,id,category,og_question,question,true_code,correct_ans,train_code
0,1,area_based,Which state (excluding UTs) has the 3rd highest PM 10 concentration per square kilometer based on the median PM 10 values?,"Which state (excluding Union Territories) shows the 3rd maximum PM10 concentration per square kilometer, using median PM10 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM10'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
    print(max_area_state)
true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM10'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
    return max_area_state
</code>"
1,4,area_based,Which state (excluding UTs) has the highest PM 2.5 concentration per square kilometer based on the variance of PM 2.5 values?,"Which state (excluding Union Territories) has the highest PM2.5 concentration per square kilometer, based on the variance of PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].var().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
    print(max_area_state)
true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].var().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
2,6,area_based,Which state (excluding UTs) has the 3rd highest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?,"Which state (excluding Union Territories) exhibits the 3rd maximum PM2.5 concentration per square kilometer, based on average PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
    print(max_area_state)
true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].mean().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
    return max_area_state
</code>"
3,10,area_based,Which state (excluding UTs) has the 3rd lowest PM 10 concentration per square kilometer based on the variance of PM 10 values?,"Which state (excluding Union Territories) exhibits the 3rd lowest PM10 concentration per square kilometer, based on the variance of PM10 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM10'].var().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
    print(max_area_state)
true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM10'].var().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
    return max_area_state
</code>"
4,16,area_based,Which state (excluding UTs) has the 2nd highest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?,"Which state (excluding Union Territories) has the 2nd maximum PM2.5 concentration per square kilometer, based on 75th percentile PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
    print(max_area_state)
true_code()
",Nagaland,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
    return max_area_state
</code>"
5,23,area_based,Which state (excluding UTs) has the 2nd lowest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?,"Which state (excluding Union Territories) presents the 2nd minimum PM10 concentration per square kilometer, according to 25th percentile PM10 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    print(max_area_state)
true_code()
",Madhya Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    return max_area_state
</code>"
6,26,area_based,Which state (excluding UTs) has the 3rd lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?,"Which state (excluding Union Territories) exhibits the 3rd lowest PM2.5 concentration per square kilometer, based on 75th percentile PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
    print(max_area_state)
true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
    return max_area_state
</code>"
7,31,area_based,Which state (excluding UTs) has the 2nd highest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?,"Which state (excluding Union Territories) presents the 2nd maximum PM2.5 concentration per square kilometer, according to median PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
    print(max_area_state)
true_code()
",Nagaland,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
    return max_area_state
</code>"
8,34,area_based,Which state (excluding UTs) has the 3rd lowest PM 2.5 concentration per square kilometer based on the variance of PM 2.5 values?,"Which state (excluding Union Territories) exhibits the 3rd lowest PM2.5 concentration per square kilometer, based on the variance of PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].var().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
    print(max_area_state)
true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].var().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
    return max_area_state
</code>"
9,40,area_based,Which union territory has the lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?,"Which union territory shows the minimum PM2.5 concentration per square kilometer, using 75th percentile PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
    print(max_area_state)
true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
    return max_area_state
</code>"
10,43,area_based,Which union territory has the highest PM 2.5 concentration per square kilometer based on the total PM 2.5 values?,"Which union territory has the highest PM2.5 concentration per square kilometer, based on total PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].sum().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
    print(max_area_state)
true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].sum().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
11,46,area_based,Which union territory has the 2nd lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?,"Which union territory presents the 2nd minimum PM2.5 concentration per square kilometer, according to 75th percentile PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    print(max_area_state)
true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    return max_area_state
</code>"
12,47,area_based,Which union territory has the 2nd highest PM 10 concentration per square kilometer based on the total PM 10 values?,"Which union territory has the 2nd highest PM10 concentration per square kilometer, based on total PM10 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM10'].sum().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
    print(max_area_state)
true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM10'].sum().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
    return max_area_state
</code>"
13,56,area_based,Which union territory has the lowest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?,"Which union territory shows the minimum PM10 concentration per square kilometer, using 25th percentile PM10 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
    print(max_area_state)
true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
    return max_area_state
</code>"
14,57,area_based,Which union territory has the 2nd lowest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?,"Which union territory exhibits the 2nd lowest PM2.5 concentration per square kilometer, based on median PM2.5 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    print(max_area_state)
true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM2.5'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    return max_area_state
</code>"
15,58,area_based,Which union territory has the 2nd lowest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?,"Which union territory presents the 2nd minimum PM10 concentration per square kilometer, according to 25th percentile PM10 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    print(max_area_state)
true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    return max_area_state
</code>"
16,64,area_based,Which union territory has the 2nd lowest PM 10 concentration per square kilometer based on the median PM 10 values?,"Which union territory shows the 2nd minimum PM10 concentration per square kilometer, using median PM10 values?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25 = main_data.groupby('state')['PM10'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    print(max_area_state)
true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25 = data.groupby('state')['PM10'].median().reset_index()
    states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm25.merge(states_area, on='state', how='inner')
    merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
    max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
    return max_area_state
</code>"
17,73,area_based,Which state has the lowest number of monitoring stations relative to its area?,Which state possesses the smallest number of monitoring stations relative to its area?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
    print(required_state)
true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
    return required_state
</code>"
18,74,area_based,Which state has the 4th highest number of monitoring stations relative to its area?,Which state has the 4th highest count of monitoring stations in proportion to its area?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
    print(required_state)
true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
    return required_state
</code>"
19,77,area_based,Which union territory has the lowest number of monitoring stations relative to its area?,Which union territory possesses the smallest number of monitoring stations relative to its area?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
    print(required_state)
true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
    return required_state
</code>"
20,78,area_based,Which union territory has the 4th highest number of monitoring stations relative to its area?,Which union territory has the 4th highest count of monitoring stations in proportion to its area?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
    print(required_state)
true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
    required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
    return required_state
</code>"
21,79,area_based,Report the total land area of the state (excluding UTs) with the highest combined PM2.5 and PM10 concentrations.,Provide the total land area of the state (excluding Union Territories) having the maximum combined PM2.5 and PM10 concentrations.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean()
    state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
    required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
    print(required_area)


true_code()
",94163,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_averages = data.groupby('state')[['PM2.5', 'PM10']].mean()
    state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
    required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
    return required_area
</code>"
22,83,area_based,Report the total land area of the union territory with the highest combined PM2.5 and PM10 concentrations.,State the total land area of the union territory with the highest combined PM2.5 and PM10 concentrations.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean()
    state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
    required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
    print(required_area)


true_code()
",1484,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_averages = data.groupby('state')[['PM2.5', 'PM10']].mean()
    state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
    required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
    return required_area
</code>"
23,91,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on median PM 10 levels?","Which state (excluding Union Territories) has the 2nd minimum land area among the top 10 most polluted states, according to median PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    print(max_area_state)


true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    return max_area_state
</code>"
24,94,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 5 most polluted states, based on 25th percentile of PM 10 levels?","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 5 most polluted states, based on the 25th percentile of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    print(max_area_state)


true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    return max_area_state
</code>"
25,95,area_based,"Which state(excuding UTs) has the lowest land area among the top 3 most polluted states, based on variance of PM 10 levels?","Which state (excluding Union Territories) has the smallest land area among the top 3 most polluted states, according to the variance of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    print(max_area_state)


true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    return max_area_state
</code>"
26,102,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on variance of PM 2.5 levels?","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 5 most polluted states, based on the variance of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    print(max_area_state)


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    return max_area_state
</code>"
27,109,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on total PM 10 levels?","Which state (excluding Union Territories) has the 2nd minimum land area among the top 10 most polluted states, according to total PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    print(max_area_state)


true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    return max_area_state
</code>"
28,110,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on 75th percentile of PM 2.5 levels?","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 10 most polluted states, based on the 75th percentile of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    print(max_area_state)


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    return max_area_state
</code>"
29,115,area_based,"Which state(excuding UTs) has the lowest land area among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?","Which state (excluding Union Territories) has the smallest land area among the top 3 most polluted states, according to the 25th percentile of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    print(max_area_state)


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    return max_area_state
</code>"
30,120,area_based,"Which state(excuding UTs) has the highest land area among the top 10 most polluted states, based on 75th percentile of PM 2.5 levels?","Which state (excluding Union Territories) possesses the largest land area among the top 10 most polluted states, based on the 75th percentile of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    print(max_area_state)


true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
31,125,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 10 most polluted states, based on variance of PM 10 levels?","Which state (excluding Union Territories) has the 3rd minimum land area among the top 10 most polluted states, according to the variance of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
    print(max_area_state)


true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
    return max_area_state
</code>"
32,127,area_based,"Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on 25th percentile of PM 10 levels?","Which state (excluding Union Territories) has the largest land area among the top 5 most polluted states, according to the 25th percentile of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    print(max_area_state)


true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
33,133,area_based,"Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on median PM 2.5 levels?","Which state (excluding Union Territories) has the smallest land area among the top 5 most polluted states, according to median PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    print(max_area_state)


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    return max_area_state
</code>"
34,138,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 3 most polluted states, based on the 25th percentile of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    print(max_area_state)


true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    return max_area_state
</code>"
35,146,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 10 most polluted states, based on 25th percentile of PM 10 levels?","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 10 most polluted states, based on the 25th percentile of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    print(max_area_state)


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    return max_area_state
</code>"
36,153,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 10 most polluted states, based on average PM 2.5 levels?","Which state (excluding Union Territories) has the 3rd highest land area among the top 10 most polluted states, according to average PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    print(max_area_state)


true_code()
",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    return max_area_state
</code>"
37,154,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 10 most polluted states, based on median PM 2.5 levels?","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 10 most polluted states, based on median PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
    print(max_area_state)


true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
    return max_area_state
</code>"
38,156,area_based,"Which state(excuding UTs) has the highest land area among the top 10 most polluted states, based on median PM 10 levels?","Which state (excluding Union Territories) possesses the largest land area among the top 10 most polluted states, based on median PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    print(max_area_state)


true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
39,177,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on total PM 10 levels?","Which state (excluding Union Territories) has the 3rd highest land area among the top 3 most polluted states, according to total PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    print(max_area_state)


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
    return max_area_state
</code>"
40,181,area_based,"Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on standard deviation of PM 10 levels?","Which state (excluding Union Territories) has the minimum land area among the top 10 most polluted states, according to the standard deviation of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    print(max_area_state)


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    return max_area_state
</code>"
41,194,area_based,"Which union territory has the highest land area among the top 4 most polluted union territories, based on average PM 2.5 levels?","Which union territory possesses the highest land area among the top 4 most polluted union territories, based on average PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    print(max_area_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
42,195,area_based,"Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on 75th percentile of PM 10 levels?","Which union territory has the 2nd minimum land area among the top 2 most polluted union territories, according to the 75th percentile of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    print(max_area_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    return max_area_state
</code>"
43,196,area_based,"Which union territory has the lowest land area among the top 4 most polluted union territories, based on 75th percentile of PM 2.5 levels?","Which union territory possesses the smallest land area among the top 4 most polluted union territories, based on the 75th percentile of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    print(max_area_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    return max_area_state
</code>"
44,209,area_based,"Which union territory has the 2nd highest land area among the top 2 most polluted union territories, based on standard deviation of PM 10 levels?","Which union territory has the 2nd highest land area among the top 2 most polluted union territories, according to the standard deviation of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    print(max_area_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    return max_area_state
</code>"
45,219,area_based,"Which union territory has the 2nd lowest land area among the top 4 most polluted union territories, based on standard deviation of PM 10 levels?","Which union territory has the 2nd minimum land area among the top 4 most polluted union territories, according to the standard deviation of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    print(max_area_state)

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    return max_area_state
</code>"
46,220,area_based,"Which union territory has the lowest land area among the top 4 most polluted union territories, based on average PM 10 levels?","Which union territory possesses the smallest land area among the top 4 most polluted union territories, based on average PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    print(max_area_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    return max_area_state
</code>"
47,221,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on median PM 2.5 levels?","Which union territory has the largest land area among the top 2 most polluted union territories, according to median PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    print(max_area_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
48,226,area_based,"Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on variance of PM 2.5 levels?","Which union territory possesses the 2nd smallest land area among the top 2 most polluted union territories, based on the variance of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    print(max_area_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
    return max_area_state
</code>"
49,230,area_based,"Which union territory has the 2nd highest land area among the top 4 most polluted union territories, based on variance of PM 10 levels?","Which union territory possesses the 2nd largest land area among the top 4 most polluted union territories, based on the variance of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    print(max_area_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
    return max_area_state
</code>"
50,232,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on 25th percentile of PM 10 levels?","Which union territory possesses the largest land area among the top 2 most polluted union territories, based on the 25th percentile of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    print(max_area_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
    return max_area_state
</code>"
51,234,area_based,"Which union territory has the lowest land area among the top 4 most polluted union territories, based on median PM 2.5 levels?","Which union territory possesses the smallest land area among the top 4 most polluted union territories, based on median PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    print(max_area_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
    return max_area_state
</code>"
52,238,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on 25th percentile of PM 2.5 level?","Which state having a land area less than 50,000 km² registers the minimum PM2.5 level, based on its 25th percentile PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
    print(required_state)

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
    return required_state
</code>"
53,240,area_based,"Which state with a land area lesser than 50,000 km² has the 5th highest PM 10 level, based on variance of PM 10 level?","Which state having a land area less than 50,000 km² registers the 5th maximum PM10 level, based on its variance of PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].var().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
    print(required_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].var().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
    return required_state
</code>"
54,250,area_based,"Which state with a land area greater than 50,000 km² has the 2nd lowest PM 2.5 level, based on total PM 2.5 level?","Which state having a land area exceeding 50,000 km² registers the 2nd minimum PM2.5 level, based on its total PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
    print(required_state)

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].sum().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
    return required_state
</code>"
55,251,area_based,"Which state with a land area greater than 50,000 km² has the 2nd highest PM 2.5 level, based on standard deviation of PM 2.5 level?","Which state with a land area greater than 50,000 km² shows the 2nd highest PM2.5 level, according to its standard deviation of PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
    return required_state
</code>"
56,256,area_based,"Which state with a land area greater than 50,000 km² has the 5th lowest PM 10 level, based on total PM 10 level?","Which state having a land area exceeding 50,000 km² registers the 5th minimum PM10 level, based on its total PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].sum().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10').iloc[4]['state']
    print(required_state)

true_code()
",Chhattisgarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].sum().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10').iloc[4]['state']
    return required_state
</code>"
57,279,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 10 level, based on median PM 10 level?","Which state with a land area below 50,000 km² shows the minimum PM10 level, according to its median PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].median().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    print(required_state)

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].median().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    return required_state
</code>"
58,287,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd highest PM 10 level, based on standard deviation of PM 10 level?","Which state with a land area below 50,000 km² shows the 2nd highest PM10 level, according to its standard deviation of PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state']
    return required_state
</code>"
59,289,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on average PM 2.5 level?","Which state with a land area below 50,000 km² shows the minimum PM2.5 level, according to its average PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
    print(required_state)

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].mean().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
    return required_state
</code>"
60,293,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd highest PM 2.5 level, based on average PM 2.5 level?","Which state with a land area below 50,000 km² shows the 3rd highest PM2.5 level, according to its average PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state']
    print(required_state)

true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].mean().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state']
    return required_state
</code>"
61,298,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd lowest PM 10 level, based on 75th percentile of PM 10 level?","Which state having a land area less than 50,000 km² registers the 3rd minimum PM10 level, based on its 75th percentile PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10').iloc[2]['state']
    print(required_state)

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].quantile(0.75).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 50000]
    required_state = filtered_data.sort_values('PM10').iloc[2]['state']
    return required_state
</code>"
62,306,area_based,"Which state with a land area greater than 50,000 km² has the 5th highest PM 10 level, based on standard deviation of PM 10 level?","Which state having a land area exceeding 50,000 km² registers the 5th maximum PM10 level, based on its standard deviation of PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
    print(required_state)

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
    return required_state
</code>"
63,311,area_based,"Which state with a land area greater than 50,000 km² has the 2nd lowest PM 2.5 level, based on standard deviation of PM 2.5 level?","Which state with a land area greater than 50,000 km² shows the 2nd lowest PM2.5 level, according to its standard deviation of PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
    print(required_state)

true_code()
",Chhattisgarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].std().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
    return required_state
</code>"
64,312,area_based,"Which state with a land area greater than 50,000 km² has the lowest PM 10 level, based on 25th percentile of PM 10 level?","Which state having a land area exceeding 50,000 km² registers the minimum PM10 level, based on its 25th percentile PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    print(required_state)

true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    return required_state
</code>"
65,315,area_based,"Which state with a land area greater than 50,000 km² has the 2nd highest PM 2.5 level, based on average PM 2.5 level?","Which state with a land area greater than 50,000 km² shows the 2nd highest PM2.5 level, according to its average PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].mean().reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
    return required_state
</code>"
66,327,area_based,"Which state with a land area greater than 50,000 km² has the highest PM 10 level, based on 25th percentile of PM 10 level?","Which state with a land area greater than 50,000 km² shows the highest PM10 level, according to its 25th percentile PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
    print(required_state)

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    merged_data = pd.merge(state_pm, states_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 50000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
    return required_state
</code>"
67,328,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 10 level, based on 25th percentile of PM 10 level?","Which union territory with a land area exceeding 1,000 km² shows the 2nd lowest PM10 level, based on its 25th percentile PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[1]['state']
    print(required_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[1]['state']
    return required_state
</code>"
68,336,area_based,"Which union territory with a land area greater than 1,000 km² has the lowest PM 10 level, based on standard deviation of PM 10 level?","Which union territory with a land area greater than 1,000 km² shows the lowest PM10 level, based on its standard deviation of PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].std().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    print(required_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].std().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    return required_state
</code>"
69,346,area_based,"Which union territory with a land area lesser than 1,000 km² has the 2nd highest PM 2.5 level, based on 25th percentile of PM 2.5 level?","Which union territory with a land area below 1,000 km² shows the 2nd highest PM2.5 level, based on its 25th percentile PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 1000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 1000]
    required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
    return required_state
</code>"
70,348,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 10 level, based on median PM 10 level?","Which union territory with a land area greater than 1,000 km² shows the 2nd lowest PM10 level, based on its median PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[1]['state']
    print(required_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].median().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[1]['state']
    return required_state
</code>"
71,352,area_based,"Which union territory with a land area lesser than 1,000 km² has the 2nd lowest PM 2.5 level, based on 75th percentile of PM 2.5 level?","Which union territory with a land area below 1,000 km² shows the 2nd lowest PM2.5 level, based on its 75th percentile PM2.5 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 1000]
    required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
    print(required_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] < 1000]
    required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
    return required_state
</code>"
72,354,area_based,"Which union territory with a land area greater than 1,000 km² has the highest PM 10 level, based on median PM 10 level?","Which union territory with a land area greater than 1,000 km² shows the maximum PM10 level, based on its median PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
    print(required_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].median().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
    return required_state
</code>"
73,361,area_based,"Which union territory with a land area greater than 1,000 km² has the lowest PM 10 level, based on total PM 10 level?","Which union territory having a land area exceeding 1,000 km² registers the minimum PM10 level, according to its total PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].sum().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    print(required_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].sum().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[0]['state']
    return required_state
</code>"
74,370,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 10 level, based on variance of PM 10 level?","Which union territory with a land area greater than 1,000 km² shows the 2nd lowest PM10 level, based on its variance of PM10 level?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[1]['state']
    print(required_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm = data.groupby('state')['PM10'].var().reset_index()
    filtered_data =  states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged_data = pd.merge(state_pm, filtered_data, on='state')
    filtered_data = merged_data[merged_data['area (km2)'] > 1000]
    required_state = filtered_data.sort_values('PM10').iloc[1]['state']
    return required_state
</code>"
75,374,funding_based,In which financial year was the variance of NCAP funding release the 2nd highest across cities?,During which financial year was the variance in NCAP funding release the 2nd highest among cities?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    df = ncap_funding_data[
        ['Amount released during FY 2019-20', 
         'Amount released during FY 2020-21', 
         'Amount released during FY 2021-22']
    ]
    avg_series = df.var()
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[len(avg_series)-2]['Year'].split()[-1]
    print(required_year)

true_code()
",2021-22,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[
    ['Amount released during FY 2019-20', 
    'Amount released during FY 2020-21', 
    'Amount released during FY 2021-22']
    ]
    avg_series = df.var()
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[len(avg_series)-2]['Year'].split()[-1]
    return required_year
</code>"
76,383,funding_based,In which financial year was the 75th percentile of NCAP funding release the lowest across cities?,In which financial year did the 75th percentile of NCAP funding release reach its minimum across cities?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    df = ncap_funding_data[
        ['Amount released during FY 2019-20', 
         'Amount released during FY 2020-21', 
         'Amount released during FY 2021-22']
    ]
    avg_series = df.quantile(0.75)
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[0]['Year'].split()[-1]
    print(required_year)

true_code()
",2021-22,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[
    ['Amount released during FY 2019-20', 
    'Amount released during FY 2020-21', 
    'Amount released during FY 2021-22']
    ]
    avg_series = df.quantile(0.75)
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[0]['Year'].split()[-1]
    return required_year
</code>"
77,385,funding_based,In which financial year was the 25th percentile of NCAP funding release the highest across cities?,In which financial year did the 25th percentile of NCAP funding release reach its peak across cities?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    df = ncap_funding_data[
        ['Amount released during FY 2019-20', 
         'Amount released during FY 2020-21', 
         'Amount released during FY 2021-22']
    ]
    avg_series = df.quantile(0.25)
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[len(avg_series)-1]['Year'].split()[-1]
    print(required_year)

true_code()
",2020-21,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[
    ['Amount released during FY 2019-20', 
    'Amount released during FY 2020-21', 
    'Amount released during FY 2021-22']
    ]
    avg_series = df.quantile(0.25)
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[len(avg_series)-1]['Year'].split()[-1]
    return required_year
</code>"
78,389,funding_based,In which financial year was the 25th percentile of NCAP funding release the 3rd lowest across cities?,In which financial year did the 25th percentile of NCAP funding release rank 3rd lowest across cities?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    df = ncap_funding_data[
        ['Amount released during FY 2019-20', 
         'Amount released during FY 2020-21', 
         'Amount released during FY 2021-22']
    ]
    avg_series = df.quantile(0.25)
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[2]['Year'].split()[-1]
    print(required_year)

true_code()
",2020-21,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[
    ['Amount released during FY 2019-20', 
    'Amount released during FY 2020-21', 
    'Amount released during FY 2021-22']
    ]
    avg_series = df.quantile(0.25)
    avg_series = avg_series.sort_values().reset_index()
    avg_series.columns = ['Year', 'Amount']
    required_year = avg_series.iloc[2]['Year'].split()[-1]
    return required_year
</code>"
79,393,funding_based,Report the state(excluding union territories) that received the highest NCAP funding relative to its land area on a per-square.,Report the state (excluding union territories) that received the maximum NCAP funding relative to its land area on a per-square basis.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged = pd.merge(funding_per_state, filtered_states_data, on='state')
    merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
    required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[0]['state']
    print(required_state)

true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged = pd.merge(funding_per_state, filtered_states_data, on='state')
    merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
    required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[0]['state']
    return required_state
</code>"
80,396,funding_based,Report the state(excluding union territories) that received the 4th highest NCAP funding relative to its land area on a per-square.,Provide the state (excluding union territories) that obtained the 4th maximum NCAP funding in proportion to its land area per square unit.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged = pd.merge(funding_per_state, filtered_states_data, on='state')
    merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
    required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[3]['state']
    print(required_state)

true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
    merged = pd.merge(funding_per_state, filtered_states_data, on='state')
    merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
    required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[3]['state']
    return required_state
</code>"
81,398,funding_based,Report the union territory that received the 2nd highest NCAP funding relative to its land area on a per-square.,Provide the union territory that obtained the 2nd highest NCAP funding in proportion to its land area per square unit.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged = pd.merge(funding_per_state, filtered_states_data, on='state')
    merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
    required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
    merged = pd.merge(funding_per_state, filtered_states_data, on='state')
    merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
    required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[1]['state']
    return required_state
</code>"
82,399,funding_based,Which state has the lowest difference between allocated NCAP funding and actual utilisation as on June 2022?,Report the state with the lowest difference between allocated NCAP funding and actual utilization as of June 2022.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
    df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
    req_loc = df.sort_values('Difference').iloc[0]['state']
    print(req_loc)

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
    df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
    req_loc = df.sort_values('Difference').iloc[0]['state']
    return req_loc
</code>"
83,404,funding_based,Which city has the 2nd lowest difference between allocated NCAP funding and actual utilisation as on June 2022?,Report the city with the second smallest difference between allocated NCAP funding and its actual utilization as of June 2022.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
    df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index()
    req_loc = df.sort_values('Difference').iloc[1]['city']
    print(req_loc)

true_code()
",Nashik,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
    df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index()
    req_loc = df.sort_values('Difference').iloc[1]['city']
    return req_loc
</code>"
84,407,funding_based,Which state has the 5th highest difference between allocated NCAP funding and actual utilisation as on June 2022?,Which state presents the 5th highest difference between allocated NCAP funds and their actual utilization as of June 2022?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
    df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
    req_loc = df.sort_values('Difference', ascending=False).iloc[4]['state']
    print(req_loc)

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
    df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
    req_loc = df.sort_values('Difference', ascending=False).iloc[4]['state']
    return req_loc
</code>"
85,413,funding_based,Which state saw the 4th highest increment in funding between FY 2020-21 and FY 2021-22?,Report the state that observed the 4th highest increment in funding between FY 2020-21 and FY 2021-22.,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    col_start = 'Amount released during FY 2020-21'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-4]['state']
    print(result)
true_code()",Madhya Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    col_start = 'Amount released during FY 2020-21'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-4]['state']
    return result
</code>"
86,416,funding_based,Which city saw the 4th highest increment in funding between FY 2019-20 and FY 2020-21?,Identify the city that saw the 4th largest rise in funding between FY 2019-20 and FY 2020-21.,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2020-21'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-4]['city']
    print(result)
true_code()",Patiala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2020-21'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-4]['city']
    return result
</code>"
87,420,funding_based,Which state saw the 3rd lowest decrement in funding between FY 2019-20 and FY 2021-22?,Identify the state that experienced the 3rd smallest decrease in funding from FY 2019-20 to FY 2021-22.,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=False)
    result = sorted_change.iloc[2]['state']
    print(result)
true_code()",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=False)
    result = sorted_change.iloc[2]['state']
    return result
</code>"
88,429,funding_based,Which state saw the 5th highest decrement in funding between FY 2019-20 and FY 2020-21?,Report the state with the 5th most significant drop in funding between FY 2019-20 and FY 2020-21.,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2020-21'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=False)
    result = sorted_change.iloc[len(sorted_change)-5]['state']
    print(result)
true_code()",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2020-21'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=False)
    result = sorted_change.iloc[len(sorted_change)-5]['state']
    return result
</code>"
89,430,funding_based,Which state saw the 4th highest increment in funding between FY 2019-20 and FY 2021-22?,Determine which state observed the 4th highest increment in funding between FY 2019-20 and FY 2021-22.,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-4]['state']
    print(result)
true_code()",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    col_start = 'Amount released during FY 2019-20'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-4]['state']
    return result
</code>"
90,431,funding_based,Which state saw the 5th highest increment in funding between FY 2020-21 and FY 2021-22?,Which state saw the 5th largest increase in funding from FY 2020-21 to FY 2021-22?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    col_start = 'Amount released during FY 2020-21'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-5]['state']
    print(result)
true_code()",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    col_start = 'Amount released during FY 2020-21'
    col_end = 'Amount released during FY 2021-22'
    ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
    funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
    sorted_change = funding_change.sort_values('change', ascending=True)
    result = sorted_change.iloc[len(sorted_change)-5]['state']
    return result
</code>"
91,444,funding_based,Which city utilised the 4th highest percentage of its allocated NCAP funding as of June 2022?,Identify the city with the 4th highest percentage use of its allocated NCAP funds as of June 2022.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
                                               ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[3]['city']
    print(ans)

true_code()
",Indore,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
    ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[3]['city']
    return ans
</code>"
92,446,funding_based,Which state utilised the 3rd lowest percentage of its allocated NCAP funding as of June 2022?,Which state had the 3rd lowest percentage utilization of its NCAP funds as of June 2022?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
                                               ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent').iloc[2]['state']
    print(ans)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
    ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent').iloc[2]['state']
    return ans
</code>"
93,447,funding_based,Which city utilised the 2nd lowest percentage of its allocated NCAP funding as of June 2022?,Report the city that showed the second smallest percentage utilization of its allocated NCAP funding by June 2022.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
                                               ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent').iloc[1]['city']
    print(ans)

true_code()
",Tuticorin,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
    ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent').iloc[1]['city']
    return ans
</code>"
94,449,funding_based,Which city utilised the highest percentage of its allocated NCAP funding as of June 2022?,Determine the city that utilized the maximum percentage of its allocated NCAP funding by June 2022.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
                                               ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[0]['city']
    print(ans)

true_code()
",Visakhapatnam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
    ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[0]['city']
    return ans
</code>"
95,450,funding_based,Which state utilised the 5th highest percentage of its allocated NCAP funding as of June 2022?,Which state exhibited the 5th highest percentage utilization of its allocated NCAP funds as of June 2022?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
                                               ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[4]['state']
    print(ans)

true_code()
",Madhya Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / 
    ncap_funding_data['Total fund released']) * 100
    ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[4]['state']
    return ans
</code>"
96,451,funding_based,Identify the state that has the 5th lowest number of cities receiving NCAP funding.,Report the state having the 5th lowest count of cities receiving NCAP funding.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    max_cities_state = state_city_counts.sort_values('city').iloc[4]['state']
    print(max_cities_state)

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    max_cities_state = state_city_counts.sort_values('city').iloc[4]['state']
    return max_cities_state
</code>"
97,452,funding_based,Identify the state that has the highest number of cities receiving NCAP funding.,Which state possesses the highest number of cities with NCAP funding?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[0]['state']
    print(max_cities_state)

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[0]['state']
    return max_cities_state
</code>"
98,453,funding_based,Identify the state that has the 3rd highest number of cities receiving NCAP funding.,Identify the state that has the 3rd highest number of cities benefiting from NCAP funding.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[2]['state']
    print(max_cities_state)

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[2]['state']
    return max_cities_state
</code>"
99,456,funding_based,Identify the union territory that has the highest number of cities receiving NCAP funding.,Identify the union territory with the highest count of cities receiving NCAP funding.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    city_count = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = pd.merge(filtered_states_data, city_count, on='state', how='inner')
    max_cities_state = merged_df.sort_values('city', ascending=False).iloc[0]['state']
    print(max_cities_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    city_count = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = pd.merge(filtered_states_data, city_count, on='state', how='inner')
    max_cities_state = merged_df.sort_values('city', ascending=False).iloc[0]['state']
    return max_cities_state
</code>"
100,464,funding_based,Which state with NCAP funding has the 5th highest PM 10 levels?,Identify the state with NCAP funding showing the 5th highest PM10 concentration.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25_avg = main_data.groupby('state')['PM10'].mean().reset_index()
    funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique()
    funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)]
    ans = funded_pm_states.sort_values('PM10', ascending=False).iloc[4]['state']
    print(ans)

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25_avg = data.groupby('state')['PM10'].mean().reset_index()
    funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique()
    funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)]
    ans = funded_pm_states.sort_values('PM10', ascending=False).iloc[4]['state']
    return ans
</code>"
101,467,funding_based,Which city with NCAP funding has the highest PM 2.5 levels?,Determine the city with NCAP funding that records the highest PM2.5 levels.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm25_avg = main_data.groupby('city')['PM2.5'].mean().reset_index()
    funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique()
    funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)]
    ans = funded_pm_states.sort_values('PM2.5', ascending=False).iloc[0]['city']
    print(ans)

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm25_avg = data.groupby('city')['PM2.5'].mean().reset_index()
    funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique()
    funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)]
    ans = funded_pm_states.sort_values('PM2.5', ascending=False).iloc[0]['city']
    return ans
</code>"
102,475,funding_based,Which city has the 3rd lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2022 (FY 2021-22)?,Identify the city with the 3rd lowest NCAP funding relative to its 25th percentile of PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city']
    print(lowest_funding_city)
true_code()",Kohima,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city']
    return lowest_funding_city
</code>"
103,487,funding_based,Which city has the 5th lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,Identify the city that received the 5th lowest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city']
    print(lowest_funding_city)
true_code()",Udaipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city']
    return lowest_funding_city
</code>"
104,490,funding_based,Which city has the 4th highest NCAP funding with respect to variance of PM 10 concentration in 2020 (FY 2019-20)?,Which city had the 4th highest NCAP funding relative to the variance of its PM10 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    print(lowest_funding_city)
true_code()",Kota,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    return lowest_funding_city
</code>"
105,491,funding_based,Which city has the highest NCAP funding with respect to total PM 10 concentration in 2020 (FY 2019-20)?,Identify the city that received the highest NCAP funding considering its total PM10 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Agra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
106,498,funding_based,Which state has the 4th highest NCAP funding with respect to variance of PM 2.5 concentration in 2020 (FY 2019-20)?,Which state had the 4th highest NCAP funding with respect to the variance of its PM2.5 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state']
    print(lowest_funding_city)
true_code()",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state']
    return lowest_funding_city
</code>"
107,505,funding_based,Which state has the 5th highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,Determine which state had the 5th highest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state']
    print(lowest_funding_city)
true_code()",Karnataka,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state']
    return lowest_funding_city
</code>"
108,506,funding_based,Which city has the 2nd lowest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,Which city got the 2nd lowest NCAP funding considering its median PM10 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    print(lowest_funding_city)
true_code()",Khanna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    return lowest_funding_city
</code>"
109,511,funding_based,Which city has the highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,Identify the city that received the highest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Srinagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
110,514,funding_based,Which state has the 3rd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,Which state received the 3rd lowest NCAP funding relative to its 75th percentile of PM10 concentration in 2021 (FY 2020-21)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state']
    print(lowest_funding_city)
true_code()",Madhya Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state']
    return lowest_funding_city
</code>"
111,522,funding_based,Which city has the highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,Which city had the highest NCAP funding with respect to its average PM10 concentration in 2021 (FY 2020-21)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Srinagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
112,528,funding_based,Which state has the 5th highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,Report the state that was granted the 5th highest NCAP funding with respect to its 75th percentile of PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state']
    print(lowest_funding_city)
true_code()",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state']
    return lowest_funding_city
</code>"
113,531,funding_based,Which city has the 3rd highest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,Identify the city that received the 3rd highest NCAP funding with respect to its total PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city']
    print(lowest_funding_city)
true_code()",Angul,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city']
    return lowest_funding_city
</code>"
114,546,funding_based,Which city has the 4th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,Which city had the 4th lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city']
    print(lowest_funding_city)
true_code()",Firozabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city']
    return lowest_funding_city
</code>"
115,547,funding_based,Which city has the highest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,Identify the city that received the highest NCAP funding relative to the variance of its PM2.5 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Srinagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
116,569,funding_based,Which city has the 4th highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,Determine which city was granted the 4th highest NCAP funding considering its average PM2.5 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    print(lowest_funding_city)
true_code()",Bhopal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    return lowest_funding_city
</code>"
117,573,funding_based,Which state has the 5th lowest NCAP funding with respect to total PM 2.5 concentration in 2022 (FY 2021-22)?,Determine which state got the 5th lowest NCAP funding with respect to its total PM2.5 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    print(lowest_funding_city)
true_code()",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    return lowest_funding_city
</code>"
118,582,funding_based,Which city has the highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,Which city had the highest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Nagpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
119,584,funding_based,Which city has the highest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,Report the city with the highest NCAP funding considering the standard deviation of its PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Solapur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
120,594,funding_based,Which city has the lowest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,Which city had the lowest NCAP funding with respect to its median PM10 concentration in 2022 (FY 2021-22)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    print(lowest_funding_city)
true_code()",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    return lowest_funding_city
</code>"
121,596,funding_based,Which city has the 4th highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,Report the city with the 4th highest NCAP funding considering its average PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    print(lowest_funding_city)
true_code()",Howrah,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    return lowest_funding_city
</code>"
122,605,funding_based,Which city has the 2nd lowest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,Determine which city was granted the 2nd lowest NCAP funding considering its median PM2.5 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    print(lowest_funding_city)
true_code()",Solapur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    return lowest_funding_city
</code>"
123,613,funding_based,Which city has the highest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,Determine which city had the highest NCAP funding relative to its median PM2.5 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Srinagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
124,627,funding_based,Which city has the lowest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,Identify the city that received the lowest NCAP funding with respect to its median PM10 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    print(lowest_funding_city)
true_code()",Muzaffarpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    return lowest_funding_city
</code>"
125,642,funding_based,Which city has the 5th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,Which city had the 5th lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city']
    print(lowest_funding_city)
true_code()",Muzaffarpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city']
    return lowest_funding_city
</code>"
126,648,funding_based,Which city has the lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,Report the city that was granted the lowest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    print(lowest_funding_city)
true_code()",Ujjain,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    return lowest_funding_city
</code>"
127,654,funding_based,Which city has the 4th highest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,Which city had the 4th highest NCAP funding with respect to the variance of its PM10 concentration in 2022 (FY 2021-22)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    print(lowest_funding_city)
true_code()",Srinagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    return lowest_funding_city
</code>"
128,658,funding_based,Which city has the 2nd lowest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,Which city received the 2nd lowest NCAP funding relative to its median PM2.5 concentration in 2022 (FY 2021-22)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    print(lowest_funding_city)
true_code()",Talcher,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    return lowest_funding_city
</code>"
129,660,funding_based,Which state has the 3rd lowest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,Report the state that was granted the 3rd lowest NCAP funding with respect to the variance of its PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state']
    print(lowest_funding_city)
true_code()",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state']
    return lowest_funding_city
</code>"
130,661,funding_based,Which city has the 5th lowest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,Determine which city had the 5th lowest NCAP funding relative to its median PM10 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city']
    print(lowest_funding_city)
true_code()",Udaipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city']
    return lowest_funding_city
</code>"
131,668,funding_based,Which city has the 2nd highest NCAP funding with respect to total PM 10 concentration in 2022 (FY 2021-22)?,Report the city with the 2nd highest NCAP funding considering its total PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city']
    print(lowest_funding_city)
true_code()",Amravati,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city']
    return lowest_funding_city
</code>"
132,673,funding_based,Which city has the lowest NCAP funding with respect to variance of PM 2.5 concentration in 2022 (FY 2021-22)?,Determine which city had the lowest NCAP funding relative to the variance of its PM2.5 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    print(lowest_funding_city)
true_code()",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    return lowest_funding_city
</code>"
133,676,funding_based,Which state has the highest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,Report the state with the highest NCAP funding relative to the standard deviation of its PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state']
    print(lowest_funding_city)
true_code()",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state']
    return lowest_funding_city
</code>"
134,679,funding_based,Which state has the 5th lowest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,Identify the state that received the 5th lowest NCAP funding relative to the variance of its PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    print(lowest_funding_city)
true_code()",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    return lowest_funding_city
</code>"
135,681,funding_based,Which city has the 3rd lowest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,Determine which city got the 3rd lowest NCAP funding with respect to its median PM2.5 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city']
    print(lowest_funding_city)
true_code()",Kohima,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city']
    return lowest_funding_city
</code>"
136,686,funding_based,Which state has the 4th highest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,Which state got the 4th highest NCAP funding considering its average PM2.5 concentration in 2022 (FY 2021-22)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state']
    print(lowest_funding_city)
true_code()",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state']
    return lowest_funding_city
</code>"
137,695,funding_based,Which city has the highest NCAP funding with respect to standard deviation of PM 10 concentration in 2021 (FY 2020-21)?,Identify the city with the highest NCAP funding considering the standard deviation of its PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
138,707,funding_based,Which state has the 3rd highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,Identify the state with the 3rd highest NCAP funding considering its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state']
    print(lowest_funding_city)
true_code()",Madhya Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state']
    return lowest_funding_city
</code>"
139,716,funding_based,Which state has the highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,Report the state with the highest NCAP funding considering its 25th percentile of PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state']
    print(lowest_funding_city)
true_code()",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state']
    return lowest_funding_city
</code>"
140,721,funding_based,Which city has the highest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,Determine which city had the highest NCAP funding relative to its median PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Gorakhpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
141,729,funding_based,Which city has the 3rd highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,Determine which city got the 3rd highest NCAP funding with respect to its average PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city']
    print(lowest_funding_city)
true_code()",Guwahati,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city']
    return lowest_funding_city
</code>"
142,738,funding_based,Which state has the highest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,Which state had the highest NCAP funding with respect to the standard deviation of its PM10 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state']
    print(lowest_funding_city)
true_code()",Telangana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state']
    return lowest_funding_city
</code>"
143,739,funding_based,Which city has the 4th lowest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,Identify the city that received the 4th lowest NCAP funding relative to its median PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city']
    print(lowest_funding_city)
true_code()",Dewas,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city']
    return lowest_funding_city
</code>"
144,742,funding_based,Which state has the 2nd lowest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,Which state received the 2nd lowest NCAP funding relative to its average PM2.5 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state']
    print(lowest_funding_city)
true_code()",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state']
    return lowest_funding_city
</code>"
145,745,funding_based,Which city has the 5th highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,Determine which city had the 5th highest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city']
    print(lowest_funding_city)
true_code()",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city']
    return lowest_funding_city
</code>"
146,749,funding_based,Which state has the 2nd highest NCAP funding with respect to standard deviation of PM 10 concentration in 2021 (FY 2020-21)?,Determine which state was granted the 2nd highest NCAP funding considering the standard deviation of its PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    print(lowest_funding_city)
true_code()",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    return lowest_funding_city
</code>"
147,758,funding_based,Which city has the lowest NCAP funding with respect to variance of PM 2.5 concentration in 2020 (FY 2019-20)?,Which city got the lowest NCAP funding considering the variance of its PM2.5 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    print(lowest_funding_city)
true_code()",Muzaffarpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city']
    return lowest_funding_city
</code>"
148,763,funding_based,Which city has the 4th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,Identify the city that received the 4th highest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    print(lowest_funding_city)
true_code()",Patiala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city']
    return lowest_funding_city
</code>"
149,764,funding_based,Which state has the lowest NCAP funding with respect to median PM 10 concentration in 2021 (FY 2020-21)?,Report the state with the lowest NCAP funding considering its median PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state']
    print(lowest_funding_city)
true_code()",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state']
    return lowest_funding_city
</code>"
150,774,funding_based,Which city has the 2nd lowest NCAP funding with respect to average PM 2.5 concentration in 2021 (FY 2020-21)?,Which city had the 2nd lowest NCAP funding with respect to its average PM2.5 concentration in 2021 (FY 2020-21)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    print(lowest_funding_city)
true_code()",Dewas,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    return lowest_funding_city
</code>"
151,775,funding_based,Which state has the 2nd highest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,Identify the state that received the 2nd highest NCAP funding relative to the variance of its PM10 concentration in 2021 (FY 2020-21).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    print(lowest_funding_city)
true_code()",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    return lowest_funding_city
</code>"
152,782,funding_based,Which state has the 5th lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,Which state got the 5th lowest NCAP funding considering its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    print(lowest_funding_city)
true_code()",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    return lowest_funding_city
</code>"
153,788,funding_based,Which state has the 2nd highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,Report the state with the 2nd highest NCAP funding considering its 25th percentile of PM2.5 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    print(lowest_funding_city)
true_code()",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    return lowest_funding_city
</code>"
154,790,funding_based,Which state has the 3rd lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,Which state received the 3rd lowest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state']
    print(lowest_funding_city)
true_code()",Madhya Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state']
    return lowest_funding_city
</code>"
155,791,funding_based,Which state has the 2nd highest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,Identify the state with the 2nd highest NCAP funding considering its total PM2.5 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    print(lowest_funding_city)
true_code()",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    return lowest_funding_city
</code>"
156,793,funding_based,Which state has the 2nd highest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,Determine which state had the 2nd highest NCAP funding relative to its median PM10 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    print(lowest_funding_city)
true_code()",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('state')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state']
    return lowest_funding_city
</code>"
157,798,funding_based,Which state has the 5th lowest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,Which state had the 5th lowest NCAP funding with respect to the variance of its PM10 concentration in 2021 (FY 2020-21)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    print(lowest_funding_city)
true_code()",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    city_pm_year = data_year.groupby('state')['PM10'].var().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state']
    return lowest_funding_city
</code>"
158,802,funding_based,Which city has the highest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,Which city received the highest NCAP funding relative to the standard deviation of its PM10 concentration in 2020 (FY 2019-20)?,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    print(lowest_funding_city)
true_code()",Nagpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].std().reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city']
    return lowest_funding_city
</code>"
159,804,funding_based,Which city has the 3rd highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,Report the city that was granted the 3rd highest NCAP funding with respect to its 75th percentile of PM10 concentration in 2020 (FY 2019-20).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city']
    print(lowest_funding_city)
true_code()",Pune,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city']
    return lowest_funding_city
</code>"
160,805,funding_based,Which state has the 4th lowest NCAP funding with respect to total PM 2.5 concentration in 2022 (FY 2021-22)?,Determine which state had the 4th lowest NCAP funding relative to its total PM2.5 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state']
    print(lowest_funding_city)
true_code()",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state']
    return lowest_funding_city
</code>"
161,807,funding_based,Which state has the 3rd highest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,Identify the state that received the 3rd highest NCAP funding with respect to its median PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state']
    print(lowest_funding_city)
true_code()",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM10'].median().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state']
    return lowest_funding_city
</code>"
162,808,funding_based,Which state has the 3rd highest NCAP funding with respect to total PM 2.5 concentration in 2022 (FY 2021-22)?,Report the state with the 3rd highest NCAP funding relative to its total PM2.5 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state']
    print(lowest_funding_city)
true_code()",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index()
    funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='state', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5']
    lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state']
    return lowest_funding_city
</code>"
163,811,funding_based,Which city has the 2nd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2022 (FY 2021-22)?,Identify the city that received the 2nd lowest NCAP funding relative to its 75th percentile of PM10 concentration in 2022 (FY 2021-22).,"def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")
    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    print(lowest_funding_city)
true_code()",Talcher,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index()
    funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']]
    merged_df = city_pm_year.merge(funding_year, on='city', how='inner')
    merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10']
    lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city']
    return lowest_funding_city
</code>"
164,817,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 10 exposure in 2023?,Report the state which was the 2nd least polluted concerning per capita PM10 exposure in 2023.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    print(required_state)

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    return required_state
</code>"
165,820,population_based,Which state was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2023?,Determine the state ranking 2nd highest in pollution from per capita PM2.5 exposure for 2023.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    return required_state
</code>"
166,822,population_based,Which state was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2024?,Report the state that was the 3rd least polluted concerning per capita PM10 exposure in 2024.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state']
    print(required_state)

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state']
    return required_state
</code>"
167,826,population_based,Which state was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2020?,Report the state ranking 2nd highest in pollution from per capita PM2.5 exposure for 2020.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    return required_state
</code>"
168,832,population_based,Which state was the highest polluted in terms of per capita PM 2.5 exposure in 2022?,Determine the most polluted state based on per capita PM2.5 exposure during 2022.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state']
    print(required_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state']
    return required_state
</code>"
169,835,population_based,Which state was the 5th highest polluted in terms of per capita PM 2.5 exposure in 2023?,Identify the state that was the 5th most polluted concerning per capita PM2.5 exposure in 2023.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[4]['state']
    print(required_state)

true_code()
",Nagaland,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[4]['state']
    return required_state
</code>"
170,839,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2021?,Identify the state that was the 2nd least polluted concerning per capita PM2.5 exposure in 2021.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    print(required_state)

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    return required_state
</code>"
171,842,population_based,Which state was the 3rd lowest polluted in terms of per capita PM 2.5 exposure in 2024?,Report the state ranking as the 3rd least polluted in terms of per capita PM2.5 exposure in 2024.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state']
    print(required_state)

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state']
    return required_state
</code>"
172,847,population_based,Which state was the highest polluted in terms of per capita PM 2.5 exposure in 2019?,Identify the most polluted state in terms of per capita PM2.5 exposure in 2019.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2019]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state']
    print(required_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2019]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state']
    return required_state
</code>"
173,848,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2020?,Determine the state that was the 2nd least polluted concerning per capita PM2.5 exposure in 2020.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    print(required_state)

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    return required_state
</code>"
174,850,population_based,Which state was the lowest polluted in terms of per capita PM 10 exposure in 2020?,Report the least polluted state regarding per capita PM10 exposure in 2020.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state']
    print(required_state)

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state']
    return required_state
</code>"
175,856,population_based,Which state was the lowest polluted in terms of per capita PM 2.5 exposure in 2023?,Determine the least polluted state concerning per capita PM2.5 exposure in 2023.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state']
    print(required_state)

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state']
    return required_state
</code>"
176,859,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 10 exposure in 2022?,Identify the 2nd least polluted state regarding per capita PM10 exposure in 2022.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2022]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    print(required_state)

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2022]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    return required_state
</code>"
177,862,population_based,Which state was the 5th highest polluted in terms of per capita PM 10 exposure in 2018?,Report the 5th most polluted state based on per capita PM10 exposure during 2018.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    data_year = main_data[main_data['Timestamp'].dt.year == 2018]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[4]['state']
    print(required_state)

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2018]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    merged_df = state_pm_avg.merge(states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[4]['state']
    return required_state
</code>"
178,863,population_based,Which union territory was the 3rd highest polluted in terms of per capita PM 2.5 exposure in 2021?,Which union territory was the 3rd most polluted in terms of per capita PM2.5 exposure in 2021?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state']
    print(required_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state']
    return required_state
</code>"
179,872,population_based,Which union territory was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2020?,Identify the 3rd least polluted union territory regarding per capita PM10 exposure for 2020.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state']
    print(required_state)


true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state']
    return required_state
</code>"
180,873,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 10 exposure in 2023?,Determine the 2nd least polluted union territory concerning per capita PM10 exposure in 2023.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    print(required_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state']
    return required_state
</code>"
181,874,population_based,Which union territory was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2023?,Which union territory was the 2nd most polluted based on per capita PM2.5 exposure during 2023?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    print(required_state)


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    return required_state
</code>"
182,880,population_based,Which union territory was the 3rd highest polluted in terms of per capita PM 2.5 exposure in 2024?,Identify the 3rd most polluted union territory regarding per capita PM2.5 exposure for 2024.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state']
    print(required_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state']
    return required_state
</code>"
183,889,population_based,Which union territory was the lowest polluted in terms of per capita PM 2.5 exposure in 2023?,Determine the least polluted union territory concerning per capita PM2.5 exposure in 2023.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state']
    print(required_state)


true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2023]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state']
    return required_state
</code>"
184,890,population_based,Which union territory was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2021?,Which union territory was the 2nd most polluted based on per capita PM2.5 exposure during 2021?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2021]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    print(required_state)


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2021]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    return required_state
</code>"
185,893,population_based,Which union territory was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2020?,Determine the 2nd most polluted union territory concerning per capita PM2.5 exposure in 2020.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    print(required_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state']
    return required_state
</code>"
186,894,population_based,Which union territory was the 3rd highest polluted in terms of per capita PM 10 exposure in 2024?,Which union territory was the 3rd most polluted based on per capita PM10 exposure during 2024?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state']
    print(required_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2024]
    state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state']
    return required_state
</code>"
187,895,population_based,Which union territory was the highest polluted in terms of per capita PM 2.5 exposure in 2020?,Report the most polluted union territory in terms of per capita PM2.5 exposure in 2020.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    data_year = main_data[main_data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state']
    print(required_state)


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data_year = data[data['Timestamp'].dt.year == 2020]
    state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner')
    merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000  
    required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state']
    return required_state
</code>"
188,899,population_based,"Among states with a population below the average population, which one receives the 3rd highest per capita NCAP funding?","Among states with a population below the average, identify the one that obtains the 3rd highest per capita NCAP funding.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].mean()
    merged_df = merged_df[merged_df['population'] < required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[2]['state']
    print(required_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].mean()
    merged_df = merged_df[merged_df['population'] < required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[2]['state']
    return required_state
</code>"
189,901,population_based,"Among states with a population above the median population, which one receives the 3rd highest per capita NCAP funding?","Which state, out of those with populations above the median, is allocated the 3rd highest per capita NCAP funding?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].median()
    merged_df = merged_df[merged_df['population'] > required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[2]['state']
    print(required_state)

true_code()
",Telangana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].median()
    merged_df = merged_df[merged_df['population'] > required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[2]['state']
    return required_state
</code>"
190,907,population_based,"Among states with a population above the average population, which one receives the 2nd highest per capita NCAP funding?","Report which state, among those with a population above the average, secures the 2nd highest per capita NCAP funding.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].mean()
    merged_df = merged_df[merged_df['population'] > required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].mean()
    merged_df = merged_df[merged_df['population'] > required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[1]['state']
    return required_state
</code>"
191,910,population_based,"Among states with a population below the 75th percentile population, which one receives the lowest per capita NCAP funding?","Identify the state, among those with a population less than the 75th percentile, which receives the lowest per capita NCAP funding.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].quantile(0.75)
    merged_df = merged_df[merged_df['population'] < required_pop]
    required_state = merged_df.sort_values('funding_per_capita').iloc[0]['state']
    print(required_state)

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    merged_df = pd.merge(states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = states_data['population'].quantile(0.75)
    merged_df = merged_df[merged_df['population'] < required_pop]
    required_state = merged_df.sort_values('funding_per_capita').iloc[0]['state']
    return required_state
</code>"
192,921,population_based,"Among union territories with a population below the 75th percentile population, which one receives the highest per capita NCAP funding?","Which union territory, among those with a population less than the 75th percentile, receives the highest per capita NCAP funding?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = pd.merge(filtered_states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = filtered_states_data['population'].quantile(0.75)
    merged_df = merged_df[merged_df['population'] < required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state']
    print(required_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = pd.merge(filtered_states_data, state_funding, on='state')
    merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population']
    required_pop = filtered_states_data['population'].quantile(0.75)
    merged_df = merged_df[merged_df['population'] < required_pop]
    required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state']
    return required_state
</code>"
193,925,population_based,Which state in India has the 5th lowest number of monitoring stations relative to its population?,Identify the state in India that has the 5th fewest monitoring stations relative to its population size.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    merged_df = station_counts.merge(states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million').iloc[4]['state']
    print(required_state)

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    merged_df = station_counts.merge(states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million').iloc[4]['state']
    return required_state
</code>"
194,927,population_based,Which state in India has the 3rd highest number of monitoring stations relative to its population?,Determine which state in India ranks 3rd highest for the number of monitoring stations proportional to its population.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    merged_df = station_counts.merge(states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[2]['state']
    print(required_state)

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    merged_df = station_counts.merge(states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[2]['state']
    return required_state
</code>"
195,930,population_based,Which union territory in India has the highest number of monitoring stations relative to its population?,Report the union territory in India with the highest density of monitoring stations relative to its population.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[0]['state']
    print(required_state)


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[0]['state']
    return required_state
</code>"
196,931,population_based,Which union territory in India has the 3rd highest number of monitoring stations relative to its population?,Determine which union territory in India ranks 3rd for the number of monitoring stations per capita.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[2]['state']
    print(required_state)


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[2]['state']
    return required_state
</code>"
197,933,population_based,Which union territory in India has the 4th highest number of monitoring stations relative to its population?,Identify the union territory in India that shows the 4th highest number of monitoring stations proportional to its population.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    station_counts = main_data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[3]['state']
    print(required_state)


true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    station_counts = data.groupby('state')['station'].nunique().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
    merged_df['stations_per_million'] = merged_df['station'] / merged_df['population']
    required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[3]['state']
    return required_state
</code>"
198,935,population_based,Which state has the 3rd lowest variance of PM 10 concentration relative to its population density?,Identify the state with the 3rd lowest variance of PM10 concentration when normalized by population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM10'].var().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    print(required_state)

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].var().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    return required_state
</code>"
199,941,population_based,Which state has the 3rd lowest 75th percentile of PM 10 concentration relative to its population density?,Determine which state shows the 3rd lowest 75th percentile of PM10 concentration in relation to its population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    print(required_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].quantile(0.75).reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    return required_state
</code>"
200,943,population_based,Which state has the 4th highest variance of PM 10 concentration relative to its population density?,Identify the state with the 4th highest variance of PM10 concentration relative to its population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM10'].var().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state']
    print(required_state)

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].var().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state']
    return required_state
</code>"
201,954,population_based,Which state has the 2nd lowest median PM 10 concentration relative to its population density?,Which state exhibits the 2nd lowest median PM10 concentration when considering population density?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM10'].median().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state']
    print(required_state)

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].median().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state']
    return required_state
</code>"
202,956,population_based,Which state has the 3rd lowest variance of PM 2.5 concentration relative to its population density?,Report the state with the 3rd lowest variance of PM2.5 concentration in relation to its population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM2.5'].var().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    print(required_state)

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].var().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    return required_state
</code>"
203,958,population_based,Which state has the 5th highest 25th percentile of PM 2.5 concentration relative to its population density?,Which state demonstrates the 5th highest 25th percentile of PM2.5 concentration relative to its population density?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[4]['state']
    print(required_state)

true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[4]['state']
    return required_state
</code>"
204,962,population_based,Which state has the highest total PM 2.5 concentration relative to its population density?,Which state shows the highest total PM2.5 concentration adjusted for population density?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM2.5'].sum().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state']
    print(required_state)

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].sum().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state']
    return required_state
</code>"
205,969,population_based,Which state has the 2nd highest standard deviation of PM 2.5 concentration relative to its population density?,Determine which state exhibits the 2nd highest standard deviation of PM2.5 concentration when considering population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM2.5'].std().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state']
    print(required_state)

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].std().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state']
    return required_state
</code>"
206,975,population_based,Which state has the 2nd lowest average PM 2.5 concentration relative to its population density?,Identify the state possessing the 2nd lowest average PM2.5 concentration normalized by population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state']
    print(required_state)

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].mean().reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state']
    return required_state
</code>"
207,984,population_based,Which state has the 4th lowest 25th percentile of PM 2.5 concentration relative to its population density?,Report the state with the 4th lowest 25th percentile of PM2.5 concentration when considering population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")


    avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state']
    print(required_state)

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    merged_df = avg_pm.merge(states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state']
    return required_state
</code>"
208,992,population_based,Which union territory has the 3rd lowest total PM 10 concentration relative to its population density?,Identify the union territory with the 3rd lowest total PM10 concentration when normalized by population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    avg_pm = main_data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    print(required_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    return required_state
</code>"
209,993,population_based,Which union territory has the lowest variance of PM 2.5 concentration relative to its population density?,Report the union territory showing the lowest variance of PM2.5 concentration in relation to its population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    avg_pm = main_data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state']
    print(required_state)

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state']
    return required_state
</code>"
210,998,population_based,Which union territory has the lowest variance of PM 10 concentration relative to its population density?,Determine which union territory shows the lowest variance of PM10 concentration in relation to its population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    avg_pm = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state']
    print(required_state)

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state']
    return required_state
</code>"
211,1012,population_based,Which union territory has the lowest 25th percentile of PM 10 concentration relative to its population density?,Identify the union territory possessing the lowest 25th percentile of PM10 concentration normalized by population density.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state']
    print(required_state)

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state']
    return required_state
</code>"
212,1015,population_based,Which union territory has the 4th lowest 75th percentile of PM 2.5 concentration relative to its population density?,Which union territory demonstrates the 4th lowest 75th percentile of PM2.5 concentration relative to its population density?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state']
    print(required_state)

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state']
    return required_state
</code>"
213,1027,population_based,Which union territory has the 3rd lowest median PM 10 concentration relative to its population density?,Which union territory demonstrates the 3rd lowest median PM10 concentration normalized by population density?,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    avg_pm = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    print(required_state)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    avg_pm = data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
    merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner')
    merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)']
    merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density']
    required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state']
    return required_state
</code>"
214,1042,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 5 most polluted states, based on average PM 10 levels?","Report the state (excluding UTs) having the 3rd smallest population within the top 5 most polluted states, when pollution is measured by average PM10 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[2]['state']
    print(max_population_state)


true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[2]['state']
    return max_population_state
</code>"
215,1048,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 3 most polluted states, based on variance of PM 2.5 levels?","Which state (excluding UTs) possesses the 3rd largest population among the top 3 most polluted states, determined by variance of PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state']
    print(max_population_state)


true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state']
    return max_population_state
</code>"
216,1050,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 10 most polluted states, based on total PM 10 levels?","Report the state (excluding UTs) having the 2nd largest population within the top 10 most polluted states, when pollution is measured by total PM10 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    print(max_population_state)


true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    return max_population_state
</code>"
217,1055,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 5 most polluted states, based on median PM 10 levels?","Determine which state (excluding UTs) has the 2nd smallest population within the top 5 most polluted states, based on median PM10 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[1]['state']
    print(max_population_state)


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[1]['state']
    return max_population_state
</code>"
218,1062,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 10 most polluted states, based on total PM 2.5 levels?","Report the state (excluding UTs) having the 3rd largest population among the top 10 most polluted states, when pollution is measured by total PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state']
    print(max_population_state)


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state']
    return max_population_state
</code>"
219,1064,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 3 most polluted states, based on variance of PM 10 levels?","Which state (excluding UTs) possesses the 3rd largest population among the top 3 most polluted states, determined by variance of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state']
    print(max_population_state)


true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state']
    return max_population_state
</code>"
220,1066,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 10 most polluted states, based on variance of PM 2.5 levels?","Report the state (excluding UTs) having the 2nd largest population within the top 10 most polluted states, when pollution is measured by variance of PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    print(max_population_state)


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    return max_population_state
</code>"
221,1067,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 5 most polluted states, based on average PM 2.5 levels?","Determine which state (excluding UTs) has the 2nd largest population among the top 5 most polluted states, based on average PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    print(max_population_state)


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    return max_population_state
</code>"
222,1078,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 3 most polluted states, based on standard deviation of PM 2.5 levels?","Report the state (excluding UTs) having the 2nd largest population within the top 3 most polluted states, when pollution is measured by standard deviation of PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    print(max_population_state)


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    return max_population_state
</code>"
223,1079,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 10 most polluted states, based on median PM 2.5 levels?","Determine which state (excluding UTs) has the 2nd largest population among the top 10 most polluted states, based on median PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    print(max_population_state)


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    return max_population_state
</code>"
224,1085,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 5 most polluted states, based on median PM 2.5 levels?","Identify the state (excluding UTs) with the 2nd largest population among the top 5 most polluted states, based on median PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    print(max_population_state)


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    return max_population_state
</code>"
225,1100,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 10 most polluted states, based on total PM 2.5 levels?","Which state (excluding UTs) possesses the 2nd largest population within the top 10 most polluted states, determined by total PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    print(max_population_state)


true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(10)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state']
    return max_population_state
</code>"
226,1116,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 3 most polluted states, based on average PM 2.5 levels?","Which state (excluding UTs) possesses the 3rd smallest population within the top 3 most polluted states, determined by average PM2.5 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[2]['state']
    print(max_population_state)


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(3)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[2]['state']
    return max_population_state
</code>"
227,1124,population_based,"Which state(excuding UTs) has the highest population among the top 5 most polluted states, based on variance of PM 10 levels?","Which state (excluding UTs) possesses the largest population within the top 5 most polluted states, determined by variance of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    print(max_population_state)


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    return max_population_state
</code>"
228,1135,population_based,"Which state(excuding UTs) has the lowest population among the top 5 most polluted states, based on variance of PM 2.5 levels?","Determine which state (excluding UTs) has the smallest population within the top 5 most polluted states, based on variance of PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[0]['state']
    print(max_population_state)


true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(5)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[0]['state']
    return max_population_state
</code>"
229,1141,population_based,"Which union territory has the 2nd lowest population among the top 2 most polluted states, based on variance of PM 10 levels?","Report the union territory with the second smallest population among the top 2 most polluted union territories, when pollution is measured by variance of PM10 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[1]['state']
    print(max_population_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[1]['state']
    return max_population_state
</code>"
230,1143,population_based,"Which union territory has the highest population among the top 4 most polluted states, based on total PM 10 levels?","Which union territory possesses the largest population among the top 4 most polluted union territories, determined by total PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    print(max_population_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].sum().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    return max_population_state
</code>"
231,1148,population_based,"Which union territory has the lowest population among the top 2 most polluted states, based on 25th percentile of PM 2.5 levels?","Identify the union territory with the smallest population among the top 2 most polluted union territories, based on 25th percentile of PM2.5 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[0]['state']
    print(max_population_state)


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM2.5', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[0]['state']
    return max_population_state
</code>"
232,1151,population_based,"Which union territory has the highest population among the top 4 most polluted states, based on 75th percentile of PM 10 levels?","Which union territory possesses the largest population among the top 4 most polluted union territories, determined by the 75th percentile of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    print(max_population_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].quantile(0.75).reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    return max_population_state
</code>"
233,1162,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on median PM 10 levels?","Determine which union territory has the largest population among the top 2 most polluted union territories, based on median PM10 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    print(max_population_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    return max_population_state
</code>"
234,1170,population_based,"Which union territory has the lowest population among the top 4 most polluted states, based on average PM 10 levels?","Determine which union territory has the smallest population within the top 4 most polluted union territories, based on average PM10 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[0]['state']
    print(max_population_state)


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].mean().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population').iloc[0]['state']
    return max_population_state
</code>"
235,1172,population_based,"Which union territory has the highest population among the top 4 most polluted states, based on median PM 10 levels?","Identify the union territory with the largest population among the top 4 most polluted union territories, based on median PM10 levels.","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    print(max_population_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].median().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(4)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    return max_population_state
</code>"
236,1183,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on standard deviation of PM 10 levels?","Which union territory possesses the largest population among the top 2 most polluted union territories, determined by standard deviation of PM10 levels?","
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    print(max_population_state)


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_pm_avg = data.groupby('state')['PM10'].std().reset_index()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
    merged_df = merged_df.sort_values('PM10', ascending=False)
    top_polluted_states = merged_df.head(2)['state'].tolist()
    top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)]
    max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state']
    return max_population_state
</code>"
237,1186,population_based,What percentage of the population lives in states where the average PM 2.5 exceeds 60?,Determine the percentage of the population residing in states where the average PM2.5 concentration is above 60.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_avg_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 60]['state'].tolist()
    total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    print(percentage)
true_code()
",29.21960675284611,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_avg_pm25 = data.groupby('state')['PM2.5'].mean().reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 60]['state'].tolist()
    total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    return percentage
</code>"
238,1188,population_based,What percentage of the population lives in states where the median PM 2.5 exceeds 100?,Identify the percentage of people dwelling in states where the median PM2.5 concentration surpasses 100.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_avg_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 100]['state'].tolist()
    total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    print(percentage)
true_code()
",0.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_avg_pm25 = data.groupby('state')['PM2.5'].median().reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 100]['state'].tolist()
    total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    return percentage
</code>"
239,1191,population_based,What percentage of the population lives in states where the 25th percentile of PM 10 exceeds 100?,Report the proportion of the population living in states where the 25th percentile of PM10 levels is above 100.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_avg_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM10'] > 100]['state'].tolist()
    total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    print(percentage)
true_code()
",1.3899548758936036,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_avg_pm25 = data.groupby('state')['PM10'].quantile(0.25).reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM10'] > 100]['state'].tolist()
    total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    return percentage
</code>"
240,1196,population_based,What percentage of the population lives in union territories where the 75th percentile of PM 10 exceeds 60?,Identify what percentage of people inhabit union territories where the 75th percentile of PM10 levels surpasses 60.,"
def true_code():
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    states_data = pd.read_pickle(""preprocessed/states_data.pkl"")
    ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"")

    state_avg_pm25 = main_data.groupby('state')['PM10'].quantile(0.75).reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM10'] > 60]['state'].tolist()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = filtered_states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    print(percentage)
true_code()
",96.02035194679725,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    state_avg_pm25 = data.groupby('state')['PM10'].quantile(0.75).reset_index()
    hazardous_states = state_avg_pm25[state_avg_pm25['PM10'] > 60]['state'].tolist()
    filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']]
    total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum()
    total_population = filtered_states_data['population'].sum()
    percentage = (total_hazardous_pop / total_population) * 100
    return percentage
</code>"
241,1208,spatial_aggregation,Which city has the 2nd lowest median PM10 in January 2022 ?,Which city had the second-most minimal median PM10 in January 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
242,1212,spatial_aggregation,Which station has the 3rd highest average PM2.5 in August 2019 ?,Find the station that had the third-highest average PM2.5 during August 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","RIICO Ind. Area III, Bhiwadi - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
243,1217,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in August 2024 ?,Determine the station exhibiting the highest 75th percentile of PM2.5 in August 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Sardar Patel Nagar, Dhanbad - JSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
244,1219,spatial_aggregation,Which city has the 3rd highest average PM10 in May 2019 ?,Which city had the third-highest mean PM10 concentration in May 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Singrauli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
245,1223,spatial_aggregation,Which state has the lowest average PM2.5 in June 2024 ?,Determine the state with the lowest average PM2.5 reading for June 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
246,1226,spatial_aggregation,Which station has the 3rd highest median PM2.5 in February 2022 ?,Name the station with the third-highest median PM2.5 concentration in February 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Nehru Nagar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
247,1229,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in September 2023 ?,Determine the state with the second-lowest 75th percentile for PM2.5 in September 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
248,1231,spatial_aggregation,Which state has the highest median PM10 in February 2020 ?,Which state recorded the highest median PM10 value in February 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
249,1233,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in December 2020 ?,Identify the station with the second-highest 75th percentile for PM2.5 in December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
250,1243,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in May 2019 ?,Which station had the third-lowest 75th percentile for PM2.5 in May 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
251,1245,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in December 2024 ?,Identify the state with the second-highest 25th percentile for PM2.5 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
252,1247,spatial_aggregation,Which state has the highest average PM2.5 in August 2019 ?,Determine the state with the highest average PM2.5 concentration for August 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
253,1251,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in August 2023 ?,Identify the state with the third-highest 25th percentile for PM2.5 during August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
254,1261,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in February 2019 ?,Which city recorded the third-smallest median PM2.5 figure in February 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Khanna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
255,1265,spatial_aggregation,Which state has the lowest median PM10 in May 2020 ?,Determine the state with the lowest median PM10 reading for May 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
256,1268,spatial_aggregation,Which station has the lowest median PM2.5 in April 2022 ?,Name the station that registered the minimum median PM2.5 level in April 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Perungudi, Chennai - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
257,1269,spatial_aggregation,Which station has the lowest median PM10 in November 2021 ?,Identify the station with the absolute lowest median PM10 in November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
258,1270,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in November 2021 ?,Which state had the second-most minimal 25th percentile of PM2.5 in November 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
259,1279,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in January 2021 ?,Which state exhibited the second-highest 25th percentile for PM10 during January 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
260,1280,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in April 2023 ?,Name the city that ranked second for the highest 25th percentile of PM10 in April 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
261,1290,spatial_aggregation,Which station has the 2nd highest average PM2.5 in August 2020 ?,Find the station that registered the second-highest average PM2.5 in August 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","DRM Office Danapur, Patna - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
262,1293,spatial_aggregation,Which station has the 3rd highest median PM2.5 in May 2021 ?,Identify the station with the third-highest median PM2.5 concentration in May 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Bawana, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
263,1295,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in December 2021 ?,Determine the city with the second-highest 75th percentile for PM2.5 in December 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Siwan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
264,1296,spatial_aggregation,Which state has the highest median PM10 in October 2024 ?,Find the state that had the highest median PM10 value in October 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
265,1299,spatial_aggregation,Which city has the lowest average PM2.5 in September 2022 ?,Identify the city with the lowest mean PM2.5 reading for September 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
266,1300,spatial_aggregation,Which city has the 2nd highest average PM10 in January 2019 ?,Which city was second in terms of highest average PM10 for January 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Talcher,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
267,1306,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in September 2022 ?,Which station recorded the second-minimum 25th percentile for PM2.5 in September 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Civic Center, Bhilai - Bhilai Steel Plant","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
268,1309,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in May 2022 ?,Which city had the second-lowest average PM2.5 value in May 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Imphal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
269,1320,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in May 2023 ?,Find the state with the highest 75th percentile for PM2.5 in May 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
270,1322,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in February 2022 ?,Name the state with the second-lowest average PM2.5 reading for February 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Chhattisgarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
271,1332,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in September 2019 ?,Find the state with the third-lowest median PM2.5 concentration in September 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
272,1334,spatial_aggregation,Which city has the 3rd highest average PM10 in October 2022 ?,Name the city that had the third-highest average PM10 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
273,1335,spatial_aggregation,Which state has the 2nd lowest median PM10 in January 2020 ?,Identify the state with the second-most minimal median PM10 in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
274,1339,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in March 2021 ?,Which state recorded the highest 25th percentile PM2.5 value in March 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
275,1344,spatial_aggregation,Which state has the 3rd lowest median PM10 in September 2021 ?,Find the state with the third-lowest median PM10 reading for September 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Telangana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
276,1349,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in December 2020 ?,Determine the state with the third-highest 25th percentile for PM2.5 in December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
277,1350,spatial_aggregation,Which station has the lowest average PM10 in November 2021 ?,Find the station with the absolute lowest average PM10 in November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
278,1351,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in December 2023 ?,Which state recorded the second-highest 25th percentile of PM2.5 for December 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
279,1352,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in March 2022 ?,Name the city with the second-lowest 25th percentile for PM10 in March 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Maihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
280,1357,spatial_aggregation,Which city has the 2nd highest average PM10 in May 2018 ?,Which city was second in terms of highest average PM10 for May 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Jodhpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
281,1366,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in October 2021 ?,Which station showed the third-lowest median PM2.5 in October 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
282,1368,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in February 2023 ?,Find the city that had the third-highest 25th percentile of PM2.5 in February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Saharsa,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
283,1375,spatial_aggregation,Which state has the lowest average PM2.5 in October 2019 ?,Which state recorded the lowest average PM2.5 reading for October 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
284,1377,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in December 2023 ?,Identify the city that ranks third for the highest 75th percentile of PM2.5 in December 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Hanumangarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
285,1379,spatial_aggregation,Which city has the 3rd lowest median PM10 in December 2024 ?,Determine the city with the third-lowest median PM10 reading for December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Tirunelveli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
286,1384,spatial_aggregation,Which station has the lowest average PM2.5 in September 2018 ?,Which station showed the minimum average PM2.5 level in September 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Chikkaballapur Rural, Chikkaballapur - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
287,1391,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in November 2022 ?,Determine the station with the second-lowest 75th percentile for PM2.5 in November 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
288,1392,spatial_aggregation,Which city has the highest median PM10 in December 2020 ?,Find the city with the highest median PM10 value in December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
289,1395,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in October 2019 ?,Identify the station with the second-lowest 75th percentile for PM2.5 in October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Hombegowda Nagar, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
290,1396,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in June 2020 ?,Which station showed the third-highest 25th percentile of PM10 for June 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","New Industrial Town, Faridabad - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
291,1399,spatial_aggregation,Which state has the 2nd highest median PM10 in June 2020 ?,Which state exhibited the second-highest median PM10 during June 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
292,1403,spatial_aggregation,Which state has the lowest average PM10 in March 2024 ?,Determine the state with the lowest average PM10 reading for March 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
293,1405,spatial_aggregation,Which state has the highest 75th percentile of PM10 in December 2018 ?,Which state exhibited the highest 75th percentile for PM10 during December 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
294,1410,spatial_aggregation,Which station has the highest median PM10 in May 2019 ?,Find the station showing the highest median PM10 for May 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","North Campus, DU, Delhi - IMD","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
295,1414,spatial_aggregation,Which state has the highest median PM10 in January 2023 ?,Which state recorded the highest median PM10 value in January 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
296,1419,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in July 2019 ?,Identify the station with the second-highest 75th percentile for PM2.5 in July 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Collectorate, Jodhpur - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
297,1427,spatial_aggregation,Which station has the lowest average PM2.5 in October 2021 ?,Determine the station with the minimum average PM2.5 level in October 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Ratanpura, Rupnagar - Ambuja Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
298,1428,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in August 2021 ?,Find the city that had the third-highest 25th percentile of PM10 in August 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
299,1430,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in September 2021 ?,Name the station with the second-lowest 75th percentile for PM2.5 in September 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
300,1433,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in August 2018 ?,Determine the city that ranked second for the highest 25th percentile of PM2.5 in August 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Patna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
301,1438,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in November 2024 ?,Which station exhibited the highest 75th percentile for PM2.5 during November 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Wazirpur, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
302,1439,spatial_aggregation,Which city has the lowest average PM10 in September 2020 ?,Determine the city with the lowest mean PM10 concentration in September 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
303,1440,spatial_aggregation,Which city has the 3rd lowest median PM10 in April 2022 ?,Find the city with the third-lowest median PM10 reading for April 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Amaravati,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
304,1448,spatial_aggregation,Which state has the 3rd lowest median PM10 in February 2024 ?,Name the state with the third-lowest median PM10 reading for February 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
305,1451,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in May 2018 ?,Determine the city with the third-most minimal 25th percentile of PM2.5 in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Rajamahendravaram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
306,1452,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in June 2020 ?,Find the station with the third-highest 25th percentile of PM2.5 for June 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Teri Gram, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
307,1454,spatial_aggregation,Which city has the highest median PM2.5 in December 2018 ?,Name the city showing the highest median PM2.5 for December 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
308,1474,spatial_aggregation,Which station has the 3rd highest average PM10 in December 2021 ?,Which station had the third-highest average PM10 in December 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Chandni Chowk, Delhi - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
309,1479,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in July 2021 ?,Identify the city with the second-lowest 25th percentile for PM2.5 in July 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
310,1487,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in May 2021 ?,Determine the station with the second-lowest average PM2.5 in May 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Devaraj Urs Badavane, Davanagere - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
311,1490,spatial_aggregation,Which station has the 2nd highest average PM10 in May 2019 ?,Name the station that registered the second-highest average PM10 in May 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Loni, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
312,1491,spatial_aggregation,Which state has the highest median PM10 in August 2018 ?,Identify the state with the highest median PM10 value in August 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
313,1496,spatial_aggregation,Which state has the highest median PM10 in February 2023 ?,Name the state with the highest median PM10 value in February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
314,1497,spatial_aggregation,Which state has the 2nd lowest average PM10 in November 2021 ?,Identify the state with the second-lowest average PM10 reading for November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
315,1498,spatial_aggregation,Which state has the 2nd lowest median PM10 in September 2022 ?,Which state had the second-most minimal median PM10 in September 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
316,1505,spatial_aggregation,Which state has the lowest median PM2.5 in May 2021 ?,Determine the state with the lowest median PM2.5 figure in May 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
317,1508,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in November 2019 ?,Name the station with the highest 75th percentile for PM2.5 in November 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Wazirpur, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
318,1509,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in April 2024 ?,Identify the city with the lowest 25th percentile for PM10 in April 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Chengalpattu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
319,1515,spatial_aggregation,Which state has the 2nd highest median PM10 in June 2019 ?,Identify the state with the second-highest median PM10 during June 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
320,1518,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in May 2024 ?,Find the state with the third-highest 75th percentile for PM2.5 in May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
321,1522,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in February 2020 ?,Which state exhibited the third-lowest 75th percentile for PM10 in February 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
322,1550,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in February 2024 ?,Name the station showing the second-highest 25th percentile of PM2.5 for February 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Samanpura, Patna - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
323,1551,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in April 2021 ?,Identify the state with the highest 75th percentile PM2.5 value in April 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
324,1554,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in June 2018 ?,Find the station showing the second-highest 25th percentile of PM10 for June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Sirifort, Delhi - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
325,1566,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in May 2021 ?,Find the station with the second-lowest median PM2.5 in May 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
326,1567,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in December 2019 ?,Which station showed the third-lowest 75th percentile for PM10 in December 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Airoli, Navi Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
327,1571,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in April 2022 ?,Determine the city with the third-lowest average PM2.5 value in April 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Amaravati,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
328,1573,spatial_aggregation,Which city has the lowest average PM2.5 in December 2023 ?,Which city recorded the lowest average PM2.5 value in December 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
329,1576,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in May 2022 ?,Which station showed the lowest 75th percentile for PM10 in May 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","ECIL Kapra, Hyderabad - TSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
330,1578,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in April 2019 ?,Find the state with the second-lowest median PM2.5 concentration in April 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
331,1587,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in September 2024 ?,Identify the state with the third-highest 75th percentile for PM2.5 during September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
332,1593,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in June 2018 ?,Identify the city that ranks third for the highest 75th percentile of PM2.5 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Rohtak,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
333,1610,spatial_aggregation,Which station has the lowest average PM2.5 in March 2024 ?,Name the station with the minimum average PM2.5 level in March 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Bhelupur, Varanasi - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
334,1614,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in December 2024 ?,Find the station with the third-lowest median PM2.5 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Kalyana Nagara, Chikkamagaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
335,1615,spatial_aggregation,Which city has the 3rd highest median PM10 in July 2024 ?,Which city ranks third for the highest median PM10 in July 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
336,1616,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in August 2022 ?,Name the city with the highest 25th percentile for PM2.5 in August 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Pune,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
337,1622,spatial_aggregation,Which city has the lowest median PM2.5 in January 2020 ?,Name the city with the lowest median PM2.5 figure in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
338,1626,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in June 2019 ?,Find the city with the second-lowest 75th percentile for PM10 in June 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Chennai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
339,1629,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in June 2019 ?,Identify the state with the third-highest 75th percentile for PM2.5 during June 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
340,1638,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in May 2024 ?,Find the city that had the third-highest 25th percentile of PM2.5 in May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Muzaffarnagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
341,1643,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in November 2020 ?,Determine the state with the lowest 25th percentile for PM2.5 in November 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
342,1650,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in November 2021 ?,Find the city with the second-lowest mean PM2.5 concentration in November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
343,1657,spatial_aggregation,Which city has the 3rd highest average PM2.5 in February 2020 ?,Which city had the third-highest mean PM2.5 concentration in February 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
344,1663,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in February 2018 ?,Which city was second in terms of highest 75th percentile for PM10 in February 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
345,1665,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in October 2018 ?,Identify the state with the third-highest 25th percentile for PM10 during October 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
346,1668,spatial_aggregation,Which city has the lowest average PM2.5 in December 2021 ?,Find the city with the lowest average PM2.5 value in December 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Gummidipoondi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
347,1671,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in December 2024 ?,Identify the state with the lowest 75th percentile for PM10 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
348,1672,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in January 2019 ?,Which station showed the second-minimum 25th percentile for PM2.5 in January 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Model Town, Patiala - PPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
349,1678,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in September 2021 ?,Which city ranks third for the highest 25th percentile of PM10 in September 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Moradabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
350,1685,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in January 2018 ?,Determine the state with the second-highest 25th percentile for PM10 during January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
351,1686,spatial_aggregation,Which state has the 2nd highest median PM10 in July 2023 ?,Find the state with the second-highest median PM10 during July 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
352,1691,spatial_aggregation,Which city has the lowest average PM2.5 in November 2024 ?,Determine the city with the lowest average PM2.5 value in November 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
353,1695,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in March 2024 ?,Identify the state with the second-lowest 75th percentile for PM2.5 in March 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
354,1697,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in October 2020 ?,Determine the station with the second-lowest 75th percentile for PM10 in October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
355,1701,spatial_aggregation,Which station has the 3rd lowest median PM10 in May 2018 ?,Identify the station that recorded the 3rd lowest median PM10 value in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Anand Kala Kshetram, Rajamahendravaram - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
356,1705,spatial_aggregation,Which city has the highest median PM10 in June 2018 ?,Which city had the highest median PM10 in June 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
357,1711,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in October 2023 ?,Report the state with the 3rd lowest average PM2.5 in October 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
358,1717,spatial_aggregation,Which state has the 2nd highest median PM2.5 in March 2021 ?,Identify the state with the 2nd highest median PM2.5 for March 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
359,1727,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in March 2019 ?,Identify the city with the lowest 25th percentile of PM2.5 for March 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
360,1730,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in November 2021 ?,Which state had the 2nd highest 75th percentile of PM2.5 in November 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
361,1733,spatial_aggregation,Which state has the lowest median PM2.5 in April 2022 ?,Which state registered the lowest median PM2.5 during April 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
362,1734,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in May 2023 ?,Determine the state exhibiting the 3rd lowest 75th percentile of PM10 in May 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
363,1738,spatial_aggregation,Which station has the 2nd highest average PM2.5 in June 2019 ?,Which station recorded the 2nd highest average PM2.5 in June 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Shadipur, Delhi - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
364,1744,spatial_aggregation,Which station has the highest average PM10 in January 2018 ?,Determine the station exhibiting the highest average PM10 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
365,1753,spatial_aggregation,Which state has the 2nd highest median PM10 in May 2018 ?,Which state registered the 2nd highest median PM10 during May 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
366,1755,spatial_aggregation,Which city has the highest average PM10 in July 2018 ?,Which city had the highest average PM10 in July 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
367,1761,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in November 2022 ?,Report the state with the 2nd highest 25th percentile of PM2.5 in November 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
368,1767,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in February 2018 ?,Identify the state with the highest 75th percentile of PM2.5 for February 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
369,1768,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in March 2024 ?,Which station recorded the 2nd lowest median PM2.5 in March 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","IESD Banaras Hindu University, Varanasi - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
370,1777,spatial_aggregation,Which state has the highest 75th percentile of PM10 in June 2019 ?,Identify the state with the highest 75th percentile of PM10 for June 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
371,1782,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in October 2023 ?,Identify the station that recorded the 3rd lowest 75th percentile of PM2.5 value in October 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Girls College, Sivasagar - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
372,1784,spatial_aggregation,Which station has the 2nd highest median PM2.5 in August 2019 ?,Determine the station exhibiting the 2nd highest median PM2.5 in August 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","F-Block, Sirsa - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
373,1786,spatial_aggregation,Which state has the lowest median PM10 in October 2024 ?,Report the state that had the lowest median PM10 in October 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
374,1789,spatial_aggregation,Which station has the lowest median PM2.5 in April 2024 ?,Determine the station with the lowest median PM2.5 in April 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","IESD Banaras Hindu University, Varanasi - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
375,1794,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in September 2023 ?,Determine the station exhibiting the 2nd highest 25th percentile of PM2.5 in September 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Sector-51, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
376,1796,spatial_aggregation,Which city has the highest 25th percentile of PM10 in December 2023 ?,Report the city that had the highest 25th percentile of PM10 in December 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
377,1798,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in February 2020 ?,Which city recorded the lowest 75th percentile of PM10 in February 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Chamarajanagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
378,1799,spatial_aggregation,Which station has the 2nd lowest median PM10 in January 2020 ?,Determine the station with the 2nd lowest median PM10 in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Sanegurava Halli, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
379,1802,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in March 2018 ?,Identify the state that recorded the 3rd highest 75th percentile of PM10 value in March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
380,1805,spatial_aggregation,Which city has the 2nd lowest average PM10 in August 2023 ?,Which city had the 2nd lowest average PM10 in August 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Gadag,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
381,1821,spatial_aggregation,Which station has the highest average PM2.5 in September 2018 ?,Report the station with the highest average PM2.5 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
382,1824,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in November 2021 ?,Determine the station exhibiting the 3rd lowest 75th percentile of PM2.5 in November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
383,1825,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in May 2021 ?,Which city had the 3rd highest 75th percentile of PM2.5 in May 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Jind,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
384,1826,spatial_aggregation,Which city has the highest 25th percentile of PM10 in December 2024 ?,Report the city that had the highest 25th percentile of PM10 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
385,1833,spatial_aggregation,Which station has the highest average PM10 in August 2021 ?,Which station registered the highest average PM10 during August 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Town Hall - Lal Bagh, Darbhanga - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
386,1842,spatial_aggregation,Which city has the 3rd lowest median PM10 in May 2020 ?,Identify the city that recorded the 3rd lowest median PM10 value in May 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Coimbatore,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
387,1844,spatial_aggregation,Which state has the 2nd lowest average PM10 in March 2023 ?,Determine the state exhibiting the 2nd lowest average PM10 in March 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
388,1853,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in November 2020 ?,Which city registered the lowest 25th percentile of PM10 during November 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
389,1855,spatial_aggregation,Which state has the 3rd highest median PM2.5 in March 2021 ?,Which state had the 3rd highest median PM2.5 in March 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
390,1857,spatial_aggregation,Which city has the 2nd highest median PM10 in December 2018 ?,Identify the city with the 2nd highest median PM10 for December 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
391,1868,spatial_aggregation,Which station has the 2nd highest average PM2.5 in October 2022 ?,Which station recorded the 2nd highest average PM2.5 in October 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Loni, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
392,1870,spatial_aggregation,Which station has the lowest median PM2.5 in November 2018 ?,Which station had the lowest median PM2.5 in November 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
393,1876,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in October 2018 ?,Report the state that had the 2nd highest 75th percentile of PM2.5 in October 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
394,1877,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in March 2020 ?,Identify the state with the 2nd lowest 25th percentile of PM10 for March 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
395,1881,spatial_aggregation,Which state has the highest median PM2.5 in August 2023 ?,Report the state with the highest median PM2.5 in August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
396,1883,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in June 2021 ?,Which station registered the 3rd highest 25th percentile of PM2.5 during June 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Pallavpuram Phase 2, Meerut - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
397,1891,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in June 2022 ?,Report the station with the 3rd highest 25th percentile of PM10 in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Chandni Chowk, Delhi - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
398,1893,spatial_aggregation,Which state has the lowest median PM10 in July 2024 ?,Which state registered the lowest median PM10 during July 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
399,1905,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in May 2022 ?,Which state had the 3rd lowest 75th percentile of PM10 in May 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
400,1918,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in January 2022 ?,Which station recorded the 2nd lowest 75th percentile of PM10 in January 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
401,1920,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in April 2024 ?,Which city had the 3rd lowest 25th percentile of PM2.5 in April 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Chengalpattu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
402,1921,spatial_aggregation,Which city has the 3rd highest median PM10 in January 2020 ?,Report the city with the 3rd highest median PM10 in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Greater Noida,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
403,1924,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in May 2020 ?,Determine the state exhibiting the lowest 75th percentile of PM10 in May 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
404,1931,spatial_aggregation,Which station has the 2nd highest median PM2.5 in August 2022 ?,Report the station with the 2nd highest median PM2.5 in August 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Kareemganj, Gaya - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
405,1933,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in April 2022 ?,Which state registered the lowest 75th percentile of PM2.5 during April 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
406,1934,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in March 2019 ?,Determine the state exhibiting the 2nd lowest 75th percentile of PM2.5 in March 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
407,1942,spatial_aggregation,Which state has the highest median PM2.5 in July 2020 ?,Identify the state that recorded the highest median PM2.5 value in July 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
408,1946,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in July 2023 ?,Report the state that had the 2nd lowest 75th percentile of PM2.5 in July 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
409,1951,spatial_aggregation,Which station has the 2nd highest median PM10 in September 2018 ?,Report the station with the 2nd highest median PM10 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","NISE Gwal Pahari, Gurugram - IMD","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
410,1956,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in August 2024 ?,Report the state that had the 2nd highest 75th percentile of PM2.5 in August 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
411,1959,spatial_aggregation,Which city has the 2nd highest median PM10 in May 2018 ?,Determine the city with the 2nd highest median PM10 in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
412,1963,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in October 2023 ?,Which state registered the 2nd lowest average PM2.5 during October 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
413,1964,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in October 2018 ?,Determine the state exhibiting the 3rd lowest 25th percentile of PM2.5 in October 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
414,1965,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in December 2019 ?,Which state had the 2nd highest 75th percentile of PM10 in December 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
415,1976,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in March 2021 ?,Report the state that had the 3rd lowest 75th percentile of PM2.5 in March 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
416,1987,spatial_aggregation,Which station has the lowest median PM2.5 in December 2022 ?,Identify the station with the lowest median PM2.5 for December 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
417,1994,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in December 2024 ?,Determine the station exhibiting the 3rd lowest 25th percentile of PM2.5 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Mahatma Basaveswar Colony, Kalaburgi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
418,1998,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in June 2019 ?,Which station recorded the 3rd lowest 25th percentile of PM2.5 in June 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Colaba, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
419,2000,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in August 2022 ?,Which state had the 3rd lowest 75th percentile of PM2.5 in August 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
420,2002,spatial_aggregation,Which city has the lowest median PM10 in September 2019 ?,Identify the city that recorded the lowest median PM10 value in September 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Kolar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
421,2003,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in March 2024 ?,Which state registered the 2nd lowest 75th percentile of PM10 during March 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
422,2005,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in August 2019 ?,Which city had the 2nd lowest 25th percentile of PM2.5 in August 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
423,2007,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in December 2020 ?,Identify the station with the lowest 25th percentile of PM2.5 for December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
424,2010,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in June 2018 ?,Which city had the 2nd lowest 75th percentile of PM10 in June 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Siliguri,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
425,2011,spatial_aggregation,Which station has the lowest average PM10 in December 2022 ?,Report the station with the lowest average PM10 in December 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
426,2012,spatial_aggregation,Which station has the 3rd highest median PM2.5 in August 2018 ?,Identify the station that recorded the 3rd highest median PM2.5 value in August 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Collectorate, Jodhpur - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
427,2028,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in December 2019 ?,Which state recorded the 2nd lowest 75th percentile of PM2.5 in December 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
428,2031,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in January 2021 ?,Report the city with the 3rd highest 75th percentile of PM10 in January 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
429,2033,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in October 2021 ?,Which state registered the 2nd lowest 25th percentile of PM2.5 during October 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
430,2038,spatial_aggregation,Which station has the 3rd highest average PM10 in January 2018 ?,Which station recorded the 3rd highest average PM10 in January 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Sector - 125, Noida - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
431,2041,spatial_aggregation,Which state has the 2nd highest average PM2.5 in April 2020 ?,Report the state with the 2nd highest average PM2.5 in April 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
432,2042,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in February 2020 ?,Identify the state that recorded the 3rd lowest 25th percentile of PM10 value in February 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
433,2056,spatial_aggregation,Which state has the highest median PM10 in May 2024 ?,Report the state that had the highest median PM10 in May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
434,2059,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in April 2024 ?,Determine the station with the 3rd lowest median PM2.5 in April 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Bhelupur, Varanasi - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
435,2077,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in November 2019 ?,Identify the station with the 3rd lowest 75th percentile of PM10 for November 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Airoli, Navi Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
436,2079,spatial_aggregation,Which city has the 3rd highest average PM10 in December 2024 ?,Determine the city with the 3rd highest average PM10 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Dhanbad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
437,2081,spatial_aggregation,Which city has the 3rd highest average PM2.5 in May 2022 ?,Report the city with the 3rd highest average PM2.5 in May 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Manesar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
438,2089,spatial_aggregation,Which city has the highest 25th percentile of PM10 in July 2024 ?,Determine the city with the highest 25th percentile of PM10 in July 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
439,2093,spatial_aggregation,Which state has the 3rd highest median PM2.5 in February 2018 ?,Which state registered the 3rd highest median PM2.5 during February 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
440,2096,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in June 2024 ?,Report the city that had the lowest 25th percentile of PM2.5 in June 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Tirupur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
441,2107,spatial_aggregation,Which city has the lowest average PM10 in January 2024 ?,Identify the city with the lowest average PM10 for January 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
442,2111,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in February 2018 ?,Report the station with the 2nd lowest 75th percentile of PM2.5 in February 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","BTM Layout, Bengaluru - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
443,2115,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in February 2019 ?,Which city had the 3rd lowest average PM2.5 in February 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Khanna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
444,2126,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in April 2018 ?,Report the state that had the 2nd lowest 75th percentile of PM10 in April 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
445,2127,spatial_aggregation,Which state has the 3rd highest average PM10 in June 2024 ?,Identify the state with the 3rd highest average PM10 for June 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
446,2131,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in January 2019 ?,Report the state with the 2nd lowest average PM2.5 in January 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Karnataka,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
447,2137,spatial_aggregation,Which city has the 3rd highest average PM2.5 in August 2020 ?,Identify the city with the 3rd highest average PM2.5 for August 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Vatva,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
448,2144,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in January 2023 ?,Determine the city exhibiting the lowest 25th percentile of PM10 in January 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
449,2149,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in July 2022 ?,Determine the station with the 2nd lowest average PM2.5 in July 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Deen Dayal Nagar, Sagar - MPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
450,2164,spatial_aggregation,Which station has the 2nd highest median PM10 in June 2020 ?,Determine the station exhibiting the 2nd highest median PM10 in June 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","New Industrial Town, Faridabad - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
451,2169,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in September 2020 ?,Determine the city with the 3rd lowest 25th percentile of PM10 in September 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Shivamogga,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
452,2177,spatial_aggregation,Which state has the 3rd highest average PM10 in February 2021 ?,Identify the state with the 3rd highest average PM10 for February 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
453,2194,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in June 2019 ?,Determine the station exhibiting the 3rd lowest 75th percentile of PM10 in June 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Sanegurava Halli, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
454,2196,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in October 2022 ?,Report the state that had the 3rd highest 25th percentile of PM10 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
455,2199,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in January 2023 ?,Determine the city with the highest 75th percentile of PM2.5 in January 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
456,2210,spatial_aggregation,Which station has the lowest average PM10 in November 2023 ?,Which station had the lowest average PM10 in November 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
457,2218,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in June 2023 ?,Which city recorded the 2nd lowest average PM2.5 in June 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
458,2220,spatial_aggregation,Which city has the 3rd lowest average PM10 in February 2018 ?,Which city had the 3rd lowest average PM10 in February 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Bengaluru,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
459,2224,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in June 2024 ?,Determine the city exhibiting the 3rd lowest median PM2.5 in June 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Pathardih,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
460,2225,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in June 2023 ?,Which state had the lowest 25th percentile of PM10 in June 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
461,2227,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in September 2018 ?,Identify the city with the 2nd highest 75th percentile of PM10 for September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
462,2229,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in September 2022 ?,Determine the state with the 2nd lowest median PM2.5 in September 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
463,2232,spatial_aggregation,Which station has the highest 75th percentile of PM10 in July 2021 ?,Identify the station that recorded the highest 75th percentile of PM10 value in July 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Nerul, Navi Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
464,2234,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in September 2018 ?,Determine the station exhibiting the highest 25th percentile of PM2.5 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
465,2238,spatial_aggregation,Which state has the 3rd lowest average PM10 in March 2021 ?,Which state recorded the 3rd lowest average PM10 in March 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
466,2241,spatial_aggregation,Which state has the lowest average PM2.5 in December 2021 ?,Report the state with the lowest average PM2.5 in December 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
467,2244,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in October 2022 ?,Determine the station exhibiting the lowest 75th percentile of PM10 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
468,2247,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in July 2021 ?,Identify the station with the 3rd highest 75th percentile of PM2.5 for July 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","IIPHG Lekawada, Gandhinagar - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
469,2252,spatial_aggregation,Which station has the 3rd highest average PM2.5 in July 2023 ?,Identify the station that recorded the 3rd highest average PM2.5 value in July 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Central Academy for SFS, Byrnihat - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
470,2255,spatial_aggregation,Which city has the 3rd highest average PM2.5 in October 2020 ?,Which city had the 3rd highest average PM2.5 in October 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Baghpat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
471,2256,spatial_aggregation,Which city has the lowest median PM10 in September 2021 ?,Report the city that had the lowest median PM10 in September 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
472,2257,spatial_aggregation,Which station has the 2nd highest average PM2.5 in August 2023 ?,Identify the station with the 2nd highest average PM2.5 for August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Central Academy for SFS, Byrnihat - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
473,2261,spatial_aggregation,Which city has the highest median PM2.5 in September 2024 ?,Report the city with the highest median PM2.5 in September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
474,2273,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in April 2023 ?,Which state registered the 3rd highest 25th percentile of PM10 during April 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
475,2274,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in December 2023 ?,Determine the state exhibiting the highest 25th percentile of PM2.5 in December 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
476,2275,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in September 2022 ?,Which city had the 3rd lowest 75th percentile of PM10 in September 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Madikeri,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
477,2284,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in September 2018 ?,Determine the city exhibiting the 3rd highest 25th percentile of PM10 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
478,2290,spatial_aggregation,Which station has the 3rd highest median PM2.5 in March 2020 ?,Which station had the 3rd highest median PM2.5 in March 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","DRM Office Danapur, Patna - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
479,2294,spatial_aggregation,Which city has the highest median PM10 in September 2022 ?,Determine the city exhibiting the highest median PM10 in September 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Tirupur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
480,2295,spatial_aggregation,Which station has the 3rd lowest average PM10 in January 2024 ?,Which station had the 3rd lowest average PM10 in January 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
481,2301,spatial_aggregation,Which city has the 3rd highest median PM10 in May 2018 ?,Report the city with the 3rd highest median PM10 in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Jodhpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
482,2303,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in January 2024 ?,Which state registered the 2nd highest 75th percentile of PM10 during January 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
483,2308,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in January 2020 ?,Which state recorded the 2nd lowest 75th percentile of PM10 in January 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
484,2315,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in August 2019 ?,Which state had the 2nd highest 25th percentile of PM10 in August 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
485,2317,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in October 2024 ?,Identify the state with the lowest 75th percentile of PM10 for October 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
486,2328,spatial_aggregation,Which state has the highest median PM10 in May 2022 ?,Which state recorded the highest median PM10 in May 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
487,2329,spatial_aggregation,Which state has the 3rd highest average PM2.5 in July 2019 ?,Determine the state with the 3rd highest average PM2.5 in July 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
488,2344,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in June 2021 ?,Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 in June 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
489,2349,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in August 2024 ?,Determine the station with the lowest 25th percentile of PM10 in August 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
490,2358,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in December 2018 ?,Which state recorded the 2nd lowest average PM2.5 in December 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
491,2362,spatial_aggregation,Which station has the highest 75th percentile of PM10 in April 2021 ?,Identify the station that recorded the highest 75th percentile of PM10 value in April 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","New Collectorate, Baghpat - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
492,2364,spatial_aggregation,Which state has the 2nd highest average PM10 in July 2021 ?,Determine the state exhibiting the 2nd highest average PM10 in July 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
493,2368,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in September 2021 ?,Which state recorded the 3rd lowest 25th percentile of PM2.5 in September 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Telangana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
494,2370,spatial_aggregation,Which station has the highest 75th percentile of PM10 in June 2022 ?,Which station had the highest 75th percentile of PM10 in June 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Loni, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
495,2373,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in February 2018 ?,Which city registered the 2nd highest 25th percentile of PM10 during February 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Talcher,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
496,2375,spatial_aggregation,Which state has the lowest median PM10 in July 2022 ?,Which state had the lowest median PM10 in July 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
497,2383,spatial_aggregation,Which state has the highest 75th percentile of PM10 in August 2020 ?,Which state registered the highest 75th percentile of PM10 during August 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
498,2392,spatial_aggregation,Which city has the 3rd highest average PM2.5 in September 2020 ?,Identify the city that recorded the 3rd highest average PM2.5 value in September 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Lucknow,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
499,2397,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in February 2018 ?,Identify the city with the 3rd lowest 25th percentile of PM10 for February 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Thiruvananthapuram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
500,2400,spatial_aggregation,Which city has the 3rd highest average PM10 in November 2021 ?,Which city had the 3rd highest average PM10 in November 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Faridabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
501,2404,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in November 2018 ?,Determine the station exhibiting the 3rd lowest average PM2.5 in November 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Chikkaballapur Rural, Chikkaballapur - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
502,2410,spatial_aggregation,Which city has the lowest median PM2.5 in February 2024 ?,Which city had the lowest median PM2.5 in February 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
503,2412,spatial_aggregation,Which city has the 2nd highest average PM10 in May 2022 ?,Identify the city that recorded the 2nd highest average PM10 value in May 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Sonipat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
504,2415,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in July 2022 ?,Which state had the 2nd lowest median PM2.5 in July 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
505,2420,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in August 2023 ?,Which city had the highest 25th percentile of PM2.5 in August 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
506,2421,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in October 2024 ?,Report the city with the 3rd lowest 75th percentile of PM10 in October 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Ramanathapuram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
507,2422,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in September 2019 ?,Identify the state that recorded the 2nd highest 25th percentile of PM2.5 value in September 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
508,2431,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in December 2024 ?,Report the station with the 2nd lowest 75th percentile of PM10 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
509,2435,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in June 2020 ?,Which state had the highest 25th percentile of PM2.5 in June 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
510,2442,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in April 2019 ?,Identify the station that recorded the 3rd lowest 75th percentile of PM2.5 value in April 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Bandra, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
511,2444,spatial_aggregation,Which city has the highest 25th percentile of PM10 in September 2024 ?,Determine the city exhibiting the highest 25th percentile of PM10 in September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
512,2453,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in March 2023 ?,Which station registered the 3rd highest 25th percentile of PM2.5 during March 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Shadipur, Delhi - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
513,2455,spatial_aggregation,Which city has the 2nd highest median PM2.5 in March 2024 ?,Which city had the 2nd highest median PM2.5 in March 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
514,2458,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in May 2021 ?,Which city recorded the 2nd lowest 75th percentile of PM2.5 in May 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Davanagere,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
515,2460,spatial_aggregation,Which state has the 3rd lowest average PM10 in March 2019 ?,Which state had the 3rd lowest average PM10 in March 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
516,2461,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in June 2018 ?,Report the city with the 2nd highest 75th percentile of PM10 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Jodhpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
517,2470,spatial_aggregation,Which state has the 2nd lowest median PM10 in March 2024 ?,Which state had the 2nd lowest median PM10 in March 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
518,2481,spatial_aggregation,Which state has the 2nd highest average PM2.5 in February 2019 ?,Report the state with the 2nd highest average PM2.5 in February 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
519,2492,spatial_aggregation,Which state has the 3rd lowest median PM10 in April 2022 ?,Identify the state that recorded the 3rd lowest median PM10 value in April 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
520,2495,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in April 2022 ?,Which state had the 2nd lowest 25th percentile of PM10 in April 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
521,2521,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in November 2022 ?,Report the station with the 2nd highest 25th percentile of PM10 in November 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Samanpura, Patna - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
522,2522,spatial_aggregation,Which state has the 3rd highest median PM10 in May 2018 ?,Identify the state that recorded the 3rd highest median PM10 value in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
523,2523,spatial_aggregation,Which state has the lowest median PM10 in March 2018 ?,Which state registered the lowest median PM10 during March 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
524,2535,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in September 2018 ?,Which state had the 2nd lowest median PM2.5 in September 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
525,2538,spatial_aggregation,Which city has the 2nd highest average PM10 in July 2021 ?,Which city recorded the 2nd highest average PM10 in July 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Narnaul,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
526,2540,spatial_aggregation,Which city has the lowest median PM10 in June 2021 ?,Which city had the lowest median PM10 in June 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
527,2545,spatial_aggregation,Which city has the lowest average PM2.5 in November 2022 ?,Which city had the lowest average PM2.5 in November 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
528,2560,spatial_aggregation,Which city has the highest median PM2.5 in November 2018 ?,Which city had the highest median PM2.5 in November 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
529,2562,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in September 2018 ?,Identify the city that recorded the 3rd lowest 75th percentile of PM2.5 value in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
530,2570,spatial_aggregation,Which city has the 3rd lowest average PM10 in May 2024 ?,Which city had the 3rd lowest average PM10 in May 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Palkalaiperur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
531,2571,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in May 2022 ?,Report the city with the 3rd lowest average PM2.5 in May 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
532,2575,spatial_aggregation,Which station has the 2nd highest average PM2.5 in November 2020 ?,Which station had the 2nd highest average PM2.5 in November 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
533,2591,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in August 2021 ?,Report the state with the lowest 25th percentile of PM2.5 in August 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
534,2605,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in October 2018 ?,Which state had the 2nd lowest 75th percentile of PM2.5 in October 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Karnataka,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
535,2606,spatial_aggregation,Which station has the highest 75th percentile of PM10 in September 2018 ?,Report the station that had the highest 75th percentile of PM10 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
536,2613,spatial_aggregation,Which station has the 2nd lowest average PM10 in November 2019 ?,Which station registered the 2nd lowest average PM10 during November 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Udyogamandal, Eloor - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
537,2617,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in February 2018 ?,Identify the station with the 2nd highest 25th percentile of PM10 for February 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Dwarka-Sector 8, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
538,2621,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in February 2023 ?,Report the station with the 2nd highest 75th percentile of PM10 in February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
539,2625,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in August 2024 ?,Which state had the 3rd lowest average PM2.5 in August 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
540,2626,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in May 2020 ?,Report the state that had the 2nd highest 75th percentile of PM2.5 in May 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
541,2632,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in July 2024 ?,Identify the city that recorded the 3rd lowest 25th percentile of PM10 value in July 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Chengalpattu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
542,2633,spatial_aggregation,Which city has the 3rd lowest average PM10 in December 2023 ?,Which city registered the 3rd lowest average PM10 during December 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
543,2636,spatial_aggregation,Which state has the lowest median PM2.5 in August 2022 ?,Report the state that had the lowest median PM2.5 in August 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
544,2637,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in December 2022 ?,Identify the city with the 3rd lowest median PM2.5 for December 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Madikeri,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
545,2644,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in January 2020 ?,Determine the state exhibiting the 2nd highest 75th percentile of PM2.5 in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
546,2669,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in February 2020 ?,Determine the station with the 3rd lowest average PM2.5 in February 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Hebbal 1st Stage, Mysuru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
547,2677,spatial_aggregation,Which city has the lowest median PM10 in October 2019 ?,Identify the city with the lowest median PM10 for October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Eloor,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
548,2679,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in February 2023 ?,Determine the state with the 3rd highest 25th percentile of PM10 in February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
549,2680,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in April 2021 ?,Which city had the 3rd lowest 25th percentile of PM10 in April 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
550,2682,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in November 2019 ?,Identify the station that recorded the 2nd highest 25th percentile of PM2.5 value in November 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Nehru Nagar, Kanpur - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
551,2687,spatial_aggregation,Which state has the highest median PM2.5 in July 2018 ?,Identify the state with the highest median PM2.5 for July 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
552,2688,spatial_aggregation,Which state has the 3rd highest average PM10 in November 2019 ?,Which state recorded the 3rd highest average PM10 in November 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
553,2689,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in August 2018 ?,Determine the station with the 2nd highest 75th percentile of PM2.5 in August 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","RIICO Ind. Area III, Bhiwadi - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
554,2697,spatial_aggregation,Which city has the lowest average PM10 in May 2024 ?,Identify the city with the lowest average PM10 for May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Chengalpattu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
555,2701,spatial_aggregation,Which station has the 3rd highest median PM2.5 in January 2024 ?,Report the station with the 3rd highest median PM2.5 in January 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
556,2707,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in May 2024 ?,Identify the city with the 2nd lowest median PM2.5 for May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
557,2713,spatial_aggregation,Which state has the lowest average PM2.5 in November 2022 ?,Which state registered the lowest average PM2.5 during November 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
558,2726,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in May 2019 ?,Report the state that had the 2nd lowest 75th percentile of PM2.5 in May 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
559,2733,spatial_aggregation,Which state has the 3rd lowest average PM10 in December 2023 ?,Which state registered the 3rd lowest average PM10 during December 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
560,2734,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in January 2019 ?,Determine the state exhibiting the 3rd lowest average PM2.5 in January 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
561,2736,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in September 2023 ?,Report the city that had the lowest 75th percentile of PM2.5 in September 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
562,2738,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in August 2023 ?,Which city recorded the lowest 75th percentile of PM10 in August 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
563,2739,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in May 2024 ?,Determine the state with the 2nd highest 75th percentile of PM10 in May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
564,2741,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in April 2023 ?,Report the city with the 2nd lowest 75th percentile of PM10 in April 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
565,2750,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in March 2021 ?,Which city had the 3rd lowest 25th percentile of PM2.5 in March 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Davanagere,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
566,2751,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in September 2019 ?,Report the state with the 3rd lowest 25th percentile of PM10 in September 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
567,2753,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in August 2024 ?,Which city registered the 2nd lowest average PM2.5 during August 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Tirupur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
568,2754,spatial_aggregation,Which state has the 3rd lowest average PM10 in December 2019 ?,Determine the state exhibiting the 3rd lowest average PM10 in December 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
569,2756,spatial_aggregation,Which state has the 3rd highest median PM2.5 in June 2022 ?,Report the state that had the 3rd highest median PM2.5 in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
570,2758,spatial_aggregation,Which state has the 2nd lowest average PM10 in September 2022 ?,Which state recorded the 2nd lowest average PM10 in September 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
571,2764,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in January 2018 ?,Determine the station exhibiting the 3rd lowest average PM2.5 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","BTM Layout, Bengaluru - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
572,2766,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in April 2020 ?,Report the city that had the highest 75th percentile of PM2.5 in April 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Guwahati,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
573,2769,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in September 2024 ?,Determine the station with the lowest 75th percentile of PM2.5 in September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
574,2783,spatial_aggregation,Which station has the highest median PM10 in November 2018 ?,Which station registered the highest median PM10 during November 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
575,2788,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in October 2022 ?,Which state recorded the 3rd highest 25th percentile of PM2.5 in October 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
576,2793,spatial_aggregation,Which city has the 3rd lowest median PM10 in May 2019 ?,Which city registered the 3rd lowest median PM10 during May 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Chennai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
577,2795,spatial_aggregation,Which station has the 3rd highest average PM10 in September 2024 ?,Which station had the 3rd highest average PM10 in September 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
578,2798,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in January 2021 ?,Which station recorded the 2nd lowest 75th percentile of PM10 in January 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Sanegurava Halli, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
579,2800,spatial_aggregation,Which station has the 3rd highest average PM2.5 in September 2020 ?,Which station had the 3rd highest average PM2.5 in September 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Lalbagh, Lucknow - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
580,2806,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in July 2024 ?,Report the state that had the 2nd lowest 25th percentile of PM2.5 in July 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
581,2810,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in May 2024 ?,Which station had the 2nd lowest 75th percentile of PM10 in May 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Sewri, Mumbai - BMC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
582,2812,spatial_aggregation,Which city has the highest median PM10 in June 2022 ?,Identify the city that recorded the highest median PM10 value in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Bihar Sharif,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
583,2818,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in February 2018 ?,Which station recorded the 2nd lowest median PM2.5 in February 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","BWSSB Kadabesanahalli, Bengaluru - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
584,2820,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in August 2018 ?,Which station had the 3rd highest 25th percentile of PM10 in August 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Dwarka-Sector 8, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
585,2823,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in August 2020 ?,Which state registered the 2nd lowest 75th percentile of PM10 during August 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
586,2827,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in March 2024 ?,Identify the station with the 2nd lowest 25th percentile of PM10 for March 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Maldahiya, Varanasi - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
587,2829,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in December 2020 ?,Determine the city with the 3rd lowest median PM2.5 in December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
588,2862,spatial_aggregation,Which city has the 2nd highest median PM2.5 in August 2018 ?,Identify the city that recorded the 2nd highest median PM2.5 value in August 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Jodhpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
589,2865,spatial_aggregation,Which station has the 3rd highest average PM2.5 in March 2019 ?,Which station had the 3rd highest average PM2.5 in March 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Mundka, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
590,2870,spatial_aggregation,Which city has the lowest average PM2.5 in January 2023 ?,Which city had the lowest average PM2.5 in January 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
591,2872,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in April 2022 ?,Identify the state that recorded the 2nd lowest average PM2.5 value in April 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
592,2875,spatial_aggregation,Which station has the 3rd highest average PM2.5 in May 2024 ?,Which station had the 3rd highest average PM2.5 in May 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Chandni Chowk, Delhi - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
593,2884,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in November 2021 ?,Determine the station exhibiting the 3rd lowest median PM2.5 in November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
594,2891,spatial_aggregation,Which city has the highest average PM2.5 in April 2022 ?,Report the city with the highest average PM2.5 in April 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
595,2897,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in February 2018 ?,Identify the station with the highest 75th percentile of PM2.5 for February 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
596,2899,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in April 2020 ?,Determine the city with the 3rd highest 25th percentile of PM2.5 in April 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Ratlam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
597,2903,spatial_aggregation,Which city has the 2nd highest average PM10 in March 2019 ?,Which city registered the 2nd highest average PM10 during March 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Talcher,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
598,2914,spatial_aggregation,Which station has the highest median PM2.5 in September 2019 ?,Determine the station exhibiting the highest median PM2.5 in September 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","F-Block, Sirsa - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
599,2921,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in September 2023 ?,Report the state with the 2nd highest 25th percentile of PM10 in September 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
600,2923,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in February 2023 ?,Which station registered the 3rd lowest 25th percentile of PM10 during February 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Sector-3B Avas Vikas Colony, Agra - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
601,2926,spatial_aggregation,Which state has the lowest median PM2.5 in March 2022 ?,Report the state that had the lowest median PM2.5 in March 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
602,2929,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in December 2022 ?,Determine the city with the 3rd highest 75th percentile of PM2.5 in December 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Bettiah,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
603,2930,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in February 2023 ?,Which station had the highest 75th percentile of PM2.5 in February 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","DRCC Anandpur, Begusarai - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
604,2938,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in April 2018 ?,Which city recorded the 3rd highest 75th percentile of PM10 in April 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Noida,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
605,2940,spatial_aggregation,Which station has the 3rd lowest average PM10 in September 2020 ?,Which station had the 3rd lowest average PM10 in September 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Hebbal 1st Stage, Mysuru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
606,2943,spatial_aggregation,Which city has the lowest median PM2.5 in October 2018 ?,Which city registered the lowest median PM2.5 during October 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
607,2944,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in September 2018 ?,Determine the station exhibiting the 3rd lowest 25th percentile of PM2.5 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Solapur, Solapur - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
608,2946,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in October 2019 ?,Report the city that had the 2nd highest 25th percentile of PM2.5 in October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Meerut,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
609,2947,spatial_aggregation,Which state has the 2nd lowest average PM10 in December 2023 ?,Identify the state with the 2nd lowest average PM10 for December 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
610,2952,spatial_aggregation,Which city has the lowest average PM10 in February 2019 ?,Identify the city that recorded the lowest average PM10 value in February 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Kolar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
611,2955,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in August 2023 ?,Which city had the 3rd highest 75th percentile of PM10 in August 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Greater Noida,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
612,2965,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in March 2022 ?,Which state had the 2nd highest 25th percentile of PM10 in March 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
613,2973,spatial_aggregation,Which city has the highest median PM10 in February 2018 ?,Which city registered the highest median PM10 during February 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Pune,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
614,2988,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in March 2022 ?,Which state recorded the 2nd lowest 25th percentile of PM10 in March 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
615,2993,spatial_aggregation,Which station has the 3rd highest median PM2.5 in October 2022 ?,Which station registered the 3rd highest median PM2.5 during October 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Loni, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
616,3007,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in October 2020 ?,Identify the state with the highest 75th percentile of PM2.5 for October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
617,3020,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in December 2024 ?,Which city had the lowest 75th percentile of PM2.5 in December 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
618,3022,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in October 2019 ?,Identify the city that recorded the 2nd lowest 75th percentile of PM10 value in October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Kolar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
619,3031,spatial_aggregation,Which station has the 2nd highest average PM10 in March 2024 ?,Report the station with the 2nd highest average PM10 in March 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Old City, Sri Ganganagar - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
620,3033,spatial_aggregation,Which city has the highest median PM2.5 in July 2018 ?,Which city registered the highest median PM2.5 during July 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Muzaffarnagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
621,3040,spatial_aggregation,Which station has the lowest median PM2.5 in February 2023 ?,Which station had the lowest median PM2.5 in February 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sahilara, Maihar - KJS Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
622,3044,spatial_aggregation,Which station has the 3rd lowest median PM10 in February 2024 ?,Determine the station exhibiting the 3rd lowest median PM10 in February 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
623,3046,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in October 2023 ?,Report the city that had the lowest 25th percentile of PM2.5 in October 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
624,3060,spatial_aggregation,Which station has the 3rd highest average PM10 in August 2021 ?,Which station had the 3rd highest average PM10 in August 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Chandni Chowk, Delhi - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
625,3070,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in August 2019 ?,Which state had the 3rd highest 75th percentile of PM10 in August 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
626,3074,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in September 2021 ?,Determine the city exhibiting the lowest 25th percentile of PM10 in September 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
627,3076,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in October 2020 ?,Report the station that had the 2nd highest 75th percentile of PM2.5 in October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Vivek Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
628,3078,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in July 2020 ?,Which state recorded the 3rd highest 75th percentile of PM2.5 in July 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
629,3086,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in April 2023 ?,Report the state that had the 2nd lowest 75th percentile of PM10 in April 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
630,3091,spatial_aggregation,Which station has the highest 25th percentile of PM10 in September 2018 ?,Report the station with the highest 25th percentile of PM10 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
631,3093,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in April 2019 ?,Which station registered the 2nd lowest 25th percentile of PM10 during April 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Anand Kala Kshetram, Rajamahendravaram - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
632,3094,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in July 2024 ?,Determine the station exhibiting the 2nd highest 75th percentile of PM10 in July 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","MIT-Daudpur Kothi, Muzaffarpur - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
633,3097,spatial_aggregation,Which station has the 2nd lowest median PM10 in February 2023 ?,Identify the station with the 2nd lowest median PM10 for February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
634,3105,spatial_aggregation,Which station has the 2nd lowest average PM10 in December 2022 ?,Which station had the 2nd lowest average PM10 in December 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Stuart Hill, Madikeri - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
635,3112,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in October 2019 ?,Identify the state that recorded the 3rd lowest 75th percentile of PM10 value in October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
636,3126,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in January 2019 ?,Report the state that had the 3rd highest 25th percentile of PM2.5 in January 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
637,3135,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in June 2022 ?,Which station had the highest 25th percentile of PM2.5 in June 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Vikas Sadan, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
638,3137,spatial_aggregation,Which state has the lowest average PM10 in August 2023 ?,Identify the state with the lowest average PM10 for August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
639,3142,spatial_aggregation,Which state has the 3rd highest median PM10 in September 2018 ?,Identify the state that recorded the 3rd highest median PM10 value in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
640,3146,spatial_aggregation,Which station has the 2nd highest average PM2.5 in July 2020 ?,Report the station that had the 2nd highest average PM2.5 in July 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Sector 11, Faridabad - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
641,3154,spatial_aggregation,Which state has the 3rd lowest median PM10 in September 2020 ?,Determine the state exhibiting the 3rd lowest median PM10 in September 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
642,3161,spatial_aggregation,Which station has the highest 75th percentile of PM10 in November 2018 ?,Report the station with the highest 75th percentile of PM10 in November 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
643,3162,spatial_aggregation,Which state has the 2nd highest average PM2.5 in May 2021 ?,Identify the state that recorded the 2nd highest average PM2.5 value in May 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
644,3164,spatial_aggregation,Which station has the 3rd highest median PM2.5 in June 2018 ?,Determine the station exhibiting the 3rd highest median PM2.5 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Lalbagh, Lucknow - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
645,3165,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in January 2022 ?,Which state had the lowest 25th percentile of PM2.5 in January 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
646,3166,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in June 2020 ?,Report the city that had the 3rd highest 25th percentile of PM10 in June 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Sonipat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
647,3171,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in July 2023 ?,Report the station with the 2nd highest 25th percentile of PM2.5 in July 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
648,3178,spatial_aggregation,Which station has the 2nd lowest average PM10 in February 2023 ?,Which station recorded the 2nd lowest average PM10 in February 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
649,3188,spatial_aggregation,Which state has the 3rd highest average PM2.5 in May 2022 ?,Which state recorded the 3rd highest average PM2.5 in May 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
650,3192,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in June 2023 ?,Identify the state that recorded the lowest 75th percentile of PM10 value in June 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
651,3202,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in May 2023 ?,Identify the state that recorded the 3rd lowest 25th percentile of PM2.5 value in May 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
652,3211,spatial_aggregation,Which city has the highest average PM10 in February 2022 ?,Report the city with the highest average PM10 in February 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Katihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
653,3221,spatial_aggregation,Which state has the highest average PM2.5 in July 2023 ?,Report the state with the highest average PM2.5 in July 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
654,3222,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in May 2018 ?,Identify the state that recorded the 2nd highest 75th percentile of PM10 value in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
655,3229,spatial_aggregation,Which state has the 3rd lowest average PM10 in July 2022 ?,Determine the state with the 3rd lowest average PM10 in July 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
656,3231,spatial_aggregation,Which station has the highest 75th percentile of PM10 in February 2022 ?,Report the station with the highest 75th percentile of PM10 in February 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
657,3234,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in March 2021 ?,Determine the city exhibiting the 3rd lowest 75th percentile of PM2.5 in March 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
658,3236,spatial_aggregation,Which city has the highest average PM10 in May 2018 ?,Report the city that had the highest average PM10 in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
659,3252,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in August 2022 ?,Identify the city that recorded the 2nd highest 75th percentile of PM2.5 value in August 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
660,3257,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in September 2019 ?,Identify the station with the 2nd lowest 25th percentile of PM10 for September 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
661,3273,spatial_aggregation,Which city has the 2nd highest average PM10 in October 2021 ?,Which city registered the 2nd highest average PM10 during October 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
662,3277,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in May 2020 ?,Identify the state with the 3rd highest 75th percentile of PM10 for May 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
663,3283,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in April 2020 ?,Which state registered the 3rd lowest median PM2.5 during April 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
664,3286,spatial_aggregation,Which state has the 2nd highest median PM10 in June 2018 ?,Report the state that had the 2nd highest median PM10 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
665,3288,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in May 2020 ?,Which city recorded the lowest 25th percentile of PM10 in May 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
666,3291,spatial_aggregation,Which state has the lowest average PM2.5 in December 2023 ?,Report the state with the lowest average PM2.5 in December 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
667,3293,spatial_aggregation,Which state has the lowest median PM2.5 in October 2018 ?,Which state registered the lowest median PM2.5 during October 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
668,3299,spatial_aggregation,Which city has the highest median PM2.5 in September 2018 ?,Determine the city with the highest median PM2.5 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Kolar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
669,3301,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in January 2018 ?,Report the state with the 2nd highest 75th percentile of PM10 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
670,3303,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in July 2022 ?,Which city registered the lowest 75th percentile of PM2.5 during July 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
671,3304,spatial_aggregation,Which city has the 3rd highest median PM2.5 in September 2018 ?,Determine the city exhibiting the 3rd highest median PM2.5 in September 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Jodhpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
672,3310,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in November 2023 ?,Which state had the 3rd lowest 75th percentile of PM2.5 in November 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
673,3318,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in March 2020 ?,Which city recorded the 2nd lowest 25th percentile of PM2.5 in March 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Bathinda,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
674,3323,spatial_aggregation,Which station has the 2nd lowest median PM10 in March 2022 ?,Which station registered the 2nd lowest median PM10 during March 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
675,3324,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in December 2024 ?,Determine the state exhibiting the highest 75th percentile of PM2.5 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
676,3331,spatial_aggregation,Which station has the lowest median PM2.5 in December 2018 ?,Report the station with the lowest median PM2.5 in December 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
677,3346,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in May 2020 ?,Report the station that had the 2nd highest 25th percentile of PM10 in May 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Sector-18, Panipat - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
678,3364,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in September 2021 ?,Determine the state exhibiting the 3rd lowest average PM2.5 in September 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
679,3378,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in April 2019 ?,Which station recorded the 2nd lowest 25th percentile of PM2.5 in April 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Alandur Bus Depot, Chennai - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
680,3391,spatial_aggregation,Which city has the highest 75th percentile of PM10 in February 2022 ?,Report the city with the highest 75th percentile of PM10 in February 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Katihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
681,3394,spatial_aggregation,Which state has the lowest average PM10 in March 2018 ?,Determine the state exhibiting the lowest average PM10 in March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
682,3395,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in July 2019 ?,Which city had the 3rd lowest median PM2.5 in July 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Chandrapur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
683,3408,spatial_aggregation,Which state has the highest median PM10 in March 2022 ?,Which state recorded the highest median PM10 in March 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
684,3411,spatial_aggregation,Which state has the highest average PM10 in September 2022 ?,Report the state with the highest average PM10 in September 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
685,3417,spatial_aggregation,Which station has the 2nd highest average PM2.5 in January 2018 ?,Identify the station with the 2nd highest average PM2.5 for January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","DTU, Delhi - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
686,3421,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in October 2024 ?,Report the station with the 2nd highest 25th percentile of PM10 in October 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Sardar Patel Nagar, Dhanbad - JSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
687,3425,spatial_aggregation,Which state has the lowest median PM10 in January 2024 ?,Which state had the lowest median PM10 in January 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
688,3426,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in August 2019 ?,Report the station that had the 2nd lowest average PM2.5 in August 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Hardev Nagar, Bathinda - PPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
689,3431,spatial_aggregation,Which state has the 3rd lowest average PM10 in October 2019 ?,Report the state with the 3rd lowest average PM10 in October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
690,3440,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in August 2020 ?,Which state had the 2nd lowest 25th percentile of PM2.5 in August 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
691,3445,spatial_aggregation,Which city has the highest 75th percentile of PM10 in September 2024 ?,Which city had the highest 75th percentile of PM10 in September 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
692,3464,spatial_aggregation,Which station has the 2nd lowest median PM10 in June 2022 ?,Determine the station exhibiting the 2nd lowest median PM10 in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Sector-19A Nerul, Navi Mumbai - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
693,3467,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in January 2020 ?,Identify the state with the 3rd highest 75th percentile of PM10 for January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
694,3468,spatial_aggregation,Which city has the 3rd lowest median PM10 in October 2019 ?,Which city recorded the 3rd lowest median PM10 in October 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
695,3474,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in March 2020 ?,Determine the city exhibiting the 2nd highest 25th percentile of PM10 in March 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Kalyan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
696,3476,spatial_aggregation,Which station has the highest 25th percentile of PM10 in January 2019 ?,Report the station that had the highest 25th percentile of PM10 in January 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
697,3487,spatial_aggregation,Which station has the 2nd highest median PM10 in March 2018 ?,Identify the station with the 2nd highest median PM10 for March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Dwarka-Sector 8, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
698,3499,spatial_aggregation,Which station has the 3rd highest average PM10 in July 2018 ?,Determine the station with the 3rd highest average PM10 in July 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Mundka, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
699,3504,spatial_aggregation,Which station has the lowest average PM2.5 in June 2022 ?,Determine the station exhibiting the lowest average PM2.5 in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
700,3505,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in February 2023 ?,Which city had the 2nd highest 25th percentile of PM10 in February 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
701,3511,spatial_aggregation,Which station has the 2nd highest median PM10 in March 2020 ?,Report the station with the 2nd highest median PM10 in March 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Khadakpada, Kalyan - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
702,3516,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in October 2022 ?,Report the city that had the highest 75th percentile of PM2.5 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
703,3517,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in February 2024 ?,Identify the station with the 3rd highest 75th percentile of PM2.5 for February 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Kharahiya Basti, Araria - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
704,3518,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in June 2022 ?,Which city recorded the 2nd highest 25th percentile of PM2.5 in June 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Muzaffarnagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
705,3521,spatial_aggregation,Which city has the highest average PM10 in April 2021 ?,Report the city with the highest average PM10 in April 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Baghpat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
706,3528,spatial_aggregation,Which city has the 3rd lowest median PM10 in June 2023 ?,Which city recorded the 3rd lowest median PM10 in June 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
707,3529,spatial_aggregation,Which city has the 2nd highest average PM2.5 in January 2022 ?,Determine the city with the 2nd highest average PM2.5 in January 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Siwan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
708,3530,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in July 2023 ?,Which station had the highest 75th percentile of PM2.5 in July 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Siddharth Nagar-Worli, Mumbai - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
709,3533,spatial_aggregation,Which state has the lowest average PM2.5 in May 2022 ?,Which state registered the lowest average PM2.5 during May 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
710,3539,spatial_aggregation,Which city has the 3rd highest average PM10 in January 2018 ?,Determine the city with the 3rd highest average PM10 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Gurugram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
711,3541,spatial_aggregation,Which city has the 2nd highest median PM10 in November 2021 ?,Report the city with the 2nd highest median PM10 in November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Faridabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
712,3542,spatial_aggregation,Which city has the highest median PM10 in July 2024 ?,Identify the city that recorded the highest median PM10 value in July 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Sri Ganganagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
713,3544,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in January 2018 ?,Determine the city exhibiting the 3rd lowest 75th percentile of PM2.5 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Vijayawada,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
714,3546,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in May 2018 ?,Report the city that had the lowest 75th percentile of PM2.5 in May 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Rajamahendravaram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
715,3551,spatial_aggregation,Which station has the lowest average PM10 in November 2024 ?,Report the station with the lowest average PM10 in November 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
716,3564,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in March 2018 ?,Determine the station exhibiting the 2nd lowest median PM2.5 in March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","BTM Layout, Bengaluru - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
717,3566,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in November 2020 ?,Report the station that had the 2nd lowest 75th percentile of PM10 in November 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Sanegurava Halli, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
718,3569,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in April 2021 ?,Determine the city with the 2nd lowest 25th percentile of PM2.5 in April 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Davanagere,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
719,3570,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in June 2020 ?,Which station had the 3rd lowest median PM2.5 in June 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Hebbal 1st Stage, Mysuru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
720,3577,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in September 2021 ?,Identify the station with the 3rd lowest 75th percentile of PM10 for September 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Kadri, Mangalore - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
721,3579,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in August 2022 ?,Determine the station with the lowest 25th percentile of PM10 in August 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
722,3584,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in January 2018 ?,Determine the state exhibiting the 2nd lowest 75th percentile of PM10 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Karnataka,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
723,3587,spatial_aggregation,Which state has the 3rd lowest average PM10 in January 2020 ?,Identify the state with the 3rd lowest average PM10 for January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Karnataka,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
724,3588,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in July 2023 ?,Which city recorded the lowest 75th percentile of PM2.5 in July 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
725,3589,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in July 2022 ?,Determine the state with the lowest 75th percentile of PM10 in July 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
726,3604,spatial_aggregation,Which state has the highest average PM2.5 in December 2020 ?,Determine the state exhibiting the highest average PM2.5 in December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
727,3607,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in May 2022 ?,Identify the state with the 2nd lowest 25th percentile of PM10 for May 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
728,3613,spatial_aggregation,Which city has the 2nd lowest average PM10 in August 2018 ?,Which city registered the 2nd lowest average PM10 during August 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Navi Mumbai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
729,3617,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in March 2019 ?,Identify the station with the 3rd lowest 75th percentile of PM2.5 for March 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Anand Kala Kshetram, Rajamahendravaram - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
730,3622,spatial_aggregation,Which station has the 2nd highest median PM10 in September 2019 ?,Identify the station that recorded the 2nd highest median PM10 value in September 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Wazirpur, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
731,3623,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in August 2018 ?,Which state registered the 2nd lowest 25th percentile of PM10 during August 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
732,3626,spatial_aggregation,Which state has the 2nd highest median PM2.5 in November 2023 ?,Report the state that had the 2nd highest median PM2.5 in November 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
733,3632,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in October 2024 ?,Identify the city that recorded the 2nd highest 75th percentile of PM2.5 value in October 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
734,3636,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in December 2023 ?,Report the station that had the 3rd lowest median PM2.5 in December 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Udyogamandal, Eloor - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
735,3639,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in January 2020 ?,Determine the state with the highest 25th percentile of PM2.5 in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
736,3645,spatial_aggregation,Which station has the 3rd highest average PM2.5 in September 2023 ?,Which station had the 3rd highest average PM2.5 in September 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Science Center, Surat - SMC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
737,3647,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in May 2021 ?,Identify the city with the lowest 75th percentile of PM10 for May 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
738,3651,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in March 2019 ?,Report the state with the 2nd highest 75th percentile of PM2.5 in March 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
739,3658,spatial_aggregation,Which station has the highest average PM2.5 in May 2023 ?,Which station recorded the highest average PM2.5 in May 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Muradpur, Patna - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
740,3662,spatial_aggregation,Which station has the 2nd highest median PM10 in June 2022 ?,Identify the station that recorded the 2nd highest median PM10 value in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
741,3663,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in August 2022 ?,Which city registered the 2nd lowest 75th percentile of PM2.5 during August 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
742,3665,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in September 2024 ?,Which state had the lowest 25th percentile of PM10 in September 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
743,3670,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in December 2024 ?,Which city had the 3rd highest 75th percentile of PM2.5 in December 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Asansol,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
744,3681,spatial_aggregation,Which station has the highest median PM2.5 in January 2021 ?,Report the station with the highest median PM2.5 in January 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
745,3684,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in January 2023 ?,Determine the station exhibiting the 3rd highest 25th percentile of PM2.5 in January 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Town Hall - Lal Bagh, Darbhanga - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
746,3687,spatial_aggregation,Which state has the 2nd highest average PM2.5 in June 2024 ?,Identify the state with the 2nd highest average PM2.5 for June 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
747,3698,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in February 2022 ?,Which station recorded the 3rd lowest average PM2.5 in February 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Urban, Chamarajanagar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
748,3703,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in October 2021 ?,Which state registered the highest 75th percentile of PM2.5 during October 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
749,3709,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in April 2024 ?,Determine the city with the 3rd lowest median PM2.5 in April 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Chengalpattu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
750,3710,spatial_aggregation,Which city has the lowest median PM10 in December 2018 ?,Which city had the lowest median PM10 in December 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Nashik,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
751,3712,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in October 2020 ?,Identify the state that recorded the lowest 25th percentile of PM10 value in October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
752,3715,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in June 2022 ?,Which station had the 3rd highest 25th percentile of PM2.5 in June 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","NSIT Dwarka, Delhi - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
753,3718,spatial_aggregation,Which city has the lowest average PM10 in March 2022 ?,Which city recorded the lowest average PM10 in March 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Maihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
754,3720,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in November 2018 ?,Which state had the 3rd highest 25th percentile of PM10 in November 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
755,3726,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in June 2022 ?,Report the state that had the 2nd lowest 25th percentile of PM2.5 in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
756,3728,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in March 2021 ?,Which city recorded the highest 25th percentile of PM2.5 in March 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
757,3740,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in June 2023 ?,Which state had the 3rd highest 25th percentile of PM2.5 in June 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
758,3747,spatial_aggregation,Which station has the lowest median PM10 in February 2019 ?,Identify the station with the lowest median PM10 for February 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
759,3755,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in July 2022 ?,Which station had the highest 25th percentile of PM2.5 in July 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Karve Road, Pune - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
760,3761,spatial_aggregation,Which city has the highest median PM10 in July 2021 ?,Report the city with the highest median PM10 in July 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Sonipat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
761,3766,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in September 2021 ?,Report the station that had the 3rd highest 75th percentile of PM10 in September 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Rajbagh, Srinagar - JKSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
762,3767,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in August 2023 ?,Identify the state with the lowest 25th percentile of PM10 for August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
763,3769,spatial_aggregation,Which station has the 3rd lowest average PM10 in January 2021 ?,Determine the station with the 3rd lowest average PM10 in January 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Meelavittan, Thoothukudi - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
764,3774,spatial_aggregation,Which state has the lowest average PM10 in September 2024 ?,Determine the state exhibiting the lowest average PM10 in September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
765,3775,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in January 2020 ?,Which city had the 2nd lowest 25th percentile of PM2.5 in January 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
766,3783,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in March 2022 ?,Which station registered the 2nd highest 75th percentile of PM10 during March 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Rohta, Agra - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
767,3786,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in February 2019 ?,Report the city that had the lowest 25th percentile of PM10 in February 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Maihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
768,3789,spatial_aggregation,Which state has the highest median PM10 in October 2022 ?,Determine the state with the highest median PM10 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
769,3791,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in April 2022 ?,Report the city with the 2nd lowest average PM2.5 in April 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
770,3794,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in May 2023 ?,Determine the city exhibiting the 3rd highest 75th percentile of PM10 in May 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Sri Ganganagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
771,3796,spatial_aggregation,Which state has the lowest median PM2.5 in October 2020 ?,Report the state that had the lowest median PM2.5 in October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
772,3798,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in December 2021 ?,Which state recorded the 2nd highest 25th percentile of PM10 in December 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
773,3805,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in May 2021 ?,Which station had the lowest 75th percentile of PM10 in May 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
774,3814,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in June 2022 ?,Determine the city exhibiting the lowest 75th percentile of PM2.5 in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Koppal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
775,3815,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in May 2024 ?,Which state had the lowest 75th percentile of PM2.5 in May 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
776,3823,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in June 2021 ?,Which station registered the 2nd highest 75th percentile of PM2.5 during June 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","RIICO Ind. Area III, Bhiwadi - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
777,3827,spatial_aggregation,Which city has the 2nd highest median PM2.5 in April 2018 ?,Identify the city with the 2nd highest median PM2.5 for April 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Gaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
778,3834,spatial_aggregation,Which state has the lowest median PM2.5 in November 2020 ?,Determine the state exhibiting the lowest median PM2.5 in November 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
779,3840,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in February 2019 ?,Which city had the 3rd lowest 25th percentile of PM10 in February 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Khanna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
780,3842,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in September 2023 ?,Identify the state that recorded the 3rd lowest average PM2.5 value in September 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Nagaland,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
781,3848,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in December 2024 ?,Which station recorded the 3rd highest 25th percentile of PM2.5 in December 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Trivenidevi Bhalotia College, Asansol - WBPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
782,3849,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in February 2021 ?,Determine the state with the 2nd highest 25th percentile of PM2.5 in February 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
783,3853,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in November 2019 ?,Which station registered the lowest 75th percentile of PM10 during November 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
784,3855,spatial_aggregation,Which state has the 3rd highest median PM10 in August 2020 ?,Which state had the 3rd highest median PM10 in August 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
785,3856,spatial_aggregation,Which station has the 3rd lowest average PM10 in July 2018 ?,Report the station that had the 3rd lowest average PM10 in July 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Zoo Park, Hyderabad - TSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
786,3859,spatial_aggregation,Which state has the 2nd lowest average PM10 in April 2020 ?,Determine the state with the 2nd lowest average PM10 in April 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
787,3864,spatial_aggregation,Which station has the lowest average PM2.5 in December 2024 ?,Determine the station exhibiting the lowest average PM2.5 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
788,3883,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in April 2020 ?,Which state registered the 2nd lowest 25th percentile of PM10 during April 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
789,3889,spatial_aggregation,Which state has the lowest median PM10 in May 2024 ?,Determine the state with the lowest median PM10 in May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
790,3892,spatial_aggregation,Which state has the 3rd highest average PM10 in February 2023 ?,Identify the state that recorded the 3rd highest average PM10 value in February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
791,3894,spatial_aggregation,Which station has the 3rd lowest median PM10 in December 2021 ?,Determine the station exhibiting the 3rd lowest median PM10 in December 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
792,3900,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in December 2020 ?,Which city had the 2nd highest 25th percentile of PM2.5 in December 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
793,3904,spatial_aggregation,Which station has the 3rd highest average PM10 in March 2018 ?,Determine the station exhibiting the 3rd highest average PM10 in March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Karve Road, Pune - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
794,3905,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in May 2024 ?,"In May 2024, report the city with the 2nd highest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Faridabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
795,3907,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in July 2019 ?,Which state recorded the 3rd lowest 75th percentile of PM2.5 in July 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
796,3926,spatial_aggregation,Which station has the 3rd highest median PM2.5 in May 2019 ?,Report the station with the 3rd highest median PM2.5 in May 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","IHBAS, Dilshad Garden, Delhi - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
797,3928,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in May 2023 ?,Which city exhibited the 2nd highest 25th percentile of PM2.5 in May 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
798,3942,spatial_aggregation,Which city has the highest 25th percentile of PM10 in February 2023 ?,Identify the city with the highest 25th percentile of PM10 in February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Hanumangarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
799,3943,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in May 2024 ?,"In May 2024, which station exhibited the 2nd lowest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Crescent University, Chengalpattu - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
800,3946,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in August 2024 ?,Which state displayed the 2nd lowest 25th percentile of PM2.5 in August 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
801,3954,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in February 2022 ?,"In February 2022, report the city with the 3rd lowest average PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Maihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
802,3963,spatial_aggregation,Which station has the 3rd highest average PM10 in March 2020 ?,Report the station with the 3rd highest average PM10 in March 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Dwarka-Sector 8, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
803,3965,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in February 2021 ?,Identify the state with the highest 25th percentile of PM2.5 in February 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
804,3967,spatial_aggregation,Which state has the 2nd highest median PM2.5 in December 2018 ?,Which state had the 2nd highest median PM2.5 in December 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
805,3989,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in March 2023 ?,Identify the station with the 2nd highest 25th percentile of PM10 in March 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","DRCC Anandpur, Begusarai - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
806,3992,spatial_aggregation,Which station has the 2nd highest average PM2.5 in June 2021 ?,"In June 2021, identify the station with the 2nd highest average PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Lodhi Road, Delhi - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
807,3998,spatial_aggregation,Which station has the 2nd highest median PM2.5 in April 2019 ?,"In April 2019, identify the station with the 2nd highest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Shyam Nagar, Palwal - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
808,4005,spatial_aggregation,Which city has the lowest average PM10 in April 2019 ?,Report the city with the lowest average PM10 in April 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Chennai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
809,4009,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in September 2021 ?,Which state showed the 2nd lowest 75th percentile of PM2.5 in September 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
810,4019,spatial_aggregation,Which city has the highest average PM2.5 in January 2020 ?,Identify the city with the highest average PM2.5 in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Muzaffarpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
811,4022,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in January 2023 ?,"In January 2023, identify the station with the 2nd lowest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Deen Dayal Nagar, Sagar - MPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
812,4025,spatial_aggregation,Which station has the 3rd lowest average PM10 in March 2018 ?,Identify the station with the 3rd lowest average PM10 in March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Tirumala, Tirupati - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
813,4030,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in February 2023 ?,"In February 2023, which station recorded the 3rd highest 75th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Central Academy for SFS, Byrnihat - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
814,4035,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in February 2018 ?,Report the city with the 3rd lowest median PM2.5 in February 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Patiala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
815,4036,spatial_aggregation,Which station has the highest average PM2.5 in January 2018 ?,"In January 2018, which station displayed the highest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Vasundhara, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
816,4040,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in July 2024 ?,"In July 2024, identify the state with the 3rd lowest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
817,4042,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in March 2019 ?,"In March 2019, which station registered the lowest 25th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Manali Village, Chennai - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
818,4045,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in November 2024 ?,Which state showed the 2nd lowest average PM2.5 in November 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
819,4050,spatial_aggregation,Which city has the 2nd highest median PM10 in October 2024 ?,"In October 2024, report the city with the 2nd highest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
820,4057,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in January 2021 ?,Which state had the 3rd lowest 75th percentile of PM10 in January 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
821,4060,spatial_aggregation,Which city has the 2nd highest average PM2.5 in April 2023 ?,"In April 2023, which city registered the 2nd highest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Dhanbad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
822,4067,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in March 2018 ?,Identify the station with the 2nd lowest 75th percentile of PM2.5 in March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Model Town, Patiala - PPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
823,4075,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in April 2022 ?,Which station had the 2nd highest 75th percentile of PM2.5 in April 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Sector 11, Faridabad - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
824,4076,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in March 2020 ?,"In March 2020, identify the city with the 2nd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
825,4087,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in February 2020 ?,Which city exhibited the 2nd lowest average PM2.5 in February 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Eloor,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
826,4089,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in January 2019 ?,Report the station with the 2nd highest 75th percentile of PM2.5 in January 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Nehru Nagar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
827,4094,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in March 2021 ?,"In March 2021, identify the state with the 2nd lowest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
828,4100,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in April 2021 ?,"In April 2021, identify the state with the lowest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
829,4102,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in May 2022 ?,"In May 2022, which city recorded the lowest 75th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
830,4116,spatial_aggregation,Which state has the lowest median PM2.5 in October 2019 ?,"In October 2019, report the state with the lowest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
831,4117,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in November 2018 ?,Which station showed the 3rd lowest 25th percentile of PM10 in November 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Chikkaballapur Rural, Chikkaballapur - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
832,4121,spatial_aggregation,Which city has the 3rd highest average PM2.5 in June 2020 ?,Identify the city with the 3rd highest average PM2.5 in June 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Kalaburagi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
833,4125,spatial_aggregation,Which station has the 3rd lowest average PM10 in June 2018 ?,Report the station with the 3rd lowest average PM10 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Jayanagar 5th Block, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
834,4132,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in March 2021 ?,"In March 2021, which state registered the 2nd highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
835,4137,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in January 2018 ?,Report the city with the 3rd lowest 25th percentile of PM10 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Tirupati,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
836,4144,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in June 2019 ?,"In June 2019, which city displayed the 3rd highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Palwal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
837,4149,spatial_aggregation,Which city has the 3rd lowest median PM10 in January 2018 ?,Report the city with the 3rd lowest median PM10 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Thiruvananthapuram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
838,4157,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in January 2019 ?,Identify the city with the 2nd highest 25th percentile of PM2.5 in January 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Kolkata,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
839,4164,spatial_aggregation,Which state has the lowest median PM2.5 in February 2022 ?,"In February 2022, report the state with the lowest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
840,4174,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in November 2024 ?,"In November 2024, which city registered the highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
841,4177,spatial_aggregation,Which city has the 3rd highest average PM10 in July 2018 ?,Which city showed the 3rd highest average PM10 in July 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
842,4188,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in July 2018 ?,"In July 2018, report the city with the 3rd highest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
843,4192,spatial_aggregation,Which city has the highest 25th percentile of PM10 in October 2020 ?,"In October 2020, which city registered the highest 25th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
844,4197,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in November 2019 ?,Report the city with the 3rd lowest median PM2.5 in November 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Vijayawada,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
845,4206,spatial_aggregation,Which city has the highest average PM10 in January 2024 ?,"In January 2024, report the city with the highest average PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Patna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
846,4208,spatial_aggregation,Which city has the 3rd lowest median PM10 in October 2021 ?,"In October 2021, identify the city with the 3rd lowest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Madikeri,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
847,4212,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in April 2019 ?,"In April 2019, report the station with the lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Manali Village, Chennai - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
848,4226,spatial_aggregation,Which state has the lowest average PM2.5 in July 2018 ?,"In July 2018, identify the state with the lowest average PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
849,4232,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in November 2022 ?,"In November 2022, identify the state with the 3rd lowest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
850,4234,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in July 2021 ?,"In July 2021, which city recorded the lowest 25th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
851,4235,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in April 2018 ?,Identify the station with the 3rd lowest average PM2.5 in April 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
852,4236,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in September 2022 ?,"In September 2022, report the state with the 3rd lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
853,4238,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in December 2023 ?,"In December 2023, identify the state with the 2nd highest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
854,4239,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in October 2020 ?,Report the city with the highest 75th percentile of PM2.5 in October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
855,4252,spatial_aggregation,Which station has the 2nd lowest average PM10 in November 2023 ?,"In November 2023, which station recorded the 2nd lowest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
856,4264,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in May 2020 ?,"In May 2020, which city registered the 2nd lowest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Navi Mumbai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
857,4274,spatial_aggregation,Which state has the highest average PM2.5 in March 2018 ?,"In March 2018, identify the state with the highest average PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
858,4284,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in August 2019 ?,"In August 2019, report the station with the highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Deshpande Nagar, Hubballi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
859,4297,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in October 2023 ?,Which state had the 2nd lowest 75th percentile of PM10 in October 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
860,4302,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in June 2019 ?,"In June 2019, report the station with the 2nd lowest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Colaba, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
861,4306,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in March 2018 ?,"In March 2018, which state recorded the lowest 75th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
862,4313,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in November 2018 ?,Identify the city with the 2nd highest 25th percentile of PM10 in November 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Noida,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
863,4314,spatial_aggregation,Which city has the highest median PM10 in June 2021 ?,"In June 2021, report the city with the highest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Sonipat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
864,4326,spatial_aggregation,Which state has the 3rd highest average PM10 in January 2019 ?,"In January 2019, report the state with the 3rd highest average PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
865,4327,spatial_aggregation,Which state has the highest 75th percentile of PM10 in June 2023 ?,Which state displayed the highest 75th percentile of PM10 in June 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
866,4329,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in January 2022 ?,Report the state with the 3rd highest 75th percentile of PM10 in January 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
867,4335,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in January 2022 ?,Report the state with the 2nd lowest average PM2.5 in January 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
868,4338,spatial_aggregation,Which city has the 3rd highest median PM2.5 in October 2021 ?,"In October 2021, report the city with the 3rd highest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Yamuna Nagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
869,4339,spatial_aggregation,Which city has the 2nd lowest average PM10 in November 2021 ?,Which city showed the 2nd lowest average PM10 in November 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
870,4344,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in April 2019 ?,"In April 2019, report the city with the 3rd highest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Singrauli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
871,4353,spatial_aggregation,Which city has the 2nd highest median PM10 in March 2022 ?,Report the city with the 2nd highest median PM10 in March 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Saharsa,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
872,4354,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in June 2024 ?,"In June 2024, which city registered the 2nd highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Rohtak,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
873,4359,spatial_aggregation,Which station has the 2nd highest average PM2.5 in June 2022 ?,Report the station with the 2nd highest average PM2.5 in June 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Vikas Sadan, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
874,4366,spatial_aggregation,Which state has the 3rd highest median PM2.5 in January 2019 ?,"In January 2019, which state exhibited the 3rd highest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
875,4373,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in December 2021 ?,Identify the city with the 2nd lowest 25th percentile of PM2.5 in December 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
876,4374,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in October 2020 ?,"In October 2020, report the state with the 3rd highest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
877,4382,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in December 2019 ?,"In December 2019, identify the state with the 3rd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
878,4387,spatial_aggregation,Which state has the 2nd highest average PM10 in January 2021 ?,Which state had the 2nd highest average PM10 in January 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
879,4393,spatial_aggregation,Which state has the lowest average PM2.5 in July 2021 ?,Which state showed the lowest average PM2.5 in July 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
880,4396,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in February 2018 ?,"In February 2018, which station recorded the 3rd highest 75th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
881,4399,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in August 2021 ?,Which city displayed the 2nd lowest 75th percentile of PM2.5 in August 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
882,4400,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in October 2022 ?,"In October 2022, identify the city with the 3rd lowest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Bidar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
883,4426,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in May 2022 ?,"In May 2022, which station registered the 2nd lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","ECIL Kapra, Hyderabad - TSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
884,4435,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in July 2021 ?,Which state displayed the 2nd lowest 25th percentile of PM2.5 in July 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
885,4438,spatial_aggregation,Which city has the 3rd lowest average PM10 in August 2021 ?,"In August 2021, which city exhibited the 3rd lowest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Mangalore,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
886,4443,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in December 2018 ?,Report the state with the 2nd highest 25th percentile of PM10 in December 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
887,4444,spatial_aggregation,Which city has the lowest median PM2.5 in December 2022 ?,"In December 2022, which city registered the lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
888,4450,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in July 2018 ?,"In July 2018, which city recorded the 2nd highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Kota,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
889,4453,spatial_aggregation,Which station has the highest average PM10 in November 2024 ?,Which station displayed the highest average PM10 in November 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
890,4463,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in September 2024 ?,Identify the station with the lowest 25th percentile of PM10 in September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","DM Office_Kasipur, Samastipur - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
891,4471,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in December 2022 ?,Which state displayed the 3rd highest 25th percentile of PM10 in December 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
892,4476,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in December 2018 ?,"In December 2018, report the city with the 2nd lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Vijayawada,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
893,4479,spatial_aggregation,Which station has the 3rd highest average PM2.5 in September 2024 ?,Report the station with the 3rd highest average PM2.5 in September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
894,4485,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in October 2019 ?,Report the state with the lowest 75th percentile of PM10 in October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
895,4521,spatial_aggregation,Which city has the lowest average PM2.5 in August 2021 ?,Report the city with the lowest average PM2.5 in August 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
896,4528,spatial_aggregation,Which station has the 3rd highest median PM2.5 in February 2023 ?,"In February 2023, which station exhibited the 3rd highest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Central Academy for SFS, Byrnihat - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
897,4533,spatial_aggregation,Which state has the highest median PM10 in March 2018 ?,Report the state with the highest median PM10 in March 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
898,4534,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in November 2024 ?,"In November 2024, which station registered the 2nd lowest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Chandni Chowk, Delhi - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
899,4536,spatial_aggregation,Which city has the lowest average PM2.5 in September 2023 ?,"In September 2023, report the city with the lowest average PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
900,4537,spatial_aggregation,Which city has the 2nd highest average PM2.5 in December 2022 ?,Which city showed the 2nd highest average PM2.5 in December 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Siwan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
901,4540,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in April 2023 ?,"In April 2023, which city recorded the lowest 75th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
902,4541,spatial_aggregation,Which station has the 3rd highest average PM2.5 in October 2022 ?,Identify the station with the 3rd highest average PM2.5 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Sector-51, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
903,4550,spatial_aggregation,Which station has the highest median PM10 in March 2021 ?,"In March 2021, identify the station with the highest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Mundka, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
904,4560,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in February 2018 ?,"In February 2018, report the station with the 2nd lowest average PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
905,4561,spatial_aggregation,Which state has the lowest median PM2.5 in December 2024 ?,Which state displayed the lowest median PM2.5 in December 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
906,4571,spatial_aggregation,Which city has the lowest average PM2.5 in May 2022 ?,Identify the city with the lowest average PM2.5 in May 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
907,4578,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in February 2022 ?,"In February 2022, report the station with the lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Sahilara, Maihar - KJS Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
908,4586,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in July 2019 ?,"In July 2019, identify the station with the lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Urban, Chamarajanagar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
909,4588,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in June 2023 ?,"In June 2023, which city registered the 2nd highest 75th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
910,4598,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in January 2020 ?,"In January 2020, identify the station with the 3rd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Sector-1, Noida - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
911,4602,spatial_aggregation,Which city has the 3rd highest median PM10 in October 2023 ?,"In October 2023, report the city with the 3rd highest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Faridabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
912,4611,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in January 2020 ?,Report the state with the lowest 25th percentile of PM2.5 in January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
913,4613,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in April 2018 ?,Identify the city with the highest 25th percentile of PM2.5 in April 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
914,4625,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in August 2023 ?,Identify the state with the 2nd lowest 25th percentile of PM2.5 in August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
915,4643,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in July 2019 ?,Identify the state with the 3rd lowest average PM2.5 in July 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Telangana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
916,4653,spatial_aggregation,Which state has the highest median PM10 in January 2022 ?,Report the state with the highest median PM10 in January 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
917,4656,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in December 2022 ?,"In December 2022, report the city with the 2nd highest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Purnia,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
918,4666,spatial_aggregation,Which state has the lowest median PM10 in March 2024 ?,"In March 2024, which state recorded the lowest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
919,4667,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in April 2019 ?,Identify the station with the 2nd highest 25th percentile of PM2.5 in April 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Shadipur, Delhi - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
920,4671,spatial_aggregation,Which state has the 3rd highest average PM10 in March 2024 ?,Report the state with the 3rd highest average PM10 in March 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
921,4677,spatial_aggregation,Which city has the lowest median PM2.5 in December 2020 ?,Report the city with the lowest median PM2.5 in December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
922,4678,spatial_aggregation,Which city has the lowest average PM10 in August 2023 ?,"In August 2023, which city registered the lowest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
923,4679,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in February 2021 ?,Identify the station with the 3rd lowest median PM2.5 in February 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Bandra, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
924,4680,spatial_aggregation,Which city has the 3rd lowest average PM10 in January 2022 ?,"In January 2022, report the city with the 3rd lowest average PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
925,4684,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in February 2020 ?,"In February 2020, which state recorded the 2nd lowest 75th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
926,4691,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in November 2024 ?,Identify the city with the 2nd lowest median PM2.5 in November 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Imphal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
927,4692,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in March 2021 ?,"In March 2021, report the state with the lowest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
928,4694,spatial_aggregation,Which city has the highest 25th percentile of PM10 in October 2018 ?,"In October 2018, identify the city with the highest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
929,4697,spatial_aggregation,Which station has the 3rd highest average PM2.5 in December 2024 ?,Identify the station with the 3rd highest average PM2.5 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Trivenidevi Bhalotia College, Asansol - WBPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
930,4699,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in November 2023 ?,Which station showed the 2nd highest 75th percentile of PM2.5 in November 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
931,4701,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in September 2024 ?,Report the state with the 3rd lowest average PM2.5 in September 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
932,4704,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in March 2021 ?,"In March 2021, report the city with the 3rd lowest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
933,4705,spatial_aggregation,Which station has the 2nd lowest average PM10 in January 2019 ?,Which station displayed the 2nd lowest average PM10 in January 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Tamaka Ind. Area, Kolar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
934,4713,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in October 2022 ?,Report the state with the 3rd lowest 25th percentile of PM2.5 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
935,4715,spatial_aggregation,Which city has the 2nd highest average PM10 in September 2020 ?,Identify the city with the 2nd highest average PM10 in September 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Greater Noida,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
936,4716,spatial_aggregation,Which station has the lowest average PM10 in September 2019 ?,"In September 2019, report the station with the lowest average PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Pimpleshwar Mandir, Thane - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
937,4720,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in December 2020 ?,"In December 2020, which city recorded the lowest 75th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
938,4724,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in June 2019 ?,"In June 2019, identify the city with the 3rd lowest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])

true_code()
",Eloor,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
939,4728,spatial_aggregation,Which state has the 3rd highest average PM10 in June 2019 ?,"In June 2019, report the state with the 3rd highest average PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
940,4729,spatial_aggregation,Which state has the 2nd lowest average PM10 in March 2018 ?,Which state had the 2nd lowest average PM10 in March 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
941,4736,spatial_aggregation,Which state has the lowest median PM10 in August 2022 ?,"In August 2022, identify the state with the lowest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
942,4740,spatial_aggregation,Which state has the 3rd highest median PM2.5 in September 2018 ?,"In September 2018, report the state with the 3rd highest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
943,4743,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in September 2020 ?,Report the state with the 2nd highest 25th percentile of PM10 in September 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
944,4744,spatial_aggregation,Which state has the highest average PM10 in April 2018 ?,"In April 2018, which state exhibited the highest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
945,4745,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in March 2020 ?,Identify the city with the lowest 75th percentile of PM2.5 in March 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])

true_code()
",Eloor,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
946,4748,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in April 2024 ?,"In April 2024, identify the city with the 2nd lowest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Varanasi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
947,4753,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in January 2023 ?,Which state showed the 2nd highest 75th percentile of PM10 in January 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
948,4757,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in January 2018 ?,Identify the city with the 2nd highest 25th percentile of PM2.5 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Kanpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
949,4759,spatial_aggregation,Which city has the highest median PM2.5 in August 2024 ?,Which city displayed the highest median PM2.5 in August 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])

true_code()
",Dhanbad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
950,4763,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in March 2022 ?,Identify the station with the 3rd lowest 25th percentile of PM2.5 in March 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])

true_code()
","Town Hall, Munger - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
951,4765,spatial_aggregation,Which state has the 3rd lowest average PM10 in May 2023 ?,Which state had the 3rd lowest average PM10 in May 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
952,4766,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in September 2022 ?,"In September 2022, identify the city with the 3rd highest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Tirupur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
953,4768,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in January 2020 ?,"In January 2020, which station registered the highest 75th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Nehru Nagar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
954,4770,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in May 2019 ?,"In May 2019, report the state with the 3rd lowest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
955,4776,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in March 2023 ?,"In March 2023, report the city with the 3rd lowest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Cuddalore,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
956,4780,spatial_aggregation,Which state has the highest median PM10 in July 2019 ?,"In July 2019, which state exhibited the highest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
957,4785,spatial_aggregation,Which city has the highest 75th percentile of PM10 in June 2020 ?,Report the city with the highest 75th percentile of PM10 in June 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Kalaburagi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
958,4787,spatial_aggregation,Which state has the 2nd highest average PM2.5 in March 2023 ?,Identify the state with the 2nd highest average PM2.5 in March 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
959,4790,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in September 2024 ?,"In September 2024, identify the state with the 2nd highest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
960,4801,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in April 2023 ?,Which city had the 2nd lowest median PM2.5 in April 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
961,4804,spatial_aggregation,Which state has the 2nd highest average PM2.5 in February 2021 ?,"In February 2021, which state registered the 2nd highest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
962,4805,spatial_aggregation,Which state has the 2nd lowest median PM10 in November 2024 ?,Identify the state with the 2nd lowest median PM10 in November 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
963,4811,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in November 2021 ?,Identify the station with the highest 25th percentile of PM2.5 in November 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
964,4816,spatial_aggregation,Which state has the 3rd highest median PM2.5 in June 2019 ?,"In June 2019, which state exhibited the 3rd highest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
965,4818,spatial_aggregation,Which city has the lowest average PM10 in August 2018 ?,"In August 2018, report the city with the lowest average PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Kolar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
966,4820,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in October 2019 ?,"In October 2019, identify the city with the 3rd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Meerut,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
967,4828,spatial_aggregation,Which state has the highest average PM2.5 in August 2022 ?,"In August 2022, which state recorded the highest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
968,4835,spatial_aggregation,Which state has the 2nd highest median PM10 in April 2019 ?,Identify the state with the 2nd highest median PM10 in April 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 4)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
969,4838,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in January 2021 ?,"In January 2021, identify the state with the 3rd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
970,4839,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in December 2024 ?,Report the state with the highest 25th percentile of PM2.5 in December 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])

true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
971,4840,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in July 2020 ?,"In July 2020, which city registered the 2nd highest 75th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Mandi Gobindgarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
972,4853,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in December 2020 ?,Identify the state with the 2nd highest 75th percentile of PM10 in December 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
973,4857,spatial_aggregation,Which state has the lowest average PM10 in July 2023 ?,Report the state with the lowest average PM10 in July 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
974,4859,spatial_aggregation,Which state has the 3rd lowest median PM10 in March 2019 ?,Identify the state with the 3rd lowest median PM10 in March 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
975,4861,spatial_aggregation,Which city has the 3rd highest median PM2.5 in November 2020 ?,Which city showed the 3rd highest median PM2.5 in November 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Baghpat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
976,4866,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in August 2018 ?,"In August 2018, report the state with the 2nd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
977,4868,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in October 2018 ?,"In October 2018, identify the state with the 2nd highest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
978,4874,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in July 2023 ?,"In July 2023, identify the station with the 2nd lowest 25th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
979,4890,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in July 2018 ?,"In July 2018, report the state with the 3rd lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
980,4893,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in May 2023 ?,Report the station with the 3rd lowest 25th percentile of PM10 in May 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Naubad, Bidar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
981,4908,spatial_aggregation,Which city has the 2nd lowest median PM10 in February 2024 ?,"In February 2024, report the city with the 2nd lowest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Maihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
982,4912,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in July 2022 ?,"In July 2022, which state registered the lowest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
983,4920,spatial_aggregation,Which station has the 2nd highest median PM10 in December 2023 ?,"In December 2023, report the station with the 2nd highest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Wazirpur, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
984,4925,spatial_aggregation,Which station has the highest median PM10 in November 2023 ?,Identify the station with the highest median PM10 in November 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
985,4927,spatial_aggregation,Which state has the lowest median PM2.5 in July 2021 ?,Which state had the lowest median PM2.5 in July 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
986,4929,spatial_aggregation,Which state has the 3rd highest average PM10 in December 2022 ?,Report the state with the 3rd highest average PM10 in December 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
987,4931,spatial_aggregation,Which city has the highest 75th percentile of PM10 in October 2022 ?,Identify the city with the highest 75th percentile of PM10 in October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
988,4933,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in January 2023 ?,Which state showed the 2nd lowest 75th percentile of PM10 in January 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])

true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
989,4939,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in December 2020 ?,Which city displayed the 2nd highest 75th percentile of PM10 in December 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
990,4942,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in May 2019 ?,"In May 2019, which city exhibited the 2nd highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Hisar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
991,4949,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in November 2024 ?,Identify the station with the lowest 25th percentile of PM2.5 in November 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
992,4950,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in August 2019 ?,"In August 2019, report the city with the 3rd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
993,4955,spatial_aggregation,Which city has the 3rd lowest median PM10 in February 2023 ?,Identify the city with the 3rd lowest median PM10 in February 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Shivamogga,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
994,4970,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in October 2018 ?,"In October 2018, identify the station with the 3rd highest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])

true_code()
","Mundka, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
995,4974,spatial_aggregation,Which station has the 3rd highest average PM10 in November 2018 ?,"In November 2018, report the station with the 3rd highest average PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Mundka, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
996,4976,spatial_aggregation,Which state has the highest 75th percentile of PM10 in October 2023 ?,"In October 2023, identify the state with the highest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 10)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
997,4979,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in June 2018 ?,Identify the station with the 2nd lowest average PM2.5 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])

true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
998,4981,spatial_aggregation,Which station has the highest average PM2.5 in November 2024 ?,Which station had the highest average PM2.5 in November 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Wazirpur, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
999,4982,spatial_aggregation,Which station has the highest median PM2.5 in May 2022 ?,"In May 2022, identify the station with the highest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","RIICO Ind. Area III, Bhiwadi - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1000,4985,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in November 2022 ?,Identify the station with the 3rd highest 25th percentile of PM10 in November 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","Kamalnath Nagar, Bettiah - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1001,4986,spatial_aggregation,Which state has the highest 75th percentile of PM10 in July 2019 ?,"In July 2019, report the state with the highest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1002,4996,spatial_aggregation,Which state has the highest average PM10 in July 2022 ?,"In July 2022, which state exhibited the highest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1003,4999,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in January 2018 ?,Which station had the 3rd lowest 75th percentile of PM10 in January 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Tirumala, Tirupati - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1004,5004,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in August 2022 ?,"In August 2022, report the station with the 3rd lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])

true_code()
","Sahilara, Maihar - KJS Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1005,5007,spatial_aggregation,Which station has the lowest median PM10 in March 2022 ?,Report the station with the lowest median PM10 in March 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])

true_code()
","Sahilara, Maihar - KJS Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1006,5008,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in March 2022 ?,"In March 2022, which state recorded the lowest 75th percentile of PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1007,5011,spatial_aggregation,Which station has the lowest average PM2.5 in July 2023 ?,Which station displayed the lowest average PM2.5 in July 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1008,5013,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in February 2018 ?,Report the state with the 2nd highest 25th percentile of PM10 in February 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1009,5024,spatial_aggregation,Which station has the highest median PM2.5 in September 2023 ?,"In September 2023, identify the station with the highest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Central Academy for SFS, Byrnihat - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1010,5027,spatial_aggregation,Which station has the highest median PM2.5 in November 2019 ?,Identify the station with the highest median PM2.5 in November 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Nehru Nagar, Kanpur - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1011,5029,spatial_aggregation,Which state has the 3rd highest median PM10 in March 2018 ?,Which state displayed the 3rd highest median PM10 in March 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1012,5030,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in June 2020 ?,"In June 2020, identify the state with the 3rd lowest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1013,5034,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in March 2018 ?,"In March 2018, report the station with the 2nd lowest 75th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Plammoodu, Thiruvananthapuram - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1014,5037,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in August 2023 ?,Report the city with the lowest 25th percentile of PM10 in August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1015,5055,spatial_aggregation,Which city has the 2nd lowest median PM10 in September 2022 ?,Report the city with the 2nd lowest median PM10 in September 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 9)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1016,5069,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in August 2019 ?,Identify the station with the 2nd lowest 75th percentile of PM10 in August 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Hardev Nagar, Bathinda - PPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1017,5072,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in December 2020 ?,"In December 2020, identify the city with the 2nd lowest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1018,5075,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in February 2022 ?,Identify the station with the 3rd highest 25th percentile of PM10 in February 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])

true_code()
","MIT-Daudpur Kothi, Muzaffarpur - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1019,5079,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in August 2023 ?,Report the state with the 2nd lowest 75th percentile of PM2.5 in August 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1020,5080,spatial_aggregation,Which station has the highest median PM10 in December 2018 ?,"In December 2018, which station recorded the highest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Mundka, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1021,5081,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in January 2018 ?,Identify the city with the 2nd lowest average PM2.5 in January 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Bengaluru,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 1)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1022,5085,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in February 2024 ?,Report the city with the lowest 25th percentile of PM10 in February 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])

true_code()
",Tumakuru,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1023,5087,spatial_aggregation,Which station has the 2nd highest average PM2.5 in July 2018 ?,Identify the station with the 2nd highest average PM2.5 in July 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","New Mandi, Muzaffarnagar - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1024,5098,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in December 2019 ?,"In December 2019, which station recorded the highest 75th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])

true_code()
","Nehru Nagar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1025,5102,spatial_aggregation,Which station has the highest 25th percentile of PM10 in May 2018 ?,"In May 2018, identify the station with the highest 25th percentile of PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","RIICO Ind. Area III, Bhiwadi - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1026,5105,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in May 2024 ?,Identify the station with the 2nd highest 25th percentile of PM2.5 in May 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Chandni Chowk, Delhi - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1027,5106,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in May 2021 ?,"In May 2021, report the state with the 2nd lowest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1028,5118,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in July 2019 ?,"In July 2019, report the state with the 2nd lowest median PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 7)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1029,5133,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in June 2020 ?,Report the station with the 2nd highest 25th percentile of PM10 in June 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Sector 30, Faridabad - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1030,5145,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in December 2023 ?,Report the city with the 2nd highest 75th percentile of PM2.5 in December 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1031,5153,spatial_aggregation,Which state has the lowest median PM10 in March 2020 ?,Identify the state with the lowest median PM10 in March 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1032,5157,spatial_aggregation,Which city has the 3rd highest median PM10 in June 2018 ?,Report the city with the 3rd highest median PM10 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])

true_code()
",Greater Noida,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1033,5158,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in February 2018 ?,"In February 2018, which state exhibited the 3rd lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])

true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1034,5161,spatial_aggregation,Which city has the highest average PM10 in August 2023 ?,Which city had the highest average PM10 in August 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Chengalpattu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1035,5167,spatial_aggregation,Which station has the 2nd highest median PM10 in November 2019 ?,Which station showed the 2nd highest median PM10 in November 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])

true_code()
","Ghusuri, Howrah - WBPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 11)]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1036,5172,spatial_aggregation,Which city has the 3rd lowest median PM10 in May 2018 ?,"In May 2018, report the city with the 3rd lowest median PM10.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])

true_code()
",Rajamahendravaram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1037,5177,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in June 2018 ?,Identify the city with the 2nd lowest average PM2.5 in June 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])

true_code()
",Thiruvananthapuram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 6)]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1038,5188,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in August 2021 ?,"In August 2021, which state recorded the 3rd highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1039,5189,spatial_aggregation,Which station has the highest 75th percentile of PM10 in March 2024 ?,Identify the station with the highest 75th percentile of PM10 in March 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","Central Academy for SFS, Byrnihat - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 3)]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1040,5196,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in August 2018 ?,"In August 2018, report the state with the lowest 75th percentile of PM2.5.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 8)]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1041,5197,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in December 2022 ?,Which station had the lowest 25th percentile of PM2.5 in December 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])

true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1042,5199,spatial_aggregation,Which station has the highest average PM10 in May 2020 ?,Report the station with the highest average PM10 in May 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])

true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 5)]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1043,5204,spatial_aggregation,In which city was average PM2.5 the 3rd highest on January 5 2021?,"Which city showed the third-highest average PM2.5 concentration on January 5, 2021?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""city""])


true_code()
",Muzaffarpur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2021) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""city""]
</code>"
1044,5210,spatial_aggregation,In which state was average PM2.5 the 2nd lowest on January 5 2022?,"Identify the state with the second-lowest average PM2.5 level on January 5, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""state""])


true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2022) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""state""]
</code>"
1045,5213,spatial_aggregation,In which city was average PM2.5 the lowest on January 5 2023?,"On January 5, 2023, which city registered the minimum average PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""city""])


true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2023) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""city""]
</code>"
1046,5216,spatial_aggregation,In which station was average PM2.5 the 3rd highest on January 5 2018?,"Which station experienced the third-highest average PM2.5 concentration on January 5, 2018?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""station""])


true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2018) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""station""]
</code>"
1047,5223,spatial_aggregation,In which city was average PM10 the 3rd highest on January 5 2023?,"On January 5, 2023, which city experienced the third-highest average PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""city""])


true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2023) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""city""]
</code>"
1048,5226,spatial_aggregation,In which station was average PM2.5 the 2nd highest on January 5 2022?,"Identify the station with the second-highest average PM2.5 level on January 5, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""station""])


true_code()
","Vivek Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2022) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""station""]
</code>"
1049,5228,spatial_aggregation,In which state was average PM10 the highest on January 5 2021?,"Which state showed the highest average PM10 concentration on January 5, 2021?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2021) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""state""]
</code>"
1050,5230,spatial_aggregation,In which city was average PM2.5 the 2nd lowest on January 5 2018?,"Identify the city that had the second-lowest average PM2.5 reading on January 5, 2018.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""city""])


true_code()
",Bengaluru,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2018) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""city""]
</code>"
1051,5235,spatial_aggregation,In which state was average PM10 the 2nd highest on January 5 2022?,"On January 5, 2022, which state registered the second-highest average PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-2][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2022) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-2][""state""]
</code>"
1052,5240,spatial_aggregation,In which state was average PM10 the 2nd lowest on January 5 2022?,"Which state registered the second-lowest average PM10 concentration on January 5, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2022) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""state""]
</code>"
1053,5253,spatial_aggregation,In which station was average PM10 the 3rd highest on January 5 2019?,"On January 5, 2019, which station had the third-highest average PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""station""])


true_code()
","Mundka, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2019) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""station""]
</code>"
1054,5260,spatial_aggregation,In which station was average PM2.5 the highest on January 5 2020?,"Which station recorded the highest average PM2.5 reading on January 5, 2020?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""station""])


true_code()
","Jahangirpuri, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2020) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""station""]
</code>"
1055,5263,spatial_aggregation,In which city was average PM2.5 the lowest on January 5 2022?,"On January 5, 2022, which city experienced the minimum average PM2.5 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""city""])


true_code()
",Gummidipoondi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2022) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""city""]
</code>"
1056,5277,spatial_aggregation,In which city was average PM2.5 the highest on January 5 2018?,"On January 5, 2018, which city recorded the highest average PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""city""])


true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2018) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""city""]
</code>"
1057,5283,spatial_aggregation,In which state was average PM2.5 the 2nd lowest on January 5 2018?,"On January 5, 2018, which state recorded the second-lowest average PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""state""])


true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2018) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""state""]
</code>"
1058,5295,spatial_aggregation,In which state was average PM2.5 the 3rd highest on January 5 2018?,"On January 5, 2018, which state showed the third-highest average PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""state""])


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2018) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""state""]
</code>"
1059,5298,spatial_aggregation,In which state was average PM10 the 2nd highest on January 5 2019?,"Identify the state with the second-highest average PM10 level on January 5, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-2][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2019) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-2][""state""]
</code>"
1060,5300,spatial_aggregation,In which station was average PM2.5 the 2nd highest on January 5 2020?,"Which station recorded the second-highest average PM2.5 concentration on January 5, 2020?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""station""])


true_code()
","Sanjay Nagar, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2020) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""station""]
</code>"
1061,5302,spatial_aggregation,In which state was average PM10 the 3rd lowest on January 5 2020?,"Identify the state that registered the third-lowest average PM10 reading on January 5, 2020.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""state""])


true_code()
",Karnataka,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.year == 2020) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.day == 5)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""state""]
</code>"
1062,5304,spatial_aggregation,Which city recorded the highest PM10 levels on New Year’s Eve ever?,"Among all New Year's Eves on record, which city registered the highest PM10 concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""city""])


true_code()
",Samastipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 12) & (data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""city""]
</code>"
1063,5309,spatial_aggregation,Which city recorded the 2nd lowest PM10 levels on New Year’s Eve ever?,"Among all New Year's Eves, which city showed the second-lowest PM10 readings?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 12) & (data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""city""]
</code>"
1064,5312,spatial_aggregation,Which city recorded the 3rd highest PM10 levels on New Year’s Eve ever?,Which city reported the third-highest PM10 levels on any New Year's Eve to date?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""city""])


true_code()
",Darbhanga,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 12) & (data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""city""]
</code>"
1065,5313,spatial_aggregation,Which state recorded the 2nd lowest PM10 levels on New Year’s Eve ever?,"Considering all New Year's Eves, which state had the second-lowest recorded PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 12) & (data[""Timestamp""].dt.day == 31)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""state""]
</code>"
1066,5318,spatial_aggregation,Which city reported the 3rd lowest PM10 readings during January 14 2022?,"On January 14, 2022, which city documented the third-lowest PM10 measurements?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[2][""city""])


true_code()
",Chennai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[2][""city""]
</code>"
1067,5326,spatial_aggregation,Which state reported the highest PM10 readings during January 14 2020?,"Which state experienced the highest PM10 values on January 14, 2020?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[-1][""state""])


true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2020)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[-1][""state""]
</code>"
1068,5328,spatial_aggregation,Which state reported the 3rd highest PM2.5 readings during January 14 2022?,"Identify the state with the third-highest PM2.5 measurements on January 14, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[-3][""state""])


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[-3][""state""]
</code>"
1069,5329,spatial_aggregation,Which station reported the 2nd lowest PM10 readings during January 14 2019?,"On January 14, 2019, which station recorded the second-lowest PM10 values?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[1][""station""])


true_code()
","Sanegurava Halli, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[1][""station""]
</code>"
1070,5340,spatial_aggregation,Which city reported the 2nd highest PM2.5 readings during January 14 2023?,"Identify the city that showed the second-highest PM2.5 values on January 14, 2023.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[-2][""city""])


true_code()
",Nalbari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2023)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[-2][""city""]
</code>"
1071,5344,spatial_aggregation,Which station reported the lowest PM2.5 readings during January 14 2022?,"Identify the station with the lowest PM2.5 measurements on January 14, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[0][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[0][""station""]
</code>"
1072,5350,spatial_aggregation,Which city reported the 2nd highest PM10 readings during January 14 2024?,"Which city recorded the second-highest PM10 measurements on January 14, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[-2][""city""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[-2][""city""]
</code>"
1073,5353,spatial_aggregation,Which state reported the lowest PM10 readings during January 14 2022?,"On January 14, 2022, which state registered the lowest PM10 measurements?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[0][""state""])


true_code()
",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[0][""state""]
</code>"
1074,5369,spatial_aggregation,Which city reported the 2nd lowest PM10 readings during January 14 2021?,"On January 14, 2021, which city registered the second-lowest PM10 readings?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[1][""city""])


true_code()
",Thiruvananthapuram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[1][""city""]
</code>"
1075,5370,spatial_aggregation,Which state reported the 2nd lowest PM10 readings during January 14 2024?,"Which state experienced the second-lowest PM10 values on January 14, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[1][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[1][""state""]
</code>"
1076,5372,spatial_aggregation,Which station reported the 2nd lowest PM2.5 readings during January 14 2024?,"Identify the station that recorded the second-lowest PM2.5 readings on January 14, 2024.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[1][""station""])


true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[1][""station""]
</code>"
1077,5373,spatial_aggregation,Which city reported the 3rd highest PM10 readings during January 14 2018?,"On January 14, 2018, which city showed the third-highest PM10 values?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[-3][""city""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2018)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[-3][""city""]
</code>"
1078,5374,spatial_aggregation,Which city reported the 3rd highest PM2.5 readings during January 14 2022?,"Which city registered the third-highest PM2.5 measurements on January 14, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[-3][""city""])


true_code()
",Faridabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[-3][""city""]
</code>"
1079,5381,spatial_aggregation,Which city reported the 3rd lowest PM2.5 readings during January 14 2022?,"On January 14, 2022, which city experienced the third-lowest PM2.5 readings?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[2][""city""])


true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[2][""city""]
</code>"
1080,5389,spatial_aggregation,Which city reported the 2nd highest PM10 readings during January 14 2018?,"On January 14, 2018, which city recorded the second-highest PM10 measurements?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[-2][""city""])


true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2018)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[-2][""city""]
</code>"
1081,5394,spatial_aggregation,Which station reported the 3rd lowest PM2.5 readings during January 14 2019?,"Which station had the third-lowest PM2.5 values on January 14, 2019?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[2][""station""])


true_code()
","Sector-12, Karnal - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[2][""station""]
</code>"
1082,5398,spatial_aggregation,Which city reported the 3rd lowest PM10 readings during January 14 2024?,"Which city experienced the third-lowest PM10 measurements on January 14, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[2][""city""])


true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[2][""city""]
</code>"
1083,5400,spatial_aggregation,Which state reported the highest PM2.5 readings during January 14 2023?,"Identify the state with the highest PM2.5 values on January 14, 2023.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[-1][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2023)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[-1][""state""]
</code>"
1084,5407,spatial_aggregation,Which city reported the 2nd lowest PM2.5 readings during January 14 2019?,"On January 14, 2019, which city showed the second-lowest PM2.5 measurements?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    print(data_sorted.iloc[1][""city""])


true_code()
",Patiala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data_sorted = data.sort_values(by=""PM2.5"")
    return data_sorted.iloc[1][""city""]
</code>"
1085,5416,spatial_aggregation,Which state reported the lowest PM10 readings during January 14 2019?,"Identify the state with the lowest PM10 measurements on January 14, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    print(data_sorted.iloc[0][""state""])


true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[ (data['Timestamp'].dt.month == 1) & (data['Timestamp'].dt.day == 14) & (data['Timestamp'].dt.year == 2019)]
    data = data.dropna(subset=[""PM10""])
    data_sorted = data.sort_values(by=""PM10"")
    return data_sorted.iloc[0][""state""]
</code>"
1086,5426,spatial_aggregation,In which city was the 75th percentile of PM10 the lowest during March 31 2020 ?,"On March 31, 2020, which city recorded the minimum 75th percentile for PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""city""])


true_code()
",Thane,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""city""]
</code>"
1087,5431,spatial_aggregation,In which station was the average PM2.5 the 3rd highest during March 31 2020 ?,"Which station showed the third-highest average PM2.5 on March 31, 2020?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""station""])


true_code()
","Ward-32 Bapupara, Siliguri - WBPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""station""]
</code>"
1088,5432,spatial_aggregation,In which city was the 75th percentile of PM10 the highest during March 31 2019 ?,"On March 31, 2019, which city had the highest 75th percentile for PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""city""])


true_code()
",Singrauli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""city""]
</code>"
1089,5440,spatial_aggregation,In which state was the median PM10 the 2nd highest during March 31 2022 ?,"On March 31, 2022, which state had the second-highest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-2][""state""])


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-2][""state""]
</code>"
1090,5444,spatial_aggregation,In which state was the median PM2.5 the 2nd lowest during March 31 2021 ?,"On March 31, 2021, which state had the second-lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""state""])


true_code()
",Nagaland,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""state""]
</code>"
1091,5463,spatial_aggregation,In which state was the 75th percentile of PM10 the lowest during March 31 2023 ?,"Which state showed the minimum 75th percentile for PM10 on March 31, 2023?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1092,5465,spatial_aggregation,In which state was the 25th percentile of PM10 the lowest during March 31 2019 ?,"Identify the state with the minimum 25th percentile for PM10 on March 31, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1093,5469,spatial_aggregation,In which station was the median PM10 the lowest during March 31 2018 ?,"Identify the station with the minimum median PM10 on March 31, 2018.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""station""])


true_code()
","Anand Kala Kshetram, Rajamahendravaram - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""station""]
</code>"
1094,5494,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 3rd lowest during March 31 2019 ?,"On March 31, 2019, which state recorded the third-lowest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""state""]
</code>"
1095,5495,spatial_aggregation,In which station was the median PM10 the highest during March 31 2023 ?,"Which station showed the highest median PM10 on March 31, 2023?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""station""])


true_code()
","Darshan Nagar, Chhapra - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""station""]
</code>"
1096,5502,spatial_aggregation,In which city was the 75th percentile of PM2.5 the highest during March 31 2021 ?,"On March 31, 2021, which city recorded the highest 75th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""city""])


true_code()
",Singrauli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""city""]
</code>"
1097,5505,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd highest during March 31 2020 ?,"Identify the state with the second-highest 75th percentile for PM2.5 on March 31, 2020.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""state""]
</code>"
1098,5506,spatial_aggregation,In which station was the average PM2.5 the 2nd lowest during March 31 2021 ?,"On March 31, 2021, which station recorded the second-lowest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""station""])


true_code()
","Velachery Res. Area, Chennai - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""station""]
</code>"
1099,5510,spatial_aggregation,In which city was the 25th percentile of PM2.5 the lowest during March 31 2018 ?,"On March 31, 2018, which city recorded the lowest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""city""])


true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""city""]
</code>"
1100,5516,spatial_aggregation,In which state was the average PM10 the lowest during March 31 2019 ?,"On March 31, 2019, which state had the minimum average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1101,5518,spatial_aggregation,In which state was the average PM10 the lowest during March 31 2024 ?,"On March 31, 2024, which state recorded the minimum average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1102,5520,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd highest during March 31 2019 ?,"On March 31, 2019, which state had the second-highest 75th percentile for PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""state""])


true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""state""]
</code>"
1103,5521,spatial_aggregation,In which station was the average PM10 the 3rd highest during March 31 2023 ?,"Identify the station with the third-highest average PM10 on March 31, 2023.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""station""])


true_code()
","DRCC Anandpur, Begusarai - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""station""]
</code>"
1104,5523,spatial_aggregation,In which state was the 75th percentile of PM10 the 3rd lowest during March 31 2022 ?,"Which state showed the third-lowest 75th percentile for PM10 on March 31, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""state""])


true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""state""]
</code>"
1105,5524,spatial_aggregation,In which city was the average PM10 the 2nd lowest during March 31 2022 ?,"On March 31, 2022, which city had the second-lowest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""city""])


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""city""]
</code>"
1106,5525,spatial_aggregation,In which city was the average PM10 the 3rd lowest during March 31 2022 ?,"Identify the city with the third-lowest average PM10 on March 31, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""city""]
</code>"
1107,5526,spatial_aggregation,In which state was the 25th percentile of PM2.5 the lowest during March 31 2020 ?,"On March 31, 2020, which state recorded the lowest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""state""])


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""state""]
</code>"
1108,5528,spatial_aggregation,In which station was the median PM10 the 3rd lowest during March 31 2023 ?,"On March 31, 2023, which station had the third-lowest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""station""])


true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""station""]
</code>"
1109,5537,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 2nd highest during March 31 2020 ?,"Identify the station with the second-highest 75th percentile for PM2.5 on March 31, 2020.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""station""])


true_code()
","Pusa, Delhi - IMD","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""station""]
</code>"
1110,5540,spatial_aggregation,In which city was the average PM10 the 3rd lowest during March 31 2019 ?,"On March 31, 2019, which city had the third-lowest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""city""])


true_code()
",Chennai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""city""]
</code>"
1111,5546,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd highest during March 31 2020 ?,"On March 31, 2020, which city recorded the third-highest 25th percentile for PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""city""])


true_code()
",Hajipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""city""]
</code>"
1112,5551,spatial_aggregation,In which station was the average PM2.5 the 2nd highest during March 31 2021 ?,"Which station showed the second-highest average PM2.5 on March 31, 2021?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""station""])


true_code()
","Suryakiran Bhawan NCL, Singrauli - MPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""station""]
</code>"
1113,5553,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 3rd lowest during March 31 2021 ?,"Identify the station with the third-lowest 75th percentile for PM2.5 on March 31, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""station""])


true_code()
","Plammoodu, Thiruvananthapuram - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""station""]
</code>"
1114,5567,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd highest during March 31 2022 ?,"Which station showed the second-highest 25th percentile for PM2.5 on March 31, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""station""])


true_code()
","Loni, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""station""]
</code>"
1115,5569,spatial_aggregation,In which station was the average PM10 the 3rd highest during March 31 2019 ?,"Identify the station with the third-highest average PM10 on March 31, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""station""])


true_code()
","Ardhali Bazar, Varanasi - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""station""]
</code>"
1116,5571,spatial_aggregation,In which state was the 75th percentile of PM10 the lowest during March 31 2021 ?,"Which state showed the minimum 75th percentile for PM10 on March 31, 2021?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Nagaland,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1117,5576,spatial_aggregation,In which station was the 75th percentile of PM10 the 2nd lowest during March 31 2023 ?,"On March 31, 2023, which station had the second-lowest 75th percentile for PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""station""]
</code>"
1118,5587,spatial_aggregation,In which city was the average PM10 the 2nd lowest during March 31 2020 ?,"Which city showed the second-lowest average PM10 on March 31, 2020?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""city""])


true_code()
",Patiala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""city""]
</code>"
1119,5593,spatial_aggregation,In which city was the median PM10 the 3rd highest during March 31 2022 ?,"Identify the city with the third-highest median PM10 on March 31, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""city""])


true_code()
",Chhapra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""city""]
</code>"
1120,5596,spatial_aggregation,In which city was the average PM2.5 the 3rd highest during March 31 2023 ?,"On March 31, 2023, which city had the third-highest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""city""])


true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""city""]
</code>"
1121,5609,spatial_aggregation,In which city was the median PM2.5 the highest during March 31 2023 ?,"Identify the city with the highest median PM2.5 on March 31, 2023.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""city""])


true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""city""]
</code>"
1122,5615,spatial_aggregation,In which city was the 75th percentile of PM2.5 the highest during March 31 2024 ?,"Which city showed the highest 75th percentile for PM2.5 on March 31, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""city""])


true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""city""]
</code>"
1123,5617,spatial_aggregation,In which state was the average PM10 the 3rd lowest during March 31 2021 ?,"Identify the state with the third-lowest average PM10 on March 31, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""state""]
</code>"
1124,5619,spatial_aggregation,In which state was the 25th percentile of PM10 the lowest during March 31 2024 ?,"Which state showed the minimum 25th percentile for PM10 on March 31, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1125,5623,spatial_aggregation,In which state was the median PM10 the highest during March 31 2018 ?,"Which state showed the highest median PM10 on March 31, 2018?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""state""]
</code>"
1126,5631,spatial_aggregation,In which city was the median PM10 the 3rd highest during March 31 2023 ?,"Which city showed the third-highest median PM10 on March 31, 2023?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""city""])


true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""city""]
</code>"
1127,5632,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 3rd highest during March 31 2021 ?,"On March 31, 2021, which city had the third-highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""city""])


true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""city""]
</code>"
1128,5637,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 2nd lowest during March 31 2023 ?,"Identify the city with the second-lowest 25th percentile for PM2.5 on March 31, 2023.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""city""])


true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""city""]
</code>"
1129,5641,spatial_aggregation,In which station was the average PM10 the highest during March 31 2022 ?,"Identify the station with the highest average PM10 on March 31, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""station""])


true_code()
","Loni, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""station""]
</code>"
1130,5646,spatial_aggregation,In which state was the median PM10 the highest during March 31 2023 ?,"On March 31, 2023, which state recorded the highest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""state""]
</code>"
1131,5662,spatial_aggregation,In which city was the median PM2.5 the 3rd lowest during March 31 2020 ?,"On March 31, 2020, which city recorded the third-lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""city""])


true_code()
",Patiala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""city""]
</code>"
1132,5670,spatial_aggregation,In which state was the median PM2.5 the 2nd highest during March 31 2023 ?,"On March 31, 2023, which state recorded the second-highest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""state""]
</code>"
1133,5672,spatial_aggregation,In which city was the average PM10 the 2nd lowest during March 31 2018 ?,"On March 31, 2018, which city had the second-lowest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""city""])


true_code()
",Amaravati,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""city""]
</code>"
1134,5676,spatial_aggregation,In which city was the average PM2.5 the 2nd highest during March 31 2022 ?,"On March 31, 2022, which city had the second-highest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""city""])


true_code()
",Faridabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""city""]
</code>"
1135,5677,spatial_aggregation,In which city was the median PM10 the lowest during March 31 2020 ?,"Identify the city with the minimum median PM10 on March 31, 2020.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""city""])


true_code()
",Thane,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""city""]
</code>"
1136,5679,spatial_aggregation,In which station was the average PM2.5 the 2nd lowest during March 31 2024 ?,"Which station showed the second-lowest average PM2.5 on March 31, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""station""])


true_code()
","Civil Lines, Bareilly - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""station""]
</code>"
1137,5684,spatial_aggregation,In which state was the median PM10 the 2nd highest during March 31 2021 ?,"On March 31, 2021, which state had the second-highest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-2][""state""])


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-2][""state""]
</code>"
1138,5685,spatial_aggregation,In which city was the 75th percentile of PM10 the 3rd lowest during March 31 2024 ?,"Identify the city with the third-lowest 75th percentile for PM10 on March 31, 2024.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""city""])


true_code()
",Ariyalur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""city""]
</code>"
1139,5694,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 3rd highest during March 31 2019 ?,"On March 31, 2019, which state recorded the third-highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""state""])


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""state""]
</code>"
1140,5698,spatial_aggregation,In which state was the median PM2.5 the highest during March 31 2020 ?,"On March 31, 2020, which state recorded the highest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""state""])


true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""state""]
</code>"
1141,5703,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd highest during March 31 2018 ?,"Which city showed the third-highest 25th percentile for PM10 on March 31, 2018?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""city""])


true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""city""]
</code>"
1142,5704,spatial_aggregation,In which station was the average PM2.5 the highest during March 31 2024 ?,"On March 31, 2024, which station had the highest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""station""])


true_code()
","Sector 11, Faridabad - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""station""]
</code>"
1143,5707,spatial_aggregation,In which state was the 75th percentile of PM10 the 3rd lowest during March 31 2021 ?,"Which state showed the third-lowest 75th percentile for PM10 on March 31, 2021?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""state""]
</code>"
1144,5718,spatial_aggregation,In which city was the median PM2.5 the 2nd lowest during March 31 2023 ?,"On March 31, 2023, which city recorded the second-lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""city""])


true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""city""]
</code>"
1145,5728,spatial_aggregation,In which city was the average PM2.5 the 2nd lowest during March 31 2020 ?,"On March 31, 2020, which city had the second-lowest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""city""])


true_code()
",Bathinda,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""city""]
</code>"
1146,5730,spatial_aggregation,In which city was the average PM2.5 the 2nd lowest during March 31 2023 ?,"On March 31, 2023, which city recorded the second-lowest average PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""city""])


true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""city""]
</code>"
1147,5737,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 3rd highest during March 31 2018 ?,"Identify the city with the third-highest 25th percentile for PM2.5 on March 31, 2018.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""city""])


true_code()
",Singrauli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""city""]
</code>"
1148,5743,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd highest during March 31 2023 ?,"Which state showed the second-highest 75th percentile for PM2.5 on March 31, 2023?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""state""]
</code>"
1149,5752,spatial_aggregation,In which state was the median PM2.5 the 3rd highest during March 31 2023 ?,"On March 31, 2023, which state had the third-highest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""state""])


true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""state""]
</code>"
1150,5758,spatial_aggregation,In which state was the average PM10 the lowest during March 31 2020 ?,"On March 31, 2020, which state recorded the minimum average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1151,5772,spatial_aggregation,In which station was the median PM10 the 2nd lowest during March 31 2023 ?,"On March 31, 2023, which station had the second-lowest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""station""]
</code>"
1152,5781,spatial_aggregation,In which state was the 25th percentile of PM10 the lowest during March 31 2022 ?,"Identify the state with the minimum 25th percentile for PM10 on March 31, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1153,5782,spatial_aggregation,In which state was the average PM10 the 3rd highest during March 31 2019 ?,"On March 31, 2019, which state recorded the third-highest average PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""state""]
</code>"
1154,5787,spatial_aggregation,In which state was the average PM10 the 3rd highest during March 31 2024 ?,"Which state showed the third-highest average PM10 on March 31, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""state""])


true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""state""]
</code>"
1155,5789,spatial_aggregation,In which station was the average PM10 the 2nd lowest during March 31 2021 ?,"Identify the station with the second-lowest average PM10 on March 31, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""station""])


true_code()
","PWD Juction, Kohima - NPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""station""]
</code>"
1156,5790,spatial_aggregation,In which state was the median PM2.5 the 3rd lowest during March 31 2024 ?,"On March 31, 2024, which state recorded the third-lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""state""]
</code>"
1157,5791,spatial_aggregation,In which station was the median PM10 the lowest during March 31 2022 ?,"Which station showed the lowest median PM10 on March 31, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""station""])


true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""station""]
</code>"
1158,5792,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd highest during March 31 2023 ?,"On March 31, 2023, which station had the second-highest 25th percentile for PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""station""])


true_code()
","Kamalnath Nagar, Bettiah - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""station""]
</code>"
1159,5795,spatial_aggregation,In which state was the average PM10 the 3rd highest during March 31 2018 ?,"Which state showed the third-highest average PM10 on March 31, 2018?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""state""])


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""state""]
</code>"
1160,5796,spatial_aggregation,In which state was the median PM2.5 the 3rd lowest during March 31 2019 ?,"On March 31, 2019, which state had the third-lowest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""state""]
</code>"
1161,5799,spatial_aggregation,In which city was the 75th percentile of PM2.5 the lowest during March 31 2022 ?,"Which city showed the lowest 75th percentile for PM2.5 on March 31, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""city""])


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""city""]
</code>"
1162,5800,spatial_aggregation,In which state was the median PM2.5 the 2nd highest during March 31 2022 ?,"On March 31, 2022, which state had the second-highest median PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""state""])


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""state""]
</code>"
1163,5804,spatial_aggregation,In which state was the median PM10 the 3rd highest during March 31 2019 ?,"On March 31, 2019, which state had the third-highest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""state""])


true_code()
",Madhya Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""state""]
</code>"
1164,5809,spatial_aggregation,In which station was the average PM10 the 3rd lowest during March 31 2021 ?,"Identify the station with the third-lowest average PM10 on March 31, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""station""])


true_code()
","Karve Road, Pune - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""station""]
</code>"
1165,5823,spatial_aggregation,In which state was the average PM2.5 the highest during March 31 2024 ?,"Which state showed the highest average PM2.5 on March 31, 2024?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2024)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""state""]
</code>"
1166,5826,spatial_aggregation,In which city was the median PM10 the 3rd lowest during March 31 2018 ?,"On March 31, 2018, which city recorded the third-lowest median PM10?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""city""])


true_code()
",Navi Mumbai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM10""].median().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""city""]
</code>"
1167,5827,spatial_aggregation,In which city was the 25th percentile of PM2.5 the lowest during March 31 2019 ?,"Which city showed the lowest 25th percentile for PM2.5 on March 31, 2019?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""city""])


true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""city""]
</code>"
1168,5830,spatial_aggregation,In which city was the 25th percentile of PM2.5 the highest during March 31 2018 ?,"On March 31, 2018, which city recorded the highest 25th percentile of PM2.5?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""city""])


true_code()
",Bhiwadi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""city""]
</code>"
1169,5831,spatial_aggregation,In which state was the 75th percentile of PM10 the highest during March 31 2023 ?,"Which state showed the highest 75th percentile for PM10 on March 31, 2023?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.month == 3) & (data[""Timestamp""].dt.day == 31) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""state""]
</code>"
1170,5850,spatial_aggregation,Which station had the highest PM2.5 levels on August 15 2022 ?,"Identify the station with the peak PM2.5 concentrations on August 15, 2022.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""station""])

true_code()
","Karve Road, Pune - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""station""]
</code>"
1171,5855,spatial_aggregation,Which state had the highest PM10 levels on August 15 2020 ?,"Which state experienced the highest PM10 levels on August 15, 2020?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""state""])

true_code()
",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""state""]
</code>"
1172,5863,spatial_aggregation,Which station had the 3rd highest PM2.5 levels on August 15 2018 ?,"Which station experienced the third-highest PM2.5 concentrations on August 15, 2018?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""station""])

true_code()
","Ashok Nagar, Udaipur - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""station""]
</code>"
1173,5866,spatial_aggregation,Which state had the 2nd highest PM10 levels on August 15 2021 ?,"On August 15, 2021, which state registered the second-highest PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-2][""state""])

true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-2][""state""]
</code>"
1174,5868,spatial_aggregation,Which state had the 2nd highest PM2.5 levels on August 15 2023 ?,"On August 15, 2023, which state recorded the second-most elevated PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""state""])

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""state""]
</code>"
1175,5880,spatial_aggregation,Which state had the lowest PM10 levels on August 15 2023 ?,"On August 15, 2023, which state showed the minimum PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[0][""state""]
</code>"
1176,5886,spatial_aggregation,Which station had the 2nd highest PM2.5 levels on August 15 2018 ?,"On August 15, 2018, which station showed the second-most elevated PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""station""])

true_code()
","Police Commissionerate, Jaipur - RSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""station""]
</code>"
1177,5887,spatial_aggregation,Which state had the 2nd lowest PM10 levels on August 15 2022 ?,"Which state registered the second-lowest PM10 concentrations on August 15, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""state""])

true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""state""]
</code>"
1178,5897,spatial_aggregation,Which state had the 3rd highest PM10 levels on August 15 2021 ?,"Identify the state with the third-highest PM10 levels on August 15, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2021)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""state""]
</code>"
1179,5903,spatial_aggregation,Which city had the 2nd lowest PM2.5 levels on August 15 2022 ?,"Which city registered the second-lowest PM2.5 levels on August 15, 2022?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""city""])

true_code()
",Haveri,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2022)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""city""]
</code>"
1180,5913,spatial_aggregation,Which station had the lowest PM2.5 levels on August 15 2018 ?,"Identify the station with the lowest PM2.5 levels on August 15, 2018.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""station""])

true_code()
","Tirumala, Tirupati - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""station"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""station""]
</code>"
1181,5915,spatial_aggregation,Which city had the 3rd highest PM2.5 levels on August 15 2018 ?,"Which city registered the third-highest PM2.5 levels on August 15, 2018?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""city""])

true_code()
",Pali,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""city""]
</code>"
1182,5925,spatial_aggregation,Which state had the 3rd highest PM2.5 levels on August 15 2019 ?,"Identify the state that showed the third-highest PM2.5 levels on August 15, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""state""])

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""state""]
</code>"
1183,5929,spatial_aggregation,Which state had the highest PM2.5 levels on August 15 2023 ?,"Identify the state with the highest PM2.5 concentrations on August 15, 2023.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""state""])

true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""state""]
</code>"
1184,5930,spatial_aggregation,Which state had the 2nd lowest PM2.5 levels on August 15 2018 ?,"On August 15, 2018, which state recorded the second-lowest PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""state""]
</code>"
1185,5934,spatial_aggregation,Which state had the 2nd lowest PM10 levels on August 15 2020 ?,"On August 15, 2020, which state had the second-lowest PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""state""])

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2020)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""state""]
</code>"
1186,5936,spatial_aggregation,Which city had the 3rd lowest PM2.5 levels on August 15 2019 ?,"On August 15, 2019, which city showed the third-lowest PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""city""])

true_code()
",Bathinda,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2019)]
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""city""]
</code>"
1187,5940,spatial_aggregation,Which state had the highest PM10 levels on August 15 2023 ?,"On August 15, 2023, which state had the highest PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""state""])

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2023)]
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""state""]
</code>"
1188,5943,spatial_aggregation,Which state had the 3rd lowest PM2.5 levels on August 15 2018 ?,"Which state registered the third-lowest PM2.5 levels on August 15, 2018?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""state""])

true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 15) & (data[""Timestamp""].dt.month == 8) & (data[""Timestamp""].dt.year == 2018)]
    data = data.groupby(""state"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""state""]
</code>"
1189,5955,spatial_aggregation,Which station recorded the 2nd lowest average PM10 level?,Which station recorded the 2nd lowest average PM10 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Semmandalam, Cuddalore - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""station"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1190,5959,spatial_aggregation,Which city recorded the 2nd highest average PM10 level?,Which city registered the 2nd maximum average PM10 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1191,5971,spatial_aggregation,How many city have only two station ?,What number of cities have only two stations?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")['station'].nunique().reset_index()
    data = data[data['station'] == 2]
    count = len(data[""city""].to_list())
    print(count)

true_code()
",24,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")['station'].nunique().reset_index()
    data = data[data['station'] == 2]
    count = len(data[""city""].to_list())
    return count
</code>"
1192,5979,spatial_aggregation,Which station recorded the 3rd lowest stablePM10 level ?,Identify the station that registered the 3rd most minimal stable PM10 level.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""station"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])




true_code()
","Urban, Chamarajanagar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""station"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1193,5980,spatial_aggregation,Which city recorded the highest stablePM10 level ?,Report which city documented the maximum stable PM10 level.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])




true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1194,5981,spatial_aggregation,Which station recorded the lowest stablePM2.5 level ?,Determine the station that recorded the minimum stable PM2.5 level.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""station"")[""PM2.5""].std().reset_index()
    data = data.dropna(subset='PM2.5')
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])




true_code()
","Municipal Corporation Office, Tirunelveli - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""station"")[""PM2.5""].std().reset_index()
    data = data.dropna(subset='PM2.5')
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1195,5988,spatial_aggregation,Which city recorded the 2nd highest stablePM10 level ?,Report which city documented the second highest stable PM10 level.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])




true_code()
",Darbhanga,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1196,5989,spatial_aggregation,Which state recorded the 3rd highest stablePM10 level ?,Determine the state that recorded the third highest stable PM10 level.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""state"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])




true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""state"")[""PM10""].std().reset_index()
    data = data.dropna(subset='PM10')
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1197,5994,spatial_aggregation,Which city had the lowest PM10 level on 27 January 2022 ?,"On 27 January 2022, which city registered the minimum PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Chennai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1198,5996,spatial_aggregation,Which city had the 2nd highest PM10 level on 27 January 2024 ?,Determine the city with the second highest PM10 level on 27 January 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Patna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1199,6001,spatial_aggregation,Which station had the 2nd lowest PM10 level on 27 January 2022 ?,"On 27 January 2022, which station had the second most minimal PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Sahilara, Maihar - KJS Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2022)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1200,6011,spatial_aggregation,Which state had the 2nd highest PM2.5 level on 27 January 2019 ?,"Report which state registered the second highest PM2.5 level on January 27, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1201,6016,spatial_aggregation,Which city had the 3rd highest PM2.5 level on 27 January 2023 ?,Determine the city that recorded the third highest PM2.5 level on 27 January 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Agartala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2023)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1202,6020,spatial_aggregation,Which station had the 2nd highest PM2.5 level on 27 January 2022 ?,Determine the station with the second highest PM2.5 level on 27 January 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Anand Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2022)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1203,6023,spatial_aggregation,Which city had the lowest PM10 level on 27 January 2019 ?,Report which city registered the minimum PM10 level on 27 January 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Aurangabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1204,6026,spatial_aggregation,Which city had the 3rd highest PM10 level on 27 January 2024 ?,Identify the city with the third highest PM10 level on 27 January 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1205,6038,spatial_aggregation,Which state had the 3rd lowest PM2.5 level on 27 January 2021 ?,Identify the state with the third lowest PM2.5 level on 27 January 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1206,6039,spatial_aggregation,Which station had the highest PM10 level on 27 January 2021 ?,"Report which station documented the maximum PM10 level on January 27, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Ratanpura, Rupnagar - Ambuja Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1207,6040,spatial_aggregation,Which state had the 2nd highest PM2.5 level on 27 January 2020 ?,Determine the state that recorded the second highest PM2.5 level on 27 January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2020)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1208,6051,spatial_aggregation,Which station had the 3rd lowest PM10 level on 27 January 2021 ?,"Report which station documented the third most minimal PM10 level on January 27, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Vidayagiri, Bagalkot - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1209,6055,spatial_aggregation,Which city had the 3rd lowest PM2.5 level on 27 January 2018 ?,"Report which city had the third lowest PM2.5 level on January 27, 2018.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Aurangabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2018)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1210,6059,spatial_aggregation,Which station had the lowest PM10 level on 27 January 2020 ?,Report which station registered the lowest PM10 level on 27 January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Urban, Chamarajanagar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2020)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1211,6063,spatial_aggregation,Which state had the lowest PM2.5 level on 27 January 2021 ?,"Report which state documented the minimum PM2.5 level on January 27, 2021.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1212,6065,spatial_aggregation,Which city had the 3rd highest PM10 level on 27 January 2018 ?,"On January 27, 2018, which city showed the third highest PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2018)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1213,6067,spatial_aggregation,Which city had the 3rd highest PM2.5 level on 27 January 2019 ?,"Report which city had the third highest PM2.5 level on January 27, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1214,6072,spatial_aggregation,Which state had the 3rd highest PM2.5 level on 27 January 2019 ?,"Determine the state showing the third highest PM2.5 level on January 27, 2019.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2019)]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1215,6074,spatial_aggregation,Which city had the lowest PM10 level on 27 January 2024 ?,Identify the city with the minimum PM10 level on 27 January 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Bihar Sharif,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2024)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1216,6080,spatial_aggregation,Which city had the 2nd highest PM10 level on 27 January 2021 ?,Determine the city with the second highest PM10 level on 27 January 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2021)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1217,6093,spatial_aggregation,Which city had the highest PM10 level on 27 January 2020 ?,"On January 27, 2020, which city documented the peak PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[(data[""Timestamp""].dt.day == 27) & (data[""Timestamp""].dt.month == 1) & (data[""Timestamp""].dt.year == 2020)]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1218,6115,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2022 ?,Identify the state that saw the least significant fall in 25th percentile PM2.5 levels when comparing December 2022 to October 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1219,6126,spatio_temporal_aggregation,Which station experienced the 3rd lowest median PM10 drop compared between October and December in the year 2022 ?,"Comparing December 2022 to October 2022, which station showed the third least significant drop in median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
","Darshan Nagar, Chhapra - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1220,6140,spatio_temporal_aggregation,Which station experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?,"Comparing December 2018 to October 2018, which station showed the least significant drop in 25th percentile PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
","Ashok Vihar, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1221,6141,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2019 ?,Which state exhibited the smallest decrease in its 25th percentile PM10 levels between October and December of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1222,6145,spatio_temporal_aggregation,Which station experienced the lowest median PM10 drop compared between October and December in the year 2018 ?,"For the period October to December 2018, which station had the smallest decrease in median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
","Talcher Coalfields,Talcher - OSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1223,6146,spatio_temporal_aggregation,Which station experienced the 3rd lowest median PM10 drop compared between October and December in the year 2020 ?,"In 2020, which station will rank third for the smallest reduction in median PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
","DRM Office Danapur, Patna - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1224,6148,spatio_temporal_aggregation,Which station experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2020 ?,Which station exhibited the second smallest decrease in its 75th percentile PM10 levels between October and December of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
","Railway Colony, Guwahati - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1225,6153,spatio_temporal_aggregation,Which city experienced the highest average PM10 drop compared between October and December in the year 2022 ?,"In 2022, which city will rank with the largest reduction in average PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Tirupur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1226,6164,spatio_temporal_aggregation,Which state experienced the 3rd lowest 75th percentile of PM10 drop compared between October and December in the year 2018 ?,Identify the state that saw the third least significant fall in 75th percentile PM10 levels when comparing December 2018 to October 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1227,6166,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2022 ?,"For the period October to December 2022, which station had the second largest decrease in 25th percentile PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
","Maharaj Bada, Gwalior - MPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1228,6167,spatio_temporal_aggregation,Which state experienced the 2nd highest 75th percentile of PM10 drop compared between October and December in the year 2023 ?,"In 2023, which state will rank with the second largest reduction in 75th percentile PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1229,6170,spatio_temporal_aggregation,Which state experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?,"In 2019, which state ranked with the third smallest decrease in 25th percentile PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1230,6173,spatio_temporal_aggregation,Which city experienced the 3rd highest median PM2.5 drop compared between October and December in the year 2019 ?,"For the period October to December 2019, which city had the third largest decrease in median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Sonipat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1231,6181,spatio_temporal_aggregation,Which state experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2020 ?,"In 2020, which state will rank with the second largest reduction in 75th percentile PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Rajasthan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1232,6190,spatio_temporal_aggregation,Which city experienced the 2nd highest 75th percentile of PM10 drop compared between October and December in the year 2018 ?,Which city exhibited the second largest decrease in its 75th percentile PM10 levels between October and December of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Kolar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1233,6193,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM2.5 drop compared between October and December in the year 2022 ?,Which state experienced the third least significant drop in its average PM2.5 levels between October and December 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1234,6197,spatio_temporal_aggregation,Which station experienced the 3rd highest average PM10 drop compared between October and December in the year 2023 ?,Which station exhibited the third largest decrease in its average PM10 levels between October and December of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
","Prabhat Colony, Jalgaon - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1235,6206,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM10 drop compared between October and December in the year 2023 ?,Identify the station that saw the second least significant fall in average PM10 levels when comparing December 2023 to October 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
","Samanpura, Patna - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1236,6208,spatio_temporal_aggregation,Which station experienced the 3rd highest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?,"For the period October to December 2023, which station had the third largest decrease in 75th percentile PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
","IIPHG Lekawada, Gandhinagar - IITM","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1237,6221,spatio_temporal_aggregation,Which state experienced the 2nd lowest median PM10 drop compared between October and December in the year 2020 ?,Which state experienced the second least significant drop in its median PM10 levels between October and December 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1238,6231,spatio_temporal_aggregation,Which station experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2023 ?,"Comparing December 2023 to October 2023, which station showed the least significant drop in 75th percentile PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
","Fertilizer Township, Rourkela - OSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1239,6238,spatio_temporal_aggregation,Which state experienced the highest average PM10 drop compared between October and December in the year 2022 ?,"Comparing December 2022 to October 2022, which state showed the most significant drop in average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1240,6246,spatio_temporal_aggregation,Which city experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2022 ?,Which city exhibited the largest decrease in its 75th percentile PM2.5 levels between October and December of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1241,6254,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2020 ?,"In 2020, which station ranked with the second smallest decrease in median PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
","Nehru Nagar, Kanpur - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1242,6260,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM10 drop compared between October and December in the year 2020 ?,Which city exhibited the third smallest decrease in its median PM10 levels between October and December of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",Howrah,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1243,6265,spatio_temporal_aggregation,Which station experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?,"In 2020, which station will rank with the smallest reduction in 25th percentile PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
","Muzaffarpur Collectorate, Muzaffarpur - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1244,6267,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2020 ?,Which state exhibited the smallest decrease in its 25th percentile PM10 levels between October and December of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1245,6270,spatio_temporal_aggregation,Which state experienced the 2nd lowest median PM10 drop compared between October and December in the year 2021 ?,Which state experienced the second least significant drop in its median PM10 levels between October and December 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1246,6275,spatio_temporal_aggregation,Which state experienced the 2nd lowest average PM10 drop compared between October and December in the year 2019 ?,"In 2019, which state ranked with the second smallest decrease in average PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1247,6276,spatio_temporal_aggregation,Which state experienced the 2nd lowest average PM10 drop compared between October and December in the year 2020 ?,Identify the state that saw the second least significant fall in average PM10 levels when comparing December 2020 to October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1248,6285,spatio_temporal_aggregation,Which city experienced the highest median PM2.5 drop compared between October and December in the year 2023 ?,"For the period October to December 2023, which city had the largest decrease in median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Jalgaon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1249,6293,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM10 drop compared between October and December in the year 2021 ?,"In 2021, which state will rank with the third smallest reduction in average PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1250,6296,spatio_temporal_aggregation,Which state experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2021 ?,"In 2021, which state ranked with the third smallest decrease in 25th percentile PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",West Bengal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1251,6305,spatio_temporal_aggregation,Which state experienced the 3rd highest median PM10 drop compared between October and December in the year 2024 ?,Which state experienced the third most significant drop in its median PM10 levels between October and December 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1252,6307,spatio_temporal_aggregation,Which city experienced the 3rd highest median PM10 drop compared between October and December in the year 2023 ?,"In 2023, which city will rank third for the largest reduction in median PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Kalyan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1253,6308,spatio_temporal_aggregation,Which city experienced the 3rd highest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?,"Comparing December 2021 to October 2021, which city showed the third most significant drop in 25th percentile PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Palwal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1254,6309,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2020 ?,Which state exhibited the second largest decrease in its median PM2.5 levels between October and December of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Gujarat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1255,6315,spatio_temporal_aggregation,Which city experienced the lowest average PM10 drop compared between October and December in the year 2024 ?,"Comparing December 2024 to October 2024, which city showed the least significant drop in average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1256,6316,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2020 ?,Which state exhibited the smallest decrease in its 75th percentile PM10 levels between October and December of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1257,6317,spatio_temporal_aggregation,Which station experienced the highest average PM10 drop compared between October and December in the year 2022 ?,"In 2022, which station ranked with the largest decrease in average PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
","Deen Dayal Nagar, Sagar - MPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1258,6326,spatio_temporal_aggregation,Which city experienced the highest average PM2.5 drop compared between October and December in the year 2022 ?,Which city experienced the most significant drop in its average PM2.5 levels between October and December 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Pune,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1259,6330,spatio_temporal_aggregation,Which station experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2023 ?,Which station exhibited the smallest decrease in its 25th percentile PM10 levels between October and December of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
","DRCC Anandpur, Begusarai - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1260,6334,spatio_temporal_aggregation,Which state experienced the highest 75th percentile of PM10 drop compared between October and December in the year 2018 ?,"For the period October to December 2018, which state had the largest decrease in 75th percentile PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1261,6336,spatio_temporal_aggregation,Which station experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2024 ?,"Comparing December 2024 to October 2024, which station showed the second most significant drop in average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
","Sanjay Nagar, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1262,6337,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM2.5 drop compared between October and December in the year 2024 ?,Which state exhibited the third smallest decrease in its average PM2.5 levels between October and December of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1263,6343,spatio_temporal_aggregation,Which station experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2019 ?,"Comparing December 2019 to October 2019, which station showed the least significant drop in 25th percentile PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
","Ghusuri, Howrah - WBPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1264,6344,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM10 drop compared between October and December in the year 2024 ?,Which state exhibited the second largest decrease in its median PM10 levels between October and December of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1265,6348,spatio_temporal_aggregation,Which city experienced the 2nd lowest average PM10 drop compared between October and December in the year 2018 ?,"For the period October to December 2018, which city had the second smallest decrease in average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",Kolkata,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1266,6349,spatio_temporal_aggregation,Which state experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?,"In 2024, which state will rank with the second largest reduction in 25th percentile PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1267,6351,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM10 drop compared between October and December in the year 2018 ?,Which station exhibited the second smallest decrease in its average PM10 levels between October and December of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
","North Campus, DU, Delhi - IMD","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1268,6356,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM10 drop compared between October and December in the year 2022 ?,"In 2022, which city will rank with the second largest reduction in average PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1269,6376,spatio_temporal_aggregation,Which station experienced the lowest median PM10 drop compared between October and December in the year 2019 ?,"For the period October to December 2019, which station had the smallest decrease in median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
","Talcher Coalfields,Talcher - OSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1270,6379,spatio_temporal_aggregation,Which city experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2018 ?,Which city exhibited the third largest decrease in its 25th percentile PM10 levels between October and December of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Mandideep,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1271,6387,spatio_temporal_aggregation,Which state experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?,"In 2018, which state ranked with the second smallest decrease in 75th percentile PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1272,6388,spatio_temporal_aggregation,Which station experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2020 ?,Identify the station that saw the second least significant fall in 75th percentile PM2.5 levels when comparing December 2020 to October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
","Dr. Karni Singh Shooting Range, Delhi - DPCC","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1273,6393,spatio_temporal_aggregation,Which city experienced the lowest average PM10 drop compared between October and December in the year 2018 ?,Which city exhibited the smallest decrease in its average PM10 levels between October and December of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",Talcher,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1274,6395,spatio_temporal_aggregation,Which station experienced the 2nd highest median PM10 drop compared between October and December in the year 2023 ?,Identify the station that saw the second most significant fall in median PM10 levels when comparing December 2023 to October 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
","Prabhat Colony, Jalgaon - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1275,6396,spatio_temporal_aggregation,Which city experienced the highest 25th percentile of PM10 drop compared between October and December in the year 2019 ?,Which city experienced the most significant drop in its 25th percentile PM10 levels between October and December 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Varanasi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1276,6400,spatio_temporal_aggregation,Which city experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2023 ?,Which city exhibited the second largest decrease in its 25th percentile PM10 levels between October and December of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Jalgaon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1277,6415,spatio_temporal_aggregation,Which state experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2019 ?,"In 2019, which state ranked with the second smallest decrease in average PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1278,6418,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM10 drop compared between October and December in the year 2022 ?,"For the period October to December 2022, which state had the third smallest decrease in average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1279,6421,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2023 ?,Which state exhibited the smallest decrease in its 25th percentile PM2.5 levels between October and December of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1280,6429,spatio_temporal_aggregation,Which city experienced the 3rd lowest average PM10 drop compared between October and December in the year 2023 ?,"In 2023, which city ranked with the third smallest decrease in average PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[2].name)

true_code()
",Chhapra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[2].name
</code>"
1281,6439,spatio_temporal_aggregation,Which city experienced the 3rd highest 75th percentile of PM10 drop compared between October and December in the year 2018 ?,"For the period October to December 2018, which city had the third largest decrease in 75th percentile PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Visakhapatnam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1282,6447,spatio_temporal_aggregation,Which state experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2019 ?,"In 2019, which state will rank with the second largest reduction in 25th percentile PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1283,6450,spatio_temporal_aggregation,Which state experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2023 ?,"In 2023, which state ranked with the second smallest decrease in 25th percentile PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1284,6451,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2019 ?,Identify the station that saw the second least significant fall in median PM2.5 levels when comparing December 2019 to October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
","Vasundhara, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1285,6458,spatio_temporal_aggregation,Which state experienced the 3rd highest average PM10 drop compared between October and December in the year 2019 ?,Identify the state that saw the third most significant fall in average PM10 levels when comparing December 2019 to October 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1286,6464,spatio_temporal_aggregation,Which state experienced the highest average PM10 drop compared between October and December in the year 2018 ?,"In 2018, which state ranked with the largest decrease in average PM10 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1287,6484,spatio_temporal_aggregation,Which city experienced the 3rd highest median PM10 drop compared between October and December in the year 2018 ?,Which city exhibited the third largest decrease in its median PM10 levels between October and December of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-3].name)

true_code()
",Khanna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-3].name
</code>"
1288,6499,spatio_temporal_aggregation,Which state experienced the highest average PM2.5 drop compared between October and December in the year 2024 ?,"In 2024, which state ranked with the largest decrease in average PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1289,6503,spatio_temporal_aggregation,Which city experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?,"In 2019, which city will rank with the smallest reduction in 25th percentile PM2.5 levels from October to December?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[0].name)

true_code()
",Ghaziabad,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[0].name
</code>"
1290,6504,spatio_temporal_aggregation,Which station experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2024 ?,"Comparing December 2024 to October 2024, which station showed the second most significant drop in median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-2].name)

true_code()
","NISE Gwal Pahari, Gurugram - IMD","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-2].name
</code>"
1291,6505,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM10 drop compared between October and December in the year 2020 ?,Which station exhibited the second smallest decrease in its average PM10 levels between October and December of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[1].name)

true_code()
","Ghusuri, Howrah - WBPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[1].name
</code>"
1292,6507,spatio_temporal_aggregation,Which state experienced the highest 75th percentile of PM10 drop compared between October and December in the year 2023 ?,Identify the state that saw the most significant fall in 75th percentile PM10 levels when comparing December 2023 to October 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)]
    data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns='Timestamp', values='PM10')
    data['diff'] = data[10] - data[12]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1293,6511,spatio_temporal_aggregation,What is the average PM10 on Monday in Madhya Pradesh ?,What is the mean PM10 value on Mondays in Madhya Pradesh?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Madhya Pradesh""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 0]
    print(data[""PM10""].mean())

true_code()
",110.66039630932656,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Madhya Pradesh""]
    data = data[data[""Timestamp""].dt.dayofweek == 0]
    return data[""PM10""].mean()
</code>"
1294,6513,spatio_temporal_aggregation,What is the average PM10 on Saturday in Uttar Pradesh ?,Calculate the average PM10 level on Saturdays in Uttar Pradesh.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Uttar Pradesh""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 5]
    print(data[""PM10""].mean())

true_code()
",144.72486535999545,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Uttar Pradesh""]
    data = data[data[""Timestamp""].dt.dayofweek == 5]
    return data[""PM10""].mean()
</code>"
1295,6517,spatio_temporal_aggregation,What is the median PM10 on Friday in West Bengal ?,What is the median PM10 value on Fridays in West Bengal?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""West Bengal""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 4]
    print(data[""PM10""].median())

true_code()
",85.62511627906976,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""West Bengal""]
    data = data[data[""Timestamp""].dt.dayofweek == 4]
    return data[""PM10""].median()
</code>"
1296,6519,spatio_temporal_aggregation,What is the average PM2.5 on Monday in Bihar ?,Calculate the average PM2.5 level on Mondays in Bihar.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Bihar""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 0]
    print(data[""PM2.5""].mean())

true_code()
",78.03937112241952,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Bihar""]
    data = data[data[""Timestamp""].dt.dayofweek == 0]
    return data[""PM2.5""].mean()
</code>"
1297,6527,spatio_temporal_aggregation,What is the median PM10 on Saturday in West Bengal ?,"In West Bengal, what is the median PM10 concentration on Saturdays?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""West Bengal""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 5]
    print(data[""PM10""].median())

true_code()
",85.62,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""West Bengal""]
    data = data[data[""Timestamp""].dt.dayofweek == 5]
    return data[""PM10""].median()
</code>"
1298,6529,spatio_temporal_aggregation,What is the average PM2.5 on Sunday in Madhya Pradesh ?,What is the mean PM2.5 value on Sundays in Madhya Pradesh?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Madhya Pradesh""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 6]
    print(data[""PM2.5""].mean())

true_code()
",45.31739680152073,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Madhya Pradesh""]
    data = data[data[""Timestamp""].dt.dayofweek == 6]
    return data[""PM2.5""].mean()
</code>"
1299,6530,spatio_temporal_aggregation,What is the average PM2.5 on Friday in Bihar ?,"In Bihar, what is the average PM2.5 concentration on Fridays?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Bihar""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 4]
    print(data[""PM2.5""].mean())

true_code()
",76.89194358231488,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Bihar""]
    data = data[data[""Timestamp""].dt.dayofweek == 4]
    return data[""PM2.5""].mean()
</code>"
1300,6532,spatio_temporal_aggregation,What is the median PM2.5 on Saturday in Madhya Pradesh ?,What is the median PM2.5 value on Saturdays in Madhya Pradesh?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Madhya Pradesh""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 5]
    print(data[""PM2.5""].median())

true_code()
",35.572916666666664,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Madhya Pradesh""]
    data = data[data[""Timestamp""].dt.dayofweek == 5]
    return data[""PM2.5""].median()
</code>"
1301,6535,spatio_temporal_aggregation,What is the average PM10 on Saturday in West Bengal ?,What is the mean PM10 value on Saturdays in West Bengal?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""West Bengal""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 5]
    print(data[""PM10""].mean())

true_code()
",109.62809365678739,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""West Bengal""]
    data = data[data[""Timestamp""].dt.dayofweek == 5]
    return data[""PM10""].mean()
</code>"
1302,6537,spatio_temporal_aggregation,What is the median PM2.5 on Friday in Uttar Pradesh ?,Calculate the median PM2.5 level on Fridays in Uttar Pradesh.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Uttar Pradesh""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 4]
    print(data[""PM2.5""].median())

true_code()
",50.67575096899225,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Uttar Pradesh""]
    data = data[data[""Timestamp""].dt.dayofweek == 4]
    return data[""PM2.5""].median()
</code>"
1303,6540,spatio_temporal_aggregation,What is the median PM2.5 on Thursday in West Bengal ?,Determine the median PM2.5 level on Thursdays in West Bengal.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""West Bengal""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 3]
    print(data[""PM2.5""].median())

true_code()
",40.51158823529412,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""West Bengal""]
    data = data[data[""Timestamp""].dt.dayofweek == 3]
    return data[""PM2.5""].median()
</code>"
1304,6542,spatio_temporal_aggregation,What is the median PM10 on Wednesday in Maharashtra ?,"In Maharashtra, what is the median PM10 concentration on Wednesdays?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Maharashtra""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 2]
    print(data[""PM10""].median())

true_code()
",86.66826315789476,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Maharashtra""]
    data = data[data[""Timestamp""].dt.dayofweek == 2]
    return data[""PM10""].median()
</code>"
1305,6547,spatio_temporal_aggregation,What is the average PM2.5 on Monday in Uttar Pradesh ?,What is the mean PM2.5 value on Mondays in Uttar Pradesh?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Uttar Pradesh""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 0]
    print(data[""PM2.5""].mean())

true_code()
",71.3707050064582,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Uttar Pradesh""]
    data = data[data[""Timestamp""].dt.dayofweek == 0]
    return data[""PM2.5""].mean()
</code>"
1306,6558,spatio_temporal_aggregation,What is the average PM2.5 on Saturday in West Bengal ?,Determine the average PM2.5 level on Saturdays in West Bengal.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""West Bengal""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 5]
    print(data[""PM2.5""].mean())

true_code()
",54.84240847989603,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""West Bengal""]
    data = data[data[""Timestamp""].dt.dayofweek == 5]
    return data[""PM2.5""].mean()
</code>"
1307,6560,spatio_temporal_aggregation,What is the median PM10 on Tuesday in Uttar Pradesh ?,"In Uttar Pradesh, what is the median PM10 concentration on Tuesdays?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""Uttar Pradesh""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 1]
    print(data[""PM10""].median())

true_code()
",117.33979166666663,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""Uttar Pradesh""]
    data = data[data[""Timestamp""].dt.dayofweek == 1]
    return data[""PM10""].median()
</code>"
1308,6561,spatio_temporal_aggregation,What is the median PM10 on Monday in West Bengal ?,Calculate the median PM10 level on Mondays in West Bengal.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""state""] == ""West Bengal""]
    data = data[main_data[""Timestamp""].dt.dayofweek == 0]
    print(data[""PM10""].median())

true_code()
",86.20321052631581,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""state""] == ""West Bengal""]
    data = data[data[""Timestamp""].dt.dayofweek == 0]
    return data[""PM10""].median()
</code>"
1309,6568,spatio_temporal_aggregation,Which state had the median PM10 level increased most compared to July 2020 from July 2019 ?,Which state experienced the most substantial growth in median PM10 levels between July 2019 and July 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1310,6569,spatio_temporal_aggregation,Which city had the 75th percentile of PM10 level increased most compared to February 2020 from February 2019 ?,Identify the city where the 75th percentile of PM10 levels rose most significantly from February 2019 to February 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 2]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Panipat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 2]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1311,6575,spatio_temporal_aggregation,Which station had the median PM2.5 level increased most compared to July 2020 from July 2019 ?,Which station recorded the most significant growth in median PM2.5 levels between July 2019 and July 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","RIMT University, Mandi Gobindgarh - PPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1312,6576,spatio_temporal_aggregation,Which state had the median PM2.5 level increased most compared to October 2020 from October 2019 ?,"Between October 2019 and October 2020, which state saw the largest upswing in median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1313,6577,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to September 2020 from September 2019 ?,Identify the city where average PM10 levels increased the most from September 2019 to September 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 9]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Hapur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 9]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1314,6579,spatio_temporal_aggregation,Which state had the 25th percentile of PM2.5 level increased most compared to September 2020 from September 2019 ?,Which state experienced the highest rise in its 25th percentile PM2.5 level between September 2019 and September 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 9]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Uttar Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 9]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1315,6582,spatio_temporal_aggregation,Which station had the median PM2.5 level increased most compared to September 2020 from September 2019 ?,"For September 2020 compared to September 2019, which station registered the highest increase in median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 9]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","RIMT University, Mandi Gobindgarh - PPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 9]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1316,6583,spatio_temporal_aggregation,Which station had the median PM10 level increased most compared to October 2020 from October 2019 ?,Which station had the most pronounced increase in median PM10 levels when comparing October 2019 to October 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","New Collectorate, Baghpat - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1317,6592,spatio_temporal_aggregation,Which city had the 25th percentile of PM2.5 level increased most compared to April 2020 from April 2019 ?,"Between April 2019 and April 2020, which city saw the largest upswing in its 25th percentile PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 4]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Ratlam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 4]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1318,6594,spatio_temporal_aggregation,Which state had the 25th percentile of PM10 level increased most compared to January 2020 from January 2019 ?,"For January 2020 relative to January 2019, which state had the most substantial increase in its 25th percentile PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 1]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 1]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1319,6595,spatio_temporal_aggregation,Which city had the average PM2.5 level increased most compared to June 2020 from June 2019 ?,Which city experienced the highest rise in average PM2.5 levels between June 2019 and June 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 6]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Brajrajnagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 6]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].mean().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1320,6604,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to October 2020 from October 2019 ?,Which city had the most substantial increase in average PM10 levels between October 2019 and October 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Kalaburagi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1321,6605,spatio_temporal_aggregation,Which city had the 25th percentile of PM10 level increased most compared to October 2020 from October 2019 ?,Identify the city that showed the highest rise in its 25th percentile PM10 level from October 2019 to October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Baghpat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1322,6606,spatio_temporal_aggregation,Which station had the 25th percentile of PM10 level increased most compared to March 2020 from March 2019 ?,"Comparing March 2019 with March 2020, which station experienced the largest increase in its 25th percentile PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 3]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","Sector-18, Panipat - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 3]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.25).reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1323,6615,spatio_temporal_aggregation,Which station had the median PM10 level increased most compared to December 2020 from December 2019 ?,Which station had the most pronounced increase in median PM10 levels when comparing December 2019 to December 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 12]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","Ardhali Bazar, Varanasi - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 12]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1324,6618,spatio_temporal_aggregation,Which station had the average PM10 level increased most compared to April 2020 from April 2019 ?,"Comparing April 2019 with April 2020, which station experienced the largest increase in average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 4]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","Manali Village, Chennai - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 4]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1325,6619,spatio_temporal_aggregation,Which city had the 75th percentile of PM2.5 level increased most compared to June 2020 from June 2019 ?,"For June 2020 relative to June 2019, which city recorded the highest increase in its 75th percentile PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 6]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Brajrajnagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 6]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1326,6627,spatio_temporal_aggregation,Which state had the median PM2.5 level increased most compared to February 2020 from February 2019 ?,Which state experienced the highest rise in median PM2.5 levels between February 2019 and February 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 2]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Assam,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 2]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1327,6629,spatio_temporal_aggregation,Which city had the 25th percentile of PM2.5 level increased most compared to March 2020 from March 2019 ?,Identify the city that saw the most significant growth in its 25th percentile PM2.5 level from March 2019 to March 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 3]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Alwar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 3]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1328,6641,spatio_temporal_aggregation,Which station had the average PM10 level increased most compared to October 2020 from October 2019 ?,Identify the station where average PM10 levels increased the most from October 2019 to October 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 10]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1329,6651,spatio_temporal_aggregation,Which state had the average PM10 level increased most compared to July 2020 from July 2019 ?,"For July 2020 relative to July 2019, which state recorded the highest increase in average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].mean().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1330,6661,spatio_temporal_aggregation,Which station had the median PM10 level increased most compared to January 2020 from January 2019 ?,Identify the station that saw the most significant growth in median PM10 levels from January 2019 to January 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 1]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","Sector-18, Panipat - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 1]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1331,6662,spatio_temporal_aggregation,Which state had the median PM10 level increased most compared to March 2020 from March 2019 ?,"For March 2020 compared to March 2019, which state registered the highest increase in median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 3]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 3]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM10""].median().reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM10"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1332,6672,spatio_temporal_aggregation,Which state had the 75th percentile of PM2.5 level increased most compared to July 2020 from July 2019 ?,"Between July 2019 and July 2020, which state saw the largest upswing in its 75th percentile PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 7]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1333,6678,spatio_temporal_aggregation,Which station had the 75th percentile of PM2.5 level increased most compared to February 2020 from February 2019 ?,"For February 2020 compared to February 2019, which station registered the highest increase in its 75th percentile PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 2]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
","Loni, Ghaziabad - UPPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 2]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1334,6679,spatio_temporal_aggregation,Which city had the median PM2.5 level increased most compared to June 2020 from June 2019 ?,Which city had the most pronounced increase in median PM2.5 levels when comparing June 2019 to June 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.month == 6]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    print(data.iloc[-1].name)


true_code()
",Brajrajnagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.month == 6]
    data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)]
    data[""year""] = data[""Timestamp""].dt.year
    data = data.groupby([""city"", ""year""])[""PM2.5""].median().reset_index()
    data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"")
    data[""diff""] = data[2020] - data[2019]
    data = data.dropna(subset=""diff"")
    data = data.sort_values(by=""diff"")
    return data.iloc[-1].name
</code>"
1335,6699,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 30 µg/m³ of PM2.5 in year 2018,How many stations in Uttar Pradesh exceeded 30 µg/m³ of PM2.5 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['station'].nunique()
    print(count)

true_code()
",16,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['station'].nunique()
    return count
</code>"
1336,6702,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the Indian guideline of PM10 in year 2018,What number of Uttar Pradesh stations exceeded the Indian guideline for PM10 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    print(count)

true_code()
",10,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    return count
</code>"
1337,6707,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 45 µg/m³ of PM2.5 in year 2020,How many stations in Maharashtra surpassed 45 µg/m³ of PM2.5 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",35,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1338,6708,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 75 µg/m³ of PM10 in year 2020,What count of Maharashtra stations exceeded 75 µg/m³ of PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    print(count)

true_code()
",36,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    return count
</code>"
1339,6710,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 90 µg/m³ of PM10 in year 2020,What number of Madhya Pradesh stations surpassed 90 µg/m³ of PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    print(count)

true_code()
",16,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    return count
</code>"
1340,6714,spatio_temporal_aggregation,How many stations of West Bengal crossed the 75 µg/m³ of PM10 in year 2022,What number of West Bengal stations exceeded 75 µg/m³ of PM10 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    print(count)

true_code()
",13,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    return count
</code>"
1341,6715,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM10 in year 2017,How many stations in Maharashtra went above 90 µg/m³ of PM10 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    print(count)

true_code()
",10,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    return count
</code>"
1342,6717,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM10 in year 2021,How many stations in Maharashtra exceeded the Indian guideline for PM10 in the year 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    print(count)

true_code()
",39,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    return count
</code>"
1343,6718,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2018,What number of Madhya Pradesh stations went above 45 µg/m³ of PM2.5 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",8,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1344,6726,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the WHO guideline of PM10 in year 2017,What number of Madhya Pradesh stations exceeded the WHO guideline for PM10 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['station'].nunique()
    print(count)

true_code()
",4,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['station'].nunique()
    return count
</code>"
1345,6729,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 45 µg/m³ of PM2.5 in year 2018,How many stations in Uttar Pradesh exceeded 45 µg/m³ of PM2.5 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",16,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1346,6733,spatio_temporal_aggregation,How many stations of West Bengal crossed the 75 µg/m³ of PM10 in year 2018,How many stations in West Bengal went above 75 µg/m³ of PM10 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    print(count)

true_code()
",7,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    return count
</code>"
1347,6734,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 45 µg/m³ of PM2.5 in year 2023,What number of Uttar Pradesh stations surpassed 45 µg/m³ of PM2.5 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",54,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1348,6740,spatio_temporal_aggregation,How many stations of Bihar crossed the 45 µg/m³ of PM2.5 in year 2022,What count of Bihar stations surpassed 45 µg/m³ of PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",34,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1349,6744,spatio_temporal_aggregation,How many stations of West Bengal crossed the WHO guideline of PM10 in year 2021,What count of West Bengal stations exceeded the WHO guideline for PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['station'].nunique()
    print(count)

true_code()
",13,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['station'].nunique()
    return count
</code>"
1350,6746,spatio_temporal_aggregation,How many stations of Bihar crossed the WHO guideline of PM10 in year 2021,What number of Bihar stations surpassed the WHO guideline for PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['station'].nunique()
    print(count)

true_code()
",28,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['station'].nunique()
    return count
</code>"
1351,6747,spatio_temporal_aggregation,How many stations of Bihar crossed the 45 µg/m³ of PM2.5 in year 2018,How many stations in Bihar exceeded 45 µg/m³ of PM2.5 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",3,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1352,6748,spatio_temporal_aggregation,How many stations of Bihar crossed the 75 µg/m³ of PM2.5 in year 2018,What count of Bihar stations went above 75 µg/m³ of PM2.5 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['station'].nunique()
    print(count)

true_code()
",3,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['station'].nunique()
    return count
</code>"
1353,6751,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2017,How many stations in Uttar Pradesh went above 90 µg/m³ of PM2.5 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['station'].nunique()
    print(count)

true_code()
",11,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['station'].nunique()
    return count
</code>"
1354,6765,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 30 µg/m³ of PM10 in year 2021,How many stations in Madhya Pradesh exceeded 30 µg/m³ of PM10 in the year 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['station'].nunique()
    print(count)

true_code()
",16,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['station'].nunique()
    return count
</code>"
1355,6775,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM10 in year 2017,How many stations in Maharashtra went above the Indian guideline for PM10 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    print(count)

true_code()
",10,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    return count
</code>"
1356,6790,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 45 µg/m³ of PM2.5 in year 2017,What number of Maharashtra stations went above 45 µg/m³ of PM2.5 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",10,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1357,6791,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM2.5 in year 2023,How many stations in West Bengal surpassed 90 µg/m³ of PM2.5 in the year 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['station'].nunique()
    print(count)

true_code()
",15,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['station'].nunique()
    return count
</code>"
1358,6792,spatio_temporal_aggregation,How many stations of West Bengal crossed the WHO guideline of PM2.5 in year 2021,What count of West Bengal stations exceeded the WHO guideline for PM2.5 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['station'].nunique()
    print(count)

true_code()
",13,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['station'].nunique()
    return count
</code>"
1359,6802,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 30 µg/m³ of PM2.5 in year 2022,What number of Maharashtra stations went above 30 µg/m³ of PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['station'].nunique()
    print(count)

true_code()
",38,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['station'].nunique()
    return count
</code>"
1360,6804,spatio_temporal_aggregation,How many stations of Maharashtra crossed the WHO guideline of PM2.5 in year 2019,What count of Maharashtra stations exceeded the WHO guideline for PM2.5 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['station'].nunique()
    print(count)

true_code()
",20,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['station'].nunique()
    return count
</code>"
1361,6824,spatio_temporal_aggregation,How many stations of West Bengal crossed the 30 µg/m³ of PM2.5 in year 2023,What count of West Bengal stations surpassed 30 µg/m³ of PM2.5 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['station'].nunique()
    print(count)

true_code()
",15,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['station'].nunique()
    return count
</code>"
1362,6826,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM10 in year 2020,What number of Maharashtra stations went above the Indian guideline for PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    print(count)

true_code()
",36,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['station'].nunique()
    return count
</code>"
1363,6833,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM2.5 in year 2020,How many stations in Maharashtra surpassed the Indian guideline for PM2.5 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 60]
    count = data['station'].nunique()
    print(count)

true_code()
",34,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 60]
    count = data['station'].nunique()
    return count
</code>"
1364,6838,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 90 µg/m³ of PM2.5 in year 2020,What number of Madhya Pradesh stations went above 90 µg/m³ of PM2.5 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['station'].nunique()
    print(count)

true_code()
",15,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['station'].nunique()
    return count
</code>"
1365,6839,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 90 µg/m³ of PM10 in year 2018,How many stations in Madhya Pradesh surpassed 90 µg/m³ of PM10 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    print(count)

true_code()
",8,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    return count
</code>"
1366,6841,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the WHO guideline of PM2.5 in year 2021,How many stations in Madhya Pradesh went above the WHO guideline for PM2.5 in the year 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['station'].nunique()
    print(count)

true_code()
",16,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['station'].nunique()
    return count
</code>"
1367,6852,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM10 in year 2019,What count of Maharashtra stations exceeded 90 µg/m³ of PM10 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    print(count)

true_code()
",21,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['station'].nunique()
    return count
</code>"
1368,6853,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 45 µg/m³ of PM10 in year 2020,How many stations in Uttar Pradesh went above 45 µg/m³ of PM10 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['station'].nunique()
    print(count)

true_code()
",21,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['station'].nunique()
    return count
</code>"
1369,6856,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 75 µg/m³ of PM10 in year 2018,What count of Maharashtra stations went above 75 µg/m³ of PM10 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    print(count)

true_code()
",9,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['station'].nunique()
    return count
</code>"
1370,6861,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Post-Monsoon season  of 2021?,Which state noted the 3rd maximum median PM10 during the Post-Monsoon season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1371,6862,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season  of 2024?,Which state recorded the 3rd highest 25th percentile of PM10 in the Post-Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1372,6883,spatio_temporal_aggregation,Which station had the highest average PM10 during the Winter season  of 2018?,Which station recorded the peak average PM10 during the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1373,6884,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Summer season  of 2019?,Which city registered the 3rd minimum average PM2.5 in the Summer season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Mumbai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1374,6887,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Summer season  of 2019?,Which station registered the 2nd minimum average PM10 during the Summer season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Manali Village, Chennai - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1375,6895,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Summer season  of 2020?,Which city recorded the peak 75th percentile of PM10 during the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1376,6899,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Monsoon season  of 2018?,Which state registered the 3rd minimum 25th percentile of PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])


true_code()
",Maharashtra,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
1377,6906,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Summer season  of 2020?,Which state noted the 3rd minimum average PM2.5 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1378,6907,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Winter season  of 2024?,Which city recorded the 3rd highest median PM10 during the Winter season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Kozhikode,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1379,6926,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Post-Monsoon season  of 2023?,Which city registered the 3rd maximum median PM10 in the Post-Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Rohtak,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1380,6928,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Post-Monsoon season  of 2018?,Which station recorded the minimum 25th percentile of PM10 in the Post-Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Talcher Coalfields,Talcher - OSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1381,6933,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season  of 2020?,Which state noted the 2nd maximum 25th percentile of PM2.5 during the Post-Monsoon season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1382,6935,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Post-Monsoon season  of 2019?,Which state registered the minimum 75th percentile of PM2.5 during the Post-Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1383,6936,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Summer season  of 2023?,Which city noted the 2nd minimum average PM10 in the Summer season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Damoh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1384,6938,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Winter season  of 2020?,Which station registered the 3rd lowest 75th percentile of PM2.5 in the Winter season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1385,6942,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Summer season  of 2020?,Which city noted the 3rd minimum 25th percentile of PM10 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1386,6953,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Summer season  of 2023?,Which state registered the 3rd highest 75th percentile of PM2.5 in the Summer season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1387,6956,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Monsoon season  of 2020?,Which city registered the 3rd maximum 75th percentile of PM10 during the Monsoon season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1388,6966,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Winter season  of 2024?,Which station noted the minimum 75th percentile of PM10 during the Winter season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Manipur University, Imphal - Manipur PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1389,6971,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Summer season  of 2020?,Which station registered the 3rd minimum 25th percentile of PM10 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1390,6976,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Winter season  of 2023?,Which station recorded the minimum 75th percentile of PM10 during the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","GIDC, Nandesari - Nandesari Ind. Association","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1391,6978,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Summer season  of 2018?,Which city noted the 3rd maximum 75th percentile of PM2.5 during the Summer season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1392,6980,spatio_temporal_aggregation,Which city had the highest median PM10 during the Winter season  of 2022?,Which city registered the peak median PM10 during the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1393,6983,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Winter season  of 2022?,Which state registered the 2nd maximum 25th percentile of PM2.5 in the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1394,7000,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Winter season  of 2022?,Which city recorded the 3rd lowest 25th percentile of PM10 during the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1395,7003,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Monsoon season  of 2019?,Which city recorded the 3rd lowest median PM2.5 in the Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1396,7012,spatio_temporal_aggregation,Which station had the highest median PM10 during the Monsoon season  of 2019?,Which station recorded the peak median PM10 in the Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1397,7013,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season  of 2020?,Which city registered the 2nd minimum 25th percentile of PM10 during the Post-Monsoon season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1398,7020,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Monsoon season  of 2019?,Which city noted the 3rd maximum 25th percentile of PM10 in the Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1399,7028,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Summer season  of 2018?,Which station registered the 2nd minimum 75th percentile of PM10 during the Summer season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","PWD Grounds, Vijayawada - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1400,7037,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Summer season  of 2024?,Which city registered the minimum average PM10 in the Summer season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Chengalpattu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1401,7041,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM2.5 during the Summer season  of 2021?,Which station noted the minimum 25th percentile of PM2.5 in the Summer season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])


true_code()
","Ratanpura, Rupnagar - Ambuja Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1402,7061,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Summer season  of 2019?,Which station registered the 3rd minimum 25th percentile of PM2.5 in the Summer season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Alandur Bus Depot, Chennai - CPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1403,7062,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Monsoon season  of 2018?,Which city noted the 2nd minimum 75th percentile of PM2.5 during the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])


true_code()
",Chikkaballapur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1404,7066,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Summer season  of 2019?,Which station recorded the 2nd highest median PM2.5 in the Summer season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1405,7070,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Winter season  of 2023?,Which city registered the 3rd maximum median PM10 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Panchkula,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1406,7071,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Post-Monsoon season  of 2019?,Which city noted the minimum 75th percentile of PM2.5 during the Post-Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])


true_code()
",Eloor,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
1407,7075,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Winter season  of 2018?,Which city recorded the 2nd highest 25th percentile of PM2.5 during the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Yadgir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1408,7076,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Winter season  of 2020?,Which city registered the minimum average PM2.5 in the Winter season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])


true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
1409,7083,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Monsoon season  of 2022?,Which state noted the 3rd minimum median PM10 during the Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])


true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
1410,7087,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Post-Monsoon season  of 2023?,Which state recorded the minimum 25th percentile of PM2.5 during the Post-Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1411,7090,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Winter season  of 2018?,Which city recorded the 2nd lowest average PM10 in the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Chikkaballapur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1412,7095,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Winter season  of 2023?,Which station noted the 3rd lowest 75th percentile of PM2.5 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Urban, Chamarajanagar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1413,7096,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season  of 2023?,Which station recorded the 3rd lowest 25th percentile of PM2.5 during the Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1414,7098,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Monsoon season  of 2024?,Which station noted the 2nd minimum 75th percentile of PM10 during the Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1415,7107,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Winter season  of 2018?,Which city noted the peak 75th percentile of PM10 during the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Yamuna Nagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1416,7114,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Summer season  of 2022?,Which station recorded the 3rd lowest 25th percentile of PM2.5 in the Summer season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","ECIL Kapra, Hyderabad - TSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1417,7115,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Winter season  of 2024?,Which city registered the peak 25th percentile of PM2.5 during the Winter season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Thoothukudi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1418,7122,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Post-Monsoon season  of 2021?,Which state noted the peak 75th percentile of PM2.5 during the Post-Monsoon season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1419,7124,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Summer season  of 2023?,Which state registered the minimum 75th percentile of PM10 during the Summer season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1420,7126,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM10 during the Winter season  of 2020?,Which state recorded the 3rd highest 75th percentile of PM10 during the Winter season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1421,7130,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Post-Monsoon season  of 2019?,Which station registered the minimum 75th percentile of PM10 during the Post-Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Udyogamandal, Eloor - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1422,7133,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Winter season  of 2020?,Which state registered the minimum average PM10 in the Winter season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1423,7140,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Post-Monsoon season  of 2024?,Which city noted the 2nd minimum median PM10 during the Post-Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Shillong,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1424,7141,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Winter season  of 2022?,Which station recorded the 2nd lowest median PM10 in the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1425,7144,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Monsoon season  of 2019?,Which station recorded the 2nd lowest 25th percentile of PM10 in the Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Rabindra Sarobar, Kolkata - WBPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1426,7147,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Summer season  of 2020?,Which state recorded the 3rd highest median PM2.5 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1427,7151,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Post-Monsoon season  of 2024?,Which station registered the peak 25th percentile of PM10 in the Post-Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Vyttila, Kochi - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1428,7152,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Winter season  of 2024?,Which city noted the minimum 75th percentile of PM10 during the Winter season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Tirunelveli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1429,7154,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Winter season  of 2018?,Which station registered the 3rd maximum 75th percentile of PM10 during the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Worli, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1430,7158,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM2.5 during the Post-Monsoon season  of 2022?,Which station noted the peak 75th percentile of PM2.5 in the Post-Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1431,7172,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Monsoon season  of 2024?,Which state registered the 2nd minimum 75th percentile of PM10 during the Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1432,7175,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM2.5 during the Summer season  of 2020?,Which state registered the 3rd minimum 25th percentile of PM2.5 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1433,7177,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Winter season  of 2024?,Which state recorded the peak average PM2.5 in the Winter season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1434,7180,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Post-Monsoon season  of 2022?,Which state recorded the 2nd lowest average PM10 in the Post-Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1435,7182,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Monsoon season  of 2024?,Which station noted the 3rd minimum 25th percentile of PM10 during the Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","DM College of Science, Imphal - Manipur PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1436,7187,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Summer season  of 2018?,Which station registered the 3rd maximum median PM10 in the Summer season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Worli, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1437,7191,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Winter season  of 2022?,Which city noted the minimum 75th percentile of PM10 during the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1438,7207,spatio_temporal_aggregation,Which state had the 3rd lowest average PM10 during the Monsoon season  of 2024?,Which state recorded the 3rd lowest average PM10 in the Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])


true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
1439,7213,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Post-Monsoon season  of 2024?,Which station recorded the minimum median PM10 in the Post-Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1440,7220,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Winter season  of 2021?,Which city registered the peak 25th percentile of PM10 during the Winter season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1441,7221,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Monsoon season  of 2024?,Which state noted the 3rd maximum 25th percentile of PM10 in the Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Punjab,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1442,7224,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Monsoon season  of 2019?,Which city noted the 2nd highest median PM10 during the Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1443,7225,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Monsoon season  of 2023?,Which station recorded the 2nd lowest median PM2.5 in the Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
1444,7232,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Summer season  of 2023?,Which state registered the peak 25th percentile of PM10 in the Summer season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1445,7243,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Monsoon season  of 2018?,Which state recorded the peak average PM2.5 during the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1446,7248,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Post-Monsoon season  of 2021?,Which state noted the minimum 25th percentile of PM10 in the Post-Monsoon season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1447,7256,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Winter season  of 2021?,Which station registered the minimum 75th percentile of PM10 in the Winter season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1448,7258,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Monsoon season  of 2018?,Which state recorded the peak 25th percentile of PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1449,7262,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Summer season  of 2019?,Which city registered the 2nd maximum average PM2.5 during the Summer season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1450,7274,spatio_temporal_aggregation,Which station had the highest median PM10 during the Monsoon season  of 2018?,Which station registered the peak median PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1451,7276,spatio_temporal_aggregation,Which city had the lowest median PM10 during the Post-Monsoon season  of 2024?,Which city recorded the minimum median PM10 during the Post-Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1452,7282,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Post-Monsoon season  of 2023?,Which state recorded the 2nd lowest median PM10 in the Post-Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1453,7286,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Summer season  of 2021?,Which state registered the 2nd maximum 25th percentile of PM2.5 in the Summer season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1454,7290,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Post-Monsoon season  of 2023?,Which station noted the 3rd minimum average PM10 during the Post-Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Tarapur, Silchar - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1455,7291,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Monsoon season  of 2018?,Which state recorded the 3rd lowest median PM2.5 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1456,7301,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season  of 2022?,Which state registered the 3rd maximum 75th percentile of PM2.5 in the Post-Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1457,7308,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Winter season  of 2023?,Which city noted the peak 25th percentile of PM10 during the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1458,7313,spatio_temporal_aggregation,Which city had the 3rd highest average PM2.5 during the Post-Monsoon season  of 2019?,Which city registered the 3rd maximum average PM2.5 during the Post-Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1459,7317,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Monsoon season  of 2020?,Which station noted the 2nd minimum average PM10 during the Monsoon season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Lumpyngngad, Shillong - Meghalaya PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1460,7318,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Winter season  of 2022?,Which state recorded the peak median PM2.5 during the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1461,7319,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Monsoon season  of 2023?,Which state registered the 2nd minimum average PM10 in the Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1462,7320,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Winter season  of 2023?,Which station noted the 3rd lowest average PM10 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Semmandalam, Cuddalore - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1463,7323,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Monsoon season  of 2021?,Which station noted the 2nd minimum median PM10 during the Monsoon season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1464,7325,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Monsoon season  of 2018?,Which state registered the minimum median PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1465,7331,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Winter season  of 2021?,Which state registered the 2nd maximum 25th percentile of PM2.5 in the Winter season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1466,7334,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Post-Monsoon season  of 2021?,Which station registered the 3rd maximum average PM2.5 during the Post-Monsoon season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])


true_code()
","Vijay Nagar, Sangli - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
1467,7338,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season  of 2023?,Identify the station that recorded the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1468,7344,spatio_temporal_aggregation,Which station had the highest median PM2.5 during the Post-Monsoon season  of 2020?,Determine the station that recorded the highest median for PM2.5 over the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1469,7346,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Winter season  of 2020?,Identify the city that registered the third highest median PM2.5 during the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1470,7348,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Winter season  of 2022?,Determine which city exhibited the 2nd highest 25th percentile of PM10 over the Winter season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1471,7355,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Summer season  of 2018?,Report which city recorded the most minimal average PM10 throughout the Summer season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Kolkata,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1472,7365,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Winter season  of 2020?,Which station recorded the 2nd highest median for PM10 in the Winter season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1473,7375,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Monsoon season  of 2019?,Report which state experienced the most minimal median PM10 throughout the Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1474,7378,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season  of 2019?,Identify the station that registered the second lowest 25th percentile for PM2.5 during the Post-Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])


true_code()
","Udyogamandal, Eloor - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
1475,7382,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Summer season  of 2018?,Identify the station that showed the second highest median PM2.5 during the Summer season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1476,7385,spatio_temporal_aggregation,Which station had the 2nd highest average PM10 during the Summer season  of 2020?,Which station experienced the 2nd highest average for PM10 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1477,7386,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Post-Monsoon season  of 2023?,Identify the city that recorded the third lowest median PM10 during the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1478,7389,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Winter season  of 2018?,Which station possessed the 3rd lowest median for PM10 in the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Tirumala, Tirupati - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1479,7390,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Winter season  of 2018?,Identify the station exhibiting the third lowest 25th percentile of PM2.5 during the Winter season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","MIDC Khutala, Chandrapur - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1480,7392,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Monsoon season  of 2023?,Determine the state that recorded the highest average for PM2.5 over the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Manipur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1481,7396,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Summer season  of 2022?,Determine which station exhibited the 2nd highest median PM2.5 over the Summer season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Vijay Nagar, Sangli - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1482,7404,spatio_temporal_aggregation,Which state had the highest average PM10 during the Summer season  of 2021?,Determine the state that showed the peak average PM10 over the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1483,7406,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Post-Monsoon season  of 2023?,Identify the city exhibiting the third highest median PM2.5 during the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Thoothukudi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1484,7419,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Post-Monsoon season  of 2019?,Report which state registered the highest 75th percentile for PM10 throughout the Post-Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1485,7422,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Post-Monsoon season  of 2019?,Identify the state exhibiting the third most minimal median PM2.5 during the Post-Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1486,7431,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Summer season  of 2022?,Report which city possessed the peak average PM2.5 throughout the Summer season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1487,7434,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM10 during the Summer season  of 2019?,Identify the state that recorded the third highest 75th percentile of PM10 during the Summer season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1488,7448,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Summer season  of 2023?,Determine the station exhibiting the 2nd lowest 25th percentile of PM2.5 over the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
1489,7449,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Monsoon season  of 2018?,Which station experienced the most minimal median for PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Hombegowda Nagar, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1490,7452,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Summer season  of 2023?,Determine the city that showed the 3rd most minimal average PM10 over the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Cuddalore,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1491,7461,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Summer season  of 2020?,Which state recorded the 2nd lowest 75th percentile for PM2.5 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])


true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1492,7463,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season  of 2018?,Report which state possessed the third most minimal 75th percentile of PM2.5 throughout the Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Telangana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1493,7467,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Winter season  of 2018?,Report which station registered the 3rd most minimal average for PM10 throughout the Winter season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Chikkaballapur Rural, Chikkaballapur - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1494,7471,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Winter season  of 2022?,Report which station experienced the third lowest average PM2.5 throughout the Winter season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Urban, Chamarajanagar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1495,7472,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Monsoon season  of 2020?,Determine the city that recorded the 3rd most minimal 75th percentile of PM10 over the Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Mysuru,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1496,7474,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Monsoon season  of 2023?,Identify the station that registered the most minimal 25th percentile for PM10 during the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Tarapur, Silchar - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1497,7475,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Summer season  of 2022?,Report which station possessed the third highest average PM10 throughout the Summer season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Vijay Nagar, Sangli - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1498,7477,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Winter season  of 2019?,Which state recorded the 2nd highest median for PM2.5 in the Winter season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1499,7479,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Monsoon season  of 2022?,Report which city possessed the 2nd most minimal average PM2.5 throughout the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])


true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1500,7484,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season  of 2020?,Determine the city that showed the 3rd lowest 25th percentile of PM10 over the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Eloor,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1501,7486,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Winter season  of 2024?,Identify the city exhibiting the peak 25th percentile of PM10 during the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Rohtak,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1502,7492,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Winter season  of 2022?,Determine the city exhibiting the 3rd highest 25th percentile of PM10 over the Winter season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Vijayawada,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1503,7495,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Summer season  of 2021?,Report which state possessed the third most minimal 25th percentile of PM10 throughout the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
1504,7498,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Winter season  of 2023?,Identify the state that recorded the lowest 75th percentile of PM2.5 during the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1505,7502,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Monsoon season  of 2021?,Identify the state exhibiting the most minimal median PM2.5 during the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1506,7516,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Summer season  of 2021?,Determine the state that showed the second highest 75th percentile of PM2.5 over the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1507,7520,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Summer season  of 2021?,Determine the city that recorded the 2nd highest 75th percentile of PM2.5 over the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1508,7527,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Winter season  of 2024?,Report which city possessed the 2nd most minimal median PM2.5 throughout the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])


true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1509,7533,spatio_temporal_aggregation,Which city had the highest median PM10 during the Summer season  of 2018?,Which city possessed the highest median for PM10 in the Summer season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Yamuna Nagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1510,7541,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Winter season  of 2022?,Which state recorded the 2nd lowest 75th percentile for PM10 in the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1511,7546,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season  of 2022?,Identify the city that recorded the second lowest 25th percentile of PM2.5 during the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])


true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1512,7547,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Summer season  of 2018?,Report which city registered the 2nd highest median for PM2.5 throughout the Summer season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Yadgir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1513,7548,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Post-Monsoon season  of 2020?,Determine the station that showed the 3rd lowest median PM2.5 over the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Palayam, Kozhikode - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1514,7550,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season  of 2023?,Identify the state exhibiting the third highest 25th percentile of PM2.5 during the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1515,7552,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Summer season  of 2019?,Determine the state that recorded the 2nd most minimal 75th percentile of PM2.5 over the Summer season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])


true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1516,7553,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Post-Monsoon season  of 2018?,Which city showed the 2nd highest average PM2.5 in the Post-Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Yadgir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1517,7562,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season  of 2021?,Identify the station that recorded the second highest 25th percentile of PM2.5 during the Post-Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1518,7572,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Post-Monsoon season  of 2022?,Determine the city exhibiting the 3rd lowest average PM10 over the Post-Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1519,7574,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Post-Monsoon season  of 2022?,Identify the city that showed the peak 25th percentile of PM10 during the Post-Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1520,7580,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Post-Monsoon season  of 2020?,Determine the station that showed the 3rd highest median PM10 over the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Vikas Sadan, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1521,7596,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Monsoon season  of 2018?,Determine the city that showed the 3rd highest 25th percentile of PM2.5 over the Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1522,7600,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Post-Monsoon season  of 2023?,Determine the state that recorded the 2nd most minimal 75th percentile of PM2.5 over the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1523,7606,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season  of 2020?,Identify the state that showed the second highest 25th percentile of PM10 during the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1524,7608,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Summer season  of 2023?,Determine the city exhibiting the 3rd most minimal median PM2.5 over the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1525,7618,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Monsoon season  of 2024?,Identify the station that registered the 2nd most minimal 25th percentile of PM10 during the Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1526,7622,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Winter season  of 2024?,Identify the city that showed the second highest median PM2.5 during the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Kozhikode,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1527,7627,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season  of 2023?,Report which city registered the 3rd most minimal 75th percentile of PM2.5 throughout the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1528,7630,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Winter season  of 2024?,Identify the state exhibiting the most minimal 25th percentile of PM2.5 during the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1529,7632,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season  of 2020?,Determine the city that recorded the 2nd highest 75th percentile of PM10 over the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1530,7636,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Winter season  of 2024?,Determine the station exhibiting the 3rd most minimal 25th percentile of PM2.5 over the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1531,7641,spatio_temporal_aggregation,Which city had the lowest median PM10 during the Summer season  of 2020?,Which city experienced the lowest median for PM10 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1532,7643,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Monsoon season  of 2020?,Report which state registered the highest 75th percentile for PM2.5 throughout the Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1533,7649,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Winter season  of 2023?,Which state showed the lowest median PM10 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1534,7653,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season  of 2023?,Which station recorded the 3rd lowest 25th percentile for PM10 in the Post-Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1535,7654,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Winter season  of 2020?,Identify the station that showed the third lowest 25th percentile of PM2.5 during the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1536,7655,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Monsoon season  of 2024?,Report which state possessed the 2nd most minimal average PM2.5 throughout the Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1537,7657,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Monsoon season  of 2023?,Which city experienced the highest median for PM2.5 in the Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1538,7659,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Post-Monsoon season  of 2022?,Report which state registered the 2nd highest average for PM10 throughout the Post-Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1539,7662,spatio_temporal_aggregation,Which city had the 3rd highest average PM2.5 during the Winter season  of 2023?,Identify the city exhibiting the third highest average PM2.5 during the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Jorapokhar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1540,7669,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Post-Monsoon season  of 2020?,Which city recorded the lowest average for PM10 in the Post-Monsoon season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1541,7672,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Monsoon season  of 2024?,Determine the city exhibiting the peak average PM2.5 over the Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Thoothukudi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1542,7673,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Monsoon season  of 2018?,Which state experienced the 2nd highest 75th percentile for PM2.5 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1543,7691,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Monsoon season  of 2020?,Report which city registered the most minimal 25th percentile of PM2.5 throughout the Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])


true_code()
",Aizawl,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
1544,7695,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Post-Monsoon season  of 2022?,Report which state experienced the third highest median PM2.5 throughout the Post-Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1545,7696,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Post-Monsoon season  of 2024?,Determine the state that recorded the 3rd highest median for PM2.5 over the Post-Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Chandigarh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1546,7697,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Monsoon season  of 2020?,Which city showed the 3rd highest 75th percentile of PM2.5 in the Monsoon season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1547,7711,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Winter season  of 2023?,Report which station experienced the third most minimal median PM2.5 throughout the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1548,7715,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Summer season  of 2020?,Report which state possessed the lowest 25th percentile of PM10 throughout the Summer season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1549,7716,spatio_temporal_aggregation,Which station had the highest average PM10 during the Winter season  of 2021?,Determine the station exhibiting the highest average PM10 over the Winter season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1550,7718,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Summer season  of 2020?,Identify the station that showed the second highest 75th percentile of PM10 during the Summer season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1551,7719,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Winter season  of 2019?,Report which city possessed the peak median PM2.5 throughout the Winter season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1552,7721,spatio_temporal_aggregation,Which city had the lowest median PM10 during the Summer season  of 2018?,Which city experienced the lowest median for PM10 in the Summer season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Kolkata,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1553,7723,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Winter season  of 2020?,Report which station registered the peak 25th percentile of PM2.5 throughout the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1554,7734,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Monsoon season  of 2021?,Identify the station that showed the most minimal average PM2.5 during the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])


true_code()
","Ratanpura, Rupnagar - Ambuja Cements","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1555,7737,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Winter season  of 2022?,Which city experienced the highest 75th percentile for PM10 in the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1556,7741,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Monsoon season  of 2018?,Which city possessed the 2nd lowest median for PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Siliguri,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1557,7749,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Winter season  of 2023?,Which city recorded the 3rd highest 25th percentile for PM2.5 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Jorapokhar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1558,7759,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Monsoon season  of 2020?,Report which city experienced the peak 75th percentile of PM2.5 throughout the Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1559,7766,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Winter season  of 2021?,Identify the city that showed the second lowest average PM10 during the Winter season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1560,7767,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Winter season  of 2020?,Report which state possessed the 2nd most minimal 75th percentile of PM10 throughout the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1561,7770,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Winter season  of 2023?,Identify the state that recorded the second lowest average PM10 during the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1562,7772,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Summer season  of 2022?,Determine the city that showed the 2nd most minimal 25th percentile of PM10 over the Summer season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1563,7774,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Monsoon season  of 2023?,Identify the station exhibiting the 2nd lowest 75th percentile of PM10 during the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1564,7781,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Winter season  of 2023?,Which city recorded the highest median for PM2.5 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1565,7787,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Winter season  of 2024?,Report which station registered the 3rd highest 75th percentile of PM10 throughout the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Sector-6, Panchkula - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1566,7791,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Monsoon season  of 2019?,Report which station experienced the most minimal average PM10 throughout the Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Urban, Chamarajanagar - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1567,7799,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Post-Monsoon season  of 2018?,Report which station possessed the peak 25th percentile of PM10 throughout the Post-Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1568,7805,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Summer season  of 2023?,Which state possessed the 2nd highest 25th percentile for PM2.5 in the Summer season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1569,7808,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Monsoon season  of 2018?,Determine the state that recorded the 3rd highest median PM2.5 over the Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1570,7814,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season  of 2019?,Identify the station that showed the third highest 25th percentile of PM10 during the Post-Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Vyttila, Kochi - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1571,7816,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Summer season  of 2021?,Determine the station exhibiting the 3rd highest average PM2.5 over the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])


true_code()
","Vijay Nagar, Sangli - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
1572,7829,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Post-Monsoon season  of 2024?,Which city recorded the 2nd highest median for PM10 in the Post-Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Rohtak,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1573,7840,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Post-Monsoon season  of 2023?,Determine the state that recorded the highest 75th percentile of PM10 over the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1574,7845,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Monsoon season  of 2023?,Which station recorded the 3rd lowest average for PM10 in the Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1575,7852,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Post-Monsoon season  of 2022?,Determine the station that showed the 2nd lowest median PM2.5 over the Post-Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
1576,7864,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Post-Monsoon season  of 2023?,Determine the city exhibiting the most minimal 75th percentile of PM2.5 over the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])


true_code()
",Karwar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
1577,7866,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Monsoon season  of 2022?,Identify the station that recorded the third lowest average PM2.5 during the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1578,7867,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Winter season  of 2020?,Report which state registered the 3rd highest average for PM10 throughout the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1579,7871,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Winter season  of 2023?,Report which city experienced the 2nd most minimal median PM10 throughout the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1580,7872,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Monsoon season  of 2018?,Determine the state that recorded the 3rd highest 75th percentile of PM2.5 over the Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1581,7876,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Winter season  of 2024?,Determine the station exhibiting the 2nd highest 25th percentile of PM10 over the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])


true_code()
","Town Hall - Lal Bagh, Darbhanga - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1582,7895,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Summer season  of 2024?,Report which city possessed the 2nd most minimal 25th percentile of PM10 throughout the Summer season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Palkalaiperur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1583,7897,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Winter season  of 2021?,Which station experienced the 3rd highest average for PM2.5 in the Winter season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])


true_code()
","Vijay Nagar, Sangli - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
1584,7902,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Winter season  of 2023?,Identify the city exhibiting the 2nd most minimal average PM10 during the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1585,7906,spatio_temporal_aggregation,Which station had the highest average PM10 during the Monsoon season  of 2018?,Identify the station that registered the peak average PM10 during the Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1586,7908,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Post-Monsoon season  of 2023?,Determine the city exhibiting the 2nd most minimal average PM10 over the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Nandesari,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1587,7928,spatio_temporal_aggregation,Which state had the highest average PM10 during the Monsoon season  of 2020?,Determine the state exhibiting the highest average PM10 over the Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1588,7930,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Winter season  of 2019?,Identify the city that recorded the third highest average PM10 during the Winter season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1589,7931,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Summer season  of 2022?,Report which station registered the 2nd most minimal median PM2.5 throughout the Summer season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
1590,7935,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Monsoon season  of 2020?,Report which state experienced the 2nd highest average PM2.5 throughout the Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1591,7942,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Monsoon season  of 2021?,Identify the city that showed the peak median PM2.5 during the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1592,7943,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Summer season  of 2021?,Report which station possessed the highest 75th percentile of PM10 throughout the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1593,7944,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Winter season  of 2022?,Determine the station exhibiting the 3rd highest 25th percentile of PM2.5 over the Winter season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])


true_code()
","Vijay Nagar Scheme-78, Indore - Glenmark","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
1594,7955,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Summer season  of 2023?,Report which station possessed the third most minimal 25th percentile of PM2.5 throughout the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""station""]
</code>"
1595,7961,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Post-Monsoon season  of 2022?,Which state experienced the lowest 25th percentile for PM2.5 in the Post-Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1596,7964,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season  of 2019?,Determine the state that showed the 3rd lowest 25th percentile of PM10 over the Post-Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])


true_code()
",Andhra Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
1597,7972,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Summer season  of 2021?,Determine the city exhibiting the 3rd most minimal median PM2.5 over the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Koppal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1598,7973,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Winter season  of 2023?,Which state recorded the highest 25th percentile for PM10 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1599,7974,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season  of 2022?,Identify the city that showed the third lowest 25th percentile of PM2.5 during the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Bhilai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1600,7982,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Summer season  of 2023?,Identify the station exhibiting the 2nd highest 25th percentile of PM2.5 during the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Velippalayam, Nagapattinam - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1601,7983,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Summer season  of 2018?,Report which city experienced the third lowest 75th percentile of PM2.5 throughout the Summer season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Kolkata,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1602,7984,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Post-Monsoon season  of 2021?,Determine the city that recorded the 3rd most minimal 25th percentile of PM2.5 over the Post-Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Koppal,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1603,7988,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Winter season  of 2024?,Determine the station exhibiting the 3rd lowest average PM10 over the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1604,7990,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season  of 2021?,Identify the state that showed the third lowest 25th percentile of PM2.5 during the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Arunachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1605,7997,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Winter season  of 2023?,Which state possessed the highest average for PM2.5 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1606,8005,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Monsoon season  of 2023?,Which city recorded the 2nd lowest 75th percentile for PM2.5 in the Monsoon season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""city""])


true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""city""]
</code>"
1607,8006,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season  of 2022?,Identify the station that showed the second highest 25th percentile of PM2.5 during the Post-Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Vijay Nagar, Sangli - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1608,8010,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Post-Monsoon season  of 2024?,Identify the state that recorded the lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1609,8013,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Winter season  of 2022?,Which station possessed the 3rd lowest 25th percentile for PM10 in the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1610,8021,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Monsoon season  of 2018?,Which city recorded the 2nd highest 75th percentile for PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Yadgir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1611,8024,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Winter season  of 2018?,Determine the station exhibiting the lowest 75th percentile of PM2.5 over the Winter season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])


true_code()
","Bandhavgar Colony, Satna - Birla Cement","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1612,8032,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Monsoon season  of 2019?,Determine the city that recorded the most minimal 25th percentile of PM2.5 over the Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])


true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
1613,8042,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Summer season  of 2021?,Identify the state that recorded the most minimal average PM2.5 during the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1614,8048,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Monsoon season  of 2022?,Determine the city that recorded the most minimal 25th percentile of PM10 over the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""city""]
</code>"
1615,8053,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season  of 2022?,Which city recorded the 3rd lowest 75th percentile for PM2.5 in the Post-Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""city""])


true_code()
",Gangtok,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""city""]
</code>"
1616,8054,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Summer season  of 2018?,Identify the state that showed the lowest 75th percentile of PM10 during the Summer season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1617,8055,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Monsoon season  of 2021?,Report which state possessed the 2nd most minimal 25th percentile of PM10 throughout the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1618,8060,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Summer season  of 2023?,Determine the state that showed the 3rd highest 25th percentile of PM10 over the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Jharkhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1619,8062,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Monsoon season  of 2023?,Identify the state exhibiting the most minimal median PM10 during the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1620,8069,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Summer season  of 2020?,Which station recorded the highest average for PM2.5 in the Summer season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1621,8075,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Post-Monsoon season  of 2020?,Report which station registered the 3rd most minimal average PM10 throughout the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Udyogamandal, Eloor - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1622,8080,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Summer season  of 2024?,Determine the state that recorded the 3rd most minimal median PM2.5 over the Summer season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1623,8081,spatio_temporal_aggregation,Which city had the highest average PM10 during the Post-Monsoon season  of 2021?,Which city showed the highest average for PM10 in the Post-Monsoon season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1624,8090,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Post-Monsoon season  of 2019?,Identify the state that recorded the second highest median PM2.5 during the Post-Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1625,8092,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Summer season  of 2020?,Determine the state that showed the 2nd highest median PM2.5 over the Summer season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1626,8096,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Monsoon season  of 2022?,Determine the city that recorded the 2nd highest 25th percentile of PM2.5 over the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1627,8102,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Post-Monsoon season  of 2018?,Identify the city that showed the second highest median PM10 during the Post-Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Yadgir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1628,8104,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Summer season  of 2024?,Determine the state exhibiting the 3rd highest average PM2.5 over the Summer season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1629,8110,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Winter season  of 2019?,Identify the station exhibiting the third highest median PM10 during the Winter season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Vyttila, Kochi - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1630,8112,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Summer season  of 2023?,Determine the state that recorded the highest 75th percentile of PM10 over the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""state""])


true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""state""]
</code>"
1631,8120,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Monsoon season  of 2024?,Determine the city exhibiting the 3rd highest median PM2.5 over the Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Ranipet,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1632,8140,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Summer season  of 2019?,Determine the station that showed the 3rd highest 75th percentile of PM2.5 over the Summer season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])


true_code()
","Worli, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
1633,8142,spatio_temporal_aggregation,Which station had the highest average PM10 during the Summer season  of 2023?,Identify the station exhibiting the peak average PM10 during the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""station""])


true_code()
","Vikas Sadan, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""station""]
</code>"
1634,8145,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Post-Monsoon season  of 2018?,Which station showed the lowest 75th percentile for PM10 in the Post-Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Plammoodu, Thiruvananthapuram - Kerala PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1635,8154,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Post-Monsoon season  of 2023?,Identify the city that recorded the third lowest average PM10 during the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""city""])


true_code()
",Silchar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""city""]
</code>"
1636,8157,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season  of 2022?,Which state possessed the 3rd lowest 75th percentile for PM10 in the Post-Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""state""]
</code>"
1637,8160,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Monsoon season  of 2022?,Determine the station that recorded the 2nd highest 25th percentile of PM10 over the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])


true_code()
","Vikas Sadan, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1638,8166,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Monsoon season  of 2020?,Identify the station that showed the second highest median PM10 during the Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""station""]
</code>"
1639,8172,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Monsoon season  of 2022?,Determine the state that showed the 3rd highest 25th percentile of PM2.5 over the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1640,8174,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season  of 2018?,Identify the city exhibiting the third highest 75th percentile of PM10 during the Post-Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1641,8186,spatio_temporal_aggregation,Which city had the highest average PM10 during the Summer season  of 2021?,Identify the city that recorded the peak average PM10 during the Summer season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1642,8193,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Winter season  of 2023?,Which state showed the 2nd lowest median for PM2.5 in the Winter season of 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])


true_code()
",Jammu and Kashmir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1643,8197,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Summer season  of 2019?,Which station recorded the 2nd lowest 75th percentile for PM2.5 in the Summer season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])


true_code()
","Bandra, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
1644,8199,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Winter season  of 2023?,Report which station possessed the peak average PM2.5 throughout the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])


true_code()
","Vijay Nagar Scheme-78, Indore - Glenmark","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1645,8201,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Winter season  of 2018?,Which state experienced the 2nd highest 75th percentile for PM10 in the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1646,8206,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season  of 2018?,Identify the city exhibiting the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Yadgir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1647,8222,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Winter season  of 2020?,Identify the city exhibiting the 2nd highest 25th percentile of PM10 during the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1648,8224,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Winter season  of 2019?,Determine the city that recorded the peak 25th percentile of PM10 over the Winter season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1649,8226,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Summer season  of 2019?,Identify the city that registered the most minimal average PM2.5 during the Summer season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])


true_code()
",Eloor,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
1650,8229,spatio_temporal_aggregation,Which city had the highest median PM10 during the Monsoon season  of 2018?,Which city recorded the highest median for PM10 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Yamuna Nagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1651,8237,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Winter season  of 2022?,Which city possessed the 3rd highest 75th percentile for PM2.5 in the Winter season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Vijayawada,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1652,8242,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Summer season  of 2019?,Identify the station that registered the third highest average PM10 during the Summer season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Worli, Mumbai - MPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1653,8244,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Monsoon season  of 2021?,Determine the city exhibiting the 2nd most minimal 75th percentile of PM10 over the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1654,8246,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season  of 2023?,Identify the city that showed the third highest 25th percentile of PM10 during the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""city""])


true_code()
",Rohtak,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""city""]
</code>"
1655,8248,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Summer season  of 2019?,Determine the state exhibiting the 2nd highest 75th percentile of PM2.5 over the Summer season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Tripura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1656,8251,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Monsoon season  of 2021?,Report which station registered the peak 25th percentile of PM2.5 throughout the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1657,8263,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Monsoon season  of 2023?,Report which city possessed the third highest median PM2.5 throughout the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Tiruchirappalli,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1658,8264,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Monsoon season  of 2019?,Determine the city exhibiting the peak 25th percentile of PM2.5 over the Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Yadgir,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1659,8265,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Summer season  of 2021?,Which city experienced the 3rd highest median for PM2.5 in the Summer season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1660,8269,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Winter season  of 2018?,Which station possessed the lowest 25th percentile for PM10 in the Winter season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""station""])


true_code()
","Sanegurava Halli, Bengaluru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""station""]
</code>"
1661,8270,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Post-Monsoon season  of 2023?,Identify the station exhibiting the third highest average PM10 during the Post-Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Velippalayam, Nagapattinam - TNPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1662,8281,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Summer season  of 2022?,Which state experienced the 3rd highest average for PM2.5 in the Summer season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1663,8290,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Winter season  of 2018?,Identify the state that registered the second lowest average PM10 during the Winter season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Karnataka,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1664,8300,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Monsoon season  of 2019?,Determine the state that showed the 3rd highest median PM2.5 over the Monsoon season of 2019.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1665,8304,spatio_temporal_aggregation,Which city had the highest median PM10 during the Winter season  of 2021?,Determine the city that recorded the peak median PM10 over the Winter season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Virudhunagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1666,8313,spatio_temporal_aggregation,Which station had the highest median PM2.5 during the Monsoon season  of 2022?,Which station experienced the highest median for PM2.5 in the Monsoon season of 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""station""]
</code>"
1667,8317,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Monsoon season  of 2018?,Which city possessed the highest 75th percentile for PM2.5 in the Monsoon season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""city""])


true_code()
",Yamuna Nagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""city""]
</code>"
1668,8322,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Summer season  of 2023?,Identify the station that registered the second lowest 25th percentile of PM10 during the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Brahmagiri, Udupi - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1669,8328,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Monsoon season  of 2022?,Determine the station exhibiting the 3rd highest 75th percentile of PM2.5 over the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""station""])


true_code()
","Vijay Nagar Scheme-78, Indore - Glenmark","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""station""]
</code>"
1670,8339,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Winter season  of 2022?,Report which state possessed the 2nd highest median PM10 throughout the Winter season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1671,8342,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season  of 2024?,Identify the city that showed the second lowest 25th percentile of PM10 during the Post-Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Maihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1672,8351,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Monsoon season  of 2022?,Report which station experienced the third lowest average PM10 throughout the Monsoon season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1673,8356,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season  of 2020?,Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 over the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""state""])


true_code()
",Puducherry,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""state""]
</code>"
1674,8357,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Post-Monsoon season  of 2019?,Which state recorded the highest 75th percentile for PM2.5 in the Post-Monsoon season of 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""state""])


true_code()
",Uttarakhand,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""state""]
</code>"
1675,8362,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Winter season  of 2023?,Identify the station that recorded the most minimal average PM2.5 during the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1676,8367,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Winter season  of 2020?,Report which city experienced the lowest 75th percentile of PM2.5 throughout the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""city""])


true_code()
",Satna,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""city""]
</code>"
1677,8369,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Winter season  of 2021?,Which state showed the 2nd lowest average for PM10 in the Winter season of 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""state""])


true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""state""]
</code>"
1678,8371,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Summer season  of 2023?,Report which city possessed the 2nd highest 25th percentile of PM10 throughout the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])


true_code()
",Virar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1679,8372,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Summer season  of 2018?,Determine the state exhibiting the 2nd most minimal 25th percentile of PM2.5 over the Summer season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""state""]
</code>"
1680,8379,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Winter season  of 2020?,Report which station registered the 3rd highest median PM10 throughout the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Vikas Sadan, Gurugram - HSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1681,8400,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Winter season  of 2023?,Determine the station that recorded the most minimal median PM2.5 over the Winter season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""station""]
</code>"
1682,8407,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Monsoon season  of 2023?,Report which city possessed the 2nd highest 75th percentile of PM2.5 throughout the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])


true_code()
",Tirupur,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1683,8408,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Winter season  of 2024?,Determine the city exhibiting the 2nd lowest 75th percentile of PM10 over the Winter season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])


true_code()
",Vijayapura,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1684,8411,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Summer season  of 2022?,Report which state registered the 2nd highest 75th percentile of PM2.5 throughout the Summer season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""state""])


true_code()
",Delhi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""state""]
</code>"
1685,8420,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Post-Monsoon season  of 2020?,Determine the station exhibiting the 2nd highest average PM2.5 over the Post-Monsoon season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1686,8431,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Monsoon season  of 2023?,Report which state experienced the third lowest average PM2.5 throughout the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""state""])


true_code()
",Meghalaya,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""state""]
</code>"
1687,8444,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Monsoon season  of 2024?,Determine the station that showed the 3rd highest 75th percentile of PM10 over the Monsoon season of 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""station""])


true_code()
","Vijay Nagar Scheme-78, Indore - Glenmark","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""station""]
</code>"
1688,8449,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Monsoon season  of 2020?,Which station showed the 3rd lowest 75th percentile for PM10 in the Monsoon season of 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""station""])


true_code()
","Hebbal 1st Stage, Mysuru - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""station""]
</code>"
1689,8468,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Winter season  of 2020?,Determine the station exhibiting the 2nd highest average PM2.5 over the Winter season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])


true_code()
","Yerramukkapalli, Kadapa - APPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([12, 1, 2])]
    data = data.groupby([""station""])[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1690,8469,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Post-Monsoon season  of 2024?,Which state recorded the 2nd highest median for PM10 in the Post-Monsoon season of 2024?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])


true_code()
",Himachal Pradesh,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data[data['Timestamp'].dt.month.isin([9, 10, 11])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1691,8470,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Monsoon season  of 2018?,Identify the state that showed the third highest median PM10 during the Monsoon season of 2018.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])


true_code()
",Tamil Nadu,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1692,8479,spatio_temporal_aggregation,Which city had the highest median PM10 during the Summer season  of 2020?,Report which city experienced the peak median PM10 throughout the Summer season of 2020.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])


true_code()
",Vrindavan,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""city""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1693,8480,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Monsoon season  of 2021?,Determine the station that recorded the 2nd lowest 75th percentile of PM2.5 over the Monsoon season of 2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""station""])


true_code()
","Sikulpuikawn, Aizawl - Mizoram PCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""station""]
</code>"
1694,8484,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Summer season  of 2022?,Determine the state exhibiting the 2nd highest median PM10 over the Summer season of 2022.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])


true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1695,8485,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Summer season  of 2018?,Which state recorded the lowest average for PM10 in the Summer season of 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Kerala,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1696,8490,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Monsoon season  of 2023?,Identify the station that recorded the second lowest 25th percentile of PM10 during the Monsoon season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])


true_code()
","Diwator Nagar, Koppal - KSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([6, 7, 8])]
    data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1697,8498,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Summer season  of 2023?,Identify the state that registered the most minimal median PM10 during the Summer season of 2023.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])


true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['Timestamp'].dt.month.isin([3, 4, 5])]
    data = data.groupby([""state""])[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1698,8503,spatio_temporal_aggregation,Which city recorded the 3rd highest 25th percentile of PM2.5 level ever?,Determine the city that recorded the third highest 25th percentile of PM2.5 across all time.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""city""])

true_code()
",Gurugram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""city""]
</code>"
1699,8512,spatio_temporal_aggregation,Which city recorded the 2nd lowest 25th percentile of PM10 level ever?,Which city showed the second most minimal 25th percentile of PM10 historically?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""city""])

true_code()
",Udupi,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""city""]
</code>"
1700,8516,spatio_temporal_aggregation,Which city recorded the 2nd highest average PM2.5 level ever?,Which city showed the second highest average PM2.5 level of all time?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""city""])

true_code()
",Begusarai,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""city""]
</code>"
1701,8519,spatio_temporal_aggregation,Which station recorded the 2nd lowest median PM10 level ever?,Determine the station that recorded the second most minimal median PM10 level across all time.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""station""])

true_code()
","Zero Point GICI, Gangtok - SSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""station"")[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""station""]
</code>"
1702,8524,spatio_temporal_aggregation,Which station recorded the 2nd highest median PM2.5 level ever?,Which station showed the second highest median PM2.5 level historically?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""station"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","Central Academy for SFS, Byrnihat - PCBA","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""station"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1703,8525,spatio_temporal_aggregation,Which city recorded the highest 75th percentile of PM10 level ever?,Identify the city that registered the maximum 75th percentile for PM10 across all time.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""city""])

true_code()
",Byrnihat,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""city""]
</code>"
1704,8527,spatio_temporal_aggregation,Which state recorded the lowest median PM2.5 level ever?,Determine the state that recorded the minimum median PM2.5 level historically.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""state""])

true_code()
",Mizoram,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""state"")[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""state""]
</code>"
1705,8533,spatio_temporal_aggregation,Which state recorded the 3rd highest average PM10 level ever?,Identify the state that registered the third highest average PM10 historically.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""state""])

true_code()
",Haryana,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""state"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""state""]
</code>"
1706,8550,spatio_temporal_aggregation,Which state recorded the 2nd highest 75th percentile of PM10 level ever?,Report which state documented the second highest 75th percentile of PM10 ever.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""state""])

true_code()
",Bihar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""state""]
</code>"
1707,8551,spatio_temporal_aggregation,Which station recorded the 2nd highest 75th percentile of PM2.5 level ever?,Determine the station that recorded the second highest 75th percentile for PM2.5 historically.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""station""])

true_code()
","DRCC Anandpur, Begusarai - BSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""station""]
</code>"
1708,8554,spatio_temporal_aggregation,Which state recorded the lowest 75th percentile of PM10 level ever?,Report which state documented the minimum 75th percentile for PM10 historically.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""state""])

true_code()
",Sikkim,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""state""]
</code>"
1709,8558,spatio_temporal_aggregation,Which city recorded the 2nd highest average PM10 level ever?,Report which city documented the second highest average PM10 of all time.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""city""])

true_code()
",Sri Ganganagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(""city"")[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""city""]
</code>"
1710,8566,specific_pattern,Which date in the last three years recorded the 3rd lowest PM10 in the Jaipur ?,"For Jaipur, what date in the last three years had the third-lowest PM10 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""Timestamp""].date())

true_code()
",2024-08-01,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Jaipur"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""Timestamp""].date()
</code>"
1711,8569,specific_pattern,Which date in the last five years recorded the highest PM10 in the Ahmedabad ?,"Over the past five years in Ahmedabad, on which date was the PM10 level the highest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""Timestamp""].date())

true_code()
",2021-09-16,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Ahmedabad"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""Timestamp""].date()
</code>"
1712,8570,specific_pattern,Which date in the last four years recorded the 2nd highest PM2.5 in the Ahmedabad ?,"For Ahmedabad, what date in the last four years showed the second-highest PM2.5 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2022-10-24,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Ahmedabad"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1713,8575,specific_pattern,Which date in the last four years recorded the 3rd lowest PM10 in the Ahmedabad ?,"In Ahmedabad, which date in the previous four years showed the third-lowest PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""Timestamp""].date())

true_code()
",2023-07-09,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Ahmedabad"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""Timestamp""].date()
</code>"
1714,8576,specific_pattern,Which date in the last three years recorded the lowest PM10 in the Pune ?,"Within the last three years in Pune, on what date was the PM10 level the lowest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2022-07-04,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Pune"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1715,8578,specific_pattern,Which date in the last four years recorded the 2nd highest PM2.5 in the Mumbai ?,"For Mumbai, what date in the last four years had the second-highest PM2.5 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2024-05-19,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Mumbai"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1716,8579,specific_pattern,Which date in the last five years recorded the lowest PM2.5 in the Kolkata ?,"In Kolkata, which date in the previous five years registered the lowest PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2020-06-11,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Kolkata"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1717,8584,specific_pattern,Which date in the last four years recorded the 2nd lowest PM10 in the Surat ?,"Within the last four years in Surat, on what date was the PM10 level the second lowest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""Timestamp""].date())

true_code()
",2023-02-02,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Surat"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""Timestamp""].date()
</code>"
1718,8585,specific_pattern,Which date in the last two years recorded the lowest PM10 in the Surat ?,"Over the past two years in Surat, on which date was the PM10 level the lowest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2023-01-30,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Surat"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1719,8590,specific_pattern,Which date in the last three years recorded the 3rd lowest PM10 in the Hyderabad ?,"For Hyderabad, what date in the last three years had the third-lowest PM10 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""Timestamp""].date())

true_code()
",2024-07-14,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Hyderabad"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""Timestamp""].date()
</code>"
1720,8593,specific_pattern,Which date in the last four years recorded the lowest PM10 in the Delhi ?,"Over the past four years in Delhi, on which date was the PM10 level the lowest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2022-09-20,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Delhi"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1721,8598,specific_pattern,Which date in the last three years recorded the 2nd lowest PM2.5 in the Surat ?,"For Surat, what date in the last three years registered the second-lowest PM2.5 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""Timestamp""].date())

true_code()
",2023-04-18,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Surat"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""Timestamp""].date()
</code>"
1722,8605,specific_pattern,Which date in the last five years recorded the 3rd highest PM10 in the Hyderabad ?,"Over the past five years in Hyderabad, on which date was the PM10 level the third highest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""Timestamp""].date())

true_code()
",2023-11-12,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Hyderabad"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""Timestamp""].date()
</code>"
1723,8620,specific_pattern,Which date in the last two years recorded the 3rd highest PM2.5 in the Pune ?,"Within the last two years in Pune, on what date was the PM2.5 level the third highest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""Timestamp""].date())

true_code()
",2023-05-26,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Pune"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""Timestamp""].date()
</code>"
1724,8626,specific_pattern,Which date in the last five years recorded the 2nd lowest PM2.5 in the Ahmedabad ?,"For Ahmedabad, what date in the last five years had the second-lowest PM2.5 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""Timestamp""].date())

true_code()
",2023-06-01,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Ahmedabad"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""Timestamp""].date()
</code>"
1725,8627,specific_pattern,Which date in the last five years recorded the lowest PM2.5 in the Ahmedabad ?,"In Ahmedabad, which date in the previous five years registered the lowest PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2023-06-03,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Ahmedabad"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1726,8628,specific_pattern,Which date in the last five years recorded the 2nd highest PM2.5 in the Ahmedabad ?,"Within the last five years in Ahmedabad, on what date was the PM2.5 level the second highest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2022-10-24,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Ahmedabad"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1727,8642,specific_pattern,Which date in the last four years recorded the 2nd highest PM2.5 in the Delhi ?,"For Delhi, what date in the last four years showed the second-highest PM2.5 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2024-06-29,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Delhi"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1728,8646,specific_pattern,Which date in the last three years recorded the 3rd highest PM2.5 in the Delhi ?,"For Delhi, what date in the last three years registered the third-highest PM2.5 reading?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""Timestamp""].date())

true_code()
",2024-11-18,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Delhi"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""Timestamp""].date()
</code>"
1729,8656,specific_pattern,Which date in the last three years recorded the 3rd highest PM2.5 in the Chennai ?,"Within the last three years in Chennai, on what date was the PM2.5 level the third highest?","
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""Timestamp""].date())

true_code()
",2022-04-13,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Chennai"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""Timestamp""].date()
</code>"
1730,8661,specific_pattern,Which date in the last two years recorded the 3rd highest PM2.5 in the Jaipur ?,What date during the last two years noted the 3rd maximum PM2.5 reading in Jaipur?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""Timestamp""].date())

true_code()
",2024-11-02,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Jaipur"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""Timestamp""].date()
</code>"
1731,8669,specific_pattern,Which date in the last three years recorded the 3rd highest PM2.5 in the Mumbai ?,What date during the last three years noted Mumbai's 3rd maximum PM2.5 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""Timestamp""].date())

true_code()
",2023-07-20,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Mumbai"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""Timestamp""].date()
</code>"
1732,8671,specific_pattern,Which date in the last five years recorded the 2nd highest PM10 in the Delhi ?,What date within the past five years showed Delhi's 2nd highest PM10 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2024-11-18,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Delhi"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1733,8675,specific_pattern,Which date in the last three years recorded the 2nd highest PM10 in the Hyderabad ?,What date during the last three years showed Hyderabad's 2nd highest PM10 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2024-11-02,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Hyderabad"") & (data['Timestamp'].dt.year >= (year - 3))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1734,8677,specific_pattern,Which date in the last two years recorded the 2nd highest PM2.5 in the Chennai ?,What date over the last two years noted Chennai's 2nd maximum PM2.5 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2023-11-12,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Chennai"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1735,8684,specific_pattern,Which date in the last four years recorded the lowest PM10 in the Hyderabad ?,On which date in the previous four years did Hyderabad record its minimum PM10 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2022-08-08,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Hyderabad"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1736,8685,specific_pattern,Which date in the last four years recorded the 2nd highest PM2.5 in the Jaipur ?,What date over the last four years noted Jaipur's 2nd maximum PM2.5 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2023-07-18,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Jaipur"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1737,8692,specific_pattern,Which date in the last four years recorded the 3rd highest PM10 in the Ahmedabad ?,On which date in the past four years did Ahmedabad record its 3rd maximum PM10 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""Timestamp""].date())

true_code()
",2021-12-23,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Ahmedabad"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""Timestamp""].date()
</code>"
1738,8700,specific_pattern,Which date in the last two years recorded the highest PM2.5 in the Surat ?,On which date in the last two years did Surat record its peak PM2.5 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""Timestamp""].date())

true_code()
",2023-12-04,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Surat"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""Timestamp""].date()
</code>"
1739,8702,specific_pattern,Which date in the last two years recorded the 2nd highest PM10 in the Pune ?,On which date in the previous two years did Pune register its 2nd peak PM10 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2024-03-05,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Pune"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1740,8703,specific_pattern,Which date in the last four years recorded the 2nd highest PM10 in the Hyderabad ?,What date during the last four years showed Hyderabad's 2nd highest PM10 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""Timestamp""].date())

true_code()
",2024-11-02,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Hyderabad"") & (data['Timestamp'].dt.year >= (year - 4))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""Timestamp""].date()
</code>"
1741,8708,specific_pattern,Which date in the last two years recorded the 2nd lowest PM2.5 in the Delhi ?,On which date in the previous two years did Delhi record its 2nd minimum PM2.5 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""Timestamp""].date())

true_code()
",2024-08-19,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Delhi"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""Timestamp""].date()
</code>"
1742,8716,specific_pattern,Which date in the last five years recorded the 3rd lowest PM10 in the Jaipur ?,On which date in the past five years did Jaipur record its 3rd minimum PM10 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""Timestamp""].date())

true_code()
",2024-08-01,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Jaipur"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""Timestamp""].date()
</code>"
1743,8719,specific_pattern,Which date in the last two years recorded the lowest PM10 in the Pune ?,What date during the past two years showed Pune's minimum PM10 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2024-06-26,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Pune"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1744,8722,specific_pattern,Which date in the last five years recorded the lowest PM10 in the Surat ?,On which date in the past five years did Surat register its minimum PM10 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2023-01-30,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Surat"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1745,8725,specific_pattern,Which date in the last two years recorded the lowest PM2.5 in the Hyderabad ?,What date over the past two years noted Hyderabad's lowest PM2.5 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""Timestamp""].date())

true_code()
",2023-11-03,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Hyderabad"") & (data['Timestamp'].dt.year >= (year - 2))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""Timestamp""].date()
</code>"
1746,8732,specific_pattern,Which date in the last five years recorded the highest PM2.5 in the Chennai ?,On which date in the previous five years did Chennai record its peak PM2.5 level?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""Timestamp""].date())

true_code()
",2021-05-24,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Chennai"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""Timestamp""].date()
</code>"
1747,8735,specific_pattern,Which date in the last five years recorded the 3rd lowest PM2.5 in the Surat ?,What date during the previous five years showed Surat's 3rd lowest PM2.5 reading?,"
def true_code():
    import numpy as np
    import pandas as pd
    import datetime
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    year = datetime.datetime.now().year
    data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""Timestamp""].date())

true_code()
",2023-04-14,"<code>
import pandas as pd
import datetime
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    year = datetime.datetime.now().year
    data = data[(data['city'] == ""Surat"") & (data['Timestamp'].dt.year >= (year - 5))]
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""Timestamp""].date()
</code>"
1748,8738,specific_pattern,Which Indian station recorded the 2nd highest PM2.5 levels for single-day in the past decade?,Which Indian station recorded the 2nd highest PM2.5 levels for a single day in the past decade?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    val = data.iloc[-2][""PM2.5""]
    data = data[data[""PM2.5""] == val]
    data = data.groupby(""station"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    print(data.iloc[0][""station""])

true_code()
","GM Office, Brajrajnagar - OSPCB","<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    val = data.iloc[-2][""PM2.5""]
    data = data[data[""PM2.5""] == val]
    data = data.groupby(""station"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    return data.iloc[0][""station""]
</code>"
1749,8742,specific_pattern,Which Indian city recorded the 2nd lowest PM10 levels for single-day in the past decade?,Which Indian city registered the 2nd minimum PM10 levels for a single day in the previous decade?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    val = data.iloc[1][""PM10""]
    data = data[data[""PM10""] == val]
    data = data.groupby(""city"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    print(data.iloc[0][""city""])

true_code()
",Talcher,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    val = data.iloc[1][""PM10""]
    data = data[data[""PM10""] == val]
    data = data.groupby(""city"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    return data.iloc[0][""city""]
</code>"
1750,8743,specific_pattern,Which Indian city recorded the highest PM2.5 levels for single-day in the past decade?,Which Indian city noted the maximum PM2.5 levels for a single day over the last decade?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    val = data.iloc[-1][""PM2.5""]
    data = data[data[""PM2.5""] == val]
    data = data.groupby(""city"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    print(data.iloc[0][""city""])

true_code()
",Brajrajnagar,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    val = data.iloc[-1][""PM2.5""]
    data = data[data[""PM2.5""] == val]
    data = data.groupby(""city"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    return data.iloc[0][""city""]
</code>"
1751,8746,specific_pattern,Which Indian state recorded the 2nd lowest PM10 levels for single-day in the past decade?,Which Indian state noted the 2nd minimum PM10 levels for a single day over the last decade?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    val = data.iloc[1][""PM10""]
    data = data[data[""PM10""] == val]
    data = data.groupby(""state"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    print(data.iloc[0][""state""])

true_code()
",Odisha,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    val = data.iloc[1][""PM10""]
    data = data[data[""PM10""] == val]
    data = data.groupby(""state"").size().reset_index(name=""count"")
    data = data.sort_values(by=""count"")
    return data.iloc[0][""state""]
</code>"
1752,8756,specific_pattern,Find a week with Mumbai's 2nd lowest PM2.5 levels for all these years,Find a week exhibiting Mumbai's 2nd minimum PM2.5 levels for all specified years.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""week""])

true_code()
",32.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""week""]
</code>"
1753,8759,specific_pattern,Find a week with Kolkata's 3rd lowest PM10 levels for all these years,Find a week exhibiting Kolkata's 3rd minimum PM10 levels for all specified years.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",28.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1754,8765,specific_pattern,Find a week with Surat's 3rd lowest PM10 levels for all these years,Find a week exhibiting Surat's 3rd minimum PM10 levels for all specified years.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",17.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1755,8773,specific_pattern,Find a week with Delhi's 3rd lowest PM10 levels for all these years,Find a week exhibiting Delhi's 3rd minimum PM10 levels for all specified years.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Delhi""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",30.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Delhi""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1756,8778,specific_pattern,Find a week with Ahmedabad's lowest PM10 levels for all these years,Find a week exhibiting Ahmedabad's minimum PM10 levels for all specified years.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""week""])

true_code()
",35.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""week""]
</code>"
1757,8779,specific_pattern,Find a week with Jaipur's 3rd highest PM2.5 levels for all these years,Identify a week showing Jaipur's 3rd maximum PM2.5 levels across the specified years.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Jaipur""]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""week""])

true_code()
",44.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Jaipur""]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""week""]
</code>"
1758,8786,specific_pattern,Find a week with Chennai's 3rd lowest PM10 levels for all these years,Determine a week with Chennai's 3rd lowest PM10 levels over all these years.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Chennai""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",17.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Chennai""]
    data = data.dropna(subset=""PM10"")
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1759,8795,specific_pattern,Identify a year in which Pune experienced the cleanest air from 2018-2024,Determine a year when Pune witnessed its cleanest air quality between 2018 and 2024.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Pune""]
    data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2024)]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""Timestamp""])

true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Pune""]
    data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2024)]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""Timestamp""]
</code>"
1760,8802,specific_pattern,Identify a year in which Hyderabad experienced the cleanest air from 2018-2021,Identify a year in which Hyderabad experienced its best air quality from 2018-2021.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""city""] == ""Hyderabad""]
    data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2021)]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""Timestamp""])

true_code()
",2020.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""city""] == ""Hyderabad""]
    data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2021)]
    data = data.dropna(subset=""PM2.5"")
    data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""Timestamp""]
</code>"
1761,8822,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on median in 2018?","In 2018, what weekday was linked to the third-lowest median PM10 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])

true_code()
",Wednesday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1762,8835,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on 25th percentile of in 2022?",Which weekday in 2022 was linked to the minimum 25th percentile of PM10 pollution levels?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])

true_code()
",Sunday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1763,8843,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on 25th percentile of in 2021?","In 2021, which weekday was associated with the second-lowest 25th percentile of PM10 pollution concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1]['Timestamp'])

true_code()
",Monday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1]['Timestamp']
</code>"
1764,8855,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on average in 2018?",Identify the weekday in 2018 that registered the second-lowest average PM2.5 pollution levels.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1]['Timestamp'])

true_code()
",Wednesday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1]['Timestamp']
</code>"
1765,8856,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on 25th percentile of in 2019?","In 2019, which weekday was associated with the third-highest 25th percentile of PM2.5 pollution concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3]['Timestamp'])

true_code()
",Monday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3]['Timestamp']
</code>"
1766,8861,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on median in 2022?",Which weekday in 2022 was linked to the highest median PM10 pollution levels?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])

true_code()
",Wednesday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1767,8863,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on 25th percentile of in 2019?","In 2019, which weekday experienced the minimum 25th percentile of PM10 pollution concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])

true_code()
",Sunday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1768,8866,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on 75th percentile of in 2023?","In 2023, which day of the week corresponded to the second-lowest 75th percentile of PM10 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1]['Timestamp'])

true_code()
",Thursday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1]['Timestamp']
</code>"
1769,8868,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on average in 2022?",Identify the weekday in 2022 that registered the highest average PM2.5 pollution levels.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])

true_code()
",Tuesday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1770,8870,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on median in 2022?","During 2022, determine the weekday that showed the lowest median PM10 pollution levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])

true_code()
",Sunday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1771,8871,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 75th percentile of in 2019?","For 2019, which weekday experienced the highest 75th percentile of PM2.5 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])

true_code()
",Thursday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1772,8875,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 75th percentile of in 2022?","For 2022, identify the weekday with the lowest 75th percentile of PM2.5 pollution levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0]['Timestamp'])

true_code()
",Sunday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0]['Timestamp']
</code>"
1773,8882,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on 25th percentile of in 2021?","In 2021, which weekday was associated with the minimum 25th percentile of PM10 pollution concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])

true_code()
",Sunday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1774,8888,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 25th percentile of in 2021?","For 2021, identify the weekday with the lowest 25th percentile of PM2.5 pollution levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0]['Timestamp'])

true_code()
",Saturday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0]['Timestamp']
</code>"
1775,8890,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on 75th percentile of in 2024?","During 2024, which weekday saw the second-highest 75th percentile of PM10 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])

true_code()
",Saturday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1776,8893,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on average in 2018?","For the year 2018, which weekday had the third-lowest average PM2.5 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2]['Timestamp'])

true_code()
",Saturday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2]['Timestamp']
</code>"
1777,8898,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on median in 2019?","Considering 2019, what day of the week had the third-lowest median PM2.5 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2]['Timestamp'])

true_code()
",Sunday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2]['Timestamp']
</code>"
1778,8900,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on 75th percentile of in 2018?",Which weekday in 2018 was linked to the highest 75th percentile of PM10 pollution levels?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])

true_code()
",Friday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1779,8908,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on average in 2019?","In 2019, which weekday was associated with the second-lowest average PM10 pollution concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1]['Timestamp'])

true_code()
",Saturday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[1]['Timestamp']
</code>"
1780,8913,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on median in 2020?",Which weekday in 2020 was linked to the second-highest median PM10 pollution levels?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])

true_code()
",Tuesday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1781,8919,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on median in 2020?","For the year 2020, which weekday had the third-lowest median PM2.5 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2]['Timestamp'])

true_code()
",Thursday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2]['Timestamp']
</code>"
1782,8921,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on median in 2024?","In 2024, which weekday was associated with the third-lowest median PM10 pollution concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])

true_code()
",Friday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1783,8925,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on 75th percentile of in 2020?","In the year 2020, which weekday recorded the highest 75th percentile for PM10 pollution levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])

true_code()
",Wednesday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM10"")
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1784,8935,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 25th percentile of in 2022?","During 2022, determine the weekday that showed the highest 25th percentile of PM2.5 pollution levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])

true_code()
",Wednesday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1785,8940,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on 75th percentile of in 2019?","For 2019, identify the weekday with the second-lowest 75th percentile of PM2.5 pollution levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1]['Timestamp'])

true_code()
",Saturday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1]['Timestamp']
</code>"
1786,8947,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on average in 2021?","In 2021, which weekday was associated with the second-highest average PM2.5 pollution concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2]['Timestamp'])

true_code()
",Thursday,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index()
    data = data.dropna(subset=""PM2.5"")
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2]['Timestamp']
</code>"
1787,8952,temporal_aggregation,What was the 2nd highest PM10 recorded in 2019 ?,What was the second-highest PM10 concentration measured in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""PM10""])

true_code()
",999.99,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""PM10""]
</code>"
1788,8959,temporal_aggregation,What was the 3rd highest PM10 recorded in 2023 ?,"For 2023, what was the third-highest recorded PM10 value?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""PM10""])

true_code()
",999.9900000000011,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""PM10""]
</code>"
1789,8967,temporal_aggregation,What was the 3rd lowest PM2.5 recorded in 2018 ?,What was the third-lowest PM2.5 level measured in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""PM2.5""])

true_code()
",0.0275,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""PM2.5""]
</code>"
1790,8968,temporal_aggregation,What was the 3rd lowest PM10 recorded in 2018 ?,"For 2018, what was the third-lowest recorded PM10 value?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""PM10""])

true_code()
",0.0320833333333333,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""PM10""]
</code>"
1791,8970,temporal_aggregation,What was the 3rd highest PM2.5 recorded in 2020 ?,What was the third-highest PM2.5 concentration measured in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""PM2.5""])

true_code()
",805.51,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""PM2.5""]
</code>"
1792,8975,temporal_aggregation,What was the highest PM2.5 recorded in 2023 ?,"In 2023, what was the peak PM2.5 concentration observed?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""PM2.5""])

true_code()
",1000.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data.dropna(subset=[""PM2.5""])
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""PM2.5""]
</code>"
1793,8980,temporal_aggregation,What was the lowest PM10 recorded in 2024 ?,"In 2024, what was the minimum recorded PM10 value?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2024]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""PM10""])

true_code()
",0.4,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2024]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""PM10""]
</code>"
1794,8982,temporal_aggregation,What was the 2nd lowest PM10 recorded in 2019 ?,What was the second-lowest PM10 reading in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""PM10""])

true_code()
",1.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data.dropna(subset=[""PM10""])
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""PM10""]
</code>"
1795,8986,temporal_aggregation,In which year was the 3rd lowest 25th percentile of PM2.5 recorded ?,During which year did the 25th percentile of PM2.5 reach its third-lowest value?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.dropna(subset=[""PM2.5""])
    data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""Timestamp""])

true_code()
",2023.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.dropna(subset=[""PM2.5""])
    data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""Timestamp""]
</code>"
1796,8990,temporal_aggregation,In which year was the 2nd highest average PM10 recorded ?,During which year did the average PM10 level register as the second highest?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.dropna(subset=[""PM10""])
    data = data.groupby(data['Timestamp'].dt.year)['PM10'].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""Timestamp""])

true_code()
",2019.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.dropna(subset=[""PM10""])
    data = data.groupby(data['Timestamp'].dt.year)['PM10'].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""Timestamp""]
</code>"
1797,8996,temporal_aggregation,In which year was the highest 25th percentile of PM10 recorded ?,Which year corresponds to the highest 25th percentile for PM10 levels?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.dropna(subset=[""PM10""])
    data = data.groupby(data['Timestamp'].dt.year)['PM10'].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""Timestamp""])

true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.dropna(subset=[""PM10""])
    data = data.groupby(data['Timestamp'].dt.year)['PM10'].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""Timestamp""]
</code>"
1798,9004,temporal_aggregation,"Across all years, which November had the 3rd highest 25th percentile of PM2.5 level?","Considering all years, which November showed the third-highest 25th percentile of PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2019.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3]['Timestamp']
</code>"
1799,9008,temporal_aggregation,"Across all years, which May had the 2nd lowest 25th percentile of PM2.5 level?","Considering all years, which May was associated with the second-lowest 25th percentile of PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1]['Timestamp'])


true_code()
",2021.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1]['Timestamp']
</code>"
1800,9010,temporal_aggregation,"Across all years, which May had the 3rd highest 75th percentile of PM10 level?","Taking all years into account, which May had the third-highest 75th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2022.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3]['Timestamp']
</code>"
1801,9011,temporal_aggregation,"Across all years, which August had the highest median PM2.5 level?","Over all years, which August registered the maximum median PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1802,9020,temporal_aggregation,"Across all years, which July had the 3rd highest median PM10 level?","Considering all years, which July had the third-highest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 7]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 7]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3]['Timestamp']
</code>"
1803,9021,temporal_aggregation,"Across all years, which February had the highest 75th percentile of PM10 level?","Across all recorded years, which February registered the maximum 75th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1804,9022,temporal_aggregation,"Across all years, which December had the lowest average PM2.5 level?","Taking all years into account, which December experienced the lowest average PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0]['Timestamp']
</code>"
1805,9030,temporal_aggregation,"Across all years, which February had the 3rd highest 75th percentile of PM10 level?","Taking all years into account, which February had the third-highest 75th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3]['Timestamp']
</code>"
1806,9032,temporal_aggregation,"Across all years, which August had the 3rd highest average PM10 level?","Considering all years, which August experienced the third-highest average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3]['Timestamp']
</code>"
1807,9043,temporal_aggregation,"Across all years, which December had the lowest average PM10 level?","Over all years, which December was associated with the minimum average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1808,9045,temporal_aggregation,"Across all years, which May had the 2nd highest 75th percentile of PM10 level?","Across all recorded years, which May had the second-highest 75th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2019.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1809,9052,temporal_aggregation,"Across all years, which June had the highest 25th percentile of PM10 level?","Considering all years, which June experienced the highest 25th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 6]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 6]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1810,9055,temporal_aggregation,"Across all years, which August had the 3rd lowest 25th percentile of PM2.5 level?","Over all years, which August had the third-lowest 25th percentile for PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2022.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2]['Timestamp']
</code>"
1811,9056,temporal_aggregation,"Across all years, which August had the 2nd lowest 25th percentile of PM2.5 level?","Considering all years, which August registered the second-lowest 25th percentile of PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1]['Timestamp'])


true_code()
",2020.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1]['Timestamp']
</code>"
1812,9059,temporal_aggregation,"Across all years, which July had the lowest average PM10 level?","Over all years, which July showed the minimum average PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 7]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 7]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1813,9071,temporal_aggregation,"Across all years, which January had the 2nd highest median PM10 level?","Over all years, which January registered the second-highest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 1]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 1]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1814,9077,temporal_aggregation,"Across all years, which August had the highest average PM10 level?","Across all recorded years, which August experienced the highest average PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1815,9078,temporal_aggregation,"Across all years, which October had the 2nd highest median PM10 level?","Taking all years into account, which October was associated with the second-highest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2020.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1816,9087,temporal_aggregation,"Across all years, which October had the lowest average PM10 level?","Over all years, which October experienced the minimum average PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1817,9098,temporal_aggregation,"Across all years, which December had the 3rd lowest median PM10 level?","Taking all years into account, which December was associated with the third-lowest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1818,9102,temporal_aggregation,"Across all years, which November had the 2nd highest median PM2.5 level?","Taking all years into account, which November experienced the second-highest median PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2]['Timestamp']
</code>"
1819,9106,temporal_aggregation,"Across all years, which December had the 3rd highest 25th percentile of PM2.5 level?","Taking all years into account, which December registered the third-highest 25th percentile of PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2019.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3]['Timestamp']
</code>"
1820,9108,temporal_aggregation,"Across all years, which October had the 2nd lowest average PM2.5 level?","Considering all years, which October was associated with the second-lowest average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1]['Timestamp'])


true_code()
",2021.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1]['Timestamp']
</code>"
1821,9109,temporal_aggregation,"Across all years, which October had the 3rd highest 25th percentile of PM10 level?","Across all recorded years, which October showed the third-highest 25th percentile of PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3]['Timestamp']
</code>"
1822,9110,temporal_aggregation,"Across all years, which January had the 3rd highest average PM2.5 level?","Taking all years into account, which January had the third-highest average PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 1]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2019.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 1]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3]['Timestamp']
</code>"
1823,9122,temporal_aggregation,"Across all years, which May had the 3rd highest average PM2.5 level?","Taking all years into account, which May experienced the third-highest average PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2019.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3]['Timestamp']
</code>"
1824,9133,temporal_aggregation,"Across all years, which October had the 3rd lowest 25th percentile of PM10 level?","Across all recorded years, which October was associated with the third-lowest 25th percentile of PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2021.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1825,9139,temporal_aggregation,"Across all years, which January had the lowest average PM10 level?","Over all years, which January showed the minimum average PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 1]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 1]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1826,9140,temporal_aggregation,"Across all years, which June had the highest 25th percentile of PM2.5 level?","Considering all years, which June had the highest 25th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 6]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 6]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1827,9143,temporal_aggregation,"Across all years, which November had the highest median PM10 level?","Over all years, which November was associated with the maximum median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1828,9145,temporal_aggregation,"Across all years, which November had the 2nd highest average PM10 level?","Across all recorded years, which November had the second-highest average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1829,9156,temporal_aggregation,"Across all years, which September had the 3rd lowest median PM10 level?","Considering all years, which September registered the third-lowest median PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 9]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2023.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 9]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1830,9162,temporal_aggregation,"Across all years, which April had the 2nd highest average PM2.5 level?","Taking all years into account, which April experienced the second-highest average PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 4]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 4]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2]['Timestamp']
</code>"
1831,9164,temporal_aggregation,"Across all years, which December had the lowest 75th percentile of PM10 level?","Considering all years, which December showed the minimum 75th percentile for PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 12]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1832,9170,temporal_aggregation,"Across all years, which August had the 3rd lowest median PM10 level?","Taking all years into account, which August had the third-lowest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2022.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1833,9173,temporal_aggregation,"Across all years, which June had the highest median PM10 level?","Across all recorded years, which June was associated with the maximum median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 6]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 6]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1834,9174,temporal_aggregation,"Across all years, which March had the 3rd highest 75th percentile of PM2.5 level?","Taking all years into account, which March showed the third-highest 75th percentile of PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 3]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2022.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 3]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3]['Timestamp']
</code>"
1835,9175,temporal_aggregation,"Across all years, which November had the 3rd lowest average PM2.5 level?","Over all years, which November had the third-lowest average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2022.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2]['Timestamp']
</code>"
1836,9177,temporal_aggregation,"Across all years, which April had the 3rd lowest median PM10 level?","Across all recorded years, which April experienced the third-lowest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 4]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2023.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 4]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1837,9179,temporal_aggregation,"Across all years, which February had the 2nd highest median PM10 level?","Over all years, which February showed the second-highest median PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1838,9183,temporal_aggregation,"Across all years, which September had the 3rd lowest average PM10 level?","Over all years, which September was associated with the third-lowest average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 9]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2022.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 9]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2]['Timestamp']
</code>"
1839,9189,temporal_aggregation,"Across all years, which October had the 2nd lowest 25th percentile of PM2.5 level?","Across all recorded years, which October showed the second-lowest 25th percentile of PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1]['Timestamp'])


true_code()
",2024.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 10]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1]['Timestamp']
</code>"
1840,9192,temporal_aggregation,"Across all years, which July had the 3rd lowest average PM2.5 level?","Considering all years, which July experienced the third-lowest average PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 7]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2]['Timestamp'])


true_code()
",2022.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 7]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2]['Timestamp']
</code>"
1841,9193,temporal_aggregation,"Across all years, which February had the highest 75th percentile of PM2.5 level?","Across all recorded years, which February was associated with the maximum 75th percentile of PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 2]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1842,9194,temporal_aggregation,"Across all years, which March had the lowest average PM2.5 level?","Taking all years into account, which March showed the minimum average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 3]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2020.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 3]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0]['Timestamp']
</code>"
1843,9210,temporal_aggregation,"Across all years, which November had the highest 25th percentile of PM2.5 level?","Taking all years into account, which November had the highest 25th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2017.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1844,9221,temporal_aggregation,"Across all years, which August had the 2nd highest 25th percentile of PM10 level?","Across all recorded years, which August registered the second-highest 25th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2023.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1845,9222,temporal_aggregation,"Across all years, which August had the highest 75th percentile of PM2.5 level?","Taking all years into account, which August experienced the maximum 75th percentile of PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1846,9225,temporal_aggregation,"Across all years, which August had the 2nd highest median PM10 level?","Across all recorded years, which August had the second-highest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2]['Timestamp'])


true_code()
",2023.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 8]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2]['Timestamp']
</code>"
1847,9232,temporal_aggregation,"Across all years, which May had the highest median PM2.5 level?","Considering all years, which May experienced the highest median PM2.5 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2018.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1]['Timestamp']
</code>"
1848,9234,temporal_aggregation,"Across all years, which May had the highest 25th percentile of PM10 level?","Taking all years into account, which May showed the maximum 25th percentile of PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1]['Timestamp'])


true_code()
",2019.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1]['Timestamp']
</code>"
1849,9235,temporal_aggregation,"Across all years, which November had the 3rd highest average PM10 level?","Over all years, which November had the third-highest average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3]['Timestamp'])


true_code()
",2020.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 11]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3]['Timestamp']
</code>"
1850,9236,temporal_aggregation,"Across all years, which May had the lowest 25th percentile of PM10 level?","Considering all years, which May registered the minimum 25th percentile for PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0]['Timestamp'])


true_code()
",2021.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.month == 5]
    data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0]['Timestamp']
</code>"
1851,9239,temporal_aggregation,"During which month (i.e. January, February, March,...) is the average PM2.5 level the lowest across India ?","Nationwide, during which calendar month does the average PM2.5 level typically reach its lowest point?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(main_data[""Timestamp""].dt.month_name())[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""Timestamp""])


true_code()
",August,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(data[""Timestamp""].dt.month_name())[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""Timestamp""]
</code>"
1852,9240,temporal_aggregation,"During which month (i.e. January, February, March,...) is the average PM10 level the 2nd lowest across India ?","Across India, which month of the year usually has the second-lowest average PM10 concentration?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data.groupby(main_data[""Timestamp""].dt.month_name())[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""Timestamp""])


true_code()
",August,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data.groupby(data[""Timestamp""].dt.month_name())[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""Timestamp""]
</code>"
1853,9244,temporal_aggregation,which week of the year 2022 has the 2nd highest average PM2.5 level ?,"In the year 2022, which week number registered the second-highest average PM2.5 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2022]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""week""])

true_code()
",52.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2022]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""week""]
</code>"
1854,9245,temporal_aggregation,which week of the year 2024 has the 2nd highest 25th percentile of PM10 level ?,"For 2024, during which week of the year was the 25th percentile of PM10 levels the second highest?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""week""])

true_code()
",1.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""week""]
</code>"
1855,9247,temporal_aggregation,which week of the year 2024 has the 2nd highest average PM10 level ?,"Considering the year 2024, which week showed the second-highest average PM10 level?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-2][""week""])

true_code()
",46.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-2][""week""]
</code>"
1856,9254,temporal_aggregation,which week of the year 2018 has the 3rd lowest median PM10 level ?,"In 2018, what week number was linked to the third-lowest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""week""])

true_code()
",30.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""week""]
</code>"
1857,9259,temporal_aggregation,which week of the year 2024 has the 3rd lowest 25th percentile of PM2.5 level ?,"In 2024, which week of the year corresponded to the third-lowest 25th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",35.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1858,9273,temporal_aggregation,which week of the year 2018 has the 3rd highest median PM2.5 level ?,"For the year 2018, which week had the third-highest median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""week""])

true_code()
",1.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""week""]
</code>"
1859,9276,temporal_aggregation,which week of the year 2024 has the lowest median PM10 level ?,"During 2024, determine the week number that showed the lowest median PM10 levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[0][""week""])

true_code()
",31.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[0][""week""]
</code>"
1860,9277,temporal_aggregation,which week of the year 2018 has the highest 75th percentile of PM2.5 level ?,"For 2018, which week of the year experienced the highest 75th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""week""])

true_code()
",52.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""week""]
</code>"
1861,9279,temporal_aggregation,which week of the year 2024 has the 3rd lowest average PM10 level ?,"In the year 2024, which week recorded the third-lowest average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""week""])

true_code()
",35.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""week""]
</code>"
1862,9290,temporal_aggregation,which week of the year 2023 has the lowest 75th percentile of PM2.5 level ?,"For 2023, which week of the year experienced the lowest 75th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""week""])

true_code()
",30.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""week""]
</code>"
1863,9293,temporal_aggregation,which week of the year 2022 has the highest median PM10 level ?,Which week in 2022 was linked to the highest median PM10 levels?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2022]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""week""])

true_code()
",48.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2022]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""week""]
</code>"
1864,9297,temporal_aggregation,which week of the year 2021 has the 3rd lowest 75th percentile of PM2.5 level ?,"Considering 2021, what week number displayed the third-lowest 75th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",35.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1865,9306,temporal_aggregation,which week of the year 2024 has the 3rd lowest median PM2.5 level ?,Which week in 2024 was linked to the third-lowest median PM2.5 levels?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",32.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1866,9326,temporal_aggregation,which week of the year 2019 has the 3rd highest 75th percentile of PM2.5 level ?,Identify the week in 2019 that registered the third-highest 75th percentile for PM2.5 levels.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2019]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""week""])

true_code()
",3.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2019]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""week""]
</code>"
1867,9335,temporal_aggregation,which week of the year 2020 has the lowest 25th percentile of PM2.5 level ?,"During 2020, which week saw the lowest 25th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2020]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[0][""week""])

true_code()
",34.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2020]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[0][""week""]
</code>"
1868,9339,temporal_aggregation,which week of the year 2018 has the 2nd highest 75th percentile of PM2.5 level ?,Identify the week in 2018 that registered the second-highest 75th percentile for PM2.5 levels.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""week""])

true_code()
",45.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""week""]
</code>"
1869,9340,temporal_aggregation,which week of the year 2019 has the 2nd lowest average PM10 level ?,"In 2019, which week of the year was associated with the second-lowest average PM10 concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2019]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[1][""week""])

true_code()
",39.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2019]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[1][""week""]
</code>"
1870,9341,temporal_aggregation,which week of the year 2021 has the highest 75th percentile of PM2.5 level ?,"During 2021, determine the week number that showed the highest 75th percentile for PM2.5 levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""week""])

true_code()
",45.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""week""]
</code>"
1871,9344,temporal_aggregation,which week of the year 2021 has the 3rd lowest 75th percentile of PM10 level ?,"In the year 2021, which week recorded the third-lowest 75th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[2][""week""])

true_code()
",36.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[2][""week""]
</code>"
1872,9346,temporal_aggregation,which week of the year 2018 has the highest 25th percentile of PM10 level ?,"For 2018, identify the week of the year with the highest 25th percentile for PM10 levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-1][""week""])

true_code()
",2.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-1][""week""]
</code>"
1873,9347,temporal_aggregation,which week of the year 2023 has the highest 25th percentile of PM2.5 level ?,"In 2023, which week experienced the maximum 25th percentile for PM2.5 concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-1][""week""])

true_code()
",1.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-1][""week""]
</code>"
1874,9350,temporal_aggregation,which week of the year 2018 has the 2nd highest average PM2.5 level ?,"In 2018, which week of the year corresponded to the second-highest average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""week""])

true_code()
",45.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""week""]
</code>"
1875,9354,temporal_aggregation,which week of the year 2023 has the 2nd lowest 25th percentile of PM2.5 level ?,"During 2023, determine the week number that showed the second-lowest 25th percentile for PM2.5 levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""week""])

true_code()
",26.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""week""]
</code>"
1876,9361,temporal_aggregation,which week of the year 2024 has the 3rd lowest 75th percentile of PM2.5 level ?,"During 2024, which week saw the third-lowest 75th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[2][""week""])

true_code()
",35.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[2][""week""]
</code>"
1877,9364,temporal_aggregation,which week of the year 2021 has the 3rd highest 25th percentile of PM2.5 level ?,"For the year 2021, which week had the third-highest 25th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-3][""week""])

true_code()
",50.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2021]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-3][""week""]
</code>"
1878,9365,temporal_aggregation,which week of the year 2020 has the 2nd highest 25th percentile of PM2.5 level ?,Identify the week in 2020 that registered the second-highest 25th percentile for PM2.5 levels.,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2020]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[-2][""week""])

true_code()
",53.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2020]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[-2][""week""]
</code>"
1879,9369,temporal_aggregation,which week of the year 2023 has the 3rd highest 75th percentile of PM10 level ?,"Considering 2023, what week number had the third-highest 75th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    print(data.iloc[-3][""week""])

true_code()
",2.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2023]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM10"")
    return data.iloc[-3][""week""]
</code>"
1880,9372,temporal_aggregation,which week of the year 2019 has the 2nd lowest 75th percentile of PM2.5 level ?,"For 2019, identify the week of the year with the second-lowest 75th percentile for PM2.5 levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2019]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    print(data.iloc[1][""week""])

true_code()
",39.0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2019]
    data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=""PM2.5"")
    return data.iloc[1][""week""]
</code>"
1881,9382,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 25th percentile of PM10  levels in 2024 ?","For the year 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) exhibited the second-highest 25th percentile of PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-2][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-2][""season""]
</code>"
1882,9385,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest median PM10  levels in 2024 ?","In 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the second-lowest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[1][""season""])

true_code()
",Post-Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[1][""season""]
</code>"
1883,9390,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest average PM10  levels in 2018 ?","Considering 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""season""])

true_code()
",Post-Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""season""]
</code>"
1884,9393,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest median PM2.5  levels in 2020 ?","In 2020, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the maximum median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2020]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""season""])

true_code()
",Winter,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2020]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""season""]
</code>"
1885,9402,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest average PM2.5  levels in 2023 ?","Considering 2023, what season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2023]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2023]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""season""]
</code>"
1886,9403,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 75th percentile of PM2.5  levels in 2022 ?","In the year 2022, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the second-highest 75th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2022]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""season""])

true_code()
",Post-Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2022]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""season""]
</code>"
1887,9410,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest median PM2.5  levels in 2018 ?","For the year 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""season""])

true_code()
",Post-Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""season""]
</code>"
1888,9417,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest average PM10  levels in 2020 ?","Which season in 2020 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the third-highest average PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2020]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2020]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].mean().reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""season""]
</code>"
1889,9429,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest average PM2.5  levels in 2021 ?","In the year 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the third-lowest average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2021]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[2][""season""])

true_code()
",Post-Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2021]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[2][""season""]
</code>"
1890,9430,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest median PM10  levels in 2022 ?","Which season in 2022 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the highest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2022]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-1][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2022]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-1][""season""]
</code>"
1891,9442,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 25th percentile of PM2.5  levels in 2024 ?","In the year 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the second-highest 25th percentile for PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-2][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-2][""season""]
</code>"
1892,9448,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest median PM2.5  levels in 2023 ?","In 2023, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the second-lowest median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2023]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[1][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2023]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[1][""season""]
</code>"
1893,9455,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest median PM2.5  levels in 2024 ?","In the year 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the third-highest median PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].median().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""season""]
</code>"
1894,9456,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest average PM2.5  levels in 2021 ?","Which season in 2021 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the third-highest average PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2021]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-3][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2021]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].mean().reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-3][""season""]
</code>"
1895,9461,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest median PM10  levels in 2019 ?","In 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the third-highest median PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""season""])

true_code()
",Post-Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""season""]
</code>"
1896,9464,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 75th percentile of PM10  levels in 2018 ?","In 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the third-highest 75th percentile of PM10 concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2018]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[-3][""season""])

true_code()
",Post-Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2018]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[-3][""season""]
</code>"
1897,9479,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 25th percentile of PM2.5  levels in 2019 ?","For 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the highest 25th percentile of PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""season""])

true_code()
",Winter,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""season""]
</code>"
1898,9485,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM2.5  levels in 2019 ?","During 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the lowest 25th percentile of PM2.5 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[0][""season""])

true_code()
",Monsoon,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[0][""season""]
</code>"
1899,9490,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest median PM10  levels in 2024 ?","In 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the third-lowest median PM10 concentrations?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2024]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].median().reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""season""]
</code>"
1900,9504,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 25th percentile of PM2.5  levels in 2022 ?","During 2022, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the highest 25th percentile of PM2.5 levels.","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2022]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    print(data.iloc[-1][""season""])

true_code()
",Winter,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2022]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index()
    data = data.sort_values(by=[""PM2.5""])
    return data.iloc[-1][""season""]
</code>"
1901,9507,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 75th percentile of PM10  levels in 2019 ?","In the year 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the third-lowest 75th percentile for PM10 levels?","
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=[""PM10""])
    print(data.iloc[2][""season""])

true_code()
",Summer,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data[""Timestamp""].dt.year == 2019]
    data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"")
    data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index()
    data = data.sort_values(by=[""PM10""])
    return data.iloc[2][""season""]
</code>"
1902,9518,temporal_aggregation,How many times Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2019,How many times did Uttar Pradesh surpass 90 µg/m³ of PM2.5 in the year 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",272,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1903,9522,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM10 in year 2023,How many times did Maharashtra exceed 90 µg/m³ of PM10 in the year 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",357,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1904,9526,temporal_aggregation,How many times Uttar Pradesh crossed the Indian guideline of PM10 in year 2017,How many times did Uttar Pradesh go above the Indian guideline for PM10 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",357,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1905,9527,temporal_aggregation,How many times Bihar crossed the 30 µg/m³ of PM2.5 in year 2018,How many times did Bihar surpass 30 µg/m³ of PM2.5 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",364,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1906,9529,temporal_aggregation,How many times Madhya Pradesh crossed the Indian guideline of PM10 in year 2018,How many times did Madhya Pradesh go above the Indian guideline for PM10 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",350,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1907,9530,temporal_aggregation,How many times West Bengal crossed the WHO guideline of PM2.5 in year 2023,How many times did West Bengal surpass the WHO guideline for PM2.5 in the year 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1908,9535,temporal_aggregation,How many times Bihar crossed the WHO guideline of PM2.5 in year 2021,How many times did Bihar go above the WHO guideline for PM2.5 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1909,9549,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM10 in year 2018,How many times did Maharashtra exceed 90 µg/m³ of PM10 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",279,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1910,9551,temporal_aggregation,How many times Madhya Pradesh crossed the 30 µg/m³ of PM2.5 in year 2020,How many times did Madhya Pradesh surpass 30 µg/m³ of PM2.5 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",313,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1911,9558,temporal_aggregation,How many times Uttar Pradesh crossed the 45 µg/m³ of PM2.5 in year 2018,How many times did Uttar Pradesh exceed 45 µg/m³ of PM2.5 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",361,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1912,9564,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM2.5 in year 2019,How many times did West Bengal exceed 90 µg/m³ of PM2.5 in the year 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",150,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1913,9565,temporal_aggregation,How many times Madhya Pradesh crossed the 75 µg/m³ of PM10 in year 2021,How many times did Madhya Pradesh go above 75 µg/m³ of PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",352,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1914,9566,temporal_aggregation,How many times Bihar crossed the WHO guideline of PM2.5 in year 2023,How many times did Bihar surpass the WHO guideline for PM2.5 in the year 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1915,9573,temporal_aggregation,How many times West Bengal crossed the WHO guideline of PM10 in year 2021,How many times did West Bengal exceed the WHO guideline for PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1916,9574,temporal_aggregation,How many times Uttar Pradesh crossed the 30 µg/m³ of PM10 in year 2021,How many times did Uttar Pradesh go above 30 µg/m³ of PM10 in the year 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1917,9583,temporal_aggregation,How many times Maharashtra crossed the WHO guideline of PM2.5 in year 2023,How many times did Maharashtra go above the WHO guideline for PM2.5 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1918,9584,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM2.5 in year 2021,How many times did Maharashtra surpass 90 µg/m³ of PM2.5 in the year 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",190,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1919,9601,temporal_aggregation,How many times Bihar crossed the 45 µg/m³ of PM2.5 in year 2017,How many times did Bihar go above 45 µg/m³ of PM2.5 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",289,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1920,9605,temporal_aggregation,How many times Madhya Pradesh crossed the 75 µg/m³ of PM2.5 in year 2018,How many times did Madhya Pradesh surpass 75 µg/m³ of PM2.5 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",228,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1921,9623,temporal_aggregation,How many times West Bengal crossed the Indian guideline of PM2.5 in year 2017,How many times did West Bengal surpass the Indian guideline for PM2.5 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1922,9628,temporal_aggregation,How many times Madhya Pradesh crossed the Indian guideline of PM10 in year 2019,How many times did Madhya Pradesh go above the Indian guideline for PM10 in the year 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",342,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1923,9630,temporal_aggregation,How many times Uttar Pradesh crossed the 75 µg/m³ of PM10 in year 2018,How many times did Uttar Pradesh exceed 75 µg/m³ of PM10 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",363,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Uttar Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1924,9632,temporal_aggregation,How many times Bihar crossed the 30 µg/m³ of PM2.5 in year 2022,How many times did Bihar surpass 30 µg/m³ of PM2.5 in the year 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1925,9638,temporal_aggregation,How many times Bihar crossed the WHO guideline of PM2.5 in year 2017,How many times did Bihar surpass the WHO guideline for PM2.5 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",307,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1926,9642,temporal_aggregation,How many times Maharashtra crossed the 30 µg/m³ of PM10 in year 2018,How many times did Maharashtra exceed 30 µg/m³ of PM10 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1927,9656,temporal_aggregation,How many times Bihar crossed the Indian guideline of PM10 in year 2019,How many times did Bihar surpass the Indian guideline for PM10 in the year 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",8,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1928,9658,temporal_aggregation,How many times Madhya Pradesh crossed the 45 µg/m³ of PM10 in year 2018,How many times did Madhya Pradesh go above 45 µg/m³ of PM10 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",361,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1929,9661,temporal_aggregation,How many times Bihar crossed the WHO guideline of PM10 in year 2019,How many times did Bihar go above the WHO guideline for PM10 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",8,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['state'] == ""Bihar""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1930,9667,temporal_aggregation,How many times Madhya Pradesh crossed the 90 µg/m³ of PM2.5 in year 2020,How many times did Madhya Pradesh go above 90 µg/m³ of PM2.5 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",150,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""Madhya Pradesh""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1931,9671,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM10 in year 2020,How many times did West Bengal surpass 90 µg/m³ of PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",205,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1932,9686,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM10 in year 2022,How many times did Maharashtra surpass the Indian guideline for PM10 in the year 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['state'] == ""Maharashtra""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1933,9687,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM2.5 in year 2020,How many times did West Bengal exceed 45 µg/m³ of PM2.5 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",201,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['state'] == ""West Bengal""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1934,9691,temporal_aggregation,How many times Mumbai city crossed the 45 µg/m³ of PM2.5 in year 2017,How many times did Mumbai city go above 45 µg/m³ of PM2.5 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",128,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1935,9697,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM10 in year 2020,How many times did Ahmedabad city go above 75 µg/m³ of PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",263,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1936,9705,temporal_aggregation,How many times Jaipur city crossed the 45 µg/m³ of PM2.5 in year 2017,How many times did Jaipur city exceed 45 µg/m³ of PM2.5 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",100,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1937,9706,temporal_aggregation,How many times Bangalore city crossed the WHO guideline of PM10 in year 2018,How many times did Bangalore city go above the WHO guideline for PM10 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1938,9713,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM2.5 in year 2023,How many times did Pune city surpass the WHO guideline for PM2.5 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",347,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1939,9714,temporal_aggregation,How many times Chennai city crossed the 45 µg/m³ of PM2.5 in year 2019,How many times did Chennai city exceed 45 µg/m³ of PM2.5 in the year 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",266,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1940,9720,temporal_aggregation,How many times Hyderabad city crossed the Indian guideline of PM10 in year 2017,How many times did Hyderabad city exceed the Indian guideline for PM10 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",331,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1941,9721,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM2.5 in year 2022,How many times did Pune city go above the WHO guideline for PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",277,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1942,9724,temporal_aggregation,How many times Pune city crossed the 45 µg/m³ of PM2.5 in year 2021,How many times did Pune city go above 45 µg/m³ of PM2.5 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",268,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1943,9728,temporal_aggregation,How many times Surat city crossed the 90 µg/m³ of PM2.5 in year 2020,How many times did Surat city surpass 90 µg/m³ of PM2.5 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1944,9730,temporal_aggregation,How many times Mumbai city crossed the 90 µg/m³ of PM2.5 in year 2022,How many times did Mumbai city go above 90 µg/m³ of PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",197,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1945,9737,temporal_aggregation,How many times Delhi city crossed the 30 µg/m³ of PM2.5 in year 2022,How many times did Delhi city surpass 30 µg/m³ of PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Delhi""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",363,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Delhi""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1946,9742,temporal_aggregation,How many times Surat city crossed the WHO guideline of PM10 in year 2021,How many times did Surat city go above the WHO guideline for PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1947,9750,temporal_aggregation,How many times Kolkata city crossed the 75 µg/m³ of PM10 in year 2023,How many times did Kolkata city exceed 75 µg/m³ of PM10 in the year 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",217,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1948,9752,temporal_aggregation,How many times Jaipur city crossed the 75 µg/m³ of PM10 in year 2021,How many times did Jaipur city surpass 75 µg/m³ of PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",313,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1949,9757,temporal_aggregation,How many times Ahmedabad city crossed the 90 µg/m³ of PM2.5 in year 2019,How many times did Ahmedabad city go above 90 µg/m³ of PM2.5 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",50,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1950,9758,temporal_aggregation,How many times Chennai city crossed the 45 µg/m³ of PM2.5 in year 2018,How many times did Chennai city surpass 45 µg/m³ of PM2.5 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",286,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1951,9774,temporal_aggregation,How many times Jaipur city crossed the 45 µg/m³ of PM2.5 in year 2023,How many times did Jaipur city exceed 45 µg/m³ of PM2.5 in the year 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",286,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1952,9775,temporal_aggregation,How many times Jaipur city crossed the Indian guideline of PM10 in year 2020,How many times did Jaipur city go above the Indian guideline for PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",335,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1953,9778,temporal_aggregation,How many times Bangalore city crossed the WHO guideline of PM10 in year 2017,How many times did Bangalore city go above the WHO guideline for PM10 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1954,9782,temporal_aggregation,How many times Kolkata city crossed the Indian guideline of PM10 in year 2022,How many times did Kolkata city surpass the Indian guideline for PM10 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",296,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1955,9791,temporal_aggregation,How many times Kolkata city crossed the 75 µg/m³ of PM10 in year 2019,How many times did Kolkata city surpass 75 µg/m³ of PM10 in the year 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",289,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1956,9794,temporal_aggregation,How many times Chennai city crossed the 45 µg/m³ of PM2.5 in year 2023,How many times did Chennai city surpass 45 µg/m³ of PM2.5 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",172,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1957,9800,temporal_aggregation,How many times Chennai city crossed the WHO guideline of PM2.5 in year 2022,How many times did Chennai city surpass the WHO guideline for PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",360,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1958,9802,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM10 in year 2023,How many times did Jaipur city go above the WHO guideline for PM10 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1959,9803,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM10 in year 2017,How many times did Kolkata city surpass the WHO guideline for PM10 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",289,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1960,9812,temporal_aggregation,How many times Mumbai city crossed the WHO guideline of PM2.5 in year 2017,How many times did Mumbai city surpass the WHO guideline for PM2.5 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",303,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1961,9815,temporal_aggregation,How many times Mumbai city crossed the Indian guideline of PM10 in year 2019,How many times did Mumbai city surpass the Indian guideline for PM10 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",320,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1962,9820,temporal_aggregation,How many times Pune city crossed the 90 µg/m³ of PM10 in year 2017,How many times did Pune city go above 90 µg/m³ of PM10 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",75,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1963,9821,temporal_aggregation,How many times Ahmedabad city crossed the 45 µg/m³ of PM2.5 in year 2021,How many times did Ahmedabad city surpass 45 µg/m³ of PM2.5 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",281,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1964,9822,temporal_aggregation,How many times Mumbai city crossed the 30 µg/m³ of PM10 in year 2023,How many times did Mumbai city exceed 30 µg/m³ of PM10 in the year 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1965,9834,temporal_aggregation,How many times Hyderabad city crossed the 45 µg/m³ of PM10 in year 2019,How many times did Hyderabad city exceed 45 µg/m³ of PM10 in the year 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",337,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1966,9836,temporal_aggregation,How many times Ahmedabad city crossed the WHO guideline of PM10 in year 2020,How many times did Ahmedabad city surpass the WHO guideline for PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",361,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1967,9842,temporal_aggregation,How many times Hyderabad city crossed the WHO guideline of PM10 in year 2023,How many times did Hyderabad city surpass the WHO guideline for PM10 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1968,9849,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM2.5 in year 2017,How many times did Bangalore city exceed the Indian guideline for PM2.5 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1969,9851,temporal_aggregation,How many times Hyderabad city crossed the 30 µg/m³ of PM2.5 in year 2022,How many times did Hyderabad city surpass 30 µg/m³ of PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",340,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1970,9852,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM10 in year 2020,How many times did Bangalore city exceed the Indian guideline for PM10 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1971,9855,temporal_aggregation,How many times Kolkata city crossed the 75 µg/m³ of PM10 in year 2022,How many times did Kolkata city exceed 75 µg/m³ of PM10 in the year 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",261,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1972,9857,temporal_aggregation,How many times Kolkata city crossed the 90 µg/m³ of PM2.5 in year 2021,How many times did Kolkata city surpass 90 µg/m³ of PM2.5 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",143,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1973,9861,temporal_aggregation,How many times Kolkata city crossed the Indian guideline of PM10 in year 2020,How many times did Kolkata city exceed the Indian guideline for PM10 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",235,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1974,9864,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM10 in year 2017,How many times did Jaipur city exceed the WHO guideline for PM10 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",142,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1975,9869,temporal_aggregation,How many times Bangalore city crossed the WHO guideline of PM10 in year 2022,How many times did Bangalore city surpass the WHO guideline for PM10 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1976,9880,temporal_aggregation,How many times Surat city crossed the 75 µg/m³ of PM2.5 in year 2021,How many times did Surat city go above 75 µg/m³ of PM2.5 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1977,9886,temporal_aggregation,How many times Kolkata city crossed the 90 µg/m³ of PM2.5 in year 2022,How many times did Kolkata city go above 90 µg/m³ of PM2.5 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",122,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1978,9887,temporal_aggregation,How many times Pune city crossed the 45 µg/m³ of PM10 in year 2021,How many times did Pune city surpass 45 µg/m³ of PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",357,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1979,9891,temporal_aggregation,How many times Chennai city crossed the 75 µg/m³ of PM10 in year 2020,How many times did Chennai city exceed 75 µg/m³ of PM10 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",79,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1980,9895,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM10 in year 2022,How many times did Pune city go above the WHO guideline for PM10 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",279,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1981,9899,temporal_aggregation,How many times Surat city crossed the 45 µg/m³ of PM10 in year 2023,How many times did Surat city surpass 45 µg/m³ of PM10 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",267,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1982,9901,temporal_aggregation,How many times Mumbai city crossed the 45 µg/m³ of PM10 in year 2017,How many times did Mumbai city go above 45 µg/m³ of PM10 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",296,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1983,9902,temporal_aggregation,How many times Surat city crossed the Indian guideline of PM10 in year 2023,How many times did Surat city surpass the Indian guideline for PM10 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",251,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1984,9905,temporal_aggregation,How many times Mumbai city crossed the 30 µg/m³ of PM2.5 in year 2023,How many times did Mumbai city surpass 30 µg/m³ of PM2.5 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",351,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1985,9919,temporal_aggregation,How many times Bangalore city crossed the 30 µg/m³ of PM10 in year 2017,How many times did Bangalore city go above 30 µg/m³ of PM10 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1986,9923,temporal_aggregation,How many times Bangalore city crossed the WHO guideline of PM10 in year 2020,How many times did Bangalore city surpass the WHO guideline for PM10 in 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1987,9924,temporal_aggregation,How many times Hyderabad city crossed the WHO guideline of PM10 in year 2018,How many times did Hyderabad city exceed the WHO guideline for PM10 in the year 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1988,9944,temporal_aggregation,How many times Ahmedabad city crossed the 90 µg/m³ of PM10 in year 2018,How many times did Ahmedabad city surpass 90 µg/m³ of PM10 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1989,9946,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM10 in year 2019,How many times did Jaipur city go above the WHO guideline for PM10 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Jaipur""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1990,9953,temporal_aggregation,How many times Chennai city crossed the WHO guideline of PM2.5 in year 2023,How many times did Chennai city surpass the WHO guideline for PM2.5 in 2023?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",363,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2023]
    data = data[data['city'] == ""Chennai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1991,9957,temporal_aggregation,How many times Delhi city crossed the 90 µg/m³ of PM2.5 in year 2020,How many times did Delhi city exceed 90 µg/m³ of PM2.5 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Delhi""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",237,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Delhi""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1992,9961,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM10 in year 2019,How many times did Bangalore city go above the Indian guideline for PM10 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 60]
    count = data['Timestamp'].nunique()
    return count
</code>"
1993,9965,temporal_aggregation,How many times Delhi city crossed the 90 µg/m³ of PM10 in year 2019,How many times did Delhi city surpass 90 µg/m³ of PM10 in 2019?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Delhi""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",361,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2019]
    data = data[data['city'] == ""Delhi""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 90]
    count = data['Timestamp'].nunique()
    return count
</code>"
1994,9971,temporal_aggregation,How many times Pune city crossed the 75 µg/m³ of PM10 in year 2021,How many times did Pune city surpass 75 µg/m³ of PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",268,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1995,9975,temporal_aggregation,How many times Hyderabad city crossed the 30 µg/m³ of PM10 in year 2017,How many times did Hyderabad city exceed 30 µg/m³ of PM10 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",361,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 30]
    count = data['Timestamp'].nunique()
    return count
</code>"
1996,9977,temporal_aggregation,How many times Mumbai city crossed the WHO guideline of PM2.5 in year 2021,How many times did Mumbai city surpass the WHO guideline for PM2.5 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 15]
    count = data['Timestamp'].nunique()
    return count
</code>"
1997,9993,temporal_aggregation,How many times Mumbai city crossed the 45 µg/m³ of PM10 in year 2021,How many times did Mumbai city exceed 45 µg/m³ of PM10 in the year 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",365,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Mumbai""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
1998,10002,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM10 in year 2017,How many times did Ahmedabad city exceed 75 µg/m³ of PM10 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
1999,10008,temporal_aggregation,How many times Kolkata city crossed the 45 µg/m³ of PM10 in year 2020,How many times did Kolkata city exceed 45 µg/m³ of PM10 in the year 2020?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",302,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2020]
    data = data[data['city'] == ""Kolkata""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 45]
    count = data['Timestamp'].nunique()
    return count
</code>"
2000,10016,temporal_aggregation,How many times Hyderabad city crossed the 75 µg/m³ of PM2.5 in year 2017,How many times did Hyderabad city surpass 75 µg/m³ of PM2.5 in 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",119,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Hyderabad""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
2001,10017,temporal_aggregation,How many times Bangalore city crossed the 75 µg/m³ of PM2.5 in year 2017,How many times did Bangalore city exceed 75 µg/m³ of PM2.5 in the year 2017?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2017]
    data = data[data['city'] == ""Bangalore""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
2002,10018,temporal_aggregation,How many times Surat city crossed the 75 µg/m³ of PM2.5 in year 2018,How many times did Surat city go above 75 µg/m³ of PM2.5 in 2018?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",0,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2018]
    data = data[data['city'] == ""Surat""]
    data = data.dropna(subset=""PM2.5"")
    data = data[data[""PM2.5""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
2003,10022,temporal_aggregation,How many times Pune city crossed the 75 µg/m³ of PM10 in year 2022,How many times did Pune city surpass 75 µg/m³ of PM10 in 2022?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",208,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2022]
    data = data[data['city'] == ""Pune""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"
2004,10028,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM10 in year 2021,How many times did Ahmedabad city surpass 75 µg/m³ of PM10 in 2021?,"
def true_code():
    import numpy as np
    import pandas as pd
    main_data = pd.read_pickle(""preprocessed/main_data.pkl"")
    data = main_data[main_data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    print(count)

true_code()
",349,"<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    data = data[data['Timestamp'].dt.year == 2021]
    data = data[data['city'] == ""Ahmedabad""]
    data = data.dropna(subset=""PM10"")
    data = data[data[""PM10""] > 75]
    count = data['Timestamp'].nunique()
    return count
</code>"