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PELAB-LiU
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JunoBench

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Yiran Wang commited on
Commit
6067878
·
1 Parent(s): ba37a6c

polish pandas buggy and fixed versions

Browse files
Files changed (29) hide show
  1. benchmark/pandas_1/pandas_1_fixed.ipynb +0 -0
  2. benchmark/pandas_1/pandas_1_reproduced.ipynb +0 -0
  3. benchmark/pandas_10/pandas_10_fixed.ipynb +42 -428
  4. benchmark/pandas_10/pandas_10_reproduced.ipynb +45 -470
  5. benchmark/pandas_11/pandas_11_fixed.ipynb +140 -143
  6. benchmark/pandas_11/pandas_11_reproduced.ipynb +12 -265
  7. benchmark/pandas_12/pandas_12_fixed.ipynb +523 -729
  8. benchmark/pandas_12/pandas_12_reproduced.ipynb +21 -227
  9. benchmark/pandas_13/pandas_13_fixed.ipynb +24 -280
  10. benchmark/pandas_13/pandas_13_reproduced.ipynb +25 -281
  11. benchmark/pandas_14/pandas_14_fixed.ipynb +23 -405
  12. benchmark/pandas_14/pandas_14_reproduced.ipynb +24 -406
  13. benchmark/pandas_15/pandas_15_fixed.ipynb +22 -656
  14. benchmark/pandas_15/pandas_15_reproduced.ipynb +23 -657
  15. benchmark/pandas_2/pandas_2_fixed.ipynb +5 -394
  16. benchmark/pandas_2/pandas_2_reproduced.ipynb +6 -395
  17. benchmark/pandas_3/pandas_3_reproduced.ipynb +5 -5
  18. benchmark/pandas_4/pandas_4_fixed.ipynb +12 -194
  19. benchmark/pandas_4/pandas_4_reproduced.ipynb +13 -195
  20. benchmark/pandas_5/pandas_5_fixed.ipynb +29 -723
  21. benchmark/pandas_5/pandas_5_reproduced.ipynb +30 -724
  22. benchmark/pandas_6/pandas_6_fixed.ipynb +19 -258
  23. benchmark/pandas_6/pandas_6_reproduced.ipynb +10 -64
  24. benchmark/pandas_7/pandas_7_fixed.ipynb +8 -76
  25. benchmark/pandas_7/pandas_7_reproduced.ipynb +8 -77
  26. benchmark/pandas_8/pandas_8_fixed.ipynb +14 -259
  27. benchmark/pandas_8/pandas_8_reproduced.ipynb +0 -0
  28. benchmark/pandas_9/pandas_9_fixed.ipynb +7 -504
  29. benchmark/pandas_9/pandas_9_reproduced.ipynb +6 -260
benchmark/pandas_1/pandas_1_fixed.ipynb CHANGED
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benchmark/pandas_1/pandas_1_reproduced.ipynb CHANGED
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benchmark/pandas_10/pandas_10_fixed.ipynb CHANGED
@@ -68,7 +68,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 3,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-10-22T12:44:58.372979Z",
@@ -78,163 +78,14 @@
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  "shell.execute_reply.started": "2023-10-22T12:44:58.372945Z"
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  }
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  },
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- "outputs": [
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- {
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- "data": {
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- "text/html": [
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- "<div>\n",
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- "<style scoped>\n",
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- " .dataframe tbody tr th:only-of-type {\n",
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- " vertical-align: middle;\n",
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- " }\n",
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- "\n",
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- " .dataframe tbody tr th {\n",
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- " vertical-align: top;\n",
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- " }\n",
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- "\n",
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- " .dataframe thead th {\n",
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- " text-align: right;\n",
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- " }\n",
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- "</style>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
100
- " <thead>\n",
101
- " <tr style=\"text-align: right;\">\n",
102
- " <th></th>\n",
103
- " <th>App</th>\n",
104
- " <th>Category</th>\n",
105
- " <th>Rating</th>\n",
106
- " <th>Reviews</th>\n",
107
- " <th>Size</th>\n",
108
- " <th>Installs</th>\n",
109
- " <th>Type</th>\n",
110
- " <th>Price</th>\n",
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- " <th>Content Rating</th>\n",
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- " <th>Genres</th>\n",
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- " <th>Last Updated</th>\n",
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- " <th>Current Ver</th>\n",
115
- " <th>Android Ver</th>\n",
116
- " </tr>\n",
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- " </thead>\n",
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- " <tbody>\n",
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- " <tr>\n",
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- " <th>0</th>\n",
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- " <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
122
- " <td>ART_AND_DESIGN</td>\n",
123
- " <td>4.1</td>\n",
124
- " <td>159</td>\n",
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- " <td>19M</td>\n",
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- " <td>10,000+</td>\n",
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- " <td>Free</td>\n",
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- " <td>0</td>\n",
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- " <td>Everyone</td>\n",
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- " <td>Art &amp; Design</td>\n",
131
- " <td>7-Jan-18</td>\n",
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- " <td>1.0.0</td>\n",
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- " <td>4.0.3 and up</td>\n",
134
- " </tr>\n",
135
- " <tr>\n",
136
- " <th>1</th>\n",
137
- " <td>Coloring book moana</td>\n",
138
- " <td>ART_AND_DESIGN</td>\n",
139
- " <td>3.9</td>\n",
140
- " <td>967</td>\n",
141
- " <td>14M</td>\n",
142
- " <td>500,000+</td>\n",
143
- " <td>Free</td>\n",
144
- " <td>0</td>\n",
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- " <td>Everyone</td>\n",
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- " <td>Art &amp; Design;Pretend Play</td>\n",
147
- " <td>15-Jan-18</td>\n",
148
- " <td>2.0.0</td>\n",
149
- " <td>4.0.3 and up</td>\n",
150
- " </tr>\n",
151
- " <tr>\n",
152
- " <th>2</th>\n",
153
- " <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
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- " <td>ART_AND_DESIGN</td>\n",
155
- " <td>4.7</td>\n",
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- " <td>87510</td>\n",
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- " <td>8.7M</td>\n",
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- " <td>5,000,000+</td>\n",
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- " <td>Free</td>\n",
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- " <td>0</td>\n",
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- " <td>Everyone</td>\n",
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- " <td>Art &amp; Design</td>\n",
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- " <td>1-Aug-18</td>\n",
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- " <td>1.2.4</td>\n",
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- " <td>4.0.3 and up</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>3</th>\n",
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- " <td>Sketch - Draw &amp; Paint</td>\n",
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- " <td>ART_AND_DESIGN</td>\n",
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- " <td>4.5</td>\n",
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- " <td>215644</td>\n",
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- " <td>25M</td>\n",
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- " <td>50,000,000+</td>\n",
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- " <td>Free</td>\n",
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- " <td>0</td>\n",
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- " <td>Teen</td>\n",
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- " <td>Art &amp; Design</td>\n",
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- " <td>8-Jun-18</td>\n",
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- " <td>Varies with device</td>\n",
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- " <td>4.2 and up</td>\n",
182
- " </tr>\n",
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- " <tr>\n",
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- " <th>4</th>\n",
185
- " <td>Pixel Draw - Number Art Coloring Book</td>\n",
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- " <td>ART_AND_DESIGN</td>\n",
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- " <td>4.3</td>\n",
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- " <td>967</td>\n",
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- " <td>2.8M</td>\n",
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- " <td>100,000+</td>\n",
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- " <td>Free</td>\n",
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- " <td>0</td>\n",
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- " <td>Everyone</td>\n",
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- " <td>Art &amp; Design;Creativity</td>\n",
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- " <td>20-Jun-18</td>\n",
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- " <td>1.1</td>\n",
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- " <td>4.4 and up</td>\n",
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- " </tr>\n",
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- " </tbody>\n",
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- "</table>\n",
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- "</div>"
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- ],
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- "text/plain": [
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- " App Category Rating \\\n",
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- "0 Photo Editor & Candy Camera & Grid & ScrapBook ART_AND_DESIGN 4.1 \n",
206
- "1 Coloring book moana ART_AND_DESIGN 3.9 \n",
207
- "2 U Launcher Lite – FREE Live Cool Themes, Hide ... ART_AND_DESIGN 4.7 \n",
208
- "3 Sketch - Draw & Paint ART_AND_DESIGN 4.5 \n",
209
- "4 Pixel Draw - Number Art Coloring Book ART_AND_DESIGN 4.3 \n",
210
- "\n",
211
- " Reviews Size Installs Type Price Content Rating \\\n",
212
- "0 159 19M 10,000+ Free 0 Everyone \n",
213
- "1 967 14M 500,000+ Free 0 Everyone \n",
214
- "2 87510 8.7M 5,000,000+ Free 0 Everyone \n",
215
- "3 215644 25M 50,000,000+ Free 0 Teen \n",
216
- "4 967 2.8M 100,000+ Free 0 Everyone \n",
217
- "\n",
218
- " Genres Last Updated Current Ver Android Ver \n",
219
- "0 Art & Design 7-Jan-18 1.0.0 4.0.3 and up \n",
220
- "1 Art & Design;Pretend Play 15-Jan-18 2.0.0 4.0.3 and up \n",
221
- "2 Art & Design 1-Aug-18 1.2.4 4.0.3 and up \n",
222
- "3 Art & Design 8-Jun-18 Varies with device 4.2 and up \n",
223
- "4 Art & Design;Creativity 20-Jun-18 1.1 4.4 and up "
224
- ]
225
- },
226
- "execution_count": 3,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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  "source": [
232
  "df.head()"
233
  ]
234
  },
235
  {
236
  "cell_type": "code",
237
- "execution_count": 4,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-10-22T12:44:58.411513Z",
@@ -244,18 +95,7 @@
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  "shell.execute_reply.started": "2023-10-22T12:44:58.411466Z"
245
  }
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  },
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "(10841, 13)"
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- ]
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- },
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- "execution_count": 4,
255
- "metadata": {},
256
- "output_type": "execute_result"
257
- }
258
- ],
259
  "source": [
260
  "# print Rows and Col\n",
261
  "df.shape"
@@ -263,7 +103,7 @@
263
  },
264
  {
265
  "cell_type": "code",
266
- "execution_count": 5,
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  "metadata": {
268
  "execution": {
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  "iopub.execute_input": "2023-10-22T12:44:58.424396Z",
@@ -273,34 +113,7 @@
273
  "shell.execute_reply.started": "2023-10-22T12:44:58.424363Z"
274
  }
275
  },
276
- "outputs": [
277
- {
278
- "name": "stdout",
279
- "output_type": "stream",
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- "text": [
281
- "<class 'pandas.core.frame.DataFrame'>\n",
282
- "RangeIndex: 10841 entries, 0 to 10840\n",
283
- "Data columns (total 13 columns):\n",
284
- " # Column Non-Null Count Dtype \n",
285
- "--- ------ -------------- ----- \n",
286
- " 0 App 10841 non-null object \n",
287
- " 1 Category 10841 non-null object \n",
288
- " 2 Rating 9367 non-null float64\n",
289
- " 3 Reviews 10841 non-null object \n",
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- " 4 Size 10841 non-null object \n",
291
- " 5 Installs 10841 non-null object \n",
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- " 6 Type 10840 non-null object \n",
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- " 7 Price 10841 non-null object \n",
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- " 8 Content Rating 10840 non-null object \n",
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- " 9 Genres 10841 non-null object \n",
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- " 10 Last Updated 10841 non-null object \n",
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- " 11 Current Ver 10833 non-null object \n",
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- " 12 Android Ver 10838 non-null object \n",
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- "dtypes: float64(1), object(12)\n",
300
- "memory usage: 1.1+ MB\n"
301
- ]
302
- }
303
- ],
304
  "source": [
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  "# Information of Data\n",
306
  "df.info()"
@@ -324,7 +137,7 @@
324
  },
325
  {
326
  "cell_type": "code",
327
- "execution_count": 6,
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  "metadata": {
329
  "execution": {
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  "iopub.execute_input": "2023-10-22T12:44:58.476239Z",
@@ -334,38 +147,14 @@
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  "shell.execute_reply.started": "2023-10-22T12:44:58.476193Z"
335
  }
336
  },
337
- "outputs": [
338
- {
339
- "data": {
340
- "text/plain": [
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- "App 0\n",
342
- "Category 0\n",
343
- "Rating 1474\n",
344
- "Reviews 0\n",
345
- "Size 0\n",
346
- "Installs 0\n",
347
- "Type 1\n",
348
- "Price 0\n",
349
- "Content Rating 1\n",
350
- "Genres 0\n",
351
- "Last Updated 0\n",
352
- "Current Ver 8\n",
353
- "Android Ver 3\n",
354
- "dtype: int64"
355
- ]
356
- },
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- "execution_count": 6,
358
- "metadata": {},
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- "output_type": "execute_result"
360
- }
361
- ],
362
  "source": [
363
  "df.isnull().sum()"
364
  ]
365
  },
366
  {
367
  "cell_type": "code",
368
- "execution_count": 7,
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  "metadata": {
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  "execution": {
371
  "iopub.execute_input": "2023-10-22T12:44:58.503907Z",
@@ -382,7 +171,7 @@
382
  },
383
  {
384
  "cell_type": "code",
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- "execution_count": 8,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-10-22T12:44:58.530211Z",
@@ -392,38 +181,14 @@
392
  "shell.execute_reply.started": "2023-10-22T12:44:58.530124Z"
393
  }
394
  },
395
- "outputs": [
396
- {
397
- "data": {
398
- "text/plain": [
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- "App 0\n",
400
- "Category 0\n",
401
- "Rating 0\n",
402
- "Reviews 0\n",
403
- "Size 0\n",
404
- "Installs 0\n",
405
- "Type 0\n",
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- "Price 0\n",
407
- "Content Rating 0\n",
408
- "Genres 0\n",
409
- "Last Updated 0\n",
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- "Current Ver 0\n",
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- "Android Ver 0\n",
412
- "dtype: int64"
413
- ]
414
- },
415
- "execution_count": 8,
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- "metadata": {},
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- "output_type": "execute_result"
418
- }
419
- ],
420
  "source": [
421
  "df.isnull().sum()"
422
  ]
423
  },
424
  {
425
  "cell_type": "code",
426
- "execution_count": 9,
427
  "metadata": {
428
  "execution": {
429
  "iopub.execute_input": "2023-10-22T12:44:58.556274Z",
@@ -433,31 +198,7 @@
433
  "shell.execute_reply.started": "2023-10-22T12:44:58.556234Z"
434
  }
435
  },
436
- "outputs": [
437
- {
438
- "data": {
439
- "text/plain": [
440
- "App object\n",
441
- "Category object\n",
442
- "Rating float64\n",
443
- "Reviews object\n",
444
- "Size object\n",
445
- "Installs object\n",
446
- "Type object\n",
447
- "Price object\n",
448
- "Content Rating object\n",
449
- "Genres object\n",
450
- "Last Updated object\n",
451
- "Current Ver object\n",
452
- "Android Ver object\n",
453
- "dtype: object"
454
- ]
455
- },
456
- "execution_count": 9,
457
- "metadata": {},
458
- "output_type": "execute_result"
459
- }
460
- ],
461
  "source": [
462
  "# Check Datatypes\n",
463
  "df.dtypes"
@@ -472,7 +213,7 @@
472
  },
473
  {
474
  "cell_type": "code",
475
- "execution_count": 10,
476
  "metadata": {
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  "execution": {
478
  "iopub.execute_input": "2023-10-22T12:44:58.568629Z",
@@ -489,7 +230,7 @@
489
  },
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  {
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  "cell_type": "code",
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- "execution_count": 11,
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  "metadata": {
494
  "execution": {
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  "iopub.execute_input": "2023-10-22T12:44:58.584706Z",
@@ -499,38 +240,14 @@
499
  "shell.execute_reply.started": "2023-10-22T12:44:58.584656Z"
500
  }
501
  },
502
- "outputs": [
503
- {
504
- "data": {
505
- "text/plain": [
506
- "App object\n",
507
- "Category object\n",
508
- "Rating float64\n",
509
- "Reviews int64\n",
510
- "Size object\n",
511
- "Installs object\n",
512
- "Type object\n",
513
- "Price object\n",
514
- "Content Rating object\n",
515
- "Genres object\n",
516
- "Last Updated object\n",
517
- "Current Ver object\n",
518
- "Android Ver object\n",
519
- "dtype: object"
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- ]
521
- },
522
- "execution_count": 11,
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- "metadata": {},
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- "output_type": "execute_result"
525
- }
526
- ],
527
  "source": [
528
  "df.dtypes"
529
  ]
530
  },
531
  {
532
  "cell_type": "code",
533
- "execution_count": 12,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-10-22T12:44:58.607554Z",
@@ -542,12 +259,13 @@
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  },
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  "outputs": [],
544
  "source": [
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- "# df['Installs']=df['Installs'].astype('int') # fix --- they fixed themselved in the following cells"
 
546
  ]
547
  },
548
  {
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  "cell_type": "code",
550
- "execution_count": 14,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-10-22T12:45:31.529064Z",
@@ -559,13 +277,13 @@
559
  },
560
  "outputs": [],
561
  "source": [
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- "df['Installs']=df['Installs'].str.replace('+','')\n",
563
- "df['Installs']=df['Installs'].str.replace(',','')"
564
  ]
565
  },
566
  {
567
  "cell_type": "code",
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- "execution_count": 15,
569
  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-10-22T12:45:34.207157Z",
@@ -577,12 +295,12 @@
577
  },
578
  "outputs": [],
579
  "source": [
580
- "df['Installs']=df['Installs'].astype('int')"
581
  ]
582
  },
583
  {
584
  "cell_type": "code",
585
- "execution_count": 16,
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  "metadata": {
587
  "execution": {
588
  "iopub.execute_input": "2023-10-22T12:45:37.326177Z",
@@ -592,38 +310,14 @@
592
  "shell.execute_reply.started": "2023-10-22T12:45:37.326147Z"
593
  }
594
  },
595
- "outputs": [
596
- {
597
- "data": {
598
- "text/plain": [
599
- "App object\n",
600
- "Category object\n",
601
- "Rating float64\n",
602
- "Reviews int64\n",
603
- "Size object\n",
604
- "Installs int64\n",
605
- "Type object\n",
606
- "Price object\n",
607
- "Content Rating object\n",
608
- "Genres object\n",
609
- "Last Updated object\n",
610
- "Current Ver object\n",
611
- "Android Ver object\n",
612
- "dtype: object"
613
- ]
614
- },
615
- "execution_count": 16,
616
- "metadata": {},
617
- "output_type": "execute_result"
618
- }
619
- ],
620
  "source": [
621
  "df.dtypes"
622
  ]
623
  },
624
  {
625
  "cell_type": "code",
626
- "execution_count": 17,
627
  "metadata": {
628
  "execution": {
629
  "iopub.execute_input": "2023-10-22T12:45:40.288946Z",
@@ -635,12 +329,12 @@
635
  },
636
  "outputs": [],
637
  "source": [
638
- "# df['Price']=df['Price'].astype('float') # fix --- they fixed themselved in the following cells"
639
  ]
640
  },
641
  {
642
  "cell_type": "code",
643
- "execution_count": 18,
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  "metadata": {
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  "execution": {
646
  "iopub.execute_input": "2023-10-22T12:45:45.746481Z",
@@ -657,7 +351,7 @@
657
  },
658
  {
659
  "cell_type": "code",
660
- "execution_count": 19,
661
  "metadata": {
662
  "execution": {
663
  "iopub.execute_input": "2023-10-22T12:45:48.333300Z",
@@ -674,7 +368,7 @@
674
  },
675
  {
676
  "cell_type": "code",
677
- "execution_count": 20,
678
  "metadata": {
679
  "execution": {
680
  "iopub.status.busy": "2023-10-22T12:45:00.128119Z",
@@ -683,38 +377,14 @@
683
  "shell.execute_reply.started": "2023-10-22T12:45:00.128407Z"
684
  }
685
  },
686
- "outputs": [
687
- {
688
- "data": {
689
- "text/plain": [
690
- "App object\n",
691
- "Category object\n",
692
- "Rating float64\n",
693
- "Reviews int64\n",
694
- "Size object\n",
695
- "Installs int64\n",
696
- "Type object\n",
697
- "Price float64\n",
698
- "Content Rating object\n",
699
- "Genres object\n",
700
- "Last Updated object\n",
701
- "Current Ver object\n",
702
- "Android Ver object\n",
703
- "dtype: object"
704
- ]
705
- },
706
- "execution_count": 20,
707
- "metadata": {},
708
- "output_type": "execute_result"
709
- }
710
- ],
711
  "source": [
712
  "df.dtypes"
713
  ]
714
  },
715
  {
716
  "cell_type": "code",
717
- "execution_count": 21,
718
  "metadata": {
719
  "execution": {
720
  "iopub.execute_input": "2023-10-22T12:45:51.282739Z",
@@ -726,12 +396,12 @@
726
  },
727
  "outputs": [],
728
  "source": [
729
- "# df['Size']=df['Size'].astype('float') # fix --- they fixed themselved in the following cells"
730
  ]
731
  },
732
  {
733
  "cell_type": "code",
734
- "execution_count": 22,
735
  "metadata": {
736
  "execution": {
737
  "iopub.status.busy": "2023-10-22T12:45:00.134339Z",
@@ -740,37 +410,14 @@
740
  "shell.execute_reply.started": "2023-10-22T12:45:00.135690Z"
741
  }
742
  },
743
- "outputs": [
744
- {
745
- "data": {
746
- "text/plain": [
747
- "Size\n",
748
- "Varies with device 1637\n",
749
- "14M 165\n",
750
- "12M 161\n",
751
- "15M 159\n",
752
- "11M 159\n",
753
- " ... \n",
754
- "383k 1\n",
755
- "454k 1\n",
756
- "812k 1\n",
757
- "442k 1\n",
758
- "619k 1\n",
759
- "Name: count, Length: 413, dtype: int64"
760
- ]
761
- },
762
- "execution_count": 22,
763
- "metadata": {},
764
- "output_type": "execute_result"
765
- }
766
- ],
767
  "source": [
768
  "df['Size'].value_counts()"
769
  ]
770
  },
771
  {
772
  "cell_type": "code",
773
- "execution_count": 23,
774
  "metadata": {
775
  "execution": {
776
  "iopub.execute_input": "2023-10-22T12:46:01.150574Z",
@@ -797,7 +444,7 @@
797
  },
798
  {
799
  "cell_type": "code",
800
- "execution_count": 24,
801
  "metadata": {
802
  "execution": {
803
  "iopub.execute_input": "2023-10-22T12:46:04.480910Z",
@@ -830,7 +477,7 @@
830
  },
831
  {
832
  "cell_type": "code",
833
- "execution_count": 25,
834
  "metadata": {
835
  "execution": {
836
  "iopub.execute_input": "2023-10-22T12:46:08.114585Z",
@@ -840,38 +487,14 @@
840
  "shell.execute_reply.started": "2023-10-22T12:46:08.114556Z"
841
  }
842
  },
843
- "outputs": [
844
- {
845
- "data": {
846
- "text/plain": [
847
- "App 0\n",
848
- "Category 0\n",
849
- "Rating 0\n",
850
- "Reviews 0\n",
851
- "Size 1637\n",
852
- "Installs 0\n",
853
- "Type 0\n",
854
- "Price 0\n",
855
- "Content Rating 0\n",
856
- "Genres 0\n",
857
- "Last Updated 0\n",
858
- "Current Ver 0\n",
859
- "Android Ver 0\n",
860
- "dtype: int64"
861
- ]
862
- },
863
- "execution_count": 25,
864
- "metadata": {},
865
- "output_type": "execute_result"
866
- }
867
- ],
868
  "source": [
869
  "df.isnull().sum()"
870
  ]
871
  },
872
  {
873
  "cell_type": "code",
874
- "execution_count": 26,
875
  "metadata": {
876
  "execution": {
877
  "iopub.execute_input": "2023-10-22T12:46:13.768815Z",
@@ -881,16 +504,7 @@
881
  "shell.execute_reply.started": "2023-10-22T12:46:13.768781Z"
882
  }
883
  },
884
- "outputs": [
885
- {
886
- "name": "stderr",
887
- "output_type": "stream",
888
- "text": [
889
- "<ipython-input-26-3323fbc28fc2>:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
890
- " df['Size'].fillna(method='ffill',inplace=True)\n"
891
- ]
892
- }
893
- ],
894
  "source": [
895
  "df['Size'].fillna(method='ffill',inplace=True)"
896
  ]
 
68
  },
69
  {
70
  "cell_type": "code",
71
+ "execution_count": null,
72
  "metadata": {
73
  "execution": {
74
  "iopub.execute_input": "2023-10-22T12:44:58.372979Z",
 
78
  "shell.execute_reply.started": "2023-10-22T12:44:58.372945Z"
79
  }
80
  },
81
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
82
  "source": [
83
  "df.head()"
84
  ]
85
  },
86
  {
87
  "cell_type": "code",
88
+ "execution_count": null,
89
  "metadata": {
90
  "execution": {
91
  "iopub.execute_input": "2023-10-22T12:44:58.411513Z",
 
95
  "shell.execute_reply.started": "2023-10-22T12:44:58.411466Z"
96
  }
97
  },
98
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
99
  "source": [
100
  "# print Rows and Col\n",
101
  "df.shape"
 
103
  },
104
  {
105
  "cell_type": "code",
106
+ "execution_count": null,
107
  "metadata": {
108
  "execution": {
109
  "iopub.execute_input": "2023-10-22T12:44:58.424396Z",
 
113
  "shell.execute_reply.started": "2023-10-22T12:44:58.424363Z"
114
  }
115
  },
116
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
117
  "source": [
118
  "# Information of Data\n",
119
  "df.info()"
 
137
  },
138
  {
139
  "cell_type": "code",
140
+ "execution_count": null,
141
  "metadata": {
142
  "execution": {
143
  "iopub.execute_input": "2023-10-22T12:44:58.476239Z",
 
147
  "shell.execute_reply.started": "2023-10-22T12:44:58.476193Z"
148
  }
149
  },
150
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
151
  "source": [
152
  "df.isnull().sum()"
153
  ]
154
  },
155
  {
156
  "cell_type": "code",
157
+ "execution_count": 3,
158
  "metadata": {
159
  "execution": {
160
  "iopub.execute_input": "2023-10-22T12:44:58.503907Z",
 
171
  },
172
  {
173
  "cell_type": "code",
174
+ "execution_count": null,
175
  "metadata": {
176
  "execution": {
177
  "iopub.execute_input": "2023-10-22T12:44:58.530211Z",
 
181
  "shell.execute_reply.started": "2023-10-22T12:44:58.530124Z"
182
  }
183
  },
184
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
185
  "source": [
186
  "df.isnull().sum()"
187
  ]
188
  },
189
  {
190
  "cell_type": "code",
191
+ "execution_count": null,
192
  "metadata": {
193
  "execution": {
194
  "iopub.execute_input": "2023-10-22T12:44:58.556274Z",
 
198
  "shell.execute_reply.started": "2023-10-22T12:44:58.556234Z"
199
  }
200
  },
201
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
202
  "source": [
203
  "# Check Datatypes\n",
204
  "df.dtypes"
 
213
  },
214
  {
215
  "cell_type": "code",
216
+ "execution_count": 4,
217
  "metadata": {
218
  "execution": {
219
  "iopub.execute_input": "2023-10-22T12:44:58.568629Z",
 
230
  },
231
  {
232
  "cell_type": "code",
233
+ "execution_count": null,
234
  "metadata": {
235
  "execution": {
236
  "iopub.execute_input": "2023-10-22T12:44:58.584706Z",
 
240
  "shell.execute_reply.started": "2023-10-22T12:44:58.584656Z"
241
  }
242
  },
243
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
244
  "source": [
245
  "df.dtypes"
246
  ]
247
  },
248
  {
249
  "cell_type": "code",
250
+ "execution_count": 5,
251
  "metadata": {
252
  "execution": {
253
  "iopub.execute_input": "2023-10-22T12:44:58.607554Z",
 
259
  },
260
  "outputs": [],
261
  "source": [
262
+ "# df['Installs']=df['Installs'].astype('int') # fix --- they fixed themselved in the following cells\n",
263
+ "df['Installs']=df['Installs'].str.replace('+','').str.replace(',','').astype('int')"
264
  ]
265
  },
266
  {
267
  "cell_type": "code",
268
+ "execution_count": null,
269
  "metadata": {
270
  "execution": {
271
  "iopub.execute_input": "2023-10-22T12:45:31.529064Z",
 
277
  },
278
  "outputs": [],
279
  "source": [
280
+ "# df['Installs']=df['Installs'].str.replace('+','')\n",
281
+ "# df['Installs']=df['Installs'].str.replace(',','')"
282
  ]
283
  },
284
  {
285
  "cell_type": "code",
286
+ "execution_count": null,
287
  "metadata": {
288
  "execution": {
289
  "iopub.execute_input": "2023-10-22T12:45:34.207157Z",
 
295
  },
296
  "outputs": [],
297
  "source": [
298
+ "# df['Installs']=df['Installs'].astype('int')"
299
  ]
300
  },
301
  {
302
  "cell_type": "code",
303
+ "execution_count": null,
304
  "metadata": {
305
  "execution": {
306
  "iopub.execute_input": "2023-10-22T12:45:37.326177Z",
 
310
  "shell.execute_reply.started": "2023-10-22T12:45:37.326147Z"
311
  }
312
  },
313
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
314
  "source": [
315
  "df.dtypes"
316
  ]
317
  },
318
  {
319
  "cell_type": "code",
320
+ "execution_count": null,
321
  "metadata": {
322
  "execution": {
323
  "iopub.execute_input": "2023-10-22T12:45:40.288946Z",
 
329
  },
330
  "outputs": [],
331
  "source": [
332
+ "# df['Price']=df['Price'].astype('float') # fix --- should be as in the following cells"
333
  ]
334
  },
335
  {
336
  "cell_type": "code",
337
+ "execution_count": null,
338
  "metadata": {
339
  "execution": {
340
  "iopub.execute_input": "2023-10-22T12:45:45.746481Z",
 
351
  },
352
  {
353
  "cell_type": "code",
354
+ "execution_count": null,
355
  "metadata": {
356
  "execution": {
357
  "iopub.execute_input": "2023-10-22T12:45:48.333300Z",
 
368
  },
369
  {
370
  "cell_type": "code",
371
+ "execution_count": null,
372
  "metadata": {
373
  "execution": {
374
  "iopub.status.busy": "2023-10-22T12:45:00.128119Z",
 
377
  "shell.execute_reply.started": "2023-10-22T12:45:00.128407Z"
378
  }
379
  },
380
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
381
  "source": [
382
  "df.dtypes"
383
  ]
384
  },
385
  {
386
  "cell_type": "code",
387
+ "execution_count": null,
388
  "metadata": {
389
  "execution": {
390
  "iopub.execute_input": "2023-10-22T12:45:51.282739Z",
 
396
  },
397
  "outputs": [],
398
  "source": [
399
+ "# df['Size']=df['Size'].astype('float') # fix --- should be as in the following cells"
400
  ]
401
  },
402
  {
403
  "cell_type": "code",
404
+ "execution_count": null,
405
  "metadata": {
406
  "execution": {
407
  "iopub.status.busy": "2023-10-22T12:45:00.134339Z",
 
410
  "shell.execute_reply.started": "2023-10-22T12:45:00.135690Z"
411
  }
412
  },
413
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
414
  "source": [
415
  "df['Size'].value_counts()"
416
  ]
417
  },
418
  {
419
  "cell_type": "code",
420
+ "execution_count": null,
421
  "metadata": {
422
  "execution": {
423
  "iopub.execute_input": "2023-10-22T12:46:01.150574Z",
 
444
  },
445
  {
446
  "cell_type": "code",
447
+ "execution_count": null,
448
  "metadata": {
449
  "execution": {
450
  "iopub.execute_input": "2023-10-22T12:46:04.480910Z",
 
477
  },
478
  {
479
  "cell_type": "code",
480
+ "execution_count": null,
481
  "metadata": {
482
  "execution": {
483
  "iopub.execute_input": "2023-10-22T12:46:08.114585Z",
 
487
  "shell.execute_reply.started": "2023-10-22T12:46:08.114556Z"
488
  }
489
  },
490
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
491
  "source": [
492
  "df.isnull().sum()"
493
  ]
494
  },
495
  {
496
  "cell_type": "code",
497
+ "execution_count": null,
498
  "metadata": {
499
  "execution": {
500
  "iopub.execute_input": "2023-10-22T12:46:13.768815Z",
 
504
  "shell.execute_reply.started": "2023-10-22T12:46:13.768781Z"
505
  }
506
  },
507
+ "outputs": [],
 
 
 
 
 
 
 
 
 
508
  "source": [
509
  "df['Size'].fillna(method='ffill',inplace=True)"
510
  ]
benchmark/pandas_10/pandas_10_reproduced.ipynb CHANGED
@@ -68,7 +68,7 @@
68
  },
69
  {
70
  "cell_type": "code",
71
- "execution_count": 3,
72
  "metadata": {
73
  "execution": {
74
  "iopub.execute_input": "2023-10-22T12:44:58.372979Z",
@@ -78,163 +78,14 @@
78
  "shell.execute_reply.started": "2023-10-22T12:44:58.372945Z"
79
  }
80
  },
81
- "outputs": [
82
- {
83
- "data": {
84
- "text/html": [
85
- "<div>\n",
86
- "<style scoped>\n",
87
- " .dataframe tbody tr th:only-of-type {\n",
88
- " vertical-align: middle;\n",
89
- " }\n",
90
- "\n",
91
- " .dataframe tbody tr th {\n",
92
- " vertical-align: top;\n",
93
- " }\n",
94
- "\n",
95
- " .dataframe thead th {\n",
96
- " text-align: right;\n",
97
- " }\n",
98
- "</style>\n",
99
- "<table border=\"1\" class=\"dataframe\">\n",
100
- " <thead>\n",
101
- " <tr style=\"text-align: right;\">\n",
102
- " <th></th>\n",
103
- " <th>App</th>\n",
104
- " <th>Category</th>\n",
105
- " <th>Rating</th>\n",
106
- " <th>Reviews</th>\n",
107
- " <th>Size</th>\n",
108
- " <th>Installs</th>\n",
109
- " <th>Type</th>\n",
110
- " <th>Price</th>\n",
111
- " <th>Content Rating</th>\n",
112
- " <th>Genres</th>\n",
113
- " <th>Last Updated</th>\n",
114
- " <th>Current Ver</th>\n",
115
- " <th>Android Ver</th>\n",
116
- " </tr>\n",
117
- " </thead>\n",
118
- " <tbody>\n",
119
- " <tr>\n",
120
- " <th>0</th>\n",
121
- " <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
122
- " <td>ART_AND_DESIGN</td>\n",
123
- " <td>4.1</td>\n",
124
- " <td>159</td>\n",
125
- " <td>19M</td>\n",
126
- " <td>10,000+</td>\n",
127
- " <td>Free</td>\n",
128
- " <td>0</td>\n",
129
- " <td>Everyone</td>\n",
130
- " <td>Art &amp; Design</td>\n",
131
- " <td>7-Jan-18</td>\n",
132
- " <td>1.0.0</td>\n",
133
- " <td>4.0.3 and up</td>\n",
134
- " </tr>\n",
135
- " <tr>\n",
136
- " <th>1</th>\n",
137
- " <td>Coloring book moana</td>\n",
138
- " <td>ART_AND_DESIGN</td>\n",
139
- " <td>3.9</td>\n",
140
- " <td>967</td>\n",
141
- " <td>14M</td>\n",
142
- " <td>500,000+</td>\n",
143
- " <td>Free</td>\n",
144
- " <td>0</td>\n",
145
- " <td>Everyone</td>\n",
146
- " <td>Art &amp; Design;Pretend Play</td>\n",
147
- " <td>15-Jan-18</td>\n",
148
- " <td>2.0.0</td>\n",
149
- " <td>4.0.3 and up</td>\n",
150
- " </tr>\n",
151
- " <tr>\n",
152
- " <th>2</th>\n",
153
- " <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
154
- " <td>ART_AND_DESIGN</td>\n",
155
- " <td>4.7</td>\n",
156
- " <td>87510</td>\n",
157
- " <td>8.7M</td>\n",
158
- " <td>5,000,000+</td>\n",
159
- " <td>Free</td>\n",
160
- " <td>0</td>\n",
161
- " <td>Everyone</td>\n",
162
- " <td>Art &amp; Design</td>\n",
163
- " <td>1-Aug-18</td>\n",
164
- " <td>1.2.4</td>\n",
165
- " <td>4.0.3 and up</td>\n",
166
- " </tr>\n",
167
- " <tr>\n",
168
- " <th>3</th>\n",
169
- " <td>Sketch - Draw &amp; Paint</td>\n",
170
- " <td>ART_AND_DESIGN</td>\n",
171
- " <td>4.5</td>\n",
172
- " <td>215644</td>\n",
173
- " <td>25M</td>\n",
174
- " <td>50,000,000+</td>\n",
175
- " <td>Free</td>\n",
176
- " <td>0</td>\n",
177
- " <td>Teen</td>\n",
178
- " <td>Art &amp; Design</td>\n",
179
- " <td>8-Jun-18</td>\n",
180
- " <td>Varies with device</td>\n",
181
- " <td>4.2 and up</td>\n",
182
- " </tr>\n",
183
- " <tr>\n",
184
- " <th>4</th>\n",
185
- " <td>Pixel Draw - Number Art Coloring Book</td>\n",
186
- " <td>ART_AND_DESIGN</td>\n",
187
- " <td>4.3</td>\n",
188
- " <td>967</td>\n",
189
- " <td>2.8M</td>\n",
190
- " <td>100,000+</td>\n",
191
- " <td>Free</td>\n",
192
- " <td>0</td>\n",
193
- " <td>Everyone</td>\n",
194
- " <td>Art &amp; Design;Creativity</td>\n",
195
- " <td>20-Jun-18</td>\n",
196
- " <td>1.1</td>\n",
197
- " <td>4.4 and up</td>\n",
198
- " </tr>\n",
199
- " </tbody>\n",
200
- "</table>\n",
201
- "</div>"
202
- ],
203
- "text/plain": [
204
- " App Category Rating \\\n",
205
- "0 Photo Editor & Candy Camera & Grid & ScrapBook ART_AND_DESIGN 4.1 \n",
206
- "1 Coloring book moana ART_AND_DESIGN 3.9 \n",
207
- "2 U Launcher Lite – FREE Live Cool Themes, Hide ... ART_AND_DESIGN 4.7 \n",
208
- "3 Sketch - Draw & Paint ART_AND_DESIGN 4.5 \n",
209
- "4 Pixel Draw - Number Art Coloring Book ART_AND_DESIGN 4.3 \n",
210
- "\n",
211
- " Reviews Size Installs Type Price Content Rating \\\n",
212
- "0 159 19M 10,000+ Free 0 Everyone \n",
213
- "1 967 14M 500,000+ Free 0 Everyone \n",
214
- "2 87510 8.7M 5,000,000+ Free 0 Everyone \n",
215
- "3 215644 25M 50,000,000+ Free 0 Teen \n",
216
- "4 967 2.8M 100,000+ Free 0 Everyone \n",
217
- "\n",
218
- " Genres Last Updated Current Ver Android Ver \n",
219
- "0 Art & Design 7-Jan-18 1.0.0 4.0.3 and up \n",
220
- "1 Art & Design;Pretend Play 15-Jan-18 2.0.0 4.0.3 and up \n",
221
- "2 Art & Design 1-Aug-18 1.2.4 4.0.3 and up \n",
222
- "3 Art & Design 8-Jun-18 Varies with device 4.2 and up \n",
223
- "4 Art & Design;Creativity 20-Jun-18 1.1 4.4 and up "
224
- ]
225
- },
226
- "execution_count": 3,
227
- "metadata": {},
228
- "output_type": "execute_result"
229
- }
230
- ],
231
  "source": [
232
  "df.head()"
233
  ]
234
  },
235
  {
236
  "cell_type": "code",
237
- "execution_count": 4,
238
  "metadata": {
239
  "execution": {
240
  "iopub.execute_input": "2023-10-22T12:44:58.411513Z",
@@ -244,18 +95,7 @@
244
  "shell.execute_reply.started": "2023-10-22T12:44:58.411466Z"
245
  }
246
  },
247
- "outputs": [
248
- {
249
- "data": {
250
- "text/plain": [
251
- "(10841, 13)"
252
- ]
253
- },
254
- "execution_count": 4,
255
- "metadata": {},
256
- "output_type": "execute_result"
257
- }
258
- ],
259
  "source": [
260
  "# print Rows and Col\n",
261
  "df.shape"
@@ -263,7 +103,7 @@
263
  },
264
  {
265
  "cell_type": "code",
266
- "execution_count": 5,
267
  "metadata": {
268
  "execution": {
269
  "iopub.execute_input": "2023-10-22T12:44:58.424396Z",
@@ -273,34 +113,7 @@
273
  "shell.execute_reply.started": "2023-10-22T12:44:58.424363Z"
274
  }
275
  },
276
- "outputs": [
277
- {
278
- "name": "stdout",
279
- "output_type": "stream",
280
- "text": [
281
- "<class 'pandas.core.frame.DataFrame'>\n",
282
- "RangeIndex: 10841 entries, 0 to 10840\n",
283
- "Data columns (total 13 columns):\n",
284
- " # Column Non-Null Count Dtype \n",
285
- "--- ------ -------------- ----- \n",
286
- " 0 App 10841 non-null object \n",
287
- " 1 Category 10841 non-null object \n",
288
- " 2 Rating 9367 non-null float64\n",
289
- " 3 Reviews 10841 non-null object \n",
290
- " 4 Size 10841 non-null object \n",
291
- " 5 Installs 10841 non-null object \n",
292
- " 6 Type 10840 non-null object \n",
293
- " 7 Price 10841 non-null object \n",
294
- " 8 Content Rating 10840 non-null object \n",
295
- " 9 Genres 10841 non-null object \n",
296
- " 10 Last Updated 10841 non-null object \n",
297
- " 11 Current Ver 10833 non-null object \n",
298
- " 12 Android Ver 10838 non-null object \n",
299
- "dtypes: float64(1), object(12)\n",
300
- "memory usage: 1.1+ MB\n"
301
- ]
302
- }
303
- ],
304
  "source": [
305
  "# Information of Data\n",
306
  "df.info()"
@@ -324,7 +137,7 @@
324
  },
325
  {
326
  "cell_type": "code",
327
- "execution_count": 6,
328
  "metadata": {
329
  "execution": {
330
  "iopub.execute_input": "2023-10-22T12:44:58.476239Z",
@@ -334,38 +147,14 @@
334
  "shell.execute_reply.started": "2023-10-22T12:44:58.476193Z"
335
  }
336
  },
337
- "outputs": [
338
- {
339
- "data": {
340
- "text/plain": [
341
- "App 0\n",
342
- "Category 0\n",
343
- "Rating 1474\n",
344
- "Reviews 0\n",
345
- "Size 0\n",
346
- "Installs 0\n",
347
- "Type 1\n",
348
- "Price 0\n",
349
- "Content Rating 1\n",
350
- "Genres 0\n",
351
- "Last Updated 0\n",
352
- "Current Ver 8\n",
353
- "Android Ver 3\n",
354
- "dtype: int64"
355
- ]
356
- },
357
- "execution_count": 6,
358
- "metadata": {},
359
- "output_type": "execute_result"
360
- }
361
- ],
362
  "source": [
363
  "df.isnull().sum()"
364
  ]
365
  },
366
  {
367
  "cell_type": "code",
368
- "execution_count": 7,
369
  "metadata": {
370
  "execution": {
371
  "iopub.execute_input": "2023-10-22T12:44:58.503907Z",
@@ -382,7 +171,7 @@
382
  },
383
  {
384
  "cell_type": "code",
385
- "execution_count": 8,
386
  "metadata": {
387
  "execution": {
388
  "iopub.execute_input": "2023-10-22T12:44:58.530211Z",
@@ -392,38 +181,14 @@
392
  "shell.execute_reply.started": "2023-10-22T12:44:58.530124Z"
393
  }
394
  },
395
- "outputs": [
396
- {
397
- "data": {
398
- "text/plain": [
399
- "App 0\n",
400
- "Category 0\n",
401
- "Rating 0\n",
402
- "Reviews 0\n",
403
- "Size 0\n",
404
- "Installs 0\n",
405
- "Type 0\n",
406
- "Price 0\n",
407
- "Content Rating 0\n",
408
- "Genres 0\n",
409
- "Last Updated 0\n",
410
- "Current Ver 0\n",
411
- "Android Ver 0\n",
412
- "dtype: int64"
413
- ]
414
- },
415
- "execution_count": 8,
416
- "metadata": {},
417
- "output_type": "execute_result"
418
- }
419
- ],
420
  "source": [
421
  "df.isnull().sum()"
422
  ]
423
  },
424
  {
425
  "cell_type": "code",
426
- "execution_count": 9,
427
  "metadata": {
428
  "execution": {
429
  "iopub.execute_input": "2023-10-22T12:44:58.556274Z",
@@ -433,31 +198,7 @@
433
  "shell.execute_reply.started": "2023-10-22T12:44:58.556234Z"
434
  }
435
  },
436
- "outputs": [
437
- {
438
- "data": {
439
- "text/plain": [
440
- "App object\n",
441
- "Category object\n",
442
- "Rating float64\n",
443
- "Reviews object\n",
444
- "Size object\n",
445
- "Installs object\n",
446
- "Type object\n",
447
- "Price object\n",
448
- "Content Rating object\n",
449
- "Genres object\n",
450
- "Last Updated object\n",
451
- "Current Ver object\n",
452
- "Android Ver object\n",
453
- "dtype: object"
454
- ]
455
- },
456
- "execution_count": 9,
457
- "metadata": {},
458
- "output_type": "execute_result"
459
- }
460
- ],
461
  "source": [
462
  "# Check Datatypes\n",
463
  "df.dtypes"
@@ -472,7 +213,7 @@
472
  },
473
  {
474
  "cell_type": "code",
475
- "execution_count": 10,
476
  "metadata": {
477
  "execution": {
478
  "iopub.execute_input": "2023-10-22T12:44:58.568629Z",
@@ -489,7 +230,7 @@
489
  },
490
  {
491
  "cell_type": "code",
492
- "execution_count": 11,
493
  "metadata": {
494
  "execution": {
495
  "iopub.execute_input": "2023-10-22T12:44:58.584706Z",
@@ -499,38 +240,14 @@
499
  "shell.execute_reply.started": "2023-10-22T12:44:58.584656Z"
500
  }
501
  },
502
- "outputs": [
503
- {
504
- "data": {
505
- "text/plain": [
506
- "App object\n",
507
- "Category object\n",
508
- "Rating float64\n",
509
- "Reviews int64\n",
510
- "Size object\n",
511
- "Installs object\n",
512
- "Type object\n",
513
- "Price object\n",
514
- "Content Rating object\n",
515
- "Genres object\n",
516
- "Last Updated object\n",
517
- "Current Ver object\n",
518
- "Android Ver object\n",
519
- "dtype: object"
520
- ]
521
- },
522
- "execution_count": 11,
523
- "metadata": {},
524
- "output_type": "execute_result"
525
- }
526
- ],
527
  "source": [
528
  "df.dtypes"
529
  ]
530
  },
531
  {
532
  "cell_type": "code",
533
- "execution_count": 13,
534
  "metadata": {
535
  "execution": {
536
  "iopub.execute_input": "2023-10-22T12:44:58.607554Z",
@@ -548,14 +265,14 @@
548
  "traceback": [
549
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
550
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
551
- "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInstalls\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mInstalls\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mint\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
552
- "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/generic.py:6324\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6317\u001b[0m results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 6318\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m 6319\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[1;32m 6320\u001b[0m ]\n\u001b[1;32m 6322\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6323\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6324\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6327\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n",
553
- "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:451\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 449\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
554
- "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:352\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 350\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 352\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 353\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 355\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n",
555
- "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/blocks.py:511\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 492\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[1;32m 493\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 509\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m--> 511\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 513\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m 515\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
556
- "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:242\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 239\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 242\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m 244\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
557
- "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:187\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 184\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 187\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
558
- "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:138\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m is_object_dtype(arr\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mor\u001b[39;00m is_object_dtype(dtype):\n\u001b[1;32m 137\u001b[0m \u001b[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
559
  "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '10,000+'"
560
  ]
561
  }
@@ -566,7 +283,7 @@
566
  },
567
  {
568
  "cell_type": "code",
569
- "execution_count": 12,
570
  "metadata": {
571
  "execution": {
572
  "iopub.execute_input": "2023-10-22T12:45:31.529064Z",
@@ -584,7 +301,7 @@
584
  },
585
  {
586
  "cell_type": "code",
587
- "execution_count": 13,
588
  "metadata": {
589
  "execution": {
590
  "iopub.execute_input": "2023-10-22T12:45:34.207157Z",
@@ -601,7 +318,7 @@
601
  },
602
  {
603
  "cell_type": "code",
604
- "execution_count": 14,
605
  "metadata": {
606
  "execution": {
607
  "iopub.execute_input": "2023-10-22T12:45:37.326177Z",
@@ -611,38 +328,14 @@
611
  "shell.execute_reply.started": "2023-10-22T12:45:37.326147Z"
612
  }
613
  },
614
- "outputs": [
615
- {
616
- "data": {
617
- "text/plain": [
618
- "App object\n",
619
- "Category object\n",
620
- "Rating float64\n",
621
- "Reviews int64\n",
622
- "Size object\n",
623
- "Installs int64\n",
624
- "Type object\n",
625
- "Price object\n",
626
- "Content Rating object\n",
627
- "Genres object\n",
628
- "Last Updated object\n",
629
- "Current Ver object\n",
630
- "Android Ver object\n",
631
- "dtype: object"
632
- ]
633
- },
634
- "execution_count": 14,
635
- "metadata": {},
636
- "output_type": "execute_result"
637
- }
638
- ],
639
  "source": [
640
  "df.dtypes"
641
  ]
642
  },
643
  {
644
  "cell_type": "code",
645
- "execution_count": 15,
646
  "metadata": {
647
  "execution": {
648
  "iopub.execute_input": "2023-10-22T12:45:40.288946Z",
@@ -652,33 +345,14 @@
652
  "shell.execute_reply.started": "2023-10-22T12:45:40.288917Z"
653
  }
654
  },
655
- "outputs": [
656
- {
657
- "ename": "ValueError",
658
- "evalue": "could not convert string to float: '$4.99 '",
659
- "output_type": "error",
660
- "traceback": [
661
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
662
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
663
- "\u001b[0;32m<ipython-input-15-6b4285799748>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
664
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
665
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
666
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
667
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m 614\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 616\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
668
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;31m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
669
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_astype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
670
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
671
- "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '$4.99 '"
672
- ]
673
- }
674
- ],
675
  "source": [
676
  "df['Price']=df['Price'].astype('float')"
677
  ]
678
  },
679
  {
680
  "cell_type": "code",
681
- "execution_count": 16,
682
  "metadata": {
683
  "execution": {
684
  "iopub.execute_input": "2023-10-22T12:45:45.746481Z",
@@ -695,7 +369,7 @@
695
  },
696
  {
697
  "cell_type": "code",
698
- "execution_count": 17,
699
  "metadata": {
700
  "execution": {
701
  "iopub.execute_input": "2023-10-22T12:45:48.333300Z",
@@ -712,7 +386,7 @@
712
  },
713
  {
714
  "cell_type": "code",
715
- "execution_count": 18,
716
  "metadata": {
717
  "execution": {
718
  "iopub.status.busy": "2023-10-22T12:45:00.128119Z",
@@ -721,38 +395,14 @@
721
  "shell.execute_reply.started": "2023-10-22T12:45:00.128407Z"
722
  }
723
  },
724
- "outputs": [
725
- {
726
- "data": {
727
- "text/plain": [
728
- "App object\n",
729
- "Category object\n",
730
- "Rating float64\n",
731
- "Reviews int64\n",
732
- "Size object\n",
733
- "Installs int64\n",
734
- "Type object\n",
735
- "Price float64\n",
736
- "Content Rating object\n",
737
- "Genres object\n",
738
- "Last Updated object\n",
739
- "Current Ver object\n",
740
- "Android Ver object\n",
741
- "dtype: object"
742
- ]
743
- },
744
- "execution_count": 18,
745
- "metadata": {},
746
- "output_type": "execute_result"
747
- }
748
- ],
749
  "source": [
750
  "df.dtypes"
751
  ]
752
  },
753
  {
754
  "cell_type": "code",
755
- "execution_count": 19,
756
  "metadata": {
757
  "execution": {
758
  "iopub.execute_input": "2023-10-22T12:45:51.282739Z",
@@ -762,33 +412,14 @@
762
  "shell.execute_reply.started": "2023-10-22T12:45:51.282704Z"
763
  }
764
  },
765
- "outputs": [
766
- {
767
- "ename": "ValueError",
768
- "evalue": "could not convert string to float: '19M'",
769
- "output_type": "error",
770
- "traceback": [
771
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
772
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
773
- "\u001b[0;32m<ipython-input-19-7b746e2217b8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
774
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
775
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
776
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
777
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m 614\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 616\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
778
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;31m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
779
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_astype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
780
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
781
- "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '19M'"
782
- ]
783
- }
784
- ],
785
  "source": [
786
  "df['Size']=df['Size'].astype('float')"
787
  ]
788
  },
789
  {
790
  "cell_type": "code",
791
- "execution_count": 20,
792
  "metadata": {
793
  "execution": {
794
  "iopub.status.busy": "2023-10-22T12:45:00.134339Z",
@@ -797,37 +428,14 @@
797
  "shell.execute_reply.started": "2023-10-22T12:45:00.135690Z"
798
  }
799
  },
800
- "outputs": [
801
- {
802
- "data": {
803
- "text/plain": [
804
- "Size\n",
805
- "Varies with device 1637\n",
806
- "14M 165\n",
807
- "12M 161\n",
808
- "15M 159\n",
809
- "11M 159\n",
810
- " ... \n",
811
- "383k 1\n",
812
- "454k 1\n",
813
- "812k 1\n",
814
- "442k 1\n",
815
- "619k 1\n",
816
- "Name: count, Length: 413, dtype: int64"
817
- ]
818
- },
819
- "execution_count": 20,
820
- "metadata": {},
821
- "output_type": "execute_result"
822
- }
823
- ],
824
  "source": [
825
  "df['Size'].value_counts()"
826
  ]
827
  },
828
  {
829
  "cell_type": "code",
830
- "execution_count": 21,
831
  "metadata": {
832
  "execution": {
833
  "iopub.execute_input": "2023-10-22T12:46:01.150574Z",
@@ -854,7 +462,7 @@
854
  },
855
  {
856
  "cell_type": "code",
857
- "execution_count": 22,
858
  "metadata": {
859
  "execution": {
860
  "iopub.execute_input": "2023-10-22T12:46:04.480910Z",
@@ -887,7 +495,7 @@
887
  },
888
  {
889
  "cell_type": "code",
890
- "execution_count": 23,
891
  "metadata": {
892
  "execution": {
893
  "iopub.execute_input": "2023-10-22T12:46:08.114585Z",
@@ -897,38 +505,14 @@
897
  "shell.execute_reply.started": "2023-10-22T12:46:08.114556Z"
898
  }
899
  },
900
- "outputs": [
901
- {
902
- "data": {
903
- "text/plain": [
904
- "App 0\n",
905
- "Category 0\n",
906
- "Rating 0\n",
907
- "Reviews 0\n",
908
- "Size 1637\n",
909
- "Installs 0\n",
910
- "Type 0\n",
911
- "Price 0\n",
912
- "Content Rating 0\n",
913
- "Genres 0\n",
914
- "Last Updated 0\n",
915
- "Current Ver 0\n",
916
- "Android Ver 0\n",
917
- "dtype: int64"
918
- ]
919
- },
920
- "execution_count": 23,
921
- "metadata": {},
922
- "output_type": "execute_result"
923
- }
924
- ],
925
  "source": [
926
  "df.isnull().sum()"
927
  ]
928
  },
929
  {
930
  "cell_type": "code",
931
- "execution_count": 24,
932
  "metadata": {
933
  "execution": {
934
  "iopub.execute_input": "2023-10-22T12:46:13.768815Z",
@@ -938,16 +522,7 @@
938
  "shell.execute_reply.started": "2023-10-22T12:46:13.768781Z"
939
  }
940
  },
941
- "outputs": [
942
- {
943
- "name": "stderr",
944
- "output_type": "stream",
945
- "text": [
946
- "<ipython-input-24-3323fbc28fc2>:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
947
- " df['Size'].fillna(method='ffill',inplace=True)\n"
948
- ]
949
- }
950
- ],
951
  "source": [
952
  "df['Size'].fillna(method='ffill',inplace=True)"
953
  ]
 
68
  },
69
  {
70
  "cell_type": "code",
71
+ "execution_count": null,
72
  "metadata": {
73
  "execution": {
74
  "iopub.execute_input": "2023-10-22T12:44:58.372979Z",
 
78
  "shell.execute_reply.started": "2023-10-22T12:44:58.372945Z"
79
  }
80
  },
81
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
82
  "source": [
83
  "df.head()"
84
  ]
85
  },
86
  {
87
  "cell_type": "code",
88
+ "execution_count": null,
89
  "metadata": {
90
  "execution": {
91
  "iopub.execute_input": "2023-10-22T12:44:58.411513Z",
 
95
  "shell.execute_reply.started": "2023-10-22T12:44:58.411466Z"
96
  }
97
  },
98
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
99
  "source": [
100
  "# print Rows and Col\n",
101
  "df.shape"
 
103
  },
104
  {
105
  "cell_type": "code",
106
+ "execution_count": null,
107
  "metadata": {
108
  "execution": {
109
  "iopub.execute_input": "2023-10-22T12:44:58.424396Z",
 
113
  "shell.execute_reply.started": "2023-10-22T12:44:58.424363Z"
114
  }
115
  },
116
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
117
  "source": [
118
  "# Information of Data\n",
119
  "df.info()"
 
137
  },
138
  {
139
  "cell_type": "code",
140
+ "execution_count": null,
141
  "metadata": {
142
  "execution": {
143
  "iopub.execute_input": "2023-10-22T12:44:58.476239Z",
 
147
  "shell.execute_reply.started": "2023-10-22T12:44:58.476193Z"
148
  }
149
  },
150
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
151
  "source": [
152
  "df.isnull().sum()"
153
  ]
154
  },
155
  {
156
  "cell_type": "code",
157
+ "execution_count": 3,
158
  "metadata": {
159
  "execution": {
160
  "iopub.execute_input": "2023-10-22T12:44:58.503907Z",
 
171
  },
172
  {
173
  "cell_type": "code",
174
+ "execution_count": null,
175
  "metadata": {
176
  "execution": {
177
  "iopub.execute_input": "2023-10-22T12:44:58.530211Z",
 
181
  "shell.execute_reply.started": "2023-10-22T12:44:58.530124Z"
182
  }
183
  },
184
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
185
  "source": [
186
  "df.isnull().sum()"
187
  ]
188
  },
189
  {
190
  "cell_type": "code",
191
+ "execution_count": null,
192
  "metadata": {
193
  "execution": {
194
  "iopub.execute_input": "2023-10-22T12:44:58.556274Z",
 
198
  "shell.execute_reply.started": "2023-10-22T12:44:58.556234Z"
199
  }
200
  },
201
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
202
  "source": [
203
  "# Check Datatypes\n",
204
  "df.dtypes"
 
213
  },
214
  {
215
  "cell_type": "code",
216
+ "execution_count": 4,
217
  "metadata": {
218
  "execution": {
219
  "iopub.execute_input": "2023-10-22T12:44:58.568629Z",
 
230
  },
231
  {
232
  "cell_type": "code",
233
+ "execution_count": null,
234
  "metadata": {
235
  "execution": {
236
  "iopub.execute_input": "2023-10-22T12:44:58.584706Z",
 
240
  "shell.execute_reply.started": "2023-10-22T12:44:58.584656Z"
241
  }
242
  },
243
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
244
  "source": [
245
  "df.dtypes"
246
  ]
247
  },
248
  {
249
  "cell_type": "code",
250
+ "execution_count": 5,
251
  "metadata": {
252
  "execution": {
253
  "iopub.execute_input": "2023-10-22T12:44:58.607554Z",
 
265
  "traceback": [
266
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
267
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
268
+ "\u001b[0;32m<ipython-input-5-169a23801569>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Installs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Installs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
269
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
270
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
271
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
272
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m 614\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 616\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
273
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;31m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
274
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_astype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
275
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/dtypes/astype.py\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
276
  "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '10,000+'"
277
  ]
278
  }
 
283
  },
284
  {
285
  "cell_type": "code",
286
+ "execution_count": null,
287
  "metadata": {
288
  "execution": {
289
  "iopub.execute_input": "2023-10-22T12:45:31.529064Z",
 
301
  },
302
  {
303
  "cell_type": "code",
304
+ "execution_count": null,
305
  "metadata": {
306
  "execution": {
307
  "iopub.execute_input": "2023-10-22T12:45:34.207157Z",
 
318
  },
319
  {
320
  "cell_type": "code",
321
+ "execution_count": null,
322
  "metadata": {
323
  "execution": {
324
  "iopub.execute_input": "2023-10-22T12:45:37.326177Z",
 
328
  "shell.execute_reply.started": "2023-10-22T12:45:37.326147Z"
329
  }
330
  },
331
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
332
  "source": [
333
  "df.dtypes"
334
  ]
335
  },
336
  {
337
  "cell_type": "code",
338
+ "execution_count": null,
339
  "metadata": {
340
  "execution": {
341
  "iopub.execute_input": "2023-10-22T12:45:40.288946Z",
 
345
  "shell.execute_reply.started": "2023-10-22T12:45:40.288917Z"
346
  }
347
  },
348
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
349
  "source": [
350
  "df['Price']=df['Price'].astype('float')"
351
  ]
352
  },
353
  {
354
  "cell_type": "code",
355
+ "execution_count": null,
356
  "metadata": {
357
  "execution": {
358
  "iopub.execute_input": "2023-10-22T12:45:45.746481Z",
 
369
  },
370
  {
371
  "cell_type": "code",
372
+ "execution_count": null,
373
  "metadata": {
374
  "execution": {
375
  "iopub.execute_input": "2023-10-22T12:45:48.333300Z",
 
386
  },
387
  {
388
  "cell_type": "code",
389
+ "execution_count": null,
390
  "metadata": {
391
  "execution": {
392
  "iopub.status.busy": "2023-10-22T12:45:00.128119Z",
 
395
  "shell.execute_reply.started": "2023-10-22T12:45:00.128407Z"
396
  }
397
  },
398
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
399
  "source": [
400
  "df.dtypes"
401
  ]
402
  },
403
  {
404
  "cell_type": "code",
405
+ "execution_count": null,
406
  "metadata": {
407
  "execution": {
408
  "iopub.execute_input": "2023-10-22T12:45:51.282739Z",
 
412
  "shell.execute_reply.started": "2023-10-22T12:45:51.282704Z"
413
  }
414
  },
415
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
416
  "source": [
417
  "df['Size']=df['Size'].astype('float')"
418
  ]
419
  },
420
  {
421
  "cell_type": "code",
422
+ "execution_count": null,
423
  "metadata": {
424
  "execution": {
425
  "iopub.status.busy": "2023-10-22T12:45:00.134339Z",
 
428
  "shell.execute_reply.started": "2023-10-22T12:45:00.135690Z"
429
  }
430
  },
431
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
432
  "source": [
433
  "df['Size'].value_counts()"
434
  ]
435
  },
436
  {
437
  "cell_type": "code",
438
+ "execution_count": null,
439
  "metadata": {
440
  "execution": {
441
  "iopub.execute_input": "2023-10-22T12:46:01.150574Z",
 
462
  },
463
  {
464
  "cell_type": "code",
465
+ "execution_count": null,
466
  "metadata": {
467
  "execution": {
468
  "iopub.execute_input": "2023-10-22T12:46:04.480910Z",
 
495
  },
496
  {
497
  "cell_type": "code",
498
+ "execution_count": null,
499
  "metadata": {
500
  "execution": {
501
  "iopub.execute_input": "2023-10-22T12:46:08.114585Z",
 
505
  "shell.execute_reply.started": "2023-10-22T12:46:08.114556Z"
506
  }
507
  },
508
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
509
  "source": [
510
  "df.isnull().sum()"
511
  ]
512
  },
513
  {
514
  "cell_type": "code",
515
+ "execution_count": null,
516
  "metadata": {
517
  "execution": {
518
  "iopub.execute_input": "2023-10-22T12:46:13.768815Z",
 
522
  "shell.execute_reply.started": "2023-10-22T12:46:13.768781Z"
523
  }
524
  },
525
+ "outputs": [],
 
 
 
 
 
 
 
 
 
526
  "source": [
527
  "df['Size'].fillna(method='ffill',inplace=True)"
528
  ]
benchmark/pandas_11/pandas_11_fixed.ipynb CHANGED
@@ -331,7 +331,7 @@
331
  },
332
  {
333
  "cell_type": "code",
334
- "execution_count": 3,
335
  "metadata": {
336
  "execution": {
337
  "iopub.execute_input": "2023-07-13T08:43:31.099091Z",
@@ -341,18 +341,7 @@
341
  "shell.execute_reply.started": "2023-07-13T08:43:31.099063Z"
342
  }
343
  },
344
- "outputs": [
345
- {
346
- "data": {
347
- "image/png": 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\n",
348
- "text/plain": [
349
- "<Figure size 640x480 with 1 Axes>"
350
- ]
351
- },
352
- "metadata": {},
353
- "output_type": "display_data"
354
- }
355
- ],
356
  "source": [
357
  "colors = [\"#0101DF\", \"#DF0101\"]\n",
358
  "\n",
@@ -420,7 +409,7 @@
420
  },
421
  {
422
  "cell_type": "code",
423
- "execution_count": 4,
424
  "metadata": {
425
  "execution": {
426
  "iopub.execute_input": "2023-07-13T08:43:35.113910Z",
@@ -544,7 +533,7 @@
544
  "4 45355.430437 "
545
  ]
546
  },
547
- "execution_count": 4,
548
  "metadata": {},
549
  "output_type": "execute_result"
550
  }
@@ -840,7 +829,7 @@
840
  },
841
  {
842
  "cell_type": "code",
843
- "execution_count": 9,
844
  "metadata": {
845
  "execution": {
846
  "iopub.execute_input": "2023-07-13T08:52:28.333770Z",
@@ -907,7 +896,7 @@
907
  },
908
  {
909
  "cell_type": "code",
910
- "execution_count": 10,
911
  "metadata": {
912
  "execution": {
913
  "iopub.execute_input": "2023-07-13T08:56:56.658604Z",
@@ -924,11 +913,11 @@
924
  "text": [
925
  "No Frauds 99.83 % of the dataset\n",
926
  "Frauds 0.17 % of the dataset\n",
927
- "Train: [ 56961 56962 56963 ... 284804 284805 284806] Test: [ 0 1 2 ... 56959 56960 57761]\n",
928
- "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 56961 56962 56963 ... 113925 113926 113927]\n",
929
- "Train: [ 0 1 2 ... 284804 284805 284806] Test: [113000 113547 113715 ... 170888 170889 170890]\n",
930
- "Train: [ 0 1 2 ... 284804 284805 284806] Test: [165526 166874 167336 ... 227848 227849 227850]\n",
931
- "Train: [ 0 1 2 ... 227848 227849 227850] Test: [226627 226956 227052 ... 284804 284805 284806]\n",
932
  "----------------------------------------------------------------------------------------------------\n",
933
  "Label Distributions: \n",
934
  "\n",
@@ -978,7 +967,7 @@
978
  },
979
  {
980
  "cell_type": "code",
981
- "execution_count": 11,
982
  "metadata": {
983
  "execution": {
984
  "iopub.execute_input": "2023-07-13T08:56:56.989037Z",
@@ -1035,122 +1024,122 @@
1035
  " </thead>\n",
1036
  " <tbody>\n",
1037
  " <tr>\n",
1038
- " <th>86065</th>\n",
1039
- " <td>61069.0</td>\n",
1040
- " <td>-1.626290</td>\n",
1041
- " <td>0.354503</td>\n",
1042
- " <td>1.416334</td>\n",
1043
- " <td>3.185015</td>\n",
1044
- " <td>-0.586516</td>\n",
1045
- " <td>0.875813</td>\n",
1046
- " <td>0.389734</td>\n",
1047
- " <td>0.080256</td>\n",
1048
- " <td>-0.631102</td>\n",
1049
  " <td>...</td>\n",
1050
- " <td>0.081040</td>\n",
1051
- " <td>0.668585</td>\n",
1052
- " <td>-0.561357</td>\n",
1053
- " <td>-0.027173</td>\n",
1054
- " <td>-0.341251</td>\n",
1055
- " <td>0.296396</td>\n",
1056
- " <td>-0.283919</td>\n",
1057
- " <td>-0.216072</td>\n",
1058
- " <td>190.16</td>\n",
1059
  " <td>0</td>\n",
1060
  " </tr>\n",
1061
  " <tr>\n",
1062
- " <th>263877</th>\n",
1063
- " <td>161154.0</td>\n",
1064
- " <td>-3.387601</td>\n",
1065
- " <td>3.977881</td>\n",
1066
- " <td>-6.978585</td>\n",
1067
- " <td>1.657766</td>\n",
1068
- " <td>-1.100500</td>\n",
1069
- " <td>-3.599487</td>\n",
1070
- " <td>-3.686651</td>\n",
1071
- " <td>1.942252</td>\n",
1072
- " <td>-3.065089</td>\n",
1073
  " <td>...</td>\n",
1074
- " <td>1.043587</td>\n",
1075
- " <td>0.262189</td>\n",
1076
- " <td>-0.479224</td>\n",
1077
- " <td>-0.326638</td>\n",
1078
- " <td>-0.156939</td>\n",
1079
- " <td>0.113807</td>\n",
1080
- " <td>0.354124</td>\n",
1081
- " <td>0.287592</td>\n",
1082
- " <td>0.38</td>\n",
1083
  " <td>1</td>\n",
1084
  " </tr>\n",
1085
  " <tr>\n",
1086
- " <th>92992</th>\n",
1087
- " <td>64207.0</td>\n",
1088
- " <td>-0.867979</td>\n",
1089
- " <td>0.623649</td>\n",
1090
- " <td>1.645458</td>\n",
1091
- " <td>-0.548019</td>\n",
1092
- " <td>-1.022026</td>\n",
1093
- " <td>-0.354154</td>\n",
1094
- " <td>-0.258762</td>\n",
1095
- " <td>0.625798</td>\n",
1096
- " <td>0.813404</td>\n",
1097
  " <td>...</td>\n",
1098
- " <td>0.122553</td>\n",
1099
- " <td>0.612630</td>\n",
1100
- " <td>-0.102687</td>\n",
1101
- " <td>0.482736</td>\n",
1102
- " <td>-0.336099</td>\n",
1103
- " <td>1.003938</td>\n",
1104
- " <td>0.210524</td>\n",
1105
- " <td>0.145754</td>\n",
1106
- " <td>15.42</td>\n",
1107
  " <td>0</td>\n",
1108
  " </tr>\n",
1109
  " <tr>\n",
1110
- " <th>197586</th>\n",
1111
- " <td>132086.0</td>\n",
1112
- " <td>-0.361428</td>\n",
1113
- " <td>1.133472</td>\n",
1114
- " <td>-2.971360</td>\n",
1115
- " <td>-0.283073</td>\n",
1116
- " <td>0.371452</td>\n",
1117
- " <td>-0.574680</td>\n",
1118
- " <td>4.031513</td>\n",
1119
- " <td>-0.934398</td>\n",
1120
- " <td>-0.768255</td>\n",
1121
  " <td>...</td>\n",
1122
- " <td>0.110815</td>\n",
1123
- " <td>0.563861</td>\n",
1124
- " <td>-0.408436</td>\n",
1125
- " <td>-0.880079</td>\n",
1126
- " <td>1.408392</td>\n",
1127
- " <td>-0.137402</td>\n",
1128
- " <td>-0.001250</td>\n",
1129
- " <td>-0.182751</td>\n",
1130
- " <td>480.72</td>\n",
1131
  " <td>1</td>\n",
1132
  " </tr>\n",
1133
  " <tr>\n",
1134
- " <th>64329</th>\n",
1135
- " <td>51112.0</td>\n",
1136
- " <td>-9.848776</td>\n",
1137
- " <td>7.365546</td>\n",
1138
- " <td>-12.898538</td>\n",
1139
- " <td>4.273323</td>\n",
1140
- " <td>-7.611991</td>\n",
1141
- " <td>-3.427045</td>\n",
1142
- " <td>-8.350808</td>\n",
1143
- " <td>6.863604</td>\n",
1144
- " <td>-2.387567</td>\n",
1145
  " <td>...</td>\n",
1146
- " <td>0.931958</td>\n",
1147
- " <td>-0.874467</td>\n",
1148
- " <td>-0.192639</td>\n",
1149
- " <td>-0.035426</td>\n",
1150
- " <td>0.538665</td>\n",
1151
- " <td>-0.263934</td>\n",
1152
- " <td>1.134095</td>\n",
1153
- " <td>0.225973</td>\n",
1154
  " <td>99.99</td>\n",
1155
  " <td>1</td>\n",
1156
  " </tr>\n",
@@ -1160,31 +1149,38 @@
1160
  "</div>"
1161
  ],
1162
  "text/plain": [
1163
- " Time V1 V2 V3 V4 V5 V6 \\\n",
1164
- "86065 61069.0 -1.626290 0.354503 1.416334 3.185015 -0.586516 0.875813 \n",
1165
- "263877 161154.0 -3.387601 3.977881 -6.978585 1.657766 -1.100500 -3.599487 \n",
1166
- "92992 64207.0 -0.867979 0.623649 1.645458 -0.548019 -1.022026 -0.354154 \n",
1167
- "197586 132086.0 -0.361428 1.133472 -2.971360 -0.283073 0.371452 -0.574680 \n",
1168
- "64329 51112.0 -9.848776 7.365546 -12.898538 4.273323 -7.611991 -3.427045 \n",
1169
  "\n",
1170
- " V7 V8 V9 ... V21 V22 V23 \\\n",
1171
- "86065 0.389734 0.080256 -0.631102 ... 0.081040 0.668585 -0.561357 \n",
1172
- "263877 -3.686651 1.942252 -3.065089 ... 1.043587 0.262189 -0.479224 \n",
1173
- "92992 -0.258762 0.625798 0.813404 ... 0.122553 0.612630 -0.102687 \n",
1174
- "197586 4.031513 -0.934398 -0.768255 ... 0.110815 0.563861 -0.408436 \n",
1175
- "64329 -8.350808 6.863604 -2.387567 ... 0.931958 -0.874467 -0.192639 \n",
1176
  "\n",
1177
- " V24 V25 V26 V27 V28 Amount Class \n",
1178
- "86065 -0.027173 -0.341251 0.296396 -0.283919 -0.216072 190.16 0 \n",
1179
- "263877 -0.326638 -0.156939 0.113807 0.354124 0.287592 0.38 1 \n",
1180
- "92992 0.482736 -0.336099 1.003938 0.210524 0.145754 15.42 0 \n",
1181
- "197586 -0.880079 1.408392 -0.137402 -0.001250 -0.182751 480.72 1 \n",
1182
- "64329 -0.035426 0.538665 -0.263934 1.134095 0.225973 99.99 1 \n",
 
 
 
 
 
 
 
1183
  "\n",
1184
  "[5 rows x 31 columns]"
1185
  ]
1186
  },
1187
- "execution_count": 11,
1188
  "metadata": {},
1189
  "output_type": "execute_result"
1190
  }
@@ -1204,7 +1200,7 @@
1204
  },
1205
  {
1206
  "cell_type": "code",
1207
- "execution_count": 12,
1208
  "metadata": {
1209
  "execution": {
1210
  "iopub.execute_input": "2023-07-13T08:56:58.495789Z",
@@ -1242,8 +1238,9 @@
1242
  "print(new_df['Class'].value_counts()/len(new_df))\n",
1243
  "\n",
1244
  "\n",
1245
- "# fix --- need keyword of arguments\n",
1246
  "# sns.countplot('Class', data=new_df, palette=colors)\n",
 
1247
  "sns.countplot(data=new_df,x='Class', palette=colors)\n",
1248
  "\n",
1249
  "plt.title('Equally Distributed Classes', fontsize=14)\n",
 
331
  },
332
  {
333
  "cell_type": "code",
334
+ "execution_count": null,
335
  "metadata": {
336
  "execution": {
337
  "iopub.execute_input": "2023-07-13T08:43:31.099091Z",
 
341
  "shell.execute_reply.started": "2023-07-13T08:43:31.099063Z"
342
  }
343
  },
344
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
345
  "source": [
346
  "colors = [\"#0101DF\", \"#DF0101\"]\n",
347
  "\n",
 
409
  },
410
  {
411
  "cell_type": "code",
412
+ "execution_count": 3,
413
  "metadata": {
414
  "execution": {
415
  "iopub.execute_input": "2023-07-13T08:43:35.113910Z",
 
533
  "4 45355.430437 "
534
  ]
535
  },
536
+ "execution_count": 3,
537
  "metadata": {},
538
  "output_type": "execute_result"
539
  }
 
829
  },
830
  {
831
  "cell_type": "code",
832
+ "execution_count": 4,
833
  "metadata": {
834
  "execution": {
835
  "iopub.execute_input": "2023-07-13T08:52:28.333770Z",
 
896
  },
897
  {
898
  "cell_type": "code",
899
+ "execution_count": 5,
900
  "metadata": {
901
  "execution": {
902
  "iopub.execute_input": "2023-07-13T08:56:56.658604Z",
 
913
  "text": [
914
  "No Frauds 99.83 % of the dataset\n",
915
  "Frauds 0.17 % of the dataset\n",
916
+ "Train: [ 30473 30496 31002 ... 284804 284805 284806] Test: [ 0 1 2 ... 57017 57018 57019]\n",
917
+ "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 30473 30496 31002 ... 113964 113965 113966]\n",
918
+ "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 81609 82400 83053 ... 170946 170947 170948]\n",
919
+ "Train: [ 0 1 2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
920
+ "Train: [ 0 1 2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n",
921
  "----------------------------------------------------------------------------------------------------\n",
922
  "Label Distributions: \n",
923
  "\n",
 
967
  },
968
  {
969
  "cell_type": "code",
970
+ "execution_count": 6,
971
  "metadata": {
972
  "execution": {
973
  "iopub.execute_input": "2023-07-13T08:56:56.989037Z",
 
1024
  " </thead>\n",
1025
  " <tbody>\n",
1026
  " <tr>\n",
1027
+ " <th>217092</th>\n",
1028
+ " <td>140758.0</td>\n",
1029
+ " <td>2.096099</td>\n",
1030
+ " <td>-0.844180</td>\n",
1031
+ " <td>-1.203557</td>\n",
1032
+ " <td>-0.712270</td>\n",
1033
+ " <td>-0.800583</td>\n",
1034
+ " <td>-0.561321</td>\n",
1035
+ " <td>-1.091436</td>\n",
1036
+ " <td>0.097218</td>\n",
1037
+ " <td>0.041781</td>\n",
1038
  " <td>...</td>\n",
1039
+ " <td>0.262719</td>\n",
1040
+ " <td>0.741864</td>\n",
1041
+ " <td>0.069487</td>\n",
1042
+ " <td>-0.532429</td>\n",
1043
+ " <td>-0.202224</td>\n",
1044
+ " <td>-0.077787</td>\n",
1045
+ " <td>0.017431</td>\n",
1046
+ " <td>-0.027308</td>\n",
1047
+ " <td>24.99</td>\n",
1048
  " <td>0</td>\n",
1049
  " </tr>\n",
1050
  " <tr>\n",
1051
+ " <th>208651</th>\n",
1052
+ " <td>137211.0</td>\n",
1053
+ " <td>0.630579</td>\n",
1054
+ " <td>1.183631</td>\n",
1055
+ " <td>-5.066283</td>\n",
1056
+ " <td>2.179903</td>\n",
1057
+ " <td>-0.703376</td>\n",
1058
+ " <td>-0.103614</td>\n",
1059
+ " <td>-3.490350</td>\n",
1060
+ " <td>1.094734</td>\n",
1061
+ " <td>-0.717418</td>\n",
1062
  " <td>...</td>\n",
1063
+ " <td>0.621622</td>\n",
1064
+ " <td>0.043807</td>\n",
1065
+ " <td>0.102711</td>\n",
1066
+ " <td>-0.601505</td>\n",
1067
+ " <td>0.127371</td>\n",
1068
+ " <td>-0.163009</td>\n",
1069
+ " <td>0.853792</td>\n",
1070
+ " <td>0.356503</td>\n",
1071
+ " <td>39.45</td>\n",
1072
  " <td>1</td>\n",
1073
  " </tr>\n",
1074
  " <tr>\n",
1075
+ " <th>71974</th>\n",
1076
+ " <td>54515.0</td>\n",
1077
+ " <td>-1.502966</td>\n",
1078
+ " <td>0.066880</td>\n",
1079
+ " <td>1.481635</td>\n",
1080
+ " <td>1.299962</td>\n",
1081
+ " <td>1.302369</td>\n",
1082
+ " <td>-0.793982</td>\n",
1083
+ " <td>0.655119</td>\n",
1084
+ " <td>-0.525607</td>\n",
1085
+ " <td>-0.432044</td>\n",
1086
  " <td>...</td>\n",
1087
+ " <td>-0.139194</td>\n",
1088
+ " <td>0.208491</td>\n",
1089
+ " <td>-0.781711</td>\n",
1090
+ " <td>0.143014</td>\n",
1091
+ " <td>0.623358</td>\n",
1092
+ " <td>-0.113667</td>\n",
1093
+ " <td>-0.109330</td>\n",
1094
+ " <td>-0.190860</td>\n",
1095
+ " <td>80.00</td>\n",
1096
  " <td>0</td>\n",
1097
  " </tr>\n",
1098
  " <tr>\n",
1099
+ " <th>6717</th>\n",
1100
+ " <td>8408.0</td>\n",
1101
+ " <td>-1.813280</td>\n",
1102
+ " <td>4.917851</td>\n",
1103
+ " <td>-5.926130</td>\n",
1104
+ " <td>5.701500</td>\n",
1105
+ " <td>1.204393</td>\n",
1106
+ " <td>-3.035138</td>\n",
1107
+ " <td>-1.713402</td>\n",
1108
+ " <td>0.561257</td>\n",
1109
+ " <td>-3.796354</td>\n",
1110
  " <td>...</td>\n",
1111
+ " <td>0.615642</td>\n",
1112
+ " <td>-0.406427</td>\n",
1113
+ " <td>-0.737018</td>\n",
1114
+ " <td>-0.279642</td>\n",
1115
+ " <td>1.106766</td>\n",
1116
+ " <td>0.323885</td>\n",
1117
+ " <td>0.894767</td>\n",
1118
+ " <td>0.569519</td>\n",
1119
+ " <td>1.00</td>\n",
1120
  " <td>1</td>\n",
1121
  " </tr>\n",
1122
  " <tr>\n",
1123
+ " <th>14170</th>\n",
1124
+ " <td>25198.0</td>\n",
1125
+ " <td>-15.903635</td>\n",
1126
+ " <td>10.393917</td>\n",
1127
+ " <td>-19.133602</td>\n",
1128
+ " <td>6.185969</td>\n",
1129
+ " <td>-12.538021</td>\n",
1130
+ " <td>-4.027030</td>\n",
1131
+ " <td>-13.897827</td>\n",
1132
+ " <td>10.662252</td>\n",
1133
+ " <td>-2.844954</td>\n",
1134
  " <td>...</td>\n",
1135
+ " <td>1.577548</td>\n",
1136
+ " <td>-1.280137</td>\n",
1137
+ " <td>-0.601295</td>\n",
1138
+ " <td>0.040404</td>\n",
1139
+ " <td>0.995502</td>\n",
1140
+ " <td>-0.273743</td>\n",
1141
+ " <td>1.688136</td>\n",
1142
+ " <td>0.527831</td>\n",
1143
  " <td>99.99</td>\n",
1144
  " <td>1</td>\n",
1145
  " </tr>\n",
 
1149
  "</div>"
1150
  ],
1151
  "text/plain": [
1152
+ " Time V1 V2 V3 V4 V5 \\\n",
1153
+ "217092 140758.0 2.096099 -0.844180 -1.203557 -0.712270 -0.800583 \n",
1154
+ "208651 137211.0 0.630579 1.183631 -5.066283 2.179903 -0.703376 \n",
1155
+ "71974 54515.0 -1.502966 0.066880 1.481635 1.299962 1.302369 \n",
1156
+ "6717 8408.0 -1.813280 4.917851 -5.926130 5.701500 1.204393 \n",
1157
+ "14170 25198.0 -15.903635 10.393917 -19.133602 6.185969 -12.538021 \n",
1158
  "\n",
1159
+ " V6 V7 V8 V9 ... V21 V22 \\\n",
1160
+ "217092 -0.561321 -1.091436 0.097218 0.041781 ... 0.262719 0.741864 \n",
1161
+ "208651 -0.103614 -3.490350 1.094734 -0.717418 ... 0.621622 0.043807 \n",
1162
+ "71974 -0.793982 0.655119 -0.525607 -0.432044 ... -0.139194 0.208491 \n",
1163
+ "6717 -3.035138 -1.713402 0.561257 -3.796354 ... 0.615642 -0.406427 \n",
1164
+ "14170 -4.027030 -13.897827 10.662252 -2.844954 ... 1.577548 -1.280137 \n",
1165
  "\n",
1166
+ " V23 V24 V25 V26 V27 V28 Amount \\\n",
1167
+ "217092 0.069487 -0.532429 -0.202224 -0.077787 0.017431 -0.027308 24.99 \n",
1168
+ "208651 0.102711 -0.601505 0.127371 -0.163009 0.853792 0.356503 39.45 \n",
1169
+ "71974 -0.781711 0.143014 0.623358 -0.113667 -0.109330 -0.190860 80.00 \n",
1170
+ "6717 -0.737018 -0.279642 1.106766 0.323885 0.894767 0.569519 1.00 \n",
1171
+ "14170 -0.601295 0.040404 0.995502 -0.273743 1.688136 0.527831 99.99 \n",
1172
+ "\n",
1173
+ " Class \n",
1174
+ "217092 0 \n",
1175
+ "208651 1 \n",
1176
+ "71974 0 \n",
1177
+ "6717 1 \n",
1178
+ "14170 1 \n",
1179
  "\n",
1180
  "[5 rows x 31 columns]"
1181
  ]
1182
  },
1183
+ "execution_count": 6,
1184
  "metadata": {},
1185
  "output_type": "execute_result"
1186
  }
 
1200
  },
1201
  {
1202
  "cell_type": "code",
1203
+ "execution_count": 8,
1204
  "metadata": {
1205
  "execution": {
1206
  "iopub.execute_input": "2023-07-13T08:56:58.495789Z",
 
1238
  "print(new_df['Class'].value_counts()/len(new_df))\n",
1239
  "\n",
1240
  "\n",
1241
+ "# fix 2 --- need keyword of arguments\n",
1242
  "# sns.countplot('Class', data=new_df, palette=colors)\n",
1243
+ "colors = [\"#0101DF\", \"#DF0101\"]\n",
1244
  "sns.countplot(data=new_df,x='Class', palette=colors)\n",
1245
  "\n",
1246
  "plt.title('Equally Distributed Classes', fontsize=14)\n",
benchmark/pandas_11/pandas_11_reproduced.ipynb CHANGED
@@ -331,7 +331,7 @@
331
  },
332
  {
333
  "cell_type": "code",
334
- "execution_count": 3,
335
  "metadata": {
336
  "execution": {
337
  "iopub.execute_input": "2023-07-13T08:43:31.099091Z",
@@ -341,18 +341,7 @@
341
  "shell.execute_reply.started": "2023-07-13T08:43:31.099063Z"
342
  }
343
  },
344
- "outputs": [
345
- {
346
- "data": {
347
- "image/png": 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\n",
348
- "text/plain": [
349
- "<Figure size 640x480 with 1 Axes>"
350
- ]
351
- },
352
- "metadata": {},
353
- "output_type": "display_data"
354
- }
355
- ],
356
  "source": [
357
  "colors = [\"#0101DF\", \"#DF0101\"]\n",
358
  "\n",
@@ -420,7 +409,7 @@
420
  },
421
  {
422
  "cell_type": "code",
423
- "execution_count": 4,
424
  "metadata": {
425
  "execution": {
426
  "iopub.execute_input": "2023-07-13T08:43:35.113910Z",
@@ -544,7 +533,7 @@
544
  "4 45355.430437 "
545
  ]
546
  },
547
- "execution_count": 4,
548
  "metadata": {},
549
  "output_type": "execute_result"
550
  }
@@ -840,7 +829,7 @@
840
  },
841
  {
842
  "cell_type": "code",
843
- "execution_count": 5,
844
  "metadata": {
845
  "execution": {
846
  "iopub.execute_input": "2023-07-13T08:52:28.333770Z",
@@ -867,7 +856,7 @@
867
  "\u001b[0;31mKeyError\u001b[0m: 'Hour'",
868
  "\nThe above exception was the direct cause of the following exception:\n",
869
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
870
- "\u001b[0;32m<ipython-input-5-1bc6ab312785>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Class = 0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Class = 1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
871
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3891\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3892\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3893\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3894\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3895\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
872
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3796\u001b[0m ):\n\u001b[1;32m 3797\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3798\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3799\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3800\u001b[0m \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
873
  "\u001b[0;31mKeyError\u001b[0m: 'Hour'"
@@ -924,7 +913,7 @@
924
  },
925
  {
926
  "cell_type": "code",
927
- "execution_count": 6,
928
  "metadata": {
929
  "execution": {
930
  "iopub.execute_input": "2023-07-13T08:56:56.658604Z",
@@ -934,26 +923,7 @@
934
  "shell.execute_reply.started": "2023-07-13T08:56:56.658531Z"
935
  }
936
  },
937
- "outputs": [
938
- {
939
- "name": "stdout",
940
- "output_type": "stream",
941
- "text": [
942
- "No Frauds 99.83 % of the dataset\n",
943
- "Frauds 0.17 % of the dataset\n",
944
- "Train: [ 30473 30496 31002 ... 284804 284805 284806] Test: [ 0 1 2 ... 57017 57018 57019]\n",
945
- "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 30473 30496 31002 ... 113964 113965 113966]\n",
946
- "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 81609 82400 83053 ... 170946 170947 170948]\n",
947
- "Train: [ 0 1 2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
948
- "Train: [ 0 1 2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n",
949
- "----------------------------------------------------------------------------------------------------\n",
950
- "Label Distributions: \n",
951
- "\n",
952
- "[0.99827076 0.00172924]\n",
953
- "[0.99827952 0.00172048]\n"
954
- ]
955
- }
956
- ],
957
  "source": [
958
  "from sklearn.model_selection import train_test_split\n",
959
  "from sklearn.model_selection import StratifiedShuffleSplit\n",
@@ -995,7 +965,7 @@
995
  },
996
  {
997
  "cell_type": "code",
998
- "execution_count": 7,
999
  "metadata": {
1000
  "execution": {
1001
  "iopub.execute_input": "2023-07-13T08:56:56.989037Z",
@@ -1005,207 +975,7 @@
1005
  "shell.execute_reply.started": "2023-07-13T08:56:56.989005Z"
1006
  }
1007
  },
1008
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1009
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- " <th></th>\n",
1030
- " <th>Time</th>\n",
1031
- " <th>V1</th>\n",
1032
- " <th>V2</th>\n",
1033
- " <th>V3</th>\n",
1034
- " <th>V4</th>\n",
1035
- " <th>V5</th>\n",
1036
- " <th>V6</th>\n",
1037
- " <th>V7</th>\n",
1038
- " <th>V8</th>\n",
1039
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1040
- " <th>...</th>\n",
1041
- " <th>V21</th>\n",
1042
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1045
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1046
- " <th>V26</th>\n",
1047
- " <th>V27</th>\n",
1048
- " <th>V28</th>\n",
1049
- " <th>Amount</th>\n",
1050
- " <th>Class</th>\n",
1051
- " </tr>\n",
1052
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1053
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1054
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1056
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1065
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1068
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1069
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1070
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1071
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1072
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1073
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1074
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1075
- " <td>107.00</td>\n",
1076
- " <td>0</td>\n",
1077
- " </tr>\n",
1078
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1079
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1080
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1081
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1082
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1083
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1084
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1087
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1089
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1090
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1091
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1092
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1093
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1095
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1096
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1097
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1098
- " <td>0.192067</td>\n",
1099
- " <td>0.77</td>\n",
1100
- " <td>1</td>\n",
1101
- " </tr>\n",
1102
- " <tr>\n",
1103
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1104
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1105
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1106
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1107
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1108
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1110
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1111
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1112
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1114
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1121
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1122
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1123
- " <td>49.00</td>\n",
1124
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1125
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1126
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1127
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1128
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1135
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1136
- " <td>-0.246922</td>\n",
1137
- " <td>-0.100523</td>\n",
1138
- " <td>...</td>\n",
1139
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1140
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1141
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1144
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1147
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1148
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1150
- " <tr>\n",
1151
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1152
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1153
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1156
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1160
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1162
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1163
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1165
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1166
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1169
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1172
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1173
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1174
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1175
- "</table>\n",
1176
- "<p>5 rows × 31 columns</p>\n",
1177
- "</div>"
1178
- ],
1179
- "text/plain": [
1180
- " Time V1 V2 V3 V4 V5 V6 \\\n",
1181
- "105142 69383.0 -0.142983 -0.664989 1.738824 -1.850861 -1.700378 -0.533451 \n",
1182
- "263274 160870.0 -0.644278 5.002352 -8.252739 7.756915 -0.216267 -2.751496 \n",
1183
- "199062 132793.0 -1.200649 0.859778 -1.414095 -1.148850 0.680327 -0.177575 \n",
1184
- "86155 61108.0 -2.756007 0.683821 -1.390169 1.501887 -1.165614 -0.131207 \n",
1185
- "140786 83934.0 -0.433222 2.428379 -3.996454 4.871299 -1.796308 -0.586868 \n",
1186
- "\n",
1187
- " V7 V8 V9 ... V21 V22 V23 \\\n",
1188
- "105142 -0.289359 -0.081633 -2.168893 ... 0.065756 0.391033 0.104155 \n",
1189
- "263274 -3.358857 1.406268 -4.403852 ... 0.587728 -0.605759 0.033746 \n",
1190
- "199062 0.525039 0.614912 0.021296 ... -0.140027 -0.345108 -0.205411 \n",
1191
- "86155 -1.478741 -0.246922 -0.100523 ... 0.320474 0.611027 0.174864 \n",
1192
- "140786 -4.654543 1.285230 -2.743539 ... 0.713559 -0.408954 -0.320890 \n",
1193
- "\n",
1194
- " V24 V25 V26 V27 V28 Amount Class \n",
1195
- "105142 0.366195 -0.057220 -0.145840 0.042170 0.038780 107.00 0 \n",
1196
- "263274 -0.756170 -0.008172 0.532772 0.663970 0.192067 0.77 1 \n",
1197
- "199062 -0.106861 0.157973 -0.148159 -0.168336 -0.403640 49.00 0 \n",
1198
- "86155 -0.502151 -0.174713 1.179242 -1.166315 0.821215 101.50 1 \n",
1199
- "140786 -0.804230 0.962852 0.199558 1.094533 0.541148 1.00 1 \n",
1200
- "\n",
1201
- "[5 rows x 31 columns]"
1202
- ]
1203
- },
1204
- "execution_count": 7,
1205
- "metadata": {},
1206
- "output_type": "execute_result"
1207
- }
1208
- ],
1209
  "source": [
1210
  "df = df.sample(frac=1)\n",
1211
  "\n",
@@ -1221,7 +991,7 @@
1221
  },
1222
  {
1223
  "cell_type": "code",
1224
- "execution_count": 8,
1225
  "metadata": {
1226
  "execution": {
1227
  "iopub.execute_input": "2023-07-13T08:56:58.495789Z",
@@ -1231,30 +1001,7 @@
1231
  "shell.execute_reply.started": "2023-07-13T08:56:58.495748Z"
1232
  }
1233
  },
1234
- "outputs": [
1235
- {
1236
- "name": "stdout",
1237
- "output_type": "stream",
1238
- "text": [
1239
- "Distribution of the Classes in the subsample dataset\n",
1240
- "Class\n",
1241
- "0 0.5\n",
1242
- "1 0.5\n",
1243
- "Name: count, dtype: float64\n"
1244
- ]
1245
- },
1246
- {
1247
- "ename": "TypeError",
1248
- "evalue": "countplot() got multiple values for argument 'data'",
1249
- "output_type": "error",
1250
- "traceback": [
1251
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1252
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
1253
- "\u001b[0;32m<ipython-input-8-ca6a74a77f36>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Class'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Equally Distributed Classes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1254
- "\u001b[0;31mTypeError\u001b[0m: countplot() got multiple values for argument 'data'"
1255
- ]
1256
- }
1257
- ],
1258
  "source": [
1259
  "print('Distribution of the Classes in the subsample dataset')\n",
1260
  "print(new_df['Class'].value_counts()/len(new_df))\n",
 
331
  },
332
  {
333
  "cell_type": "code",
334
+ "execution_count": null,
335
  "metadata": {
336
  "execution": {
337
  "iopub.execute_input": "2023-07-13T08:43:31.099091Z",
 
341
  "shell.execute_reply.started": "2023-07-13T08:43:31.099063Z"
342
  }
343
  },
344
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
345
  "source": [
346
  "colors = [\"#0101DF\", \"#DF0101\"]\n",
347
  "\n",
 
409
  },
410
  {
411
  "cell_type": "code",
412
+ "execution_count": 3,
413
  "metadata": {
414
  "execution": {
415
  "iopub.execute_input": "2023-07-13T08:43:35.113910Z",
 
533
  "4 45355.430437 "
534
  ]
535
  },
536
+ "execution_count": 3,
537
  "metadata": {},
538
  "output_type": "execute_result"
539
  }
 
829
  },
830
  {
831
  "cell_type": "code",
832
+ "execution_count": 4,
833
  "metadata": {
834
  "execution": {
835
  "iopub.execute_input": "2023-07-13T08:52:28.333770Z",
 
856
  "\u001b[0;31mKeyError\u001b[0m: 'Hour'",
857
  "\nThe above exception was the direct cause of the following exception:\n",
858
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
859
+ "\u001b[0;32m<ipython-input-4-1bc6ab312785>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Class = 0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Class = 1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
860
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3891\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3892\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3893\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3894\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3895\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
861
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3796\u001b[0m ):\n\u001b[1;32m 3797\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3798\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3799\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3800\u001b[0m \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
862
  "\u001b[0;31mKeyError\u001b[0m: 'Hour'"
 
913
  },
914
  {
915
  "cell_type": "code",
916
+ "execution_count": null,
917
  "metadata": {
918
  "execution": {
919
  "iopub.execute_input": "2023-07-13T08:56:56.658604Z",
 
923
  "shell.execute_reply.started": "2023-07-13T08:56:56.658531Z"
924
  }
925
  },
926
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
927
  "source": [
928
  "from sklearn.model_selection import train_test_split\n",
929
  "from sklearn.model_selection import StratifiedShuffleSplit\n",
 
965
  },
966
  {
967
  "cell_type": "code",
968
+ "execution_count": null,
969
  "metadata": {
970
  "execution": {
971
  "iopub.execute_input": "2023-07-13T08:56:56.989037Z",
 
975
  "shell.execute_reply.started": "2023-07-13T08:56:56.989005Z"
976
  }
977
  },
978
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
979
  "source": [
980
  "df = df.sample(frac=1)\n",
981
  "\n",
 
991
  },
992
  {
993
  "cell_type": "code",
994
+ "execution_count": null,
995
  "metadata": {
996
  "execution": {
997
  "iopub.execute_input": "2023-07-13T08:56:58.495789Z",
 
1001
  "shell.execute_reply.started": "2023-07-13T08:56:58.495748Z"
1002
  }
1003
  },
1004
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1005
  "source": [
1006
  "print('Distribution of the Classes in the subsample dataset')\n",
1007
  "print(new_df['Class'].value_counts()/len(new_df))\n",
benchmark/pandas_12/pandas_12_fixed.ipynb CHANGED
@@ -222,7 +222,7 @@
222
  },
223
  {
224
  "cell_type": "code",
225
- "execution_count": 3,
226
  "metadata": {
227
  "execution": {
228
  "iopub.execute_input": "2023-12-15T14:49:11.184383Z",
@@ -232,18 +232,7 @@
232
  "shell.execute_reply.started": "2023-12-15T14:49:11.184259Z"
233
  }
234
  },
235
- "outputs": [
236
- {
237
- "data": {
238
- "text/plain": [
239
- "(375703, 16)"
240
- ]
241
- },
242
- "execution_count": 3,
243
- "metadata": {},
244
- "output_type": "execute_result"
245
- }
246
- ],
247
  "source": [
248
  "df.shape"
249
  ]
@@ -294,7 +283,7 @@
294
  },
295
  {
296
  "cell_type": "code",
297
- "execution_count": 4,
298
  "metadata": {
299
  "execution": {
300
  "iopub.execute_input": "2023-12-15T15:07:27.714708Z",
@@ -331,7 +320,7 @@
331
  },
332
  {
333
  "cell_type": "code",
334
- "execution_count": 5,
335
  "metadata": {
336
  "execution": {
337
  "iopub.execute_input": "2023-12-15T15:07:30.113075Z",
@@ -400,7 +389,7 @@
400
  },
401
  {
402
  "cell_type": "code",
403
- "execution_count": 6,
404
  "metadata": {
405
  "execution": {
406
  "iopub.execute_input": "2023-12-15T15:07:37.208238Z",
@@ -457,7 +446,7 @@
457
  },
458
  {
459
  "cell_type": "code",
460
- "execution_count": 7,
461
  "metadata": {
462
  "execution": {
463
  "iopub.execute_input": "2023-12-15T15:07:49.912389Z",
@@ -480,7 +469,7 @@
480
  },
481
  {
482
  "cell_type": "code",
483
- "execution_count": 8,
484
  "metadata": {
485
  "execution": {
486
  "iopub.execute_input": "2023-12-15T15:07:53.138237Z",
@@ -497,7 +486,7 @@
497
  "False"
498
  ]
499
  },
500
- "execution_count": 8,
501
  "metadata": {},
502
  "output_type": "execute_result"
503
  }
@@ -904,7 +893,7 @@
904
  },
905
  {
906
  "cell_type": "code",
907
- "execution_count": 9,
908
  "metadata": {
909
  "execution": {
910
  "iopub.execute_input": "2023-12-15T15:08:08.362107Z",
@@ -914,160 +903,7 @@
914
  "shell.execute_reply.started": "2023-12-15T15:08:08.362069Z"
915
  }
916
  },
917
- "outputs": [
918
- {
919
- "data": {
920
- "text/html": [
921
- "<div>\n",
922
- "<style scoped>\n",
923
- " .dataframe tbody tr th:only-of-type {\n",
924
- " vertical-align: middle;\n",
925
- " }\n",
926
- "\n",
927
- " .dataframe tbody tr th {\n",
928
- " vertical-align: top;\n",
929
- " }\n",
930
- "\n",
931
- " .dataframe thead th {\n",
932
- " text-align: right;\n",
933
- " }\n",
934
- "</style>\n",
935
- "<table border=\"1\" class=\"dataframe\">\n",
936
- " <thead>\n",
937
- " <tr style=\"text-align: right;\">\n",
938
- " <th></th>\n",
939
- " <th>RecipeId</th>\n",
940
- " <th>Name</th>\n",
941
- " <th>CookTime</th>\n",
942
- " <th>PrepTime</th>\n",
943
- " <th>TotalTime</th>\n",
944
- " <th>RecipeIngredientParts</th>\n",
945
- " <th>Calories</th>\n",
946
- " <th>FatContent</th>\n",
947
- " <th>SaturatedFatContent</th>\n",
948
- " <th>CholesterolContent</th>\n",
949
- " <th>SodiumContent</th>\n",
950
- " <th>CarbohydrateContent</th>\n",
951
- " <th>FiberContent</th>\n",
952
- " <th>SugarContent</th>\n",
953
- " <th>ProteinContent</th>\n",
954
- " <th>RecipeInstructions</th>\n",
955
- " </tr>\n",
956
- " </thead>\n",
957
- " <tbody>\n",
958
- " <tr>\n",
959
- " <th>0</th>\n",
960
- " <td>38.00</td>\n",
961
- " <td>Low-Fat Berry Blue Frozen Dessert</td>\n",
962
- " <td>1200</td>\n",
963
- " <td>45</td>\n",
964
- " <td>1485.00</td>\n",
965
- " <td>c(\"blueberries\", \"granulated sugar\", \"vanilla ...</td>\n",
966
- " <td>170.90</td>\n",
967
- " <td>2.50</td>\n",
968
- " <td>1.30</td>\n",
969
- " <td>8.00</td>\n",
970
- " <td>29.80</td>\n",
971
- " <td>37.10</td>\n",
972
- " <td>3.60</td>\n",
973
- " <td>30.20</td>\n",
974
- " <td>3.20</td>\n",
975
- " <td>c(\"Toss 2 cups berries with sugar.\", \"Let stan...</td>\n",
976
- " </tr>\n",
977
- " <tr>\n",
978
- " <th>1</th>\n",
979
- " <td>41.00</td>\n",
980
- " <td>Carina's Tofu-Vegetable Kebabs</td>\n",
981
- " <td>20</td>\n",
982
- " <td>600</td>\n",
983
- " <td>1460.00</td>\n",
984
- " <td>c(\"extra firm tofu\", \"eggplant\", \"zucchini\", \"...</td>\n",
985
- " <td>536.10</td>\n",
986
- " <td>24.00</td>\n",
987
- " <td>3.80</td>\n",
988
- " <td>0.00</td>\n",
989
- " <td>1558.60</td>\n",
990
- " <td>64.20</td>\n",
991
- " <td>17.30</td>\n",
992
- " <td>32.10</td>\n",
993
- " <td>29.30</td>\n",
994
- " <td>c(\"Drain the tofu, carefully squeezing out exc...</td>\n",
995
- " </tr>\n",
996
- " <tr>\n",
997
- " <th>2</th>\n",
998
- " <td>42.00</td>\n",
999
- " <td>Cabbage Soup</td>\n",
1000
- " <td>30</td>\n",
1001
- " <td>20</td>\n",
1002
- " <td>50.00</td>\n",
1003
- " <td>c(\"plain tomato juice\", \"cabbage\", \"onion\", \"c...</td>\n",
1004
- " <td>103.60</td>\n",
1005
- " <td>0.40</td>\n",
1006
- " <td>0.10</td>\n",
1007
- " <td>0.00</td>\n",
1008
- " <td>959.30</td>\n",
1009
- " <td>25.10</td>\n",
1010
- " <td>4.80</td>\n",
1011
- " <td>17.70</td>\n",
1012
- " <td>4.30</td>\n",
1013
- " <td>c(\"Mix everything together and bring to a boil...</td>\n",
1014
- " </tr>\n",
1015
- " <tr>\n",
1016
- " <th>3</th>\n",
1017
- " <td>45.00</td>\n",
1018
- " <td>Buttermilk Pie With Gingersnap Crumb Crust</td>\n",
1019
- " <td>50</td>\n",
1020
- " <td>30</td>\n",
1021
- " <td>80.00</td>\n",
1022
- " <td>c(\"sugar\", \"margarine\", \"egg\", \"flour\", \"salt\"...</td>\n",
1023
- " <td>228.00</td>\n",
1024
- " <td>7.10</td>\n",
1025
- " <td>1.70</td>\n",
1026
- " <td>24.50</td>\n",
1027
- " <td>281.80</td>\n",
1028
- " <td>37.50</td>\n",
1029
- " <td>0.50</td>\n",
1030
- " <td>24.70</td>\n",
1031
- " <td>4.20</td>\n",
1032
- " <td>c(\"Preheat oven to 350°F.\", \"Make pie crust, u...</td>\n",
1033
- " </tr>\n",
1034
- " <tr>\n",
1035
- " <th>4</th>\n",
1036
- " <td>46.00</td>\n",
1037
- " <td>A Jad - Cucumber Pickle</td>\n",
1038
- " <td>0</td>\n",
1039
- " <td>25</td>\n",
1040
- " <td>25.00</td>\n",
1041
- " <td>c(\"rice vinegar\", \"haeo\")</td>\n",
1042
- " <td>4.30</td>\n",
1043
- " <td>0.00</td>\n",
1044
- " <td>0.00</td>\n",
1045
- " <td>0.00</td>\n",
1046
- " <td>0.70</td>\n",
1047
- " <td>1.10</td>\n",
1048
- " <td>0.20</td>\n",
1049
- " <td>0.20</td>\n",
1050
- " <td>0.10</td>\n",
1051
- " <td>c(\"Slice the cucumber in four lengthwise, then...</td>\n",
1052
- " </tr>\n",
1053
- " </tbody>\n",
1054
- "</table>\n",
1055
- "</div>"
1056
- ],
1057
- "text/plain": [
1058
- " RecipeId Name CookTime PrepTime TotalTime RecipeIngredientParts Calories FatContent SaturatedFatContent CholesterolContent SodiumContent CarbohydrateContent FiberContent SugarContent ProteinContent RecipeInstructions\n",
1059
- "0 38.00 Low-Fat Berry Blue Frozen Dessert 1200 45 1485.00 c(\"blueberries\", \"granulated sugar\", \"vanilla ... 170.90 2.50 1.30 8.00 29.80 37.10 3.60 30.20 3.20 c(\"Toss 2 cups berries with sugar.\", \"Let stan...\n",
1060
- "1 41.00 Carina's Tofu-Vegetable Kebabs 20 600 1460.00 c(\"extra firm tofu\", \"eggplant\", \"zucchini\", \"... 536.10 24.00 3.80 0.00 1558.60 64.20 17.30 32.10 29.30 c(\"Drain the tofu, carefully squeezing out exc...\n",
1061
- "2 42.00 Cabbage Soup 30 20 50.00 c(\"plain tomato juice\", \"cabbage\", \"onion\", \"c... 103.60 0.40 0.10 0.00 959.30 25.10 4.80 17.70 4.30 c(\"Mix everything together and bring to a boil...\n",
1062
- "3 45.00 Buttermilk Pie With Gingersnap Crumb Crust 50 30 80.00 c(\"sugar\", \"margarine\", \"egg\", \"flour\", \"salt\"... 228.00 7.10 1.70 24.50 281.80 37.50 0.50 24.70 4.20 c(\"Preheat oven to 350°F.\", \"Make pie crust, u...\n",
1063
- "4 46.00 A Jad - Cucumber Pickle 0 25 25.00 c(\"rice vinegar\", \"haeo\") 4.30 0.00 0.00 0.00 0.70 1.10 0.20 0.20 0.10 c(\"Slice the cucumber in four lengthwise, then..."
1064
- ]
1065
- },
1066
- "execution_count": 9,
1067
- "metadata": {},
1068
- "output_type": "execute_result"
1069
- }
1070
- ],
1071
  "source": [
1072
  "df.head()"
1073
  ]
@@ -1091,7 +927,7 @@
1091
  },
1092
  {
1093
  "cell_type": "code",
1094
- "execution_count": 10,
1095
  "metadata": {
1096
  "execution": {
1097
  "iopub.execute_input": "2023-12-15T15:08:16.614139Z",
@@ -1115,7 +951,7 @@
1115
  },
1116
  {
1117
  "cell_type": "code",
1118
- "execution_count": 11,
1119
  "metadata": {
1120
  "execution": {
1121
  "iopub.execute_input": "2023-12-15T15:08:11.060002Z",
@@ -1141,7 +977,7 @@
1141
  },
1142
  {
1143
  "cell_type": "code",
1144
- "execution_count": 12,
1145
  "metadata": {
1146
  "execution": {
1147
  "iopub.execute_input": "2023-12-15T15:08:20.283538Z",
@@ -1171,7 +1007,7 @@
1171
  },
1172
  {
1173
  "cell_type": "code",
1174
- "execution_count": 13,
1175
  "metadata": {
1176
  "execution": {
1177
  "iopub.execute_input": "2023-12-15T15:33:42.657241Z",
@@ -1205,7 +1041,7 @@
1205
  },
1206
  {
1207
  "cell_type": "code",
1208
- "execution_count": 14,
1209
  "metadata": {
1210
  "execution": {
1211
  "iopub.execute_input": "2023-12-15T15:12:35.466716Z",
@@ -1328,7 +1164,7 @@
1328
  },
1329
  {
1330
  "cell_type": "code",
1331
- "execution_count": 15,
1332
  "metadata": {
1333
  "execution": {
1334
  "iopub.execute_input": "2023-12-15T15:39:26.995591Z",
@@ -1345,7 +1181,7 @@
1345
  },
1346
  {
1347
  "cell_type": "code",
1348
- "execution_count": 16,
1349
  "metadata": {
1350
  "execution": {
1351
  "iopub.execute_input": "2023-12-15T15:39:30.884915Z",
@@ -1359,10 +1195,10 @@
1359
  {
1360
  "data": {
1361
  "text/plain": [
1362
- "array([12, 12, 8, ..., 20, 23, 16], dtype=int32)"
1363
  ]
1364
  },
1365
- "execution_count": 16,
1366
  "metadata": {},
1367
  "output_type": "execute_result"
1368
  }
@@ -1374,7 +1210,7 @@
1374
  },
1375
  {
1376
  "cell_type": "code",
1377
- "execution_count": 17,
1378
  "metadata": {
1379
  "execution": {
1380
  "iopub.execute_input": "2023-12-15T15:39:33.957001Z",
@@ -1442,7 +1278,7 @@
1442
  " <td>30.20</td>\n",
1443
  " <td>3.20</td>\n",
1444
  " <td>c(\"Toss 2 cups berries with sugar.\", \"Let stan...</td>\n",
1445
- " <td>13</td>\n",
1446
  " </tr>\n",
1447
  " <tr>\n",
1448
  " <th>1</th>\n",
@@ -1461,7 +1297,7 @@
1461
  " <td>32.10</td>\n",
1462
  " <td>29.30</td>\n",
1463
  " <td>c(\"Drain the tofu, carefully squeezing out exc...</td>\n",
1464
- " <td>13</td>\n",
1465
  " </tr>\n",
1466
  " <tr>\n",
1467
  " <th>2</th>\n",
@@ -1480,7 +1316,7 @@
1480
  " <td>17.70</td>\n",
1481
  " <td>4.30</td>\n",
1482
  " <td>c(\"Mix everything together and bring to a boil...</td>\n",
1483
- " <td>9</td>\n",
1484
  " </tr>\n",
1485
  " <tr>\n",
1486
  " <th>3</th>\n",
@@ -1499,7 +1335,7 @@
1499
  " <td>24.70</td>\n",
1500
  " <td>4.20</td>\n",
1501
  " <td>c(\"Preheat oven to 350°F.\", \"Make pie crust, u...</td>\n",
1502
- " <td>21</td>\n",
1503
  " </tr>\n",
1504
  " <tr>\n",
1505
  " <th>4</th>\n",
@@ -1518,7 +1354,7 @@
1518
  " <td>0.20</td>\n",
1519
  " <td>0.10</td>\n",
1520
  " <td>c(\"Slice the cucumber in four lengthwise, then...</td>\n",
1521
- " <td>17</td>\n",
1522
  " </tr>\n",
1523
  " </tbody>\n",
1524
  "</table>\n",
@@ -1526,14 +1362,14 @@
1526
  ],
1527
  "text/plain": [
1528
  " Name CookTime PrepTime TotalTime RecipeIngredientParts Calories FatContent SaturatedFatContent CholesterolContent SodiumContent CarbohydrateContent FiberContent SugarContent ProteinContent RecipeInstructions kmeans_cluster\n",
1529
- "0 Low-Fat Berry Blue Frozen Dessert 1200 45 1485.00 c(\"blueberries\", \"granulated sugar\", \"vanilla ... 170.90 2.50 1.30 8.00 29.80 37.10 3.60 30.20 3.20 c(\"Toss 2 cups berries with sugar.\", \"Let stan... 13\n",
1530
- "1 Carina's Tofu-Vegetable Kebabs 20 600 1460.00 c(\"extra firm tofu\", \"eggplant\", \"zucchini\", \"... 536.10 24.00 3.80 0.00 1558.60 64.20 17.30 32.10 29.30 c(\"Drain the tofu, carefully squeezing out exc... 13\n",
1531
- "2 Cabbage Soup 30 20 50.00 c(\"plain tomato juice\", \"cabbage\", \"onion\", \"c... 103.60 0.40 0.10 0.00 959.30 25.10 4.80 17.70 4.30 c(\"Mix everything together and bring to a boil... 9\n",
1532
- "3 Buttermilk Pie With Gingersnap Crumb Crust 50 30 80.00 c(\"sugar\", \"margarine\", \"egg\", \"flour\", \"salt\"... 228.00 7.10 1.70 24.50 281.80 37.50 0.50 24.70 4.20 c(\"Preheat oven to 350°F.\", \"Make pie crust, u... 21\n",
1533
- "4 A Jad - Cucumber Pickle 0 25 25.00 c(\"rice vinegar\", \"haeo\") 4.30 0.00 0.00 0.00 0.70 1.10 0.20 0.20 0.10 c(\"Slice the cucumber in four lengthwise, then... 17"
1534
  ]
1535
  },
1536
- "execution_count": 17,
1537
  "metadata": {},
1538
  "output_type": "execute_result"
1539
  }
@@ -1563,7 +1399,7 @@
1563
  },
1564
  {
1565
  "cell_type": "code",
1566
- "execution_count": 18,
1567
  "metadata": {
1568
  "execution": {
1569
  "iopub.execute_input": "2023-12-15T15:39:45.812785Z",
@@ -1573,49 +1409,7 @@
1573
  "shell.execute_reply.started": "2023-12-15T15:39:45.812755Z"
1574
  }
1575
  },
1576
- "outputs": [
1577
- {
1578
- "data": {
1579
- "text/plain": [
1580
- "kmeans_cluster\n",
1581
- "1 39609\n",
1582
- "17 37812\n",
1583
- "14 28694\n",
1584
- "25 28350\n",
1585
- "16 28011\n",
1586
- "11 22610\n",
1587
- "8 19120\n",
1588
- "5 18189\n",
1589
- "28 17199\n",
1590
- "18 17199\n",
1591
- "9 15456\n",
1592
- "21 12824\n",
1593
- "24 12747\n",
1594
- "12 11706\n",
1595
- "3 10654\n",
1596
- "15 10040\n",
1597
- "6 8629\n",
1598
- "20 6746\n",
1599
- "27 6377\n",
1600
- "7 5329\n",
1601
- "10 3543\n",
1602
- "30 2921\n",
1603
- "23 2587\n",
1604
- "19 2454\n",
1605
- "2 2453\n",
1606
- "29 1298\n",
1607
- "26 1263\n",
1608
- "4 807\n",
1609
- "22 663\n",
1610
- "13 413\n",
1611
- "Name: count, dtype: int64"
1612
- ]
1613
- },
1614
- "execution_count": 18,
1615
- "metadata": {},
1616
- "output_type": "execute_result"
1617
- }
1618
- ],
1619
  "source": [
1620
  "df[\"kmeans_cluster\"].value_counts()"
1621
  ]
@@ -1717,7 +1511,7 @@
1717
  },
1718
  {
1719
  "cell_type": "code",
1720
- "execution_count": 23,
1721
  "metadata": {
1722
  "execution": {
1723
  "iopub.execute_input": "2023-12-15T15:03:01.714109Z",
@@ -1800,615 +1594,615 @@
1800
  " <tbody>\n",
1801
  " <tr>\n",
1802
  " <th>1</th>\n",
1803
- " <td>39609</td>\n",
1804
- " <td>15.66</td>\n",
1805
- " <td>10.00</td>\n",
1806
- " <td>620457</td>\n",
1807
- " <td>39609</td>\n",
1808
- " <td>38.54</td>\n",
1809
- " <td>30.00</td>\n",
1810
- " <td>1526374.00</td>\n",
1811
- " <td>39609</td>\n",
1812
- " <td>221.01</td>\n",
1813
- " <td>218.30</td>\n",
1814
- " <td>8753895.30</td>\n",
1815
- " <td>39609</td>\n",
1816
- " <td>11.37</td>\n",
1817
- " <td>11.20</td>\n",
1818
- " <td>450306.20</td>\n",
1819
  " </tr>\n",
1820
  " <tr>\n",
1821
  " <th>2</th>\n",
1822
- " <td>2453</td>\n",
1823
- " <td>23.35</td>\n",
1824
  " <td>15.00</td>\n",
1825
- " <td>57267</td>\n",
1826
- " <td>2453</td>\n",
1827
- " <td>67.51</td>\n",
1828
- " <td>45.00</td>\n",
1829
- " <td>165595.00</td>\n",
1830
- " <td>2453</td>\n",
1831
- " <td>725.97</td>\n",
1832
- " <td>668.50</td>\n",
1833
- " <td>1780808.30</td>\n",
1834
- " <td>2453</td>\n",
1835
- " <td>32.56</td>\n",
1836
- " <td>30.30</td>\n",
1837
- " <td>79875.80</td>\n",
1838
  " </tr>\n",
1839
  " <tr>\n",
1840
  " <th>3</th>\n",
1841
- " <td>10654</td>\n",
1842
- " <td>20.16</td>\n",
1843
  " <td>15.00</td>\n",
1844
- " <td>214809</td>\n",
1845
- " <td>10654</td>\n",
1846
- " <td>59.25</td>\n",
1847
  " <td>45.00</td>\n",
1848
- " <td>631255.00</td>\n",
1849
- " <td>10654</td>\n",
1850
- " <td>507.57</td>\n",
1851
- " <td>497.00</td>\n",
1852
- " <td>5407637.50</td>\n",
1853
- " <td>10654</td>\n",
1854
- " <td>22.29</td>\n",
1855
- " <td>21.30</td>\n",
1856
- " <td>237508.50</td>\n",
1857
  " </tr>\n",
1858
  " <tr>\n",
1859
  " <th>4</th>\n",
1860
- " <td>807</td>\n",
1861
- " <td>301.72</td>\n",
1862
- " <td>90.00</td>\n",
1863
- " <td>243491</td>\n",
1864
- " <td>807</td>\n",
1865
- " <td>1827.92</td>\n",
1866
- " <td>1834.50</td>\n",
1867
- " <td>1475133.00</td>\n",
1868
- " <td>807</td>\n",
1869
- " <td>212.49</td>\n",
1870
- " <td>144.50</td>\n",
1871
- " <td>171481.00</td>\n",
1872
- " <td>807</td>\n",
1873
- " <td>8.59</td>\n",
1874
- " <td>3.40</td>\n",
1875
- " <td>6932.40</td>\n",
1876
  " </tr>\n",
1877
  " <tr>\n",
1878
  " <th>5</th>\n",
1879
- " <td>18189</td>\n",
1880
- " <td>16.83</td>\n",
1881
  " <td>15.00</td>\n",
1882
- " <td>306209</td>\n",
1883
- " <td>18189</td>\n",
1884
- " <td>41.08</td>\n",
1885
  " <td>30.00</td>\n",
1886
- " <td>747289.00</td>\n",
1887
- " <td>18189</td>\n",
1888
- " <td>135.35</td>\n",
1889
- " <td>132.80</td>\n",
1890
- " <td>2461839.80</td>\n",
1891
- " <td>18189</td>\n",
1892
- " <td>5.38</td>\n",
1893
- " <td>5.10</td>\n",
1894
- " <td>97773.20</td>\n",
1895
  " </tr>\n",
1896
  " <tr>\n",
1897
  " <th>6</th>\n",
1898
- " <td>8629</td>\n",
1899
- " <td>20.72</td>\n",
1900
  " <td>15.00</td>\n",
1901
- " <td>178794</td>\n",
1902
- " <td>8629</td>\n",
1903
- " <td>63.76</td>\n",
1904
- " <td>50.00</td>\n",
1905
- " <td>550162.00</td>\n",
1906
- " <td>8629</td>\n",
1907
- " <td>370.55</td>\n",
1908
- " <td>367.80</td>\n",
1909
- " <td>3197473.30</td>\n",
1910
- " <td>8629</td>\n",
1911
- " <td>14.20</td>\n",
1912
- " <td>14.10</td>\n",
1913
- " <td>122552.40</td>\n",
1914
  " </tr>\n",
1915
  " <tr>\n",
1916
  " <th>7</th>\n",
1917
- " <td>5329</td>\n",
1918
- " <td>21.47</td>\n",
1919
  " <td>15.00</td>\n",
1920
- " <td>114430</td>\n",
1921
- " <td>5329</td>\n",
1922
- " <td>57.17</td>\n",
1923
- " <td>40.00</td>\n",
1924
- " <td>304675.00</td>\n",
1925
- " <td>5329</td>\n",
1926
- " <td>838.74</td>\n",
1927
- " <td>799.70</td>\n",
1928
- " <td>4469645.50</td>\n",
1929
- " <td>5329</td>\n",
1930
- " <td>35.12</td>\n",
1931
- " <td>32.20</td>\n",
1932
- " <td>187146.80</td>\n",
1933
  " </tr>\n",
1934
  " <tr>\n",
1935
  " <th>8</th>\n",
1936
- " <td>19120</td>\n",
1937
- " <td>17.13</td>\n",
1938
  " <td>15.00</td>\n",
1939
- " <td>327437</td>\n",
1940
- " <td>19120</td>\n",
1941
- " <td>43.60</td>\n",
1942
- " <td>35.00</td>\n",
1943
- " <td>833574.00</td>\n",
1944
- " <td>19120</td>\n",
1945
- " <td>198.74</td>\n",
1946
- " <td>191.00</td>\n",
1947
- " <td>3799861.30</td>\n",
1948
- " <td>19120</td>\n",
1949
- " <td>8.19</td>\n",
1950
- " <td>7.50</td>\n",
1951
- " <td>156514.60</td>\n",
1952
  " </tr>\n",
1953
  " <tr>\n",
1954
  " <th>9</th>\n",
1955
- " <td>15456</td>\n",
1956
- " <td>17.36</td>\n",
1957
  " <td>15.00</td>\n",
1958
- " <td>268263</td>\n",
1959
- " <td>15456</td>\n",
1960
- " <td>45.27</td>\n",
1961
- " <td>35.00</td>\n",
1962
- " <td>699616.00</td>\n",
1963
- " <td>15456</td>\n",
1964
- " <td>200.14</td>\n",
1965
- " <td>199.40</td>\n",
1966
- " <td>3093305.40</td>\n",
1967
- " <td>15456</td>\n",
1968
- " <td>7.78</td>\n",
1969
- " <td>7.50</td>\n",
1970
- " <td>120192.20</td>\n",
1971
  " </tr>\n",
1972
  " <tr>\n",
1973
  " <th>10</th>\n",
1974
- " <td>3543</td>\n",
1975
- " <td>54.69</td>\n",
1976
  " <td>15.00</td>\n",
1977
- " <td>193766</td>\n",
1978
- " <td>3543</td>\n",
1979
- " <td>401.67</td>\n",
1980
- " <td>380.00</td>\n",
1981
- " <td>1423126.00</td>\n",
1982
- " <td>3543</td>\n",
1983
- " <td>460.59</td>\n",
1984
- " <td>440.70</td>\n",
1985
- " <td>1631859.20</td>\n",
1986
- " <td>3543</td>\n",
1987
- " <td>20.78</td>\n",
1988
- " <td>20.30</td>\n",
1989
- " <td>73616.10</td>\n",
1990
  " </tr>\n",
1991
  " <tr>\n",
1992
  " <th>11</th>\n",
1993
- " <td>22610</td>\n",
1994
- " <td>16.56</td>\n",
1995
  " <td>15.00</td>\n",
1996
- " <td>374471</td>\n",
1997
- " <td>22610</td>\n",
1998
- " <td>39.54</td>\n",
1999
- " <td>30.00</td>\n",
2000
- " <td>894070.00</td>\n",
2001
- " <td>22610</td>\n",
2002
- " <td>123.76</td>\n",
2003
- " <td>120.90</td>\n",
2004
- " <td>2798297.70</td>\n",
2005
- " <td>22610</td>\n",
2006
- " <td>5.24</td>\n",
2007
- " <td>4.60</td>\n",
2008
- " <td>118532.20</td>\n",
2009
  " </tr>\n",
2010
  " <tr>\n",
2011
  " <th>12</th>\n",
2012
- " <td>11706</td>\n",
2013
- " <td>18.51</td>\n",
2014
- " <td>15.00</td>\n",
2015
- " <td>216623</td>\n",
2016
- " <td>11706</td>\n",
2017
- " <td>50.22</td>\n",
2018
- " <td>40.00</td>\n",
2019
- " <td>587889.00</td>\n",
2020
- " <td>11706</td>\n",
2021
- " <td>583.59</td>\n",
2022
- " <td>569.50</td>\n",
2023
- " <td>6831448.50</td>\n",
2024
- " <td>11706</td>\n",
2025
- " <td>28.11</td>\n",
2026
- " <td>26.90</td>\n",
2027
- " <td>329108.80</td>\n",
2028
  " </tr>\n",
2029
  " <tr>\n",
2030
  " <th>13</th>\n",
2031
- " <td>413</td>\n",
2032
- " <td>310.28</td>\n",
2033
- " <td>240.00</td>\n",
2034
- " <td>128145</td>\n",
2035
- " <td>413</td>\n",
2036
- " <td>1580.56</td>\n",
2037
  " <td>1470.00</td>\n",
2038
- " <td>652770.00</td>\n",
2039
- " <td>413</td>\n",
2040
- " <td>351.22</td>\n",
2041
- " <td>281.70</td>\n",
2042
- " <td>145053.30</td>\n",
2043
- " <td>413</td>\n",
2044
- " <td>11.02</td>\n",
2045
- " <td>8.40</td>\n",
2046
- " <td>4549.90</td>\n",
2047
  " </tr>\n",
2048
  " <tr>\n",
2049
  " <th>14</th>\n",
2050
- " <td>28694</td>\n",
2051
- " <td>17.47</td>\n",
2052
  " <td>15.00</td>\n",
2053
- " <td>501162</td>\n",
2054
- " <td>28694</td>\n",
2055
- " <td>46.26</td>\n",
2056
  " <td>40.00</td>\n",
2057
- " <td>1327277.00</td>\n",
2058
- " <td>28694</td>\n",
2059
- " <td>286.13</td>\n",
2060
- " <td>283.60</td>\n",
2061
- " <td>8210347.00</td>\n",
2062
- " <td>28694</td>\n",
2063
- " <td>13.13</td>\n",
2064
- " <td>12.80</td>\n",
2065
- " <td>376843.90</td>\n",
2066
  " </tr>\n",
2067
  " <tr>\n",
2068
  " <th>15</th>\n",
2069
- " <td>10040</td>\n",
2070
- " <td>18.96</td>\n",
2071
  " <td>15.00</td>\n",
2072
- " <td>190348</td>\n",
2073
- " <td>10040</td>\n",
2074
- " <td>54.24</td>\n",
2075
  " <td>45.00</td>\n",
2076
- " <td>544615.00</td>\n",
2077
- " <td>10040</td>\n",
2078
- " <td>305.50</td>\n",
2079
- " <td>308.90</td>\n",
2080
- " <td>3067179.30</td>\n",
2081
- " <td>10040</td>\n",
2082
- " <td>11.31</td>\n",
2083
- " <td>11.50</td>\n",
2084
- " <td>113572.40</td>\n",
2085
  " </tr>\n",
2086
  " <tr>\n",
2087
  " <th>16</th>\n",
2088
- " <td>28011</td>\n",
2089
- " <td>17.49</td>\n",
2090
  " <td>15.00</td>\n",
2091
- " <td>490040</td>\n",
2092
- " <td>28011</td>\n",
2093
- " <td>46.95</td>\n",
2094
- " <td>40.00</td>\n",
2095
- " <td>1315152.00</td>\n",
2096
- " <td>28011</td>\n",
2097
- " <td>385.84</td>\n",
2098
- " <td>382.40</td>\n",
2099
- " <td>10807663.60</td>\n",
2100
- " <td>28011</td>\n",
2101
- " <td>19.39</td>\n",
2102
- " <td>19.30</td>\n",
2103
- " <td>543139.20</td>\n",
2104
  " </tr>\n",
2105
  " <tr>\n",
2106
  " <th>17</th>\n",
2107
- " <td>37812</td>\n",
2108
- " <td>13.88</td>\n",
2109
  " <td>10.00</td>\n",
2110
- " <td>524745</td>\n",
2111
- " <td>37812</td>\n",
2112
- " <td>29.77</td>\n",
2113
- " <td>20.00</td>\n",
2114
- " <td>1125480.00</td>\n",
2115
- " <td>37812</td>\n",
2116
- " <td>66.58</td>\n",
2117
- " <td>66.40</td>\n",
2118
- " <td>2517419.00</td>\n",
2119
- " <td>37812</td>\n",
2120
- " <td>3.55</td>\n",
2121
- " <td>2.70</td>\n",
2122
- " <td>134205.00</td>\n",
2123
  " </tr>\n",
2124
  " <tr>\n",
2125
  " <th>18</th>\n",
2126
- " <td>17199</td>\n",
2127
- " <td>19.29</td>\n",
2128
  " <td>15.00</td>\n",
2129
- " <td>331685</td>\n",
2130
- " <td>17199</td>\n",
2131
- " <td>55.60</td>\n",
2132
- " <td>42.00</td>\n",
2133
- " <td>956338.00</td>\n",
2134
- " <td>17199</td>\n",
2135
- " <td>316.97</td>\n",
2136
- " <td>316.30</td>\n",
2137
- " <td>5451584.40</td>\n",
2138
- " <td>17199</td>\n",
2139
- " <td>13.87</td>\n",
2140
- " <td>13.50</td>\n",
2141
- " <td>238578.50</td>\n",
2142
  " </tr>\n",
2143
  " <tr>\n",
2144
  " <th>19</th>\n",
2145
- " <td>2454</td>\n",
2146
- " <td>22.58</td>\n",
2147
  " <td>15.00</td>\n",
2148
- " <td>55415</td>\n",
2149
- " <td>2454</td>\n",
2150
- " <td>73.10</td>\n",
2151
- " <td>45.00</td>\n",
2152
- " <td>179392.00</td>\n",
2153
- " <td>2454</td>\n",
2154
- " <td>861.91</td>\n",
2155
- " <td>810.55</td>\n",
2156
- " <td>2115132.70</td>\n",
2157
- " <td>2454</td>\n",
2158
- " <td>35.09</td>\n",
2159
- " <td>31.50</td>\n",
2160
- " <td>86105.10</td>\n",
2161
  " </tr>\n",
2162
  " <tr>\n",
2163
  " <th>20</th>\n",
2164
- " <td>6746</td>\n",
2165
- " <td>20.34</td>\n",
2166
  " <td>15.00</td>\n",
2167
- " <td>137209</td>\n",
2168
- " <td>6746</td>\n",
2169
- " <td>58.09</td>\n",
2170
- " <td>45.00</td>\n",
2171
- " <td>391857.00</td>\n",
2172
- " <td>6746</td>\n",
2173
- " <td>476.70</td>\n",
2174
- " <td>458.60</td>\n",
2175
- " <td>3215832.80</td>\n",
2176
- " <td>6746</td>\n",
2177
- " <td>21.42</td>\n",
2178
- " <td>20.60</td>\n",
2179
- " <td>144477.50</td>\n",
2180
  " </tr>\n",
2181
  " <tr>\n",
2182
  " <th>21</th>\n",
2183
- " <td>12824</td>\n",
2184
- " <td>17.74</td>\n",
2185
  " <td>15.00</td>\n",
2186
- " <td>227534</td>\n",
2187
- " <td>12824</td>\n",
2188
- " <td>50.28</td>\n",
2189
  " <td>40.00</td>\n",
2190
- " <td>644839.00</td>\n",
2191
- " <td>12824</td>\n",
2192
- " <td>287.32</td>\n",
2193
- " <td>286.20</td>\n",
2194
- " <td>3684543.10</td>\n",
2195
- " <td>12824</td>\n",
2196
- " <td>11.32</td>\n",
2197
- " <td>11.25</td>\n",
2198
- " <td>145153.50</td>\n",
2199
  " </tr>\n",
2200
  " <tr>\n",
2201
  " <th>22</th>\n",
2202
- " <td>663</td>\n",
2203
- " <td>321.83</td>\n",
2204
- " <td>600.00</td>\n",
2205
- " <td>213372</td>\n",
2206
- " <td>663</td>\n",
2207
- " <td>1568.77</td>\n",
2208
- " <td>1470.00</td>\n",
2209
- " <td>1040094.00</td>\n",
2210
- " <td>663</td>\n",
2211
- " <td>284.78</td>\n",
2212
- " <td>233.80</td>\n",
2213
- " <td>188811.10</td>\n",
2214
- " <td>663</td>\n",
2215
- " <td>11.79</td>\n",
2216
- " <td>8.30</td>\n",
2217
- " <td>7819.40</td>\n",
2218
  " </tr>\n",
2219
  " <tr>\n",
2220
  " <th>23</th>\n",
2221
- " <td>2587</td>\n",
2222
- " <td>76.90</td>\n",
2223
- " <td>20.00</td>\n",
2224
- " <td>198945</td>\n",
2225
- " <td>2587</td>\n",
2226
- " <td>446.03</td>\n",
2227
- " <td>435.00</td>\n",
2228
- " <td>1153872.00</td>\n",
2229
- " <td>2587</td>\n",
2230
- " <td>312.39</td>\n",
2231
- " <td>300.30</td>\n",
2232
- " <td>808150.20</td>\n",
2233
- " <td>2587</td>\n",
2234
- " <td>11.53</td>\n",
2235
- " <td>9.60</td>\n",
2236
- " <td>29833.90</td>\n",
2237
  " </tr>\n",
2238
  " <tr>\n",
2239
  " <th>24</th>\n",
2240
- " <td>12747</td>\n",
2241
- " <td>17.70</td>\n",
2242
- " <td>15.00</td>\n",
2243
- " <td>225627</td>\n",
2244
- " <td>12747</td>\n",
2245
- " <td>48.11</td>\n",
2246
- " <td>40.00</td>\n",
2247
- " <td>613310.00</td>\n",
2248
- " <td>12747</td>\n",
2249
- " <td>233.59</td>\n",
2250
- " <td>235.50</td>\n",
2251
- " <td>2977516.40</td>\n",
2252
- " <td>12747</td>\n",
2253
- " <td>9.05</td>\n",
2254
- " <td>8.90</td>\n",
2255
- " <td>115354.80</td>\n",
2256
  " </tr>\n",
2257
  " <tr>\n",
2258
  " <th>25</th>\n",
2259
- " <td>28350</td>\n",
2260
- " <td>15.18</td>\n",
2261
- " <td>10.00</td>\n",
2262
- " <td>430312</td>\n",
2263
- " <td>28350</td>\n",
2264
- " <td>35.18</td>\n",
2265
- " <td>30.00</td>\n",
2266
- " <td>997463.00</td>\n",
2267
- " <td>28350</td>\n",
2268
- " <td>108.84</td>\n",
2269
- " <td>110.70</td>\n",
2270
- " <td>3085498.70</td>\n",
2271
- " <td>28350</td>\n",
2272
- " <td>5.09</td>\n",
2273
- " <td>4.40</td>\n",
2274
- " <td>144210.90</td>\n",
2275
  " </tr>\n",
2276
  " <tr>\n",
2277
  " <th>26</th>\n",
2278
- " <td>1263</td>\n",
2279
- " <td>357.75</td>\n",
2280
- " <td>600.00</td>\n",
2281
- " <td>451839</td>\n",
2282
- " <td>1263</td>\n",
2283
- " <td>1445.58</td>\n",
2284
- " <td>1455.00</td>\n",
2285
- " <td>1825763.00</td>\n",
2286
- " <td>1263</td>\n",
2287
- " <td>236.65</td>\n",
2288
- " <td>190.90</td>\n",
2289
- " <td>298890.60</td>\n",
2290
- " <td>1263</td>\n",
2291
- " <td>12.16</td>\n",
2292
- " <td>8.30</td>\n",
2293
- " <td>15360.00</td>\n",
2294
  " </tr>\n",
2295
  " <tr>\n",
2296
  " <th>27</th>\n",
2297
- " <td>6377</td>\n",
2298
- " <td>60.66</td>\n",
 
 
 
 
2299
  " <td>20.00</td>\n",
2300
- " <td>386820</td>\n",
2301
- " <td>6377</td>\n",
2302
- " <td>253.93</td>\n",
2303
- " <td>250.00</td>\n",
2304
- " <td>1619297.00</td>\n",
2305
- " <td>6377</td>\n",
2306
- " <td>168.61</td>\n",
2307
- " <td>167.80</td>\n",
2308
- " <td>1075204.10</td>\n",
2309
- " <td>6377</td>\n",
2310
- " <td>6.97</td>\n",
2311
- " <td>5.30</td>\n",
2312
- " <td>44450.60</td>\n",
2313
  " </tr>\n",
2314
  " <tr>\n",
2315
  " <th>28</th>\n",
2316
- " <td>17199</td>\n",
2317
- " <td>18.97</td>\n",
2318
- " <td>15.00</td>\n",
2319
- " <td>326184</td>\n",
2320
- " <td>17199</td>\n",
2321
- " <td>52.46</td>\n",
2322
- " <td>40.00</td>\n",
2323
- " <td>902260.00</td>\n",
2324
- " <td>17199</td>\n",
2325
- " <td>491.62</td>\n",
2326
- " <td>480.80</td>\n",
2327
- " <td>8455314.40</td>\n",
2328
- " <td>17199</td>\n",
2329
- " <td>22.20</td>\n",
2330
- " <td>21.60</td>\n",
2331
- " <td>381852.90</td>\n",
2332
  " </tr>\n",
2333
  " <tr>\n",
2334
  " <th>29</th>\n",
2335
- " <td>1298</td>\n",
2336
- " <td>97.79</td>\n",
2337
- " <td>20.00</td>\n",
2338
- " <td>126929</td>\n",
2339
- " <td>1298</td>\n",
2340
- " <td>485.28</td>\n",
2341
- " <td>484.50</td>\n",
2342
- " <td>629887.00</td>\n",
2343
- " <td>1298</td>\n",
2344
- " <td>328.40</td>\n",
2345
- " <td>308.35</td>\n",
2346
- " <td>426261.50</td>\n",
2347
- " <td>1298</td>\n",
2348
- " <td>11.92</td>\n",
2349
- " <td>11.10</td>\n",
2350
- " <td>15470.60</td>\n",
2351
  " </tr>\n",
2352
  " <tr>\n",
2353
  " <th>30</th>\n",
2354
- " <td>2921</td>\n",
2355
- " <td>128.34</td>\n",
2356
  " <td>20.00</td>\n",
2357
- " <td>374873</td>\n",
2358
- " <td>2921</td>\n",
2359
- " <td>579.56</td>\n",
2360
- " <td>510.00</td>\n",
2361
- " <td>1692886.00</td>\n",
2362
- " <td>2921</td>\n",
2363
- " <td>207.10</td>\n",
2364
- " <td>209.50</td>\n",
2365
- " <td>604934.50</td>\n",
2366
- " <td>2921</td>\n",
2367
- " <td>8.62</td>\n",
2368
- " <td>7.00</td>\n",
2369
- " <td>25179.80</td>\n",
2370
  " </tr>\n",
2371
  " </tbody>\n",
2372
  "</table>\n",
2373
  "</div>"
2374
  ],
2375
  "text/plain": [
2376
- " PrepTime TotalTime Calories FatContent \n",
2377
- " count mean median sum count mean median sum count mean median sum count mean median sum\n",
2378
- "kmeans_cluster \n",
2379
- "1 39609 15.66 10.00 620457 39609 38.54 30.00 1526374.00 39609 221.01 218.30 8753895.30 39609 11.37 11.20 450306.20\n",
2380
- "2 2453 23.35 15.00 57267 2453 67.51 45.00 165595.00 2453 725.97 668.50 1780808.30 2453 32.56 30.30 79875.80\n",
2381
- "3 10654 20.16 15.00 214809 10654 59.25 45.00 631255.00 10654 507.57 497.00 5407637.50 10654 22.29 21.30 237508.50\n",
2382
- "4 807 301.72 90.00 243491 807 1827.92 1834.50 1475133.00 807 212.49 144.50 171481.00 807 8.59 3.40 6932.40\n",
2383
- "5 18189 16.83 15.00 306209 18189 41.08 30.00 747289.00 18189 135.35 132.80 2461839.80 18189 5.38 5.10 97773.20\n",
2384
- "6 8629 20.72 15.00 178794 8629 63.76 50.00 550162.00 8629 370.55 367.80 3197473.30 8629 14.20 14.10 122552.40\n",
2385
- "7 5329 21.47 15.00 114430 5329 57.17 40.00 304675.00 5329 838.74 799.70 4469645.50 5329 35.12 32.20 187146.80\n",
2386
- "8 19120 17.13 15.00 327437 19120 43.60 35.00 833574.00 19120 198.74 191.00 3799861.30 19120 8.19 7.50 156514.60\n",
2387
- "9 15456 17.36 15.00 268263 15456 45.27 35.00 699616.00 15456 200.14 199.40 3093305.40 15456 7.78 7.50 120192.20\n",
2388
- "10 3543 54.69 15.00 193766 3543 401.67 380.00 1423126.00 3543 460.59 440.70 1631859.20 3543 20.78 20.30 73616.10\n",
2389
- "11 22610 16.56 15.00 374471 22610 39.54 30.00 894070.00 22610 123.76 120.90 2798297.70 22610 5.24 4.60 118532.20\n",
2390
- "12 11706 18.51 15.00 216623 11706 50.22 40.00 587889.00 11706 583.59 569.50 6831448.50 11706 28.11 26.90 329108.80\n",
2391
- "13 413 310.28 240.00 128145 413 1580.56 1470.00 652770.00 413 351.22 281.70 145053.30 413 11.02 8.40 4549.90\n",
2392
- "14 28694 17.47 15.00 501162 28694 46.26 40.00 1327277.00 28694 286.13 283.60 8210347.00 28694 13.13 12.80 376843.90\n",
2393
- "15 10040 18.96 15.00 190348 10040 54.24 45.00 544615.00 10040 305.50 308.90 3067179.30 10040 11.31 11.50 113572.40\n",
2394
- "16 28011 17.49 15.00 490040 28011 46.95 40.00 1315152.00 28011 385.84 382.40 10807663.60 28011 19.39 19.30 543139.20\n",
2395
- "17 37812 13.88 10.00 524745 37812 29.77 20.00 1125480.00 37812 66.58 66.40 2517419.00 37812 3.55 2.70 134205.00\n",
2396
- "18 17199 19.29 15.00 331685 17199 55.60 42.00 956338.00 17199 316.97 316.30 5451584.40 17199 13.87 13.50 238578.50\n",
2397
- "19 2454 22.58 15.00 55415 2454 73.10 45.00 179392.00 2454 861.91 810.55 2115132.70 2454 35.09 31.50 86105.10\n",
2398
- "20 6746 20.34 15.00 137209 6746 58.09 45.00 391857.00 6746 476.70 458.60 3215832.80 6746 21.42 20.60 144477.50\n",
2399
- "21 12824 17.74 15.00 227534 12824 50.28 40.00 644839.00 12824 287.32 286.20 3684543.10 12824 11.32 11.25 145153.50\n",
2400
- "22 663 321.83 600.00 213372 663 1568.77 1470.00 1040094.00 663 284.78 233.80 188811.10 663 11.79 8.30 7819.40\n",
2401
- "23 2587 76.90 20.00 198945 2587 446.03 435.00 1153872.00 2587 312.39 300.30 808150.20 2587 11.53 9.60 29833.90\n",
2402
- "24 12747 17.70 15.00 225627 12747 48.11 40.00 613310.00 12747 233.59 235.50 2977516.40 12747 9.05 8.90 115354.80\n",
2403
- "25 28350 15.18 10.00 430312 28350 35.18 30.00 997463.00 28350 108.84 110.70 3085498.70 28350 5.09 4.40 144210.90\n",
2404
- "26 1263 357.75 600.00 451839 1263 1445.58 1455.00 1825763.00 1263 236.65 190.90 298890.60 1263 12.16 8.30 15360.00\n",
2405
- "27 6377 60.66 20.00 386820 6377 253.93 250.00 1619297.00 6377 168.61 167.80 1075204.10 6377 6.97 5.30 44450.60\n",
2406
- "28 17199 18.97 15.00 326184 17199 52.46 40.00 902260.00 17199 491.62 480.80 8455314.40 17199 22.20 21.60 381852.90\n",
2407
- "29 1298 97.79 20.00 126929 1298 485.28 484.50 629887.00 1298 328.40 308.35 426261.50 1298 11.92 11.10 15470.60\n",
2408
- "30 2921 128.34 20.00 374873 2921 579.56 510.00 1692886.00 2921 207.10 209.50 604934.50 2921 8.62 7.00 25179.80"
2409
  ]
2410
  },
2411
- "execution_count": 23,
2412
  "metadata": {},
2413
  "output_type": "execute_result"
2414
  }
 
222
  },
223
  {
224
  "cell_type": "code",
225
+ "execution_count": null,
226
  "metadata": {
227
  "execution": {
228
  "iopub.execute_input": "2023-12-15T14:49:11.184383Z",
 
232
  "shell.execute_reply.started": "2023-12-15T14:49:11.184259Z"
233
  }
234
  },
235
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
236
  "source": [
237
  "df.shape"
238
  ]
 
283
  },
284
  {
285
  "cell_type": "code",
286
+ "execution_count": 3,
287
  "metadata": {
288
  "execution": {
289
  "iopub.execute_input": "2023-12-15T15:07:27.714708Z",
 
320
  },
321
  {
322
  "cell_type": "code",
323
+ "execution_count": 4,
324
  "metadata": {
325
  "execution": {
326
  "iopub.execute_input": "2023-12-15T15:07:30.113075Z",
 
389
  },
390
  {
391
  "cell_type": "code",
392
+ "execution_count": 5,
393
  "metadata": {
394
  "execution": {
395
  "iopub.execute_input": "2023-12-15T15:07:37.208238Z",
 
446
  },
447
  {
448
  "cell_type": "code",
449
+ "execution_count": 6,
450
  "metadata": {
451
  "execution": {
452
  "iopub.execute_input": "2023-12-15T15:07:49.912389Z",
 
469
  },
470
  {
471
  "cell_type": "code",
472
+ "execution_count": 7,
473
  "metadata": {
474
  "execution": {
475
  "iopub.execute_input": "2023-12-15T15:07:53.138237Z",
 
486
  "False"
487
  ]
488
  },
489
+ "execution_count": 7,
490
  "metadata": {},
491
  "output_type": "execute_result"
492
  }
 
893
  },
894
  {
895
  "cell_type": "code",
896
+ "execution_count": null,
897
  "metadata": {
898
  "execution": {
899
  "iopub.execute_input": "2023-12-15T15:08:08.362107Z",
 
903
  "shell.execute_reply.started": "2023-12-15T15:08:08.362069Z"
904
  }
905
  },
906
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
907
  "source": [
908
  "df.head()"
909
  ]
 
927
  },
928
  {
929
  "cell_type": "code",
930
+ "execution_count": 8,
931
  "metadata": {
932
  "execution": {
933
  "iopub.execute_input": "2023-12-15T15:08:16.614139Z",
 
951
  },
952
  {
953
  "cell_type": "code",
954
+ "execution_count": 9,
955
  "metadata": {
956
  "execution": {
957
  "iopub.execute_input": "2023-12-15T15:08:11.060002Z",
 
977
  },
978
  {
979
  "cell_type": "code",
980
+ "execution_count": 10,
981
  "metadata": {
982
  "execution": {
983
  "iopub.execute_input": "2023-12-15T15:08:20.283538Z",
 
1007
  },
1008
  {
1009
  "cell_type": "code",
1010
+ "execution_count": 11,
1011
  "metadata": {
1012
  "execution": {
1013
  "iopub.execute_input": "2023-12-15T15:33:42.657241Z",
 
1041
  },
1042
  {
1043
  "cell_type": "code",
1044
+ "execution_count": 12,
1045
  "metadata": {
1046
  "execution": {
1047
  "iopub.execute_input": "2023-12-15T15:12:35.466716Z",
 
1164
  },
1165
  {
1166
  "cell_type": "code",
1167
+ "execution_count": 13,
1168
  "metadata": {
1169
  "execution": {
1170
  "iopub.execute_input": "2023-12-15T15:39:26.995591Z",
 
1181
  },
1182
  {
1183
  "cell_type": "code",
1184
+ "execution_count": 14,
1185
  "metadata": {
1186
  "execution": {
1187
  "iopub.execute_input": "2023-12-15T15:39:30.884915Z",
 
1195
  {
1196
  "data": {
1197
  "text/plain": [
1198
+ "array([28, 28, 6, ..., 18, 20, 26], dtype=int32)"
1199
  ]
1200
  },
1201
+ "execution_count": 14,
1202
  "metadata": {},
1203
  "output_type": "execute_result"
1204
  }
 
1210
  },
1211
  {
1212
  "cell_type": "code",
1213
+ "execution_count": 15,
1214
  "metadata": {
1215
  "execution": {
1216
  "iopub.execute_input": "2023-12-15T15:39:33.957001Z",
 
1278
  " <td>30.20</td>\n",
1279
  " <td>3.20</td>\n",
1280
  " <td>c(\"Toss 2 cups berries with sugar.\", \"Let stan...</td>\n",
1281
+ " <td>29</td>\n",
1282
  " </tr>\n",
1283
  " <tr>\n",
1284
  " <th>1</th>\n",
 
1297
  " <td>32.10</td>\n",
1298
  " <td>29.30</td>\n",
1299
  " <td>c(\"Drain the tofu, carefully squeezing out exc...</td>\n",
1300
+ " <td>29</td>\n",
1301
  " </tr>\n",
1302
  " <tr>\n",
1303
  " <th>2</th>\n",
 
1316
  " <td>17.70</td>\n",
1317
  " <td>4.30</td>\n",
1318
  " <td>c(\"Mix everything together and bring to a boil...</td>\n",
1319
+ " <td>7</td>\n",
1320
  " </tr>\n",
1321
  " <tr>\n",
1322
  " <th>3</th>\n",
 
1335
  " <td>24.70</td>\n",
1336
  " <td>4.20</td>\n",
1337
  " <td>c(\"Preheat oven to 350°F.\", \"Make pie crust, u...</td>\n",
1338
+ " <td>11</td>\n",
1339
  " </tr>\n",
1340
  " <tr>\n",
1341
  " <th>4</th>\n",
 
1354
  " <td>0.20</td>\n",
1355
  " <td>0.10</td>\n",
1356
  " <td>c(\"Slice the cucumber in four lengthwise, then...</td>\n",
1357
+ " <td>27</td>\n",
1358
  " </tr>\n",
1359
  " </tbody>\n",
1360
  "</table>\n",
 
1362
  ],
1363
  "text/plain": [
1364
  " Name CookTime PrepTime TotalTime RecipeIngredientParts Calories FatContent SaturatedFatContent CholesterolContent SodiumContent CarbohydrateContent FiberContent SugarContent ProteinContent RecipeInstructions kmeans_cluster\n",
1365
+ "0 Low-Fat Berry Blue Frozen Dessert 1200 45 1485.00 c(\"blueberries\", \"granulated sugar\", \"vanilla ... 170.90 2.50 1.30 8.00 29.80 37.10 3.60 30.20 3.20 c(\"Toss 2 cups berries with sugar.\", \"Let stan... 29\n",
1366
+ "1 Carina's Tofu-Vegetable Kebabs 20 600 1460.00 c(\"extra firm tofu\", \"eggplant\", \"zucchini\", \"... 536.10 24.00 3.80 0.00 1558.60 64.20 17.30 32.10 29.30 c(\"Drain the tofu, carefully squeezing out exc... 29\n",
1367
+ "2 Cabbage Soup 30 20 50.00 c(\"plain tomato juice\", \"cabbage\", \"onion\", \"c... 103.60 0.40 0.10 0.00 959.30 25.10 4.80 17.70 4.30 c(\"Mix everything together and bring to a boil... 7\n",
1368
+ "3 Buttermilk Pie With Gingersnap Crumb Crust 50 30 80.00 c(\"sugar\", \"margarine\", \"egg\", \"flour\", \"salt\"... 228.00 7.10 1.70 24.50 281.80 37.50 0.50 24.70 4.20 c(\"Preheat oven to 350°F.\", \"Make pie crust, u... 11\n",
1369
+ "4 A Jad - Cucumber Pickle 0 25 25.00 c(\"rice vinegar\", \"haeo\") 4.30 0.00 0.00 0.00 0.70 1.10 0.20 0.20 0.10 c(\"Slice the cucumber in four lengthwise, then... 27"
1370
  ]
1371
  },
1372
+ "execution_count": 15,
1373
  "metadata": {},
1374
  "output_type": "execute_result"
1375
  }
 
1399
  },
1400
  {
1401
  "cell_type": "code",
1402
+ "execution_count": null,
1403
  "metadata": {
1404
  "execution": {
1405
  "iopub.execute_input": "2023-12-15T15:39:45.812785Z",
 
1409
  "shell.execute_reply.started": "2023-12-15T15:39:45.812755Z"
1410
  }
1411
  },
1412
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1413
  "source": [
1414
  "df[\"kmeans_cluster\"].value_counts()"
1415
  ]
 
1511
  },
1512
  {
1513
  "cell_type": "code",
1514
+ "execution_count": 16,
1515
  "metadata": {
1516
  "execution": {
1517
  "iopub.execute_input": "2023-12-15T15:03:01.714109Z",
 
1594
  " <tbody>\n",
1595
  " <tr>\n",
1596
  " <th>1</th>\n",
1597
+ " <td>10150</td>\n",
1598
+ " <td>18.91</td>\n",
1599
+ " <td>15.00</td>\n",
1600
+ " <td>191893</td>\n",
1601
+ " <td>10150</td>\n",
1602
+ " <td>53.82</td>\n",
1603
+ " <td>45.00</td>\n",
1604
+ " <td>546294.00</td>\n",
1605
+ " <td>10150</td>\n",
1606
+ " <td>302.73</td>\n",
1607
+ " <td>304.45</td>\n",
1608
+ " <td>3072734.00</td>\n",
1609
+ " <td>10150</td>\n",
1610
+ " <td>11.16</td>\n",
1611
+ " <td>11.30</td>\n",
1612
+ " <td>113300.40</td>\n",
1613
  " </tr>\n",
1614
  " <tr>\n",
1615
  " <th>2</th>\n",
1616
+ " <td>27204</td>\n",
1617
+ " <td>17.06</td>\n",
1618
  " <td>15.00</td>\n",
1619
+ " <td>464067</td>\n",
1620
+ " <td>27204</td>\n",
1621
+ " <td>45.24</td>\n",
1622
+ " <td>35.00</td>\n",
1623
+ " <td>1230656.00</td>\n",
1624
+ " <td>27204</td>\n",
1625
+ " <td>345.01</td>\n",
1626
+ " <td>341.20</td>\n",
1627
+ " <td>9385782.10</td>\n",
1628
+ " <td>27204</td>\n",
1629
+ " <td>17.55</td>\n",
1630
+ " <td>17.50</td>\n",
1631
+ " <td>477328.10</td>\n",
1632
  " </tr>\n",
1633
  " <tr>\n",
1634
  " <th>3</th>\n",
1635
+ " <td>10319</td>\n",
1636
+ " <td>19.85</td>\n",
1637
  " <td>15.00</td>\n",
1638
+ " <td>204813</td>\n",
1639
+ " <td>10319</td>\n",
1640
+ " <td>56.78</td>\n",
1641
  " <td>45.00</td>\n",
1642
+ " <td>585963.00</td>\n",
1643
+ " <td>10319</td>\n",
1644
+ " <td>445.80</td>\n",
1645
+ " <td>435.90</td>\n",
1646
+ " <td>4600185.50</td>\n",
1647
+ " <td>10319</td>\n",
1648
+ " <td>19.69</td>\n",
1649
+ " <td>19.00</td>\n",
1650
+ " <td>203168.40</td>\n",
1651
  " </tr>\n",
1652
  " <tr>\n",
1653
  " <th>4</th>\n",
1654
+ " <td>595</td>\n",
1655
+ " <td>386.09</td>\n",
1656
+ " <td>600.00</td>\n",
1657
+ " <td>229724</td>\n",
1658
+ " <td>595</td>\n",
1659
+ " <td>1582.14</td>\n",
1660
+ " <td>1500.00</td>\n",
1661
+ " <td>941373.00</td>\n",
1662
+ " <td>595</td>\n",
1663
+ " <td>508.17</td>\n",
1664
+ " <td>461.90</td>\n",
1665
+ " <td>302359.00</td>\n",
1666
+ " <td>595</td>\n",
1667
+ " <td>24.40</td>\n",
1668
+ " <td>23.00</td>\n",
1669
+ " <td>14517.20</td>\n",
1670
  " </tr>\n",
1671
  " <tr>\n",
1672
  " <th>5</th>\n",
1673
+ " <td>24230</td>\n",
1674
+ " <td>16.87</td>\n",
1675
  " <td>15.00</td>\n",
1676
+ " <td>408745</td>\n",
1677
+ " <td>24230</td>\n",
1678
+ " <td>40.80</td>\n",
1679
  " <td>30.00</td>\n",
1680
+ " <td>988566.00</td>\n",
1681
+ " <td>24230</td>\n",
1682
+ " <td>127.92</td>\n",
1683
+ " <td>124.90</td>\n",
1684
+ " <td>3099574.30</td>\n",
1685
+ " <td>24230</td>\n",
1686
+ " <td>5.29</td>\n",
1687
+ " <td>4.80</td>\n",
1688
+ " <td>128225.60</td>\n",
1689
  " </tr>\n",
1690
  " <tr>\n",
1691
  " <th>6</th>\n",
1692
+ " <td>30092</td>\n",
1693
+ " <td>17.67</td>\n",
1694
  " <td>15.00</td>\n",
1695
+ " <td>531826</td>\n",
1696
+ " <td>30092</td>\n",
1697
+ " <td>47.17</td>\n",
1698
+ " <td>40.00</td>\n",
1699
+ " <td>1419518.00</td>\n",
1700
+ " <td>30092</td>\n",
1701
+ " <td>306.21</td>\n",
1702
+ " <td>300.60</td>\n",
1703
+ " <td>9214324.70</td>\n",
1704
+ " <td>30092</td>\n",
1705
+ " <td>14.05</td>\n",
1706
+ " <td>13.70</td>\n",
1707
+ " <td>422862.10</td>\n",
1708
  " </tr>\n",
1709
  " <tr>\n",
1710
  " <th>7</th>\n",
1711
+ " <td>17691</td>\n",
1712
+ " <td>17.31</td>\n",
1713
  " <td>15.00</td>\n",
1714
+ " <td>306216</td>\n",
1715
+ " <td>17691</td>\n",
1716
+ " <td>44.53</td>\n",
1717
+ " <td>35.00</td>\n",
1718
+ " <td>787806.00</td>\n",
1719
+ " <td>17691</td>\n",
1720
+ " <td>202.20</td>\n",
1721
+ " <td>198.10</td>\n",
1722
+ " <td>3577067.10</td>\n",
1723
+ " <td>17691</td>\n",
1724
+ " <td>8.02</td>\n",
1725
+ " <td>7.60</td>\n",
1726
+ " <td>141915.20</td>\n",
1727
  " </tr>\n",
1728
  " <tr>\n",
1729
  " <th>8</th>\n",
1730
+ " <td>1470</td>\n",
1731
+ " <td>25.63</td>\n",
1732
  " <td>15.00</td>\n",
1733
+ " <td>37671</td>\n",
1734
+ " <td>1470</td>\n",
1735
+ " <td>74.37</td>\n",
1736
+ " <td>45.00</td>\n",
1737
+ " <td>109330.00</td>\n",
1738
+ " <td>1470</td>\n",
1739
+ " <td>831.67</td>\n",
1740
+ " <td>777.90</td>\n",
1741
+ " <td>1222548.40</td>\n",
1742
+ " <td>1470</td>\n",
1743
+ " <td>36.25</td>\n",
1744
+ " <td>33.60</td>\n",
1745
+ " <td>53286.50</td>\n",
1746
  " </tr>\n",
1747
  " <tr>\n",
1748
  " <th>9</th>\n",
1749
+ " <td>3510</td>\n",
1750
+ " <td>55.09</td>\n",
1751
  " <td>15.00</td>\n",
1752
+ " <td>193381</td>\n",
1753
+ " <td>3510</td>\n",
1754
+ " <td>403.11</td>\n",
1755
+ " <td>380.00</td>\n",
1756
+ " <td>1414928.00</td>\n",
1757
+ " <td>3510</td>\n",
1758
+ " <td>459.85</td>\n",
1759
+ " <td>439.70</td>\n",
1760
+ " <td>1614084.40</td>\n",
1761
+ " <td>3510</td>\n",
1762
+ " <td>20.82</td>\n",
1763
+ " <td>20.30</td>\n",
1764
+ " <td>73062.50</td>\n",
1765
  " </tr>\n",
1766
  " <tr>\n",
1767
  " <th>10</th>\n",
1768
+ " <td>3220</td>\n",
1769
+ " <td>22.47</td>\n",
1770
  " <td>15.00</td>\n",
1771
+ " <td>72364</td>\n",
1772
+ " <td>3220</td>\n",
1773
+ " <td>70.24</td>\n",
1774
+ " <td>45.00</td>\n",
1775
+ " <td>226182.00</td>\n",
1776
+ " <td>3220</td>\n",
1777
+ " <td>855.32</td>\n",
1778
+ " <td>805.35</td>\n",
1779
+ " <td>2754126.40</td>\n",
1780
+ " <td>3220</td>\n",
1781
+ " <td>34.67</td>\n",
1782
+ " <td>31.10</td>\n",
1783
+ " <td>111645.40</td>\n",
1784
  " </tr>\n",
1785
  " <tr>\n",
1786
  " <th>11</th>\n",
1787
+ " <td>10522</td>\n",
1788
+ " <td>16.93</td>\n",
1789
  " <td>15.00</td>\n",
1790
+ " <td>178097</td>\n",
1791
+ " <td>10522</td>\n",
1792
+ " <td>46.67</td>\n",
1793
+ " <td>35.00</td>\n",
1794
+ " <td>491095.00</td>\n",
1795
+ " <td>10522</td>\n",
1796
+ " <td>224.54</td>\n",
1797
+ " <td>233.80</td>\n",
1798
+ " <td>2362630.30</td>\n",
1799
+ " <td>10522</td>\n",
1800
+ " <td>7.50</td>\n",
1801
+ " <td>7.40</td>\n",
1802
+ " <td>78956.80</td>\n",
1803
  " </tr>\n",
1804
  " <tr>\n",
1805
  " <th>12</th>\n",
1806
+ " <td>39868</td>\n",
1807
+ " <td>15.53</td>\n",
1808
+ " <td>10.00</td>\n",
1809
+ " <td>618985</td>\n",
1810
+ " <td>39868</td>\n",
1811
+ " <td>37.67</td>\n",
1812
+ " <td>30.00</td>\n",
1813
+ " <td>1501918.00</td>\n",
1814
+ " <td>39868</td>\n",
1815
+ " <td>200.38</td>\n",
1816
+ " <td>200.00</td>\n",
1817
+ " <td>7988725.40</td>\n",
1818
+ " <td>39868</td>\n",
1819
+ " <td>10.28</td>\n",
1820
+ " <td>10.00</td>\n",
1821
+ " <td>409733.90</td>\n",
1822
  " </tr>\n",
1823
  " <tr>\n",
1824
  " <th>13</th>\n",
1825
+ " <td>696</td>\n",
1826
+ " <td>315.80</td>\n",
1827
+ " <td>600.00</td>\n",
1828
+ " <td>219799</td>\n",
1829
+ " <td>696</td>\n",
1830
+ " <td>1569.19</td>\n",
1831
  " <td>1470.00</td>\n",
1832
+ " <td>1092154.50</td>\n",
1833
+ " <td>696</td>\n",
1834
+ " <td>253.00</td>\n",
1835
+ " <td>217.90</td>\n",
1836
+ " <td>176089.00</td>\n",
1837
+ " <td>696</td>\n",
1838
+ " <td>10.50</td>\n",
1839
+ " <td>7.55</td>\n",
1840
+ " <td>7305.50</td>\n",
1841
  " </tr>\n",
1842
  " <tr>\n",
1843
  " <th>14</th>\n",
1844
+ " <td>5987</td>\n",
1845
+ " <td>20.70</td>\n",
1846
  " <td>15.00</td>\n",
1847
+ " <td>123917</td>\n",
1848
+ " <td>5987</td>\n",
1849
+ " <td>53.96</td>\n",
1850
  " <td>40.00</td>\n",
1851
+ " <td>323084.00</td>\n",
1852
+ " <td>5987</td>\n",
1853
+ " <td>800.43</td>\n",
1854
+ " <td>763.20</td>\n",
1855
+ " <td>4792188.40</td>\n",
1856
+ " <td>5987</td>\n",
1857
+ " <td>35.13</td>\n",
1858
+ " <td>32.30</td>\n",
1859
+ " <td>210313.20</td>\n",
1860
  " </tr>\n",
1861
  " <tr>\n",
1862
  " <th>15</th>\n",
1863
+ " <td>16806</td>\n",
1864
+ " <td>19.38</td>\n",
1865
  " <td>15.00</td>\n",
1866
+ " <td>325719</td>\n",
1867
+ " <td>16806</td>\n",
1868
+ " <td>55.95</td>\n",
1869
  " <td>45.00</td>\n",
1870
+ " <td>940309.00</td>\n",
1871
+ " <td>16806</td>\n",
1872
+ " <td>322.58</td>\n",
1873
+ " <td>320.50</td>\n",
1874
+ " <td>5421314.00</td>\n",
1875
+ " <td>16806</td>\n",
1876
+ " <td>14.12</td>\n",
1877
+ " <td>13.70</td>\n",
1878
+ " <td>237314.40</td>\n",
1879
  " </tr>\n",
1880
  " <tr>\n",
1881
  " <th>16</th>\n",
1882
+ " <td>5304</td>\n",
1883
+ " <td>20.64</td>\n",
1884
  " <td>15.00</td>\n",
1885
+ " <td>109466</td>\n",
1886
+ " <td>5304</td>\n",
1887
+ " <td>61.24</td>\n",
1888
+ " <td>45.00</td>\n",
1889
+ " <td>324843.00</td>\n",
1890
+ " <td>5304</td>\n",
1891
+ " <td>530.90</td>\n",
1892
+ " <td>509.05</td>\n",
1893
+ " <td>2815888.00</td>\n",
1894
+ " <td>5304</td>\n",
1895
+ " <td>23.75</td>\n",
1896
+ " <td>22.35</td>\n",
1897
+ " <td>125945.80</td>\n",
1898
  " </tr>\n",
1899
  " <tr>\n",
1900
  " <th>17</th>\n",
1901
+ " <td>30773</td>\n",
1902
+ " <td>15.46</td>\n",
1903
  " <td>10.00</td>\n",
1904
+ " <td>475666</td>\n",
1905
+ " <td>30773</td>\n",
1906
+ " <td>35.91</td>\n",
1907
+ " <td>30.00</td>\n",
1908
+ " <td>1105185.00</td>\n",
1909
+ " <td>30773</td>\n",
1910
+ " <td>112.53</td>\n",
1911
+ " <td>111.70</td>\n",
1912
+ " <td>3462883.00</td>\n",
1913
+ " <td>30773</td>\n",
1914
+ " <td>5.10</td>\n",
1915
+ " <td>4.40</td>\n",
1916
+ " <td>156846.50</td>\n",
1917
  " </tr>\n",
1918
  " <tr>\n",
1919
  " <th>18</th>\n",
1920
+ " <td>13966</td>\n",
1921
+ " <td>19.59</td>\n",
1922
  " <td>15.00</td>\n",
1923
+ " <td>273562</td>\n",
1924
+ " <td>13966</td>\n",
1925
+ " <td>55.51</td>\n",
1926
+ " <td>45.00</td>\n",
1927
+ " <td>775184.00</td>\n",
1928
+ " <td>13966</td>\n",
1929
+ " <td>535.79</td>\n",
1930
+ " <td>525.60</td>\n",
1931
+ " <td>7482843.10</td>\n",
1932
+ " <td>13966</td>\n",
1933
+ " <td>23.71</td>\n",
1934
+ " <td>23.00</td>\n",
1935
+ " <td>331071.90</td>\n",
1936
  " </tr>\n",
1937
  " <tr>\n",
1938
  " <th>19</th>\n",
1939
+ " <td>6085</td>\n",
1940
+ " <td>20.09</td>\n",
1941
  " <td>15.00</td>\n",
1942
+ " <td>122219</td>\n",
1943
+ " <td>6085</td>\n",
1944
+ " <td>59.09</td>\n",
1945
+ " <td>50.00</td>\n",
1946
+ " <td>359566.00</td>\n",
1947
+ " <td>6085</td>\n",
1948
+ " <td>431.25</td>\n",
1949
+ " <td>409.20</td>\n",
1950
+ " <td>2624172.00</td>\n",
1951
+ " <td>6085</td>\n",
1952
+ " <td>19.81</td>\n",
1953
+ " <td>19.20</td>\n",
1954
+ " <td>120567.00</td>\n",
1955
  " </tr>\n",
1956
  " <tr>\n",
1957
  " <th>20</th>\n",
1958
+ " <td>19476</td>\n",
1959
+ " <td>18.23</td>\n",
1960
  " <td>15.00</td>\n",
1961
+ " <td>354998</td>\n",
1962
+ " <td>19476</td>\n",
1963
+ " <td>49.63</td>\n",
1964
+ " <td>40.00</td>\n",
1965
+ " <td>966647.00</td>\n",
1966
+ " <td>19476</td>\n",
1967
+ " <td>509.76</td>\n",
1968
+ " <td>497.20</td>\n",
1969
+ " <td>9928174.30</td>\n",
1970
+ " <td>19476</td>\n",
1971
+ " <td>24.66</td>\n",
1972
+ " <td>23.90</td>\n",
1973
+ " <td>480274.20</td>\n",
1974
  " </tr>\n",
1975
  " <tr>\n",
1976
  " <th>21</th>\n",
1977
+ " <td>14600</td>\n",
1978
+ " <td>17.77</td>\n",
1979
  " <td>15.00</td>\n",
1980
+ " <td>259437</td>\n",
1981
+ " <td>14600</td>\n",
1982
+ " <td>48.08</td>\n",
1983
  " <td>40.00</td>\n",
1984
+ " <td>702018.00</td>\n",
1985
+ " <td>14600</td>\n",
1986
+ " <td>231.30</td>\n",
1987
+ " <td>233.10</td>\n",
1988
+ " <td>3377004.30</td>\n",
1989
+ " <td>14600</td>\n",
1990
+ " <td>9.20</td>\n",
1991
+ " <td>9.10</td>\n",
1992
+ " <td>134388.50</td>\n",
1993
  " </tr>\n",
1994
  " <tr>\n",
1995
  " <th>22</th>\n",
1996
+ " <td>20822</td>\n",
1997
+ " <td>16.82</td>\n",
1998
+ " <td>15.00</td>\n",
1999
+ " <td>350273</td>\n",
2000
+ " <td>20822</td>\n",
2001
+ " <td>41.73</td>\n",
2002
+ " <td>30.00</td>\n",
2003
+ " <td>868924.00</td>\n",
2004
+ " <td>20822</td>\n",
2005
+ " <td>163.19</td>\n",
2006
+ " <td>159.20</td>\n",
2007
+ " <td>3397983.00</td>\n",
2008
+ " <td>20822</td>\n",
2009
+ " <td>6.55</td>\n",
2010
+ " <td>6.20</td>\n",
2011
+ " <td>136466.70</td>\n",
2012
  " </tr>\n",
2013
  " <tr>\n",
2014
  " <th>23</th>\n",
2015
+ " <td>1400</td>\n",
2016
+ " <td>318.49</td>\n",
2017
+ " <td>600.00</td>\n",
2018
+ " <td>445884</td>\n",
2019
+ " <td>1400</td>\n",
2020
+ " <td>1608.64</td>\n",
2021
+ " <td>1500.00</td>\n",
2022
+ " <td>2252098.50</td>\n",
2023
+ " <td>1400</td>\n",
2024
+ " <td>120.29</td>\n",
2025
+ " <td>108.35</td>\n",
2026
+ " <td>168410.10</td>\n",
2027
+ " <td>1400</td>\n",
2028
+ " <td>5.57</td>\n",
2029
+ " <td>3.50</td>\n",
2030
+ " <td>7792.60</td>\n",
2031
  " </tr>\n",
2032
  " <tr>\n",
2033
  " <th>24</th>\n",
2034
+ " <td>6427</td>\n",
2035
+ " <td>60.41</td>\n",
2036
+ " <td>20.00</td>\n",
2037
+ " <td>388270</td>\n",
2038
+ " <td>6427</td>\n",
2039
+ " <td>252.82</td>\n",
2040
+ " <td>250.00</td>\n",
2041
+ " <td>1624854.00</td>\n",
2042
+ " <td>6427</td>\n",
2043
+ " <td>167.79</td>\n",
2044
+ " <td>167.00</td>\n",
2045
+ " <td>1078381.30</td>\n",
2046
+ " <td>6427</td>\n",
2047
+ " <td>6.91</td>\n",
2048
+ " <td>5.30</td>\n",
2049
+ " <td>44428.80</td>\n",
2050
  " </tr>\n",
2051
  " <tr>\n",
2052
  " <th>25</th>\n",
2053
+ " <td>8768</td>\n",
2054
+ " <td>20.49</td>\n",
2055
+ " <td>15.00</td>\n",
2056
+ " <td>179683</td>\n",
2057
+ " <td>8768</td>\n",
2058
+ " <td>63.16</td>\n",
2059
+ " <td>50.00</td>\n",
2060
+ " <td>553766.00</td>\n",
2061
+ " <td>8768</td>\n",
2062
+ " <td>365.91</td>\n",
2063
+ " <td>365.45</td>\n",
2064
+ " <td>3208261.10</td>\n",
2065
+ " <td>8768</td>\n",
2066
+ " <td>13.94</td>\n",
2067
+ " <td>13.90</td>\n",
2068
+ " <td>122190.50</td>\n",
2069
  " </tr>\n",
2070
  " <tr>\n",
2071
  " <th>26</th>\n",
2072
+ " <td>2924</td>\n",
2073
+ " <td>130.59</td>\n",
2074
+ " <td>20.00</td>\n",
2075
+ " <td>381848</td>\n",
2076
+ " <td>2924</td>\n",
2077
+ " <td>583.80</td>\n",
2078
+ " <td>515.00</td>\n",
2079
+ " <td>1707044.00</td>\n",
2080
+ " <td>2924</td>\n",
2081
+ " <td>206.56</td>\n",
2082
+ " <td>208.45</td>\n",
2083
+ " <td>603978.10</td>\n",
2084
+ " <td>2924</td>\n",
2085
+ " <td>8.59</td>\n",
2086
+ " <td>7.00</td>\n",
2087
+ " <td>25118.10</td>\n",
2088
  " </tr>\n",
2089
  " <tr>\n",
2090
  " <th>27</th>\n",
2091
+ " <td>38481</td>\n",
2092
+ " <td>13.79</td>\n",
2093
+ " <td>10.00</td>\n",
2094
+ " <td>530584</td>\n",
2095
+ " <td>38481</td>\n",
2096
+ " <td>29.59</td>\n",
2097
  " <td>20.00</td>\n",
2098
+ " <td>1138564.00</td>\n",
2099
+ " <td>38481</td>\n",
2100
+ " <td>62.92</td>\n",
2101
+ " <td>63.70</td>\n",
2102
+ " <td>2421143.60</td>\n",
2103
+ " <td>38481</td>\n",
2104
+ " <td>3.33</td>\n",
2105
+ " <td>2.50</td>\n",
2106
+ " <td>128307.00</td>\n",
 
 
 
 
2107
  " </tr>\n",
2108
  " <tr>\n",
2109
  " <th>28</th>\n",
2110
+ " <td>1266</td>\n",
2111
+ " <td>98.47</td>\n",
2112
+ " <td>20.00</td>\n",
2113
+ " <td>124664</td>\n",
2114
+ " <td>1266</td>\n",
2115
+ " <td>491.70</td>\n",
2116
+ " <td>485.00</td>\n",
2117
+ " <td>622495.00</td>\n",
2118
+ " <td>1266</td>\n",
2119
+ " <td>325.24</td>\n",
2120
+ " <td>304.25</td>\n",
2121
+ " <td>411751.80</td>\n",
2122
+ " <td>1266</td>\n",
2123
+ " <td>11.76</td>\n",
2124
+ " <td>10.90</td>\n",
2125
+ " <td>14883.00</td>\n",
2126
  " </tr>\n",
2127
  " <tr>\n",
2128
  " <th>29</th>\n",
2129
+ " <td>433</td>\n",
2130
+ " <td>307.81</td>\n",
2131
+ " <td>180.00</td>\n",
2132
+ " <td>133280</td>\n",
2133
+ " <td>433</td>\n",
2134
+ " <td>1582.24</td>\n",
2135
+ " <td>1470.00</td>\n",
2136
+ " <td>685111.00</td>\n",
2137
+ " <td>433</td>\n",
2138
+ " <td>353.34</td>\n",
2139
+ " <td>284.00</td>\n",
2140
+ " <td>152997.10</td>\n",
2141
+ " <td>433</td>\n",
2142
+ " <td>11.26</td>\n",
2143
+ " <td>8.70</td>\n",
2144
+ " <td>4873.90</td>\n",
2145
  " </tr>\n",
2146
  " <tr>\n",
2147
  " <th>30</th>\n",
2148
+ " <td>2618</td>\n",
2149
+ " <td>76.45</td>\n",
2150
  " <td>20.00</td>\n",
2151
+ " <td>200150</td>\n",
2152
+ " <td>2618</td>\n",
2153
+ " <td>445.31</td>\n",
2154
+ " <td>435.00</td>\n",
2155
+ " <td>1165834.00</td>\n",
2156
+ " <td>2618</td>\n",
2157
+ " <td>311.42</td>\n",
2158
+ " <td>297.60</td>\n",
2159
+ " <td>815285.70</td>\n",
2160
+ " <td>2618</td>\n",
2161
+ " <td>11.51</td>\n",
2162
+ " <td>9.60</td>\n",
2163
+ " <td>30127.40</td>\n",
2164
  " </tr>\n",
2165
  " </tbody>\n",
2166
  "</table>\n",
2167
  "</div>"
2168
  ],
2169
  "text/plain": [
2170
+ " PrepTime TotalTime Calories FatContent \n",
2171
+ " count mean median sum count mean median sum count mean median sum count mean median sum\n",
2172
+ "kmeans_cluster \n",
2173
+ "1 10150 18.91 15.00 191893 10150 53.82 45.00 546294.00 10150 302.73 304.45 3072734.00 10150 11.16 11.30 113300.40\n",
2174
+ "2 27204 17.06 15.00 464067 27204 45.24 35.00 1230656.00 27204 345.01 341.20 9385782.10 27204 17.55 17.50 477328.10\n",
2175
+ "3 10319 19.85 15.00 204813 10319 56.78 45.00 585963.00 10319 445.80 435.90 4600185.50 10319 19.69 19.00 203168.40\n",
2176
+ "4 595 386.09 600.00 229724 595 1582.14 1500.00 941373.00 595 508.17 461.90 302359.00 595 24.40 23.00 14517.20\n",
2177
+ "5 24230 16.87 15.00 408745 24230 40.80 30.00 988566.00 24230 127.92 124.90 3099574.30 24230 5.29 4.80 128225.60\n",
2178
+ "6 30092 17.67 15.00 531826 30092 47.17 40.00 1419518.00 30092 306.21 300.60 9214324.70 30092 14.05 13.70 422862.10\n",
2179
+ "7 17691 17.31 15.00 306216 17691 44.53 35.00 787806.00 17691 202.20 198.10 3577067.10 17691 8.02 7.60 141915.20\n",
2180
+ "8 1470 25.63 15.00 37671 1470 74.37 45.00 109330.00 1470 831.67 777.90 1222548.40 1470 36.25 33.60 53286.50\n",
2181
+ "9 3510 55.09 15.00 193381 3510 403.11 380.00 1414928.00 3510 459.85 439.70 1614084.40 3510 20.82 20.30 73062.50\n",
2182
+ "10 3220 22.47 15.00 72364 3220 70.24 45.00 226182.00 3220 855.32 805.35 2754126.40 3220 34.67 31.10 111645.40\n",
2183
+ "11 10522 16.93 15.00 178097 10522 46.67 35.00 491095.00 10522 224.54 233.80 2362630.30 10522 7.50 7.40 78956.80\n",
2184
+ "12 39868 15.53 10.00 618985 39868 37.67 30.00 1501918.00 39868 200.38 200.00 7988725.40 39868 10.28 10.00 409733.90\n",
2185
+ "13 696 315.80 600.00 219799 696 1569.19 1470.00 1092154.50 696 253.00 217.90 176089.00 696 10.50 7.55 7305.50\n",
2186
+ "14 5987 20.70 15.00 123917 5987 53.96 40.00 323084.00 5987 800.43 763.20 4792188.40 5987 35.13 32.30 210313.20\n",
2187
+ "15 16806 19.38 15.00 325719 16806 55.95 45.00 940309.00 16806 322.58 320.50 5421314.00 16806 14.12 13.70 237314.40\n",
2188
+ "16 5304 20.64 15.00 109466 5304 61.24 45.00 324843.00 5304 530.90 509.05 2815888.00 5304 23.75 22.35 125945.80\n",
2189
+ "17 30773 15.46 10.00 475666 30773 35.91 30.00 1105185.00 30773 112.53 111.70 3462883.00 30773 5.10 4.40 156846.50\n",
2190
+ "18 13966 19.59 15.00 273562 13966 55.51 45.00 775184.00 13966 535.79 525.60 7482843.10 13966 23.71 23.00 331071.90\n",
2191
+ "19 6085 20.09 15.00 122219 6085 59.09 50.00 359566.00 6085 431.25 409.20 2624172.00 6085 19.81 19.20 120567.00\n",
2192
+ "20 19476 18.23 15.00 354998 19476 49.63 40.00 966647.00 19476 509.76 497.20 9928174.30 19476 24.66 23.90 480274.20\n",
2193
+ "21 14600 17.77 15.00 259437 14600 48.08 40.00 702018.00 14600 231.30 233.10 3377004.30 14600 9.20 9.10 134388.50\n",
2194
+ "22 20822 16.82 15.00 350273 20822 41.73 30.00 868924.00 20822 163.19 159.20 3397983.00 20822 6.55 6.20 136466.70\n",
2195
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2196
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2197
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2198
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2199
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2200
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2201
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2202
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benchmark/pandas_12/pandas_12_reproduced.ipynb CHANGED
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1059
- "0 38.00 Low-Fat Berry Blue Frozen Dessert 1200 45 1485.00 c(\"blueberries\", \"granulated sugar\", \"vanilla ... 170.90 2.50 1.30 8.00 29.80 37.10 3.60 30.20 3.20 c(\"Toss 2 cups berries with sugar.\", \"Let stan...\n",
1060
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1061
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1062
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1063
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1069
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1073
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1480
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1483
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1484
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1486
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1499
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1500
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1501
  " <td>c(\"Preheat oven to 350°F.\", \"Make pie crust, u...</td>\n",
1502
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1503
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1518
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1521
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1522
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1524
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@@ -1528,9 +1364,9 @@
1528
  " Name CookTime PrepTime TotalTime RecipeIngredientParts Calories FatContent SaturatedFatContent CholesterolContent SodiumContent CarbohydrateContent FiberContent SugarContent ProteinContent RecipeInstructions kmeans_cluster\n",
1529
  "0 Low-Fat Berry Blue Frozen Dessert 1200 45 1485.00 c(\"blueberries\", \"granulated sugar\", \"vanilla ... 170.90 2.50 1.30 8.00 29.80 37.10 3.60 30.20 3.20 c(\"Toss 2 cups berries with sugar.\", \"Let stan... 13\n",
1530
  "1 Carina's Tofu-Vegetable Kebabs 20 600 1460.00 c(\"extra firm tofu\", \"eggplant\", \"zucchini\", \"... 536.10 24.00 3.80 0.00 1558.60 64.20 17.30 32.10 29.30 c(\"Drain the tofu, carefully squeezing out exc... 13\n",
1531
- "2 Cabbage Soup 30 20 50.00 c(\"plain tomato juice\", \"cabbage\", \"onion\", \"c... 103.60 0.40 0.10 0.00 959.30 25.10 4.80 17.70 4.30 c(\"Mix everything together and bring to a boil... 9\n",
1532
- "3 Buttermilk Pie With Gingersnap Crumb Crust 50 30 80.00 c(\"sugar\", \"margarine\", \"egg\", \"flour\", \"salt\"... 228.00 7.10 1.70 24.50 281.80 37.50 0.50 24.70 4.20 c(\"Preheat oven to 350°F.\", \"Make pie crust, u... 21\n",
1533
- "4 A Jad - Cucumber Pickle 0 25 25.00 c(\"rice vinegar\", \"haeo\") 4.30 0.00 0.00 0.00 0.70 1.10 0.20 0.20 0.10 c(\"Slice the cucumber in four lengthwise, then... 17"
1534
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1536
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1591
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  "source": [
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  "df[\"kmeans_cluster\"].value_counts()"
1621
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@@ -1717,7 +1511,7 @@
1717
  },
1718
  {
1719
  "cell_type": "code",
1720
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1721
  "metadata": {
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@@ -1735,7 +1529,7 @@
1735
  "traceback": [
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  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
1738
- "\u001b[0;32m<ipython-input-19-a9d85aff27dd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m df.groupby('kmeans_cluster').agg({1: ['count','mean', 'median', 'sum'],\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'median'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sum'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'median'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sum'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 4: ['count','mean','median', 'sum']})\n",
1739
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1444\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGroupByApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1445\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1446\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1447\u001b[0m \u001b[0;31m# GH #52849\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1740
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36magg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;31m# we require a list, but not a 'str'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1741
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36magg_dict_like\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[0mResult\u001b[0m \u001b[0mof\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 405\u001b[0m \"\"\"\n\u001b[0;32m--> 406\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg_or_apply_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"agg\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 408\u001b[0m def compute_dict_like(\n",
 
222
  },
223
  {
224
  "cell_type": "code",
225
+ "execution_count": null,
226
  "metadata": {
227
  "execution": {
228
  "iopub.execute_input": "2023-12-15T14:49:11.184383Z",
 
232
  "shell.execute_reply.started": "2023-12-15T14:49:11.184259Z"
233
  }
234
  },
235
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
236
  "source": [
237
  "df.shape"
238
  ]
 
283
  },
284
  {
285
  "cell_type": "code",
286
+ "execution_count": 3,
287
  "metadata": {
288
  "execution": {
289
  "iopub.execute_input": "2023-12-15T15:07:27.714708Z",
 
320
  },
321
  {
322
  "cell_type": "code",
323
+ "execution_count": 6,
324
  "metadata": {
325
  "execution": {
326
  "iopub.execute_input": "2023-12-15T15:07:30.113075Z",
 
389
  },
390
  {
391
  "cell_type": "code",
392
+ "execution_count": 7,
393
  "metadata": {
394
  "execution": {
395
  "iopub.execute_input": "2023-12-15T15:07:37.208238Z",
 
446
  },
447
  {
448
  "cell_type": "code",
449
+ "execution_count": 8,
450
  "metadata": {
451
  "execution": {
452
  "iopub.execute_input": "2023-12-15T15:07:49.912389Z",
 
469
  },
470
  {
471
  "cell_type": "code",
472
+ "execution_count": 9,
473
  "metadata": {
474
  "execution": {
475
  "iopub.execute_input": "2023-12-15T15:07:53.138237Z",
 
486
  "False"
487
  ]
488
  },
489
+ "execution_count": 9,
490
  "metadata": {},
491
  "output_type": "execute_result"
492
  }
 
893
  },
894
  {
895
  "cell_type": "code",
896
+ "execution_count": null,
897
  "metadata": {
898
  "execution": {
899
  "iopub.execute_input": "2023-12-15T15:08:08.362107Z",
 
903
  "shell.execute_reply.started": "2023-12-15T15:08:08.362069Z"
904
  }
905
  },
906
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
907
  "source": [
908
  "df.head()"
909
  ]
 
1195
  {
1196
  "data": {
1197
  "text/plain": [
1198
+ "array([12, 12, 6, ..., 13, 15, 23], dtype=int32)"
1199
  ]
1200
  },
1201
  "execution_count": 16,
 
1316
  " <td>17.70</td>\n",
1317
  " <td>4.30</td>\n",
1318
  " <td>c(\"Mix everything together and bring to a boil...</td>\n",
1319
+ " <td>7</td>\n",
1320
  " </tr>\n",
1321
  " <tr>\n",
1322
  " <th>3</th>\n",
 
1335
  " <td>24.70</td>\n",
1336
  " <td>4.20</td>\n",
1337
  " <td>c(\"Preheat oven to 350°F.\", \"Make pie crust, u...</td>\n",
1338
+ " <td>16</td>\n",
1339
  " </tr>\n",
1340
  " <tr>\n",
1341
  " <th>4</th>\n",
 
1354
  " <td>0.20</td>\n",
1355
  " <td>0.10</td>\n",
1356
  " <td>c(\"Slice the cucumber in four lengthwise, then...</td>\n",
1357
+ " <td>24</td>\n",
1358
  " </tr>\n",
1359
  " </tbody>\n",
1360
  "</table>\n",
 
1364
  " Name CookTime PrepTime TotalTime RecipeIngredientParts Calories FatContent SaturatedFatContent CholesterolContent SodiumContent CarbohydrateContent FiberContent SugarContent ProteinContent RecipeInstructions kmeans_cluster\n",
1365
  "0 Low-Fat Berry Blue Frozen Dessert 1200 45 1485.00 c(\"blueberries\", \"granulated sugar\", \"vanilla ... 170.90 2.50 1.30 8.00 29.80 37.10 3.60 30.20 3.20 c(\"Toss 2 cups berries with sugar.\", \"Let stan... 13\n",
1366
  "1 Carina's Tofu-Vegetable Kebabs 20 600 1460.00 c(\"extra firm tofu\", \"eggplant\", \"zucchini\", \"... 536.10 24.00 3.80 0.00 1558.60 64.20 17.30 32.10 29.30 c(\"Drain the tofu, carefully squeezing out exc... 13\n",
1367
+ "2 Cabbage Soup 30 20 50.00 c(\"plain tomato juice\", \"cabbage\", \"onion\", \"c... 103.60 0.40 0.10 0.00 959.30 25.10 4.80 17.70 4.30 c(\"Mix everything together and bring to a boil... 7\n",
1368
+ "3 Buttermilk Pie With Gingersnap Crumb Crust 50 30 80.00 c(\"sugar\", \"margarine\", \"egg\", \"flour\", \"salt\"... 228.00 7.10 1.70 24.50 281.80 37.50 0.50 24.70 4.20 c(\"Preheat oven to 350°F.\", \"Make pie crust, u... 16\n",
1369
+ "4 A Jad - Cucumber Pickle 0 25 25.00 c(\"rice vinegar\", \"haeo\") 4.30 0.00 0.00 0.00 0.70 1.10 0.20 0.20 0.10 c(\"Slice the cucumber in four lengthwise, then... 24"
1370
  ]
1371
  },
1372
  "execution_count": 17,
 
1399
  },
1400
  {
1401
  "cell_type": "code",
1402
+ "execution_count": null,
1403
  "metadata": {
1404
  "execution": {
1405
  "iopub.execute_input": "2023-12-15T15:39:45.812785Z",
 
1409
  "shell.execute_reply.started": "2023-12-15T15:39:45.812755Z"
1410
  }
1411
  },
1412
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1413
  "source": [
1414
  "df[\"kmeans_cluster\"].value_counts()"
1415
  ]
 
1511
  },
1512
  {
1513
  "cell_type": "code",
1514
+ "execution_count": 18,
1515
  "metadata": {
1516
  "execution": {
1517
  "iopub.execute_input": "2023-12-15T15:03:01.714109Z",
 
1529
  "traceback": [
1530
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1531
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
1532
+ "\u001b[0;32m<ipython-input-18-a9d85aff27dd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m df.groupby('kmeans_cluster').agg({1: ['count','mean', 'median', 'sum'],\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'median'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sum'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'median'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sum'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 4: ['count','mean','median', 'sum']})\n",
1533
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1444\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGroupByApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1445\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1446\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1447\u001b[0m \u001b[0;31m# GH #52849\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1534
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36magg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;31m# we require a list, but not a 'str'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1535
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36magg_dict_like\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[0mResult\u001b[0m \u001b[0mof\u001b[0m \u001b[0maggregation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 405\u001b[0m \"\"\"\n\u001b[0;32m--> 406\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg_or_apply_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"agg\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 408\u001b[0m def compute_dict_like(\n",
benchmark/pandas_13/pandas_13_fixed.ipynb CHANGED
@@ -232,7 +232,7 @@
232
  },
233
  {
234
  "cell_type": "code",
235
- "execution_count": 3,
236
  "metadata": {
237
  "execution": {
238
  "iopub.execute_input": "2023-08-11T09:05:54.311108Z",
@@ -242,30 +242,7 @@
242
  "shell.execute_reply.started": "2023-08-11T09:05:54.311087Z"
243
  }
244
  },
245
- "outputs": [
246
- {
247
- "name": "stdout",
248
- "output_type": "stream",
249
- "text": [
250
- "<class 'pandas.core.frame.DataFrame'>\n",
251
- "RangeIndex: 301 entries, 0 to 300\n",
252
- "Data columns (total 9 columns):\n",
253
- " # Column Non-Null Count Dtype \n",
254
- "--- ------ -------------- ----- \n",
255
- " 0 Car_Name 301 non-null object \n",
256
- " 1 Year 301 non-null int64 \n",
257
- " 2 Selling_Price 301 non-null float64\n",
258
- " 3 Present_Price 301 non-null float64\n",
259
- " 4 Driven_kms 301 non-null int64 \n",
260
- " 5 Fuel_Type 301 non-null object \n",
261
- " 6 Selling_type 301 non-null object \n",
262
- " 7 Transmission 301 non-null object \n",
263
- " 8 Owner 301 non-null int64 \n",
264
- "dtypes: float64(2), int64(3), object(4)\n",
265
- "memory usage: 21.3+ KB\n"
266
- ]
267
- }
268
- ],
269
  "source": [
270
  "# 观察数据类型\n",
271
  "df.info()"
@@ -273,7 +250,7 @@
273
  },
274
  {
275
  "cell_type": "code",
276
- "execution_count": 4,
277
  "metadata": {
278
  "execution": {
279
  "iopub.execute_input": "2023-08-11T09:05:54.326848Z",
@@ -283,27 +260,7 @@
283
  "shell.execute_reply.started": "2023-08-11T09:05:54.326824Z"
284
  }
285
  },
286
- "outputs": [
287
- {
288
- "data": {
289
- "text/plain": [
290
- "Car_Name 0\n",
291
- "Year 0\n",
292
- "Selling_Price 0\n",
293
- "Present_Price 0\n",
294
- "Driven_kms 0\n",
295
- "Fuel_Type 0\n",
296
- "Selling_type 0\n",
297
- "Transmission 0\n",
298
- "Owner 0\n",
299
- "dtype: int64"
300
- ]
301
- },
302
- "execution_count": 4,
303
- "metadata": {},
304
- "output_type": "execute_result"
305
- }
306
- ],
307
  "source": [
308
  "# 检查缺失值\n",
309
  "df.isnull().sum()"
@@ -311,7 +268,7 @@
311
  },
312
  {
313
  "cell_type": "code",
314
- "execution_count": 5,
315
  "metadata": {
316
  "execution": {
317
  "iopub.execute_input": "2023-08-11T09:05:54.338268Z",
@@ -321,209 +278,7 @@
321
  "shell.execute_reply.started": "2023-08-11T09:05:54.338225Z"
322
  }
323
  },
324
- "outputs": [
325
- {
326
- "data": {
327
- "text/html": [
328
- "<div>\n",
329
- "<style scoped>\n",
330
- " .dataframe tbody tr th:only-of-type {\n",
331
- " vertical-align: middle;\n",
332
- " }\n",
333
- "\n",
334
- " .dataframe tbody tr th {\n",
335
- " vertical-align: top;\n",
336
- " }\n",
337
- "\n",
338
- " .dataframe thead th {\n",
339
- " text-align: right;\n",
340
- " }\n",
341
- "</style>\n",
342
- "<table border=\"1\" class=\"dataframe\">\n",
343
- " <thead>\n",
344
- " <tr style=\"text-align: right;\">\n",
345
- " <th></th>\n",
346
- " <th>Car_Name</th>\n",
347
- " <th>Year</th>\n",
348
- " <th>Selling_Price</th>\n",
349
- " <th>Present_Price</th>\n",
350
- " <th>Driven_kms</th>\n",
351
- " <th>Fuel_Type</th>\n",
352
- " <th>Selling_type</th>\n",
353
- " <th>Transmission</th>\n",
354
- " <th>Owner</th>\n",
355
- " </tr>\n",
356
- " </thead>\n",
357
- " <tbody>\n",
358
- " <tr>\n",
359
- " <th>count</th>\n",
360
- " <td>301</td>\n",
361
- " <td>301.000000</td>\n",
362
- " <td>301.000000</td>\n",
363
- " <td>301.000000</td>\n",
364
- " <td>301.000000</td>\n",
365
- " <td>301</td>\n",
366
- " <td>301</td>\n",
367
- " <td>301</td>\n",
368
- " <td>301.000000</td>\n",
369
- " </tr>\n",
370
- " <tr>\n",
371
- " <th>unique</th>\n",
372
- " <td>98</td>\n",
373
- " <td>NaN</td>\n",
374
- " <td>NaN</td>\n",
375
- " <td>NaN</td>\n",
376
- " <td>NaN</td>\n",
377
- " <td>3</td>\n",
378
- " <td>2</td>\n",
379
- " <td>2</td>\n",
380
- " <td>NaN</td>\n",
381
- " </tr>\n",
382
- " <tr>\n",
383
- " <th>top</th>\n",
384
- " <td>city</td>\n",
385
- " <td>NaN</td>\n",
386
- " <td>NaN</td>\n",
387
- " <td>NaN</td>\n",
388
- " <td>NaN</td>\n",
389
- " <td>Petrol</td>\n",
390
- " <td>Dealer</td>\n",
391
- " <td>Manual</td>\n",
392
- " <td>NaN</td>\n",
393
- " </tr>\n",
394
- " <tr>\n",
395
- " <th>freq</th>\n",
396
- " <td>26</td>\n",
397
- " <td>NaN</td>\n",
398
- " <td>NaN</td>\n",
399
- " <td>NaN</td>\n",
400
- " <td>NaN</td>\n",
401
- " <td>239</td>\n",
402
- " <td>195</td>\n",
403
- " <td>261</td>\n",
404
- " <td>NaN</td>\n",
405
- " </tr>\n",
406
- " <tr>\n",
407
- " <th>mean</th>\n",
408
- " <td>NaN</td>\n",
409
- " <td>2013.627907</td>\n",
410
- " <td>4.661296</td>\n",
411
- " <td>7.628472</td>\n",
412
- " <td>36947.205980</td>\n",
413
- " <td>NaN</td>\n",
414
- " <td>NaN</td>\n",
415
- " <td>NaN</td>\n",
416
- " <td>0.043189</td>\n",
417
- " </tr>\n",
418
- " <tr>\n",
419
- " <th>std</th>\n",
420
- " <td>NaN</td>\n",
421
- " <td>2.891554</td>\n",
422
- " <td>5.082812</td>\n",
423
- " <td>8.642584</td>\n",
424
- " <td>38886.883882</td>\n",
425
- " <td>NaN</td>\n",
426
- " <td>NaN</td>\n",
427
- " <td>NaN</td>\n",
428
- " <td>0.247915</td>\n",
429
- " </tr>\n",
430
- " <tr>\n",
431
- " <th>min</th>\n",
432
- " <td>NaN</td>\n",
433
- " <td>2003.000000</td>\n",
434
- " <td>0.100000</td>\n",
435
- " <td>0.320000</td>\n",
436
- " <td>500.000000</td>\n",
437
- " <td>NaN</td>\n",
438
- " <td>NaN</td>\n",
439
- " <td>NaN</td>\n",
440
- " <td>0.000000</td>\n",
441
- " </tr>\n",
442
- " <tr>\n",
443
- " <th>25%</th>\n",
444
- " <td>NaN</td>\n",
445
- " <td>2012.000000</td>\n",
446
- " <td>0.900000</td>\n",
447
- " <td>1.200000</td>\n",
448
- " <td>15000.000000</td>\n",
449
- " <td>NaN</td>\n",
450
- " <td>NaN</td>\n",
451
- " <td>NaN</td>\n",
452
- " <td>0.000000</td>\n",
453
- " </tr>\n",
454
- " <tr>\n",
455
- " <th>50%</th>\n",
456
- " <td>NaN</td>\n",
457
- " <td>2014.000000</td>\n",
458
- " <td>3.600000</td>\n",
459
- " <td>6.400000</td>\n",
460
- " <td>32000.000000</td>\n",
461
- " <td>NaN</td>\n",
462
- " <td>NaN</td>\n",
463
- " <td>NaN</td>\n",
464
- " <td>0.000000</td>\n",
465
- " </tr>\n",
466
- " <tr>\n",
467
- " <th>75%</th>\n",
468
- " <td>NaN</td>\n",
469
- " <td>2016.000000</td>\n",
470
- " <td>6.000000</td>\n",
471
- " <td>9.900000</td>\n",
472
- " <td>48767.000000</td>\n",
473
- " <td>NaN</td>\n",
474
- " <td>NaN</td>\n",
475
- " <td>NaN</td>\n",
476
- " <td>0.000000</td>\n",
477
- " </tr>\n",
478
- " <tr>\n",
479
- " <th>max</th>\n",
480
- " <td>NaN</td>\n",
481
- " <td>2018.000000</td>\n",
482
- " <td>35.000000</td>\n",
483
- " <td>92.600000</td>\n",
484
- " <td>500000.000000</td>\n",
485
- " <td>NaN</td>\n",
486
- " <td>NaN</td>\n",
487
- " <td>NaN</td>\n",
488
- " <td>3.000000</td>\n",
489
- " </tr>\n",
490
- " </tbody>\n",
491
- "</table>\n",
492
- "</div>"
493
- ],
494
- "text/plain": [
495
- " Car_Name Year Selling_Price Present_Price Driven_kms \\\n",
496
- "count 301 301.000000 301.000000 301.000000 301.000000 \n",
497
- "unique 98 NaN NaN NaN NaN \n",
498
- "top city NaN NaN NaN NaN \n",
499
- "freq 26 NaN NaN NaN NaN \n",
500
- "mean NaN 2013.627907 4.661296 7.628472 36947.205980 \n",
501
- "std NaN 2.891554 5.082812 8.642584 38886.883882 \n",
502
- "min NaN 2003.000000 0.100000 0.320000 500.000000 \n",
503
- "25% NaN 2012.000000 0.900000 1.200000 15000.000000 \n",
504
- "50% NaN 2014.000000 3.600000 6.400000 32000.000000 \n",
505
- "75% NaN 2016.000000 6.000000 9.900000 48767.000000 \n",
506
- "max NaN 2018.000000 35.000000 92.600000 500000.000000 \n",
507
- "\n",
508
- " Fuel_Type Selling_type Transmission Owner \n",
509
- "count 301 301 301 301.000000 \n",
510
- "unique 3 2 2 NaN \n",
511
- "top Petrol Dealer Manual NaN \n",
512
- "freq 239 195 261 NaN \n",
513
- "mean NaN NaN NaN 0.043189 \n",
514
- "std NaN NaN NaN 0.247915 \n",
515
- "min NaN NaN NaN 0.000000 \n",
516
- "25% NaN NaN NaN 0.000000 \n",
517
- "50% NaN NaN NaN 0.000000 \n",
518
- "75% NaN NaN NaN 0.000000 \n",
519
- "max NaN NaN NaN 3.000000 "
520
- ]
521
- },
522
- "execution_count": 5,
523
- "metadata": {},
524
- "output_type": "execute_result"
525
- }
526
- ],
527
  "source": [
528
  "# 观察数据的详细信息\n",
529
  "df.describe(include='all')"
@@ -607,7 +362,7 @@
607
  },
608
  {
609
  "cell_type": "code",
610
- "execution_count": 6,
611
  "metadata": {
612
  "execution": {
613
  "iopub.execute_input": "2023-08-11T09:05:55.855050Z",
@@ -617,18 +372,7 @@
617
  "shell.execute_reply.started": "2023-08-11T09:05:55.855014Z"
618
  }
619
  },
620
- "outputs": [
621
- {
622
- "data": {
623
- "text/plain": [
624
- "2"
625
- ]
626
- },
627
- "execution_count": 6,
628
- "metadata": {},
629
- "output_type": "execute_result"
630
- }
631
- ],
632
  "source": [
633
  "# 检查是否有重复数据\n",
634
  "df.duplicated().sum()"
@@ -636,7 +380,7 @@
636
  },
637
  {
638
  "cell_type": "code",
639
- "execution_count": 7,
640
  "metadata": {
641
  "execution": {
642
  "iopub.execute_input": "2023-08-11T09:05:55.865903Z",
@@ -653,7 +397,7 @@
653
  "0"
654
  ]
655
  },
656
- "execution_count": 7,
657
  "metadata": {},
658
  "output_type": "execute_result"
659
  }
@@ -736,7 +480,7 @@
736
  },
737
  {
738
  "cell_type": "code",
739
- "execution_count": 8,
740
  "metadata": {
741
  "execution": {
742
  "iopub.execute_input": "2023-08-11T09:05:55.914238Z",
@@ -764,7 +508,7 @@
764
  },
765
  {
766
  "cell_type": "code",
767
- "execution_count": 9,
768
  "metadata": {
769
  "execution": {
770
  "iopub.execute_input": "2023-08-11T09:05:55.962811Z",
@@ -800,7 +544,7 @@
800
  },
801
  {
802
  "cell_type": "code",
803
- "execution_count": 10,
804
  "metadata": {
805
  "execution": {
806
  "iopub.execute_input": "2023-08-11T09:05:55.986146Z",
@@ -925,7 +669,7 @@
925
  " 'Present_Price']"
926
  ]
927
  },
928
- "execution_count": 10,
929
  "metadata": {},
930
  "output_type": "execute_result"
931
  }
@@ -951,7 +695,7 @@
951
  },
952
  {
953
  "cell_type": "code",
954
- "execution_count": 11,
955
  "metadata": {
956
  "execution": {
957
  "iopub.execute_input": "2023-08-11T09:05:56.545107Z",
@@ -998,7 +742,7 @@
998
  },
999
  {
1000
  "cell_type": "code",
1001
- "execution_count": 12,
1002
  "metadata": {
1003
  "execution": {
1004
  "iopub.execute_input": "2023-08-11T09:05:56.568558Z",
@@ -1032,7 +776,7 @@
1032
  },
1033
  {
1034
  "cell_type": "code",
1035
- "execution_count": 13,
1036
  "metadata": {
1037
  "execution": {
1038
  "iopub.execute_input": "2023-08-11T09:05:56.586725Z",
@@ -1088,7 +832,7 @@
1088
  },
1089
  {
1090
  "cell_type": "code",
1091
- "execution_count": 14,
1092
  "metadata": {
1093
  "execution": {
1094
  "iopub.execute_input": "2023-08-11T09:13:14.556627Z",
@@ -1107,7 +851,7 @@
1107
  },
1108
  {
1109
  "cell_type": "code",
1110
- "execution_count": 15,
1111
  "metadata": {
1112
  "execution": {
1113
  "iopub.execute_input": "2023-08-11T09:13:14.629117Z",
@@ -1124,7 +868,7 @@
1124
  "0.9740787800578905"
1125
  ]
1126
  },
1127
- "execution_count": 15,
1128
  "metadata": {},
1129
  "output_type": "execute_result"
1130
  }
@@ -1145,7 +889,7 @@
1145
  },
1146
  {
1147
  "cell_type": "code",
1148
- "execution_count": 16,
1149
  "metadata": {
1150
  "execution": {
1151
  "iopub.execute_input": "2023-08-11T09:13:14.719423Z",
@@ -1165,7 +909,7 @@
1165
  },
1166
  {
1167
  "cell_type": "code",
1168
- "execution_count": 17,
1169
  "metadata": {
1170
  "execution": {
1171
  "iopub.execute_input": "2023-08-11T09:13:14.925993Z",
@@ -1215,7 +959,7 @@
1215
  },
1216
  {
1217
  "cell_type": "code",
1218
- "execution_count": 18,
1219
  "metadata": {
1220
  "execution": {
1221
  "iopub.execute_input": "2023-08-11T09:13:15.105155Z",
@@ -1326,7 +1070,7 @@
1326
  },
1327
  {
1328
  "cell_type": "code",
1329
- "execution_count": 29,
1330
  "metadata": {
1331
  "execution": {
1332
  "iopub.execute_input": "2023-08-11T09:13:16.111279Z",
 
232
  },
233
  {
234
  "cell_type": "code",
235
+ "execution_count": null,
236
  "metadata": {
237
  "execution": {
238
  "iopub.execute_input": "2023-08-11T09:05:54.311108Z",
 
242
  "shell.execute_reply.started": "2023-08-11T09:05:54.311087Z"
243
  }
244
  },
245
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
246
  "source": [
247
  "# 观察数据类型\n",
248
  "df.info()"
 
250
  },
251
  {
252
  "cell_type": "code",
253
+ "execution_count": null,
254
  "metadata": {
255
  "execution": {
256
  "iopub.execute_input": "2023-08-11T09:05:54.326848Z",
 
260
  "shell.execute_reply.started": "2023-08-11T09:05:54.326824Z"
261
  }
262
  },
263
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
264
  "source": [
265
  "# 检查缺失值\n",
266
  "df.isnull().sum()"
 
268
  },
269
  {
270
  "cell_type": "code",
271
+ "execution_count": null,
272
  "metadata": {
273
  "execution": {
274
  "iopub.execute_input": "2023-08-11T09:05:54.338268Z",
 
278
  "shell.execute_reply.started": "2023-08-11T09:05:54.338225Z"
279
  }
280
  },
281
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
282
  "source": [
283
  "# 观察数据的详细信息\n",
284
  "df.describe(include='all')"
 
362
  },
363
  {
364
  "cell_type": "code",
365
+ "execution_count": null,
366
  "metadata": {
367
  "execution": {
368
  "iopub.execute_input": "2023-08-11T09:05:55.855050Z",
 
372
  "shell.execute_reply.started": "2023-08-11T09:05:55.855014Z"
373
  }
374
  },
375
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
376
  "source": [
377
  "# 检查是否有重复数据\n",
378
  "df.duplicated().sum()"
 
380
  },
381
  {
382
  "cell_type": "code",
383
+ "execution_count": 3,
384
  "metadata": {
385
  "execution": {
386
  "iopub.execute_input": "2023-08-11T09:05:55.865903Z",
 
397
  "0"
398
  ]
399
  },
400
+ "execution_count": 3,
401
  "metadata": {},
402
  "output_type": "execute_result"
403
  }
 
480
  },
481
  {
482
  "cell_type": "code",
483
+ "execution_count": 4,
484
  "metadata": {
485
  "execution": {
486
  "iopub.execute_input": "2023-08-11T09:05:55.914238Z",
 
508
  },
509
  {
510
  "cell_type": "code",
511
+ "execution_count": 5,
512
  "metadata": {
513
  "execution": {
514
  "iopub.execute_input": "2023-08-11T09:05:55.962811Z",
 
544
  },
545
  {
546
  "cell_type": "code",
547
+ "execution_count": 6,
548
  "metadata": {
549
  "execution": {
550
  "iopub.execute_input": "2023-08-11T09:05:55.986146Z",
 
669
  " 'Present_Price']"
670
  ]
671
  },
672
+ "execution_count": 6,
673
  "metadata": {},
674
  "output_type": "execute_result"
675
  }
 
695
  },
696
  {
697
  "cell_type": "code",
698
+ "execution_count": 7,
699
  "metadata": {
700
  "execution": {
701
  "iopub.execute_input": "2023-08-11T09:05:56.545107Z",
 
742
  },
743
  {
744
  "cell_type": "code",
745
+ "execution_count": 8,
746
  "metadata": {
747
  "execution": {
748
  "iopub.execute_input": "2023-08-11T09:05:56.568558Z",
 
776
  },
777
  {
778
  "cell_type": "code",
779
+ "execution_count": 9,
780
  "metadata": {
781
  "execution": {
782
  "iopub.execute_input": "2023-08-11T09:05:56.586725Z",
 
832
  },
833
  {
834
  "cell_type": "code",
835
+ "execution_count": 10,
836
  "metadata": {
837
  "execution": {
838
  "iopub.execute_input": "2023-08-11T09:13:14.556627Z",
 
851
  },
852
  {
853
  "cell_type": "code",
854
+ "execution_count": 11,
855
  "metadata": {
856
  "execution": {
857
  "iopub.execute_input": "2023-08-11T09:13:14.629117Z",
 
868
  "0.9740787800578905"
869
  ]
870
  },
871
+ "execution_count": 11,
872
  "metadata": {},
873
  "output_type": "execute_result"
874
  }
 
889
  },
890
  {
891
  "cell_type": "code",
892
+ "execution_count": 12,
893
  "metadata": {
894
  "execution": {
895
  "iopub.execute_input": "2023-08-11T09:13:14.719423Z",
 
909
  },
910
  {
911
  "cell_type": "code",
912
+ "execution_count": 13,
913
  "metadata": {
914
  "execution": {
915
  "iopub.execute_input": "2023-08-11T09:13:14.925993Z",
 
959
  },
960
  {
961
  "cell_type": "code",
962
+ "execution_count": 14,
963
  "metadata": {
964
  "execution": {
965
  "iopub.execute_input": "2023-08-11T09:13:15.105155Z",
 
1070
  },
1071
  {
1072
  "cell_type": "code",
1073
+ "execution_count": 15,
1074
  "metadata": {
1075
  "execution": {
1076
  "iopub.execute_input": "2023-08-11T09:13:16.111279Z",
benchmark/pandas_13/pandas_13_reproduced.ipynb CHANGED
@@ -232,7 +232,7 @@
232
  },
233
  {
234
  "cell_type": "code",
235
- "execution_count": 3,
236
  "metadata": {
237
  "execution": {
238
  "iopub.execute_input": "2023-08-11T09:05:54.311108Z",
@@ -242,30 +242,7 @@
242
  "shell.execute_reply.started": "2023-08-11T09:05:54.311087Z"
243
  }
244
  },
245
- "outputs": [
246
- {
247
- "name": "stdout",
248
- "output_type": "stream",
249
- "text": [
250
- "<class 'pandas.core.frame.DataFrame'>\n",
251
- "RangeIndex: 301 entries, 0 to 300\n",
252
- "Data columns (total 9 columns):\n",
253
- " # Column Non-Null Count Dtype \n",
254
- "--- ------ -------------- ----- \n",
255
- " 0 Car_Name 301 non-null object \n",
256
- " 1 Year 301 non-null int64 \n",
257
- " 2 Selling_Price 301 non-null float64\n",
258
- " 3 Present_Price 301 non-null float64\n",
259
- " 4 Driven_kms 301 non-null int64 \n",
260
- " 5 Fuel_Type 301 non-null object \n",
261
- " 6 Selling_type 301 non-null object \n",
262
- " 7 Transmission 301 non-null object \n",
263
- " 8 Owner 301 non-null int64 \n",
264
- "dtypes: float64(2), int64(3), object(4)\n",
265
- "memory usage: 21.3+ KB\n"
266
- ]
267
- }
268
- ],
269
  "source": [
270
  "# 观察数据类型\n",
271
  "df.info()"
@@ -273,7 +250,7 @@
273
  },
274
  {
275
  "cell_type": "code",
276
- "execution_count": 4,
277
  "metadata": {
278
  "execution": {
279
  "iopub.execute_input": "2023-08-11T09:05:54.326848Z",
@@ -283,27 +260,7 @@
283
  "shell.execute_reply.started": "2023-08-11T09:05:54.326824Z"
284
  }
285
  },
286
- "outputs": [
287
- {
288
- "data": {
289
- "text/plain": [
290
- "Car_Name 0\n",
291
- "Year 0\n",
292
- "Selling_Price 0\n",
293
- "Present_Price 0\n",
294
- "Driven_kms 0\n",
295
- "Fuel_Type 0\n",
296
- "Selling_type 0\n",
297
- "Transmission 0\n",
298
- "Owner 0\n",
299
- "dtype: int64"
300
- ]
301
- },
302
- "execution_count": 4,
303
- "metadata": {},
304
- "output_type": "execute_result"
305
- }
306
- ],
307
  "source": [
308
  "# 检查缺失值\n",
309
  "df.isnull().sum()"
@@ -311,7 +268,7 @@
311
  },
312
  {
313
  "cell_type": "code",
314
- "execution_count": 5,
315
  "metadata": {
316
  "execution": {
317
  "iopub.execute_input": "2023-08-11T09:05:54.338268Z",
@@ -321,209 +278,7 @@
321
  "shell.execute_reply.started": "2023-08-11T09:05:54.338225Z"
322
  }
323
  },
324
- "outputs": [
325
- {
326
- "data": {
327
- "text/html": [
328
- "<div>\n",
329
- "<style scoped>\n",
330
- " .dataframe tbody tr th:only-of-type {\n",
331
- " vertical-align: middle;\n",
332
- " }\n",
333
- "\n",
334
- " .dataframe tbody tr th {\n",
335
- " vertical-align: top;\n",
336
- " }\n",
337
- "\n",
338
- " .dataframe thead th {\n",
339
- " text-align: right;\n",
340
- " }\n",
341
- "</style>\n",
342
- "<table border=\"1\" class=\"dataframe\">\n",
343
- " <thead>\n",
344
- " <tr style=\"text-align: right;\">\n",
345
- " <th></th>\n",
346
- " <th>Car_Name</th>\n",
347
- " <th>Year</th>\n",
348
- " <th>Selling_Price</th>\n",
349
- " <th>Present_Price</th>\n",
350
- " <th>Driven_kms</th>\n",
351
- " <th>Fuel_Type</th>\n",
352
- " <th>Selling_type</th>\n",
353
- " <th>Transmission</th>\n",
354
- " <th>Owner</th>\n",
355
- " </tr>\n",
356
- " </thead>\n",
357
- " <tbody>\n",
358
- " <tr>\n",
359
- " <th>count</th>\n",
360
- " <td>301</td>\n",
361
- " <td>301.000000</td>\n",
362
- " <td>301.000000</td>\n",
363
- " <td>301.000000</td>\n",
364
- " <td>301.000000</td>\n",
365
- " <td>301</td>\n",
366
- " <td>301</td>\n",
367
- " <td>301</td>\n",
368
- " <td>301.000000</td>\n",
369
- " </tr>\n",
370
- " <tr>\n",
371
- " <th>unique</th>\n",
372
- " <td>98</td>\n",
373
- " <td>NaN</td>\n",
374
- " <td>NaN</td>\n",
375
- " <td>NaN</td>\n",
376
- " <td>NaN</td>\n",
377
- " <td>3</td>\n",
378
- " <td>2</td>\n",
379
- " <td>2</td>\n",
380
- " <td>NaN</td>\n",
381
- " </tr>\n",
382
- " <tr>\n",
383
- " <th>top</th>\n",
384
- " <td>city</td>\n",
385
- " <td>NaN</td>\n",
386
- " <td>NaN</td>\n",
387
- " <td>NaN</td>\n",
388
- " <td>NaN</td>\n",
389
- " <td>Petrol</td>\n",
390
- " <td>Dealer</td>\n",
391
- " <td>Manual</td>\n",
392
- " <td>NaN</td>\n",
393
- " </tr>\n",
394
- " <tr>\n",
395
- " <th>freq</th>\n",
396
- " <td>26</td>\n",
397
- " <td>NaN</td>\n",
398
- " <td>NaN</td>\n",
399
- " <td>NaN</td>\n",
400
- " <td>NaN</td>\n",
401
- " <td>239</td>\n",
402
- " <td>195</td>\n",
403
- " <td>261</td>\n",
404
- " <td>NaN</td>\n",
405
- " </tr>\n",
406
- " <tr>\n",
407
- " <th>mean</th>\n",
408
- " <td>NaN</td>\n",
409
- " <td>2013.627907</td>\n",
410
- " <td>4.661296</td>\n",
411
- " <td>7.628472</td>\n",
412
- " <td>36947.205980</td>\n",
413
- " <td>NaN</td>\n",
414
- " <td>NaN</td>\n",
415
- " <td>NaN</td>\n",
416
- " <td>0.043189</td>\n",
417
- " </tr>\n",
418
- " <tr>\n",
419
- " <th>std</th>\n",
420
- " <td>NaN</td>\n",
421
- " <td>2.891554</td>\n",
422
- " <td>5.082812</td>\n",
423
- " <td>8.642584</td>\n",
424
- " <td>38886.883882</td>\n",
425
- " <td>NaN</td>\n",
426
- " <td>NaN</td>\n",
427
- " <td>NaN</td>\n",
428
- " <td>0.247915</td>\n",
429
- " </tr>\n",
430
- " <tr>\n",
431
- " <th>min</th>\n",
432
- " <td>NaN</td>\n",
433
- " <td>2003.000000</td>\n",
434
- " <td>0.100000</td>\n",
435
- " <td>0.320000</td>\n",
436
- " <td>500.000000</td>\n",
437
- " <td>NaN</td>\n",
438
- " <td>NaN</td>\n",
439
- " <td>NaN</td>\n",
440
- " <td>0.000000</td>\n",
441
- " </tr>\n",
442
- " <tr>\n",
443
- " <th>25%</th>\n",
444
- " <td>NaN</td>\n",
445
- " <td>2012.000000</td>\n",
446
- " <td>0.900000</td>\n",
447
- " <td>1.200000</td>\n",
448
- " <td>15000.000000</td>\n",
449
- " <td>NaN</td>\n",
450
- " <td>NaN</td>\n",
451
- " <td>NaN</td>\n",
452
- " <td>0.000000</td>\n",
453
- " </tr>\n",
454
- " <tr>\n",
455
- " <th>50%</th>\n",
456
- " <td>NaN</td>\n",
457
- " <td>2014.000000</td>\n",
458
- " <td>3.600000</td>\n",
459
- " <td>6.400000</td>\n",
460
- " <td>32000.000000</td>\n",
461
- " <td>NaN</td>\n",
462
- " <td>NaN</td>\n",
463
- " <td>NaN</td>\n",
464
- " <td>0.000000</td>\n",
465
- " </tr>\n",
466
- " <tr>\n",
467
- " <th>75%</th>\n",
468
- " <td>NaN</td>\n",
469
- " <td>2016.000000</td>\n",
470
- " <td>6.000000</td>\n",
471
- " <td>9.900000</td>\n",
472
- " <td>48767.000000</td>\n",
473
- " <td>NaN</td>\n",
474
- " <td>NaN</td>\n",
475
- " <td>NaN</td>\n",
476
- " <td>0.000000</td>\n",
477
- " </tr>\n",
478
- " <tr>\n",
479
- " <th>max</th>\n",
480
- " <td>NaN</td>\n",
481
- " <td>2018.000000</td>\n",
482
- " <td>35.000000</td>\n",
483
- " <td>92.600000</td>\n",
484
- " <td>500000.000000</td>\n",
485
- " <td>NaN</td>\n",
486
- " <td>NaN</td>\n",
487
- " <td>NaN</td>\n",
488
- " <td>3.000000</td>\n",
489
- " </tr>\n",
490
- " </tbody>\n",
491
- "</table>\n",
492
- "</div>"
493
- ],
494
- "text/plain": [
495
- " Car_Name Year Selling_Price Present_Price Driven_kms \\\n",
496
- "count 301 301.000000 301.000000 301.000000 301.000000 \n",
497
- "unique 98 NaN NaN NaN NaN \n",
498
- "top city NaN NaN NaN NaN \n",
499
- "freq 26 NaN NaN NaN NaN \n",
500
- "mean NaN 2013.627907 4.661296 7.628472 36947.205980 \n",
501
- "std NaN 2.891554 5.082812 8.642584 38886.883882 \n",
502
- "min NaN 2003.000000 0.100000 0.320000 500.000000 \n",
503
- "25% NaN 2012.000000 0.900000 1.200000 15000.000000 \n",
504
- "50% NaN 2014.000000 3.600000 6.400000 32000.000000 \n",
505
- "75% NaN 2016.000000 6.000000 9.900000 48767.000000 \n",
506
- "max NaN 2018.000000 35.000000 92.600000 500000.000000 \n",
507
- "\n",
508
- " Fuel_Type Selling_type Transmission Owner \n",
509
- "count 301 301 301 301.000000 \n",
510
- "unique 3 2 2 NaN \n",
511
- "top Petrol Dealer Manual NaN \n",
512
- "freq 239 195 261 NaN \n",
513
- "mean NaN NaN NaN 0.043189 \n",
514
- "std NaN NaN NaN 0.247915 \n",
515
- "min NaN NaN NaN 0.000000 \n",
516
- "25% NaN NaN NaN 0.000000 \n",
517
- "50% NaN NaN NaN 0.000000 \n",
518
- "75% NaN NaN NaN 0.000000 \n",
519
- "max NaN NaN NaN 3.000000 "
520
- ]
521
- },
522
- "execution_count": 5,
523
- "metadata": {},
524
- "output_type": "execute_result"
525
- }
526
- ],
527
  "source": [
528
  "# 观察数据的详细信息\n",
529
  "df.describe(include='all')"
@@ -607,7 +362,7 @@
607
  },
608
  {
609
  "cell_type": "code",
610
- "execution_count": 6,
611
  "metadata": {
612
  "execution": {
613
  "iopub.execute_input": "2023-08-11T09:05:55.855050Z",
@@ -617,18 +372,7 @@
617
  "shell.execute_reply.started": "2023-08-11T09:05:55.855014Z"
618
  }
619
  },
620
- "outputs": [
621
- {
622
- "data": {
623
- "text/plain": [
624
- "2"
625
- ]
626
- },
627
- "execution_count": 6,
628
- "metadata": {},
629
- "output_type": "execute_result"
630
- }
631
- ],
632
  "source": [
633
  "# 检查是否有重复数据\n",
634
  "df.duplicated().sum()"
@@ -636,7 +380,7 @@
636
  },
637
  {
638
  "cell_type": "code",
639
- "execution_count": 7,
640
  "metadata": {
641
  "execution": {
642
  "iopub.execute_input": "2023-08-11T09:05:55.865903Z",
@@ -653,7 +397,7 @@
653
  "0"
654
  ]
655
  },
656
- "execution_count": 7,
657
  "metadata": {},
658
  "output_type": "execute_result"
659
  }
@@ -736,7 +480,7 @@
736
  },
737
  {
738
  "cell_type": "code",
739
- "execution_count": 8,
740
  "metadata": {
741
  "execution": {
742
  "iopub.execute_input": "2023-08-11T09:05:55.914238Z",
@@ -764,7 +508,7 @@
764
  },
765
  {
766
  "cell_type": "code",
767
- "execution_count": 9,
768
  "metadata": {
769
  "execution": {
770
  "iopub.execute_input": "2023-08-11T09:05:55.962811Z",
@@ -800,7 +544,7 @@
800
  },
801
  {
802
  "cell_type": "code",
803
- "execution_count": 10,
804
  "metadata": {
805
  "execution": {
806
  "iopub.execute_input": "2023-08-11T09:05:55.986146Z",
@@ -925,7 +669,7 @@
925
  " 'Present_Price']"
926
  ]
927
  },
928
- "execution_count": 10,
929
  "metadata": {},
930
  "output_type": "execute_result"
931
  }
@@ -951,7 +695,7 @@
951
  },
952
  {
953
  "cell_type": "code",
954
- "execution_count": 11,
955
  "metadata": {
956
  "execution": {
957
  "iopub.execute_input": "2023-08-11T09:05:56.545107Z",
@@ -998,7 +742,7 @@
998
  },
999
  {
1000
  "cell_type": "code",
1001
- "execution_count": 12,
1002
  "metadata": {
1003
  "execution": {
1004
  "iopub.execute_input": "2023-08-11T09:05:56.568558Z",
@@ -1032,7 +776,7 @@
1032
  },
1033
  {
1034
  "cell_type": "code",
1035
- "execution_count": 13,
1036
  "metadata": {
1037
  "execution": {
1038
  "iopub.execute_input": "2023-08-11T09:05:56.586725Z",
@@ -1088,7 +832,7 @@
1088
  },
1089
  {
1090
  "cell_type": "code",
1091
- "execution_count": 14,
1092
  "metadata": {
1093
  "execution": {
1094
  "iopub.execute_input": "2023-08-11T09:13:14.556627Z",
@@ -1107,7 +851,7 @@
1107
  },
1108
  {
1109
  "cell_type": "code",
1110
- "execution_count": 15,
1111
  "metadata": {
1112
  "execution": {
1113
  "iopub.execute_input": "2023-08-11T09:13:14.629117Z",
@@ -1124,7 +868,7 @@
1124
  "0.9740787800578905"
1125
  ]
1126
  },
1127
- "execution_count": 15,
1128
  "metadata": {},
1129
  "output_type": "execute_result"
1130
  }
@@ -1145,7 +889,7 @@
1145
  },
1146
  {
1147
  "cell_type": "code",
1148
- "execution_count": 16,
1149
  "metadata": {
1150
  "execution": {
1151
  "iopub.execute_input": "2023-08-11T09:13:14.719423Z",
@@ -1165,7 +909,7 @@
1165
  },
1166
  {
1167
  "cell_type": "code",
1168
- "execution_count": 17,
1169
  "metadata": {
1170
  "execution": {
1171
  "iopub.execute_input": "2023-08-11T09:13:14.925993Z",
@@ -1215,7 +959,7 @@
1215
  },
1216
  {
1217
  "cell_type": "code",
1218
- "execution_count": 18,
1219
  "metadata": {
1220
  "execution": {
1221
  "iopub.execute_input": "2023-08-11T09:13:15.105155Z",
@@ -1326,7 +1070,7 @@
1326
  },
1327
  {
1328
  "cell_type": "code",
1329
- "execution_count": 19,
1330
  "metadata": {
1331
  "execution": {
1332
  "iopub.execute_input": "2023-08-11T09:13:16.111279Z",
@@ -1344,7 +1088,7 @@
1344
  "traceback": [
1345
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1346
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
1347
- "\u001b[0;32m<ipython-input-19-a14b7acf6d82>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mf_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'gbr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gbr_gs'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rfr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rfr_gs'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'X_train'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'gbr2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rfr2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mfeature_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfearture_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1348
  "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (4,) + inhomogeneous part."
1349
  ]
1350
  }
 
232
  },
233
  {
234
  "cell_type": "code",
235
+ "execution_count": null,
236
  "metadata": {
237
  "execution": {
238
  "iopub.execute_input": "2023-08-11T09:05:54.311108Z",
 
242
  "shell.execute_reply.started": "2023-08-11T09:05:54.311087Z"
243
  }
244
  },
245
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
246
  "source": [
247
  "# 观察数据类型\n",
248
  "df.info()"
 
250
  },
251
  {
252
  "cell_type": "code",
253
+ "execution_count": null,
254
  "metadata": {
255
  "execution": {
256
  "iopub.execute_input": "2023-08-11T09:05:54.326848Z",
 
260
  "shell.execute_reply.started": "2023-08-11T09:05:54.326824Z"
261
  }
262
  },
263
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
264
  "source": [
265
  "# 检查缺失值\n",
266
  "df.isnull().sum()"
 
268
  },
269
  {
270
  "cell_type": "code",
271
+ "execution_count": null,
272
  "metadata": {
273
  "execution": {
274
  "iopub.execute_input": "2023-08-11T09:05:54.338268Z",
 
278
  "shell.execute_reply.started": "2023-08-11T09:05:54.338225Z"
279
  }
280
  },
281
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
282
  "source": [
283
  "# 观察数据的详细信息\n",
284
  "df.describe(include='all')"
 
362
  },
363
  {
364
  "cell_type": "code",
365
+ "execution_count": null,
366
  "metadata": {
367
  "execution": {
368
  "iopub.execute_input": "2023-08-11T09:05:55.855050Z",
 
372
  "shell.execute_reply.started": "2023-08-11T09:05:55.855014Z"
373
  }
374
  },
375
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
376
  "source": [
377
  "# 检查是否有重复数据\n",
378
  "df.duplicated().sum()"
 
380
  },
381
  {
382
  "cell_type": "code",
383
+ "execution_count": 3,
384
  "metadata": {
385
  "execution": {
386
  "iopub.execute_input": "2023-08-11T09:05:55.865903Z",
 
397
  "0"
398
  ]
399
  },
400
+ "execution_count": 3,
401
  "metadata": {},
402
  "output_type": "execute_result"
403
  }
 
480
  },
481
  {
482
  "cell_type": "code",
483
+ "execution_count": 4,
484
  "metadata": {
485
  "execution": {
486
  "iopub.execute_input": "2023-08-11T09:05:55.914238Z",
 
508
  },
509
  {
510
  "cell_type": "code",
511
+ "execution_count": 5,
512
  "metadata": {
513
  "execution": {
514
  "iopub.execute_input": "2023-08-11T09:05:55.962811Z",
 
544
  },
545
  {
546
  "cell_type": "code",
547
+ "execution_count": 6,
548
  "metadata": {
549
  "execution": {
550
  "iopub.execute_input": "2023-08-11T09:05:55.986146Z",
 
669
  " 'Present_Price']"
670
  ]
671
  },
672
+ "execution_count": 6,
673
  "metadata": {},
674
  "output_type": "execute_result"
675
  }
 
695
  },
696
  {
697
  "cell_type": "code",
698
+ "execution_count": 7,
699
  "metadata": {
700
  "execution": {
701
  "iopub.execute_input": "2023-08-11T09:05:56.545107Z",
 
742
  },
743
  {
744
  "cell_type": "code",
745
+ "execution_count": 8,
746
  "metadata": {
747
  "execution": {
748
  "iopub.execute_input": "2023-08-11T09:05:56.568558Z",
 
776
  },
777
  {
778
  "cell_type": "code",
779
+ "execution_count": 9,
780
  "metadata": {
781
  "execution": {
782
  "iopub.execute_input": "2023-08-11T09:05:56.586725Z",
 
832
  },
833
  {
834
  "cell_type": "code",
835
+ "execution_count": 10,
836
  "metadata": {
837
  "execution": {
838
  "iopub.execute_input": "2023-08-11T09:13:14.556627Z",
 
851
  },
852
  {
853
  "cell_type": "code",
854
+ "execution_count": 11,
855
  "metadata": {
856
  "execution": {
857
  "iopub.execute_input": "2023-08-11T09:13:14.629117Z",
 
868
  "0.9740787800578905"
869
  ]
870
  },
871
+ "execution_count": 11,
872
  "metadata": {},
873
  "output_type": "execute_result"
874
  }
 
889
  },
890
  {
891
  "cell_type": "code",
892
+ "execution_count": 12,
893
  "metadata": {
894
  "execution": {
895
  "iopub.execute_input": "2023-08-11T09:13:14.719423Z",
 
909
  },
910
  {
911
  "cell_type": "code",
912
+ "execution_count": 13,
913
  "metadata": {
914
  "execution": {
915
  "iopub.execute_input": "2023-08-11T09:13:14.925993Z",
 
959
  },
960
  {
961
  "cell_type": "code",
962
+ "execution_count": 14,
963
  "metadata": {
964
  "execution": {
965
  "iopub.execute_input": "2023-08-11T09:13:15.105155Z",
 
1070
  },
1071
  {
1072
  "cell_type": "code",
1073
+ "execution_count": 15,
1074
  "metadata": {
1075
  "execution": {
1076
  "iopub.execute_input": "2023-08-11T09:13:16.111279Z",
 
1088
  "traceback": [
1089
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1090
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
1091
+ "\u001b[0;32m<ipython-input-15-a14b7acf6d82>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mf_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'gbr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gbr_gs'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rfr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rfr_gs'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'X_train'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'gbr2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rfr2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mfeature_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfearture_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1092
  "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (4,) + inhomogeneous part."
1093
  ]
1094
  }
benchmark/pandas_14/pandas_14_fixed.ipynb CHANGED
@@ -69,7 +69,7 @@
69
  },
70
  {
71
  "cell_type": "code",
72
- "execution_count": 2,
73
  "metadata": {
74
  "execution": {
75
  "iopub.execute_input": "2023-06-08T04:31:45.299138Z",
@@ -80,16 +80,7 @@
80
  },
81
  "id": "grAO86gTmnjJ"
82
  },
83
- "outputs": [
84
- {
85
- "name": "stdout",
86
- "output_type": "stream",
87
- "text": [
88
- "TensorFlow v2.17.0\n",
89
- "TensorFlow Decision Forests v1.10.0\n"
90
- ]
91
- }
92
- ],
93
  "source": [
94
  "print(\"TensorFlow v\" + tf.__version__)\n",
95
  "print(\"TensorFlow Decision Forests v\" + tfdf.__version__)"
@@ -106,7 +97,7 @@
106
  },
107
  {
108
  "cell_type": "code",
109
- "execution_count": 3,
110
  "metadata": {
111
  "execution": {
112
  "iopub.execute_input": "2023-06-08T04:31:45.313150Z",
@@ -143,7 +134,7 @@
143
  },
144
  {
145
  "cell_type": "code",
146
- "execution_count": 4,
147
  "metadata": {
148
  "execution": {
149
  "iopub.execute_input": "2023-06-08T04:31:45.382127Z",
@@ -154,162 +145,7 @@
154
  },
155
  "id": "nCx3PE1xmnjM"
156
  },
157
- "outputs": [
158
- {
159
- "data": {
160
- "text/html": [
161
- "<div>\n",
162
- "<style scoped>\n",
163
- " .dataframe tbody tr th:only-of-type {\n",
164
- " vertical-align: middle;\n",
165
- " }\n",
166
- "\n",
167
- " .dataframe tbody tr th {\n",
168
- " vertical-align: top;\n",
169
- " }\n",
170
- "\n",
171
- " .dataframe thead th {\n",
172
- " text-align: right;\n",
173
- " }\n",
174
- "</style>\n",
175
- "<table border=\"1\" class=\"dataframe\">\n",
176
- " <thead>\n",
177
- " <tr style=\"text-align: right;\">\n",
178
- " <th></th>\n",
179
- " <th>PassengerId</th>\n",
180
- " <th>HomePlanet</th>\n",
181
- " <th>CryoSleep</th>\n",
182
- " <th>Cabin</th>\n",
183
- " <th>Destination</th>\n",
184
- " <th>Age</th>\n",
185
- " <th>VIP</th>\n",
186
- " <th>RoomService</th>\n",
187
- " <th>FoodCourt</th>\n",
188
- " <th>ShoppingMall</th>\n",
189
- " <th>Spa</th>\n",
190
- " <th>VRDeck</th>\n",
191
- " <th>Name</th>\n",
192
- " <th>Transported</th>\n",
193
- " </tr>\n",
194
- " </thead>\n",
195
- " <tbody>\n",
196
- " <tr>\n",
197
- " <th>0</th>\n",
198
- " <td>0001_01</td>\n",
199
- " <td>Europa</td>\n",
200
- " <td>False</td>\n",
201
- " <td>B/0/P</td>\n",
202
- " <td>TRAPPIST-1e</td>\n",
203
- " <td>39.0</td>\n",
204
- " <td>False</td>\n",
205
- " <td>0.0</td>\n",
206
- " <td>0.0</td>\n",
207
- " <td>0.0</td>\n",
208
- " <td>0.0</td>\n",
209
- " <td>0.0</td>\n",
210
- " <td>Maham Ofracculy</td>\n",
211
- " <td>False</td>\n",
212
- " </tr>\n",
213
- " <tr>\n",
214
- " <th>1</th>\n",
215
- " <td>0002_01</td>\n",
216
- " <td>Earth</td>\n",
217
- " <td>False</td>\n",
218
- " <td>F/0/S</td>\n",
219
- " <td>TRAPPIST-1e</td>\n",
220
- " <td>24.0</td>\n",
221
- " <td>False</td>\n",
222
- " <td>109.0</td>\n",
223
- " <td>9.0</td>\n",
224
- " <td>25.0</td>\n",
225
- " <td>549.0</td>\n",
226
- " <td>44.0</td>\n",
227
- " <td>Juanna Vines</td>\n",
228
- " <td>True</td>\n",
229
- " </tr>\n",
230
- " <tr>\n",
231
- " <th>2</th>\n",
232
- " <td>0003_01</td>\n",
233
- " <td>Europa</td>\n",
234
- " <td>False</td>\n",
235
- " <td>A/0/S</td>\n",
236
- " <td>TRAPPIST-1e</td>\n",
237
- " <td>58.0</td>\n",
238
- " <td>True</td>\n",
239
- " <td>43.0</td>\n",
240
- " <td>3576.0</td>\n",
241
- " <td>0.0</td>\n",
242
- " <td>6715.0</td>\n",
243
- " <td>49.0</td>\n",
244
- " <td>Altark Susent</td>\n",
245
- " <td>False</td>\n",
246
- " </tr>\n",
247
- " <tr>\n",
248
- " <th>3</th>\n",
249
- " <td>0003_02</td>\n",
250
- " <td>Europa</td>\n",
251
- " <td>False</td>\n",
252
- " <td>A/0/S</td>\n",
253
- " <td>TRAPPIST-1e</td>\n",
254
- " <td>33.0</td>\n",
255
- " <td>False</td>\n",
256
- " <td>0.0</td>\n",
257
- " <td>1283.0</td>\n",
258
- " <td>371.0</td>\n",
259
- " <td>3329.0</td>\n",
260
- " <td>193.0</td>\n",
261
- " <td>Solam Susent</td>\n",
262
- " <td>False</td>\n",
263
- " </tr>\n",
264
- " <tr>\n",
265
- " <th>4</th>\n",
266
- " <td>0004_01</td>\n",
267
- " <td>Earth</td>\n",
268
- " <td>False</td>\n",
269
- " <td>F/1/S</td>\n",
270
- " <td>TRAPPIST-1e</td>\n",
271
- " <td>16.0</td>\n",
272
- " <td>False</td>\n",
273
- " <td>303.0</td>\n",
274
- " <td>70.0</td>\n",
275
- " <td>151.0</td>\n",
276
- " <td>565.0</td>\n",
277
- " <td>2.0</td>\n",
278
- " <td>Willy Santantines</td>\n",
279
- " <td>True</td>\n",
280
- " </tr>\n",
281
- " </tbody>\n",
282
- "</table>\n",
283
- "</div>"
284
- ],
285
- "text/plain": [
286
- " PassengerId HomePlanet CryoSleep Cabin Destination Age VIP \\\n",
287
- "0 0001_01 Europa False B/0/P TRAPPIST-1e 39.0 False \n",
288
- "1 0002_01 Earth False F/0/S TRAPPIST-1e 24.0 False \n",
289
- "2 0003_01 Europa False A/0/S TRAPPIST-1e 58.0 True \n",
290
- "3 0003_02 Europa False A/0/S TRAPPIST-1e 33.0 False \n",
291
- "4 0004_01 Earth False F/1/S TRAPPIST-1e 16.0 False \n",
292
- "\n",
293
- " RoomService FoodCourt ShoppingMall Spa VRDeck Name \\\n",
294
- "0 0.0 0.0 0.0 0.0 0.0 Maham Ofracculy \n",
295
- "1 109.0 9.0 25.0 549.0 44.0 Juanna Vines \n",
296
- "2 43.0 3576.0 0.0 6715.0 49.0 Altark Susent \n",
297
- "3 0.0 1283.0 371.0 3329.0 193.0 Solam Susent \n",
298
- "4 303.0 70.0 151.0 565.0 2.0 Willy Santantines \n",
299
- "\n",
300
- " Transported \n",
301
- "0 False \n",
302
- "1 True \n",
303
- "2 False \n",
304
- "3 False \n",
305
- "4 True "
306
- ]
307
- },
308
- "execution_count": 4,
309
- "metadata": {},
310
- "output_type": "execute_result"
311
- }
312
- ],
313
  "source": [
314
  "# Display the first 5 examples\n",
315
  "df.head(5)"
@@ -341,7 +177,7 @@
341
  },
342
  {
343
  "cell_type": "code",
344
- "execution_count": 5,
345
  "metadata": {
346
  "execution": {
347
  "iopub.execute_input": "2023-06-08T04:31:45.426363Z",
@@ -352,140 +188,7 @@
352
  },
353
  "id": "XjwG5wjfmnjO"
354
  },
355
- "outputs": [
356
- {
357
- "data": {
358
- "text/html": [
359
- "<div>\n",
360
- "<style scoped>\n",
361
- " .dataframe tbody tr th:only-of-type {\n",
362
- " vertical-align: middle;\n",
363
- " }\n",
364
- "\n",
365
- " .dataframe tbody tr th {\n",
366
- " vertical-align: top;\n",
367
- " }\n",
368
- "\n",
369
- " .dataframe thead th {\n",
370
- " text-align: right;\n",
371
- " }\n",
372
- "</style>\n",
373
- "<table border=\"1\" class=\"dataframe\">\n",
374
- " <thead>\n",
375
- " <tr style=\"text-align: right;\">\n",
376
- " <th></th>\n",
377
- " <th>Age</th>\n",
378
- " <th>RoomService</th>\n",
379
- " <th>FoodCourt</th>\n",
380
- " <th>ShoppingMall</th>\n",
381
- " <th>Spa</th>\n",
382
- " <th>VRDeck</th>\n",
383
- " </tr>\n",
384
- " </thead>\n",
385
- " <tbody>\n",
386
- " <tr>\n",
387
- " <th>count</th>\n",
388
- " <td>8514.000000</td>\n",
389
- " <td>8512.000000</td>\n",
390
- " <td>8510.000000</td>\n",
391
- " <td>8485.000000</td>\n",
392
- " <td>8510.000000</td>\n",
393
- " <td>8505.000000</td>\n",
394
- " </tr>\n",
395
- " <tr>\n",
396
- " <th>mean</th>\n",
397
- " <td>28.827930</td>\n",
398
- " <td>224.687617</td>\n",
399
- " <td>458.077203</td>\n",
400
- " <td>173.729169</td>\n",
401
- " <td>311.138778</td>\n",
402
- " <td>304.854791</td>\n",
403
- " </tr>\n",
404
- " <tr>\n",
405
- " <th>std</th>\n",
406
- " <td>14.489021</td>\n",
407
- " <td>666.717663</td>\n",
408
- " <td>1611.489240</td>\n",
409
- " <td>604.696458</td>\n",
410
- " <td>1136.705535</td>\n",
411
- " <td>1145.717189</td>\n",
412
- " </tr>\n",
413
- " <tr>\n",
414
- " <th>min</th>\n",
415
- " <td>0.000000</td>\n",
416
- " <td>0.000000</td>\n",
417
- " <td>0.000000</td>\n",
418
- " <td>0.000000</td>\n",
419
- " <td>0.000000</td>\n",
420
- " <td>0.000000</td>\n",
421
- " </tr>\n",
422
- " <tr>\n",
423
- " <th>25%</th>\n",
424
- " <td>19.000000</td>\n",
425
- " <td>0.000000</td>\n",
426
- " <td>0.000000</td>\n",
427
- " <td>0.000000</td>\n",
428
- " <td>0.000000</td>\n",
429
- " <td>0.000000</td>\n",
430
- " </tr>\n",
431
- " <tr>\n",
432
- " <th>50%</th>\n",
433
- " <td>27.000000</td>\n",
434
- " <td>0.000000</td>\n",
435
- " <td>0.000000</td>\n",
436
- " <td>0.000000</td>\n",
437
- " <td>0.000000</td>\n",
438
- " <td>0.000000</td>\n",
439
- " </tr>\n",
440
- " <tr>\n",
441
- " <th>75%</th>\n",
442
- " <td>38.000000</td>\n",
443
- " <td>47.000000</td>\n",
444
- " <td>76.000000</td>\n",
445
- " <td>27.000000</td>\n",
446
- " <td>59.000000</td>\n",
447
- " <td>46.000000</td>\n",
448
- " </tr>\n",
449
- " <tr>\n",
450
- " <th>max</th>\n",
451
- " <td>79.000000</td>\n",
452
- " <td>14327.000000</td>\n",
453
- " <td>29813.000000</td>\n",
454
- " <td>23492.000000</td>\n",
455
- " <td>22408.000000</td>\n",
456
- " <td>24133.000000</td>\n",
457
- " </tr>\n",
458
- " </tbody>\n",
459
- "</table>\n",
460
- "</div>"
461
- ],
462
- "text/plain": [
463
- " Age RoomService FoodCourt ShoppingMall Spa \\\n",
464
- "count 8514.000000 8512.000000 8510.000000 8485.000000 8510.000000 \n",
465
- "mean 28.827930 224.687617 458.077203 173.729169 311.138778 \n",
466
- "std 14.489021 666.717663 1611.489240 604.696458 1136.705535 \n",
467
- "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
468
- "25% 19.000000 0.000000 0.000000 0.000000 0.000000 \n",
469
- "50% 27.000000 0.000000 0.000000 0.000000 0.000000 \n",
470
- "75% 38.000000 47.000000 76.000000 27.000000 59.000000 \n",
471
- "max 79.000000 14327.000000 29813.000000 23492.000000 22408.000000 \n",
472
- "\n",
473
- " VRDeck \n",
474
- "count 8505.000000 \n",
475
- "mean 304.854791 \n",
476
- "std 1145.717189 \n",
477
- "min 0.000000 \n",
478
- "25% 0.000000 \n",
479
- "50% 0.000000 \n",
480
- "75% 46.000000 \n",
481
- "max 24133.000000 "
482
- ]
483
- },
484
- "execution_count": 5,
485
- "metadata": {},
486
- "output_type": "execute_result"
487
- }
488
- ],
489
  "source": [
490
  "df.describe()"
491
  ]
@@ -502,7 +205,7 @@
502
  },
503
  {
504
  "cell_type": "code",
505
- "execution_count": 6,
506
  "metadata": {
507
  "execution": {
508
  "iopub.execute_input": "2023-06-08T04:31:45.482430Z",
@@ -512,28 +215,7 @@
512
  "shell.execute_reply.started": "2023-06-08T04:31:45.482386Z"
513
  }
514
  },
515
- "outputs": [
516
- {
517
- "name": "stdout",
518
- "output_type": "stream",
519
- "text": [
520
- "Total de valores nulos de PassengerId = 0 equivale al 0.00% del total de la columna.\n",
521
- "Total de valores nulos de HomePlanet = 201 equivale al 2.31% del total de la columna.\n",
522
- "Total de valores nulos de CryoSleep = 217 equivale al 2.50% del total de la columna.\n",
523
- "Total de valores nulos de Cabin = 199 equivale al 2.29% del total de la columna.\n",
524
- "Total de valores nulos de Destination = 182 equivale al 2.09% del total de la columna.\n",
525
- "Total de valores nulos de Age = 179 equivale al 2.06% del total de la columna.\n",
526
- "Total de valores nulos de VIP = 203 equivale al 2.34% del total de la columna.\n",
527
- "Total de valores nulos de RoomService = 181 equivale al 2.08% del total de la columna.\n",
528
- "Total de valores nulos de FoodCourt = 183 equivale al 2.11% del total de la columna.\n",
529
- "Total de valores nulos de ShoppingMall = 208 equivale al 2.39% del total de la columna.\n",
530
- "Total de valores nulos de Spa = 183 equivale al 2.11% del total de la columna.\n",
531
- "Total de valores nulos de VRDeck = 188 equivale al 2.16% del total de la columna.\n",
532
- "Total de valores nulos de Name = 200 equivale al 2.30% del total de la columna.\n",
533
- "Total de valores nulos de Transported = 0 equivale al 0.00% del total de la columna.\n"
534
- ]
535
- }
536
- ],
537
  "source": [
538
  "for feature in df.columns:\n",
539
  " x = df[feature].isna().sum()\n",
@@ -542,7 +224,7 @@
542
  },
543
  {
544
  "cell_type": "code",
545
- "execution_count": 7,
546
  "metadata": {
547
  "execution": {
548
  "iopub.execute_input": "2023-06-08T04:31:45.504577Z",
@@ -553,35 +235,7 @@
553
  },
554
  "id": "UmWpnVxQmnjO"
555
  },
556
- "outputs": [
557
- {
558
- "name": "stdout",
559
- "output_type": "stream",
560
- "text": [
561
- "<class 'pandas.core.frame.DataFrame'>\n",
562
- "RangeIndex: 8693 entries, 0 to 8692\n",
563
- "Data columns (total 14 columns):\n",
564
- " # Column Non-Null Count Dtype \n",
565
- "--- ------ -------------- ----- \n",
566
- " 0 PassengerId 8693 non-null object \n",
567
- " 1 HomePlanet 8492 non-null object \n",
568
- " 2 CryoSleep 8476 non-null object \n",
569
- " 3 Cabin 8494 non-null object \n",
570
- " 4 Destination 8511 non-null object \n",
571
- " 5 Age 8514 non-null float64\n",
572
- " 6 VIP 8490 non-null object \n",
573
- " 7 RoomService 8512 non-null float64\n",
574
- " 8 FoodCourt 8510 non-null float64\n",
575
- " 9 ShoppingMall 8485 non-null float64\n",
576
- " 10 Spa 8510 non-null float64\n",
577
- " 11 VRDeck 8505 non-null float64\n",
578
- " 12 Name 8493 non-null object \n",
579
- " 13 Transported 8693 non-null bool \n",
580
- "dtypes: bool(1), float64(6), object(7)\n",
581
- "memory usage: 891.5+ KB\n"
582
- ]
583
- }
584
- ],
585
  "source": [
586
  "df.info()"
587
  ]
@@ -879,7 +533,7 @@
879
  },
880
  {
881
  "cell_type": "code",
882
- "execution_count": 8,
883
  "metadata": {
884
  "execution": {
885
  "iopub.execute_input": "2023-06-08T04:31:47.665505Z",
@@ -1021,7 +675,7 @@
1021
  "4 70.0 151.0 565.0 2.0 True "
1022
  ]
1023
  },
1024
- "execution_count": 8,
1025
  "metadata": {},
1026
  "output_type": "execute_result"
1027
  }
@@ -1040,7 +694,7 @@
1040
  },
1041
  {
1042
  "cell_type": "code",
1043
- "execution_count": 9,
1044
  "metadata": {
1045
  "execution": {
1046
  "iopub.execute_input": "2023-06-08T04:31:47.695912Z",
@@ -1069,7 +723,7 @@
1069
  "dtype: int64"
1070
  ]
1071
  },
1072
- "execution_count": 9,
1073
  "metadata": {},
1074
  "output_type": "execute_result"
1075
  }
@@ -1081,7 +735,7 @@
1081
  },
1082
  {
1083
  "cell_type": "code",
1084
- "execution_count": 10,
1085
  "metadata": {
1086
  "execution": {
1087
  "iopub.execute_input": "2023-06-08T04:31:47.715118Z",
@@ -1091,20 +745,7 @@
1091
  "shell.execute_reply.started": "2023-06-08T04:31:47.715064Z"
1092
  }
1093
  },
1094
- "outputs": [
1095
- {
1096
- "name": "stdout",
1097
- "output_type": "stream",
1098
- "text": [
1099
- "Columna HomePlanet - ['Europa' 'Earth' 'Mars' nan]\n",
1100
- "Columna Cabin - ['B/0/P' 'F/0/S' 'A/0/S' ... 'G/1499/S' 'G/1500/S' 'E/608/S']\n",
1101
- "Columna Destination - ['TRAPPIST-1e' 'PSO J318.5-22' '55 Cancri e' nan]\n",
1102
- "Columna CryoSleep - [False True nan]\n",
1103
- "Columna VIP - [False True nan]\n",
1104
- "Columna Transported - [False True]\n"
1105
- ]
1106
- }
1107
- ],
1108
  "source": [
1109
  "for column in ('HomePlanet', 'Cabin', 'Destination','CryoSleep','VIP','Transported'):\n",
1110
  " print(\"Columna\",column,\"-\" , df[column].unique())"
@@ -1119,7 +760,7 @@
1119
  },
1120
  {
1121
  "cell_type": "code",
1122
- "execution_count": 11,
1123
  "metadata": {
1124
  "execution": {
1125
  "iopub.execute_input": "2023-06-08T04:31:47.730956Z",
@@ -1181,7 +822,7 @@
1181
  },
1182
  {
1183
  "cell_type": "code",
1184
- "execution_count": 12,
1185
  "metadata": {
1186
  "execution": {
1187
  "iopub.execute_input": "2023-06-08T04:31:47.971942Z",
@@ -1191,37 +832,14 @@
1191
  "shell.execute_reply.started": "2023-06-08T04:31:47.971909Z"
1192
  }
1193
  },
1194
- "outputs": [
1195
- {
1196
- "data": {
1197
- "text/plain": [
1198
- "HomePlanet int64\n",
1199
- "CryoSleep int64\n",
1200
- "Cabin int64\n",
1201
- "Destination int64\n",
1202
- "Age float64\n",
1203
- "VIP int64\n",
1204
- "RoomService float64\n",
1205
- "FoodCourt float64\n",
1206
- "ShoppingMall float64\n",
1207
- "Spa float64\n",
1208
- "VRDeck float64\n",
1209
- "Transported int64\n",
1210
- "dtype: object"
1211
- ]
1212
- },
1213
- "execution_count": 12,
1214
- "metadata": {},
1215
- "output_type": "execute_result"
1216
- }
1217
- ],
1218
  "source": [
1219
  "df.dtypes"
1220
  ]
1221
  },
1222
  {
1223
  "cell_type": "code",
1224
- "execution_count": 13,
1225
  "metadata": {
1226
  "execution": {
1227
  "iopub.execute_input": "2023-06-08T04:31:47.983075Z",
@@ -1250,7 +868,7 @@
1250
  "dtype: object"
1251
  ]
1252
  },
1253
- "execution_count": 13,
1254
  "metadata": {},
1255
  "output_type": "execute_result"
1256
  }
@@ -1610,7 +1228,7 @@
1610
  },
1611
  {
1612
  "cell_type": "code",
1613
- "execution_count": 15,
1614
  "metadata": {
1615
  "execution": {
1616
  "iopub.execute_input": "2023-06-08T04:58:12.183623Z",
 
69
  },
70
  {
71
  "cell_type": "code",
72
+ "execution_count": null,
73
  "metadata": {
74
  "execution": {
75
  "iopub.execute_input": "2023-06-08T04:31:45.299138Z",
 
80
  },
81
  "id": "grAO86gTmnjJ"
82
  },
83
+ "outputs": [],
 
 
 
 
 
 
 
 
 
84
  "source": [
85
  "print(\"TensorFlow v\" + tf.__version__)\n",
86
  "print(\"TensorFlow Decision Forests v\" + tfdf.__version__)"
 
97
  },
98
  {
99
  "cell_type": "code",
100
+ "execution_count": 2,
101
  "metadata": {
102
  "execution": {
103
  "iopub.execute_input": "2023-06-08T04:31:45.313150Z",
 
134
  },
135
  {
136
  "cell_type": "code",
137
+ "execution_count": null,
138
  "metadata": {
139
  "execution": {
140
  "iopub.execute_input": "2023-06-08T04:31:45.382127Z",
 
145
  },
146
  "id": "nCx3PE1xmnjM"
147
  },
148
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
149
  "source": [
150
  "# Display the first 5 examples\n",
151
  "df.head(5)"
 
177
  },
178
  {
179
  "cell_type": "code",
180
+ "execution_count": null,
181
  "metadata": {
182
  "execution": {
183
  "iopub.execute_input": "2023-06-08T04:31:45.426363Z",
 
188
  },
189
  "id": "XjwG5wjfmnjO"
190
  },
191
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
192
  "source": [
193
  "df.describe()"
194
  ]
 
205
  },
206
  {
207
  "cell_type": "code",
208
+ "execution_count": null,
209
  "metadata": {
210
  "execution": {
211
  "iopub.execute_input": "2023-06-08T04:31:45.482430Z",
 
215
  "shell.execute_reply.started": "2023-06-08T04:31:45.482386Z"
216
  }
217
  },
218
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
219
  "source": [
220
  "for feature in df.columns:\n",
221
  " x = df[feature].isna().sum()\n",
 
224
  },
225
  {
226
  "cell_type": "code",
227
+ "execution_count": null,
228
  "metadata": {
229
  "execution": {
230
  "iopub.execute_input": "2023-06-08T04:31:45.504577Z",
 
235
  },
236
  "id": "UmWpnVxQmnjO"
237
  },
238
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
239
  "source": [
240
  "df.info()"
241
  ]
 
533
  },
534
  {
535
  "cell_type": "code",
536
+ "execution_count": 3,
537
  "metadata": {
538
  "execution": {
539
  "iopub.execute_input": "2023-06-08T04:31:47.665505Z",
 
675
  "4 70.0 151.0 565.0 2.0 True "
676
  ]
677
  },
678
+ "execution_count": 3,
679
  "metadata": {},
680
  "output_type": "execute_result"
681
  }
 
694
  },
695
  {
696
  "cell_type": "code",
697
+ "execution_count": 4,
698
  "metadata": {
699
  "execution": {
700
  "iopub.execute_input": "2023-06-08T04:31:47.695912Z",
 
723
  "dtype: int64"
724
  ]
725
  },
726
+ "execution_count": 4,
727
  "metadata": {},
728
  "output_type": "execute_result"
729
  }
 
735
  },
736
  {
737
  "cell_type": "code",
738
+ "execution_count": null,
739
  "metadata": {
740
  "execution": {
741
  "iopub.execute_input": "2023-06-08T04:31:47.715118Z",
 
745
  "shell.execute_reply.started": "2023-06-08T04:31:47.715064Z"
746
  }
747
  },
748
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
749
  "source": [
750
  "for column in ('HomePlanet', 'Cabin', 'Destination','CryoSleep','VIP','Transported'):\n",
751
  " print(\"Columna\",column,\"-\" , df[column].unique())"
 
760
  },
761
  {
762
  "cell_type": "code",
763
+ "execution_count": 5,
764
  "metadata": {
765
  "execution": {
766
  "iopub.execute_input": "2023-06-08T04:31:47.730956Z",
 
822
  },
823
  {
824
  "cell_type": "code",
825
+ "execution_count": null,
826
  "metadata": {
827
  "execution": {
828
  "iopub.execute_input": "2023-06-08T04:31:47.971942Z",
 
832
  "shell.execute_reply.started": "2023-06-08T04:31:47.971909Z"
833
  }
834
  },
835
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
836
  "source": [
837
  "df.dtypes"
838
  ]
839
  },
840
  {
841
  "cell_type": "code",
842
+ "execution_count": 6,
843
  "metadata": {
844
  "execution": {
845
  "iopub.execute_input": "2023-06-08T04:31:47.983075Z",
 
868
  "dtype: object"
869
  ]
870
  },
871
+ "execution_count": 6,
872
  "metadata": {},
873
  "output_type": "execute_result"
874
  }
 
1228
  },
1229
  {
1230
  "cell_type": "code",
1231
+ "execution_count": 7,
1232
  "metadata": {
1233
  "execution": {
1234
  "iopub.execute_input": "2023-06-08T04:58:12.183623Z",
benchmark/pandas_14/pandas_14_reproduced.ipynb CHANGED
@@ -69,7 +69,7 @@
69
  },
70
  {
71
  "cell_type": "code",
72
- "execution_count": 2,
73
  "metadata": {
74
  "execution": {
75
  "iopub.execute_input": "2023-06-08T04:31:45.299138Z",
@@ -80,16 +80,7 @@
80
  },
81
  "id": "grAO86gTmnjJ"
82
  },
83
- "outputs": [
84
- {
85
- "name": "stdout",
86
- "output_type": "stream",
87
- "text": [
88
- "TensorFlow v2.17.0\n",
89
- "TensorFlow Decision Forests v1.10.0\n"
90
- ]
91
- }
92
- ],
93
  "source": [
94
  "print(\"TensorFlow v\" + tf.__version__)\n",
95
  "print(\"TensorFlow Decision Forests v\" + tfdf.__version__)"
@@ -106,7 +97,7 @@
106
  },
107
  {
108
  "cell_type": "code",
109
- "execution_count": 3,
110
  "metadata": {
111
  "execution": {
112
  "iopub.execute_input": "2023-06-08T04:31:45.313150Z",
@@ -143,7 +134,7 @@
143
  },
144
  {
145
  "cell_type": "code",
146
- "execution_count": 4,
147
  "metadata": {
148
  "execution": {
149
  "iopub.execute_input": "2023-06-08T04:31:45.382127Z",
@@ -154,162 +145,7 @@
154
  },
155
  "id": "nCx3PE1xmnjM"
156
  },
157
- "outputs": [
158
- {
159
- "data": {
160
- "text/html": [
161
- "<div>\n",
162
- "<style scoped>\n",
163
- " .dataframe tbody tr th:only-of-type {\n",
164
- " vertical-align: middle;\n",
165
- " }\n",
166
- "\n",
167
- " .dataframe tbody tr th {\n",
168
- " vertical-align: top;\n",
169
- " }\n",
170
- "\n",
171
- " .dataframe thead th {\n",
172
- " text-align: right;\n",
173
- " }\n",
174
- "</style>\n",
175
- "<table border=\"1\" class=\"dataframe\">\n",
176
- " <thead>\n",
177
- " <tr style=\"text-align: right;\">\n",
178
- " <th></th>\n",
179
- " <th>PassengerId</th>\n",
180
- " <th>HomePlanet</th>\n",
181
- " <th>CryoSleep</th>\n",
182
- " <th>Cabin</th>\n",
183
- " <th>Destination</th>\n",
184
- " <th>Age</th>\n",
185
- " <th>VIP</th>\n",
186
- " <th>RoomService</th>\n",
187
- " <th>FoodCourt</th>\n",
188
- " <th>ShoppingMall</th>\n",
189
- " <th>Spa</th>\n",
190
- " <th>VRDeck</th>\n",
191
- " <th>Name</th>\n",
192
- " <th>Transported</th>\n",
193
- " </tr>\n",
194
- " </thead>\n",
195
- " <tbody>\n",
196
- " <tr>\n",
197
- " <th>0</th>\n",
198
- " <td>0001_01</td>\n",
199
- " <td>Europa</td>\n",
200
- " <td>False</td>\n",
201
- " <td>B/0/P</td>\n",
202
- " <td>TRAPPIST-1e</td>\n",
203
- " <td>39.0</td>\n",
204
- " <td>False</td>\n",
205
- " <td>0.0</td>\n",
206
- " <td>0.0</td>\n",
207
- " <td>0.0</td>\n",
208
- " <td>0.0</td>\n",
209
- " <td>0.0</td>\n",
210
- " <td>Maham Ofracculy</td>\n",
211
- " <td>False</td>\n",
212
- " </tr>\n",
213
- " <tr>\n",
214
- " <th>1</th>\n",
215
- " <td>0002_01</td>\n",
216
- " <td>Earth</td>\n",
217
- " <td>False</td>\n",
218
- " <td>F/0/S</td>\n",
219
- " <td>TRAPPIST-1e</td>\n",
220
- " <td>24.0</td>\n",
221
- " <td>False</td>\n",
222
- " <td>109.0</td>\n",
223
- " <td>9.0</td>\n",
224
- " <td>25.0</td>\n",
225
- " <td>549.0</td>\n",
226
- " <td>44.0</td>\n",
227
- " <td>Juanna Vines</td>\n",
228
- " <td>True</td>\n",
229
- " </tr>\n",
230
- " <tr>\n",
231
- " <th>2</th>\n",
232
- " <td>0003_01</td>\n",
233
- " <td>Europa</td>\n",
234
- " <td>False</td>\n",
235
- " <td>A/0/S</td>\n",
236
- " <td>TRAPPIST-1e</td>\n",
237
- " <td>58.0</td>\n",
238
- " <td>True</td>\n",
239
- " <td>43.0</td>\n",
240
- " <td>3576.0</td>\n",
241
- " <td>0.0</td>\n",
242
- " <td>6715.0</td>\n",
243
- " <td>49.0</td>\n",
244
- " <td>Altark Susent</td>\n",
245
- " <td>False</td>\n",
246
- " </tr>\n",
247
- " <tr>\n",
248
- " <th>3</th>\n",
249
- " <td>0003_02</td>\n",
250
- " <td>Europa</td>\n",
251
- " <td>False</td>\n",
252
- " <td>A/0/S</td>\n",
253
- " <td>TRAPPIST-1e</td>\n",
254
- " <td>33.0</td>\n",
255
- " <td>False</td>\n",
256
- " <td>0.0</td>\n",
257
- " <td>1283.0</td>\n",
258
- " <td>371.0</td>\n",
259
- " <td>3329.0</td>\n",
260
- " <td>193.0</td>\n",
261
- " <td>Solam Susent</td>\n",
262
- " <td>False</td>\n",
263
- " </tr>\n",
264
- " <tr>\n",
265
- " <th>4</th>\n",
266
- " <td>0004_01</td>\n",
267
- " <td>Earth</td>\n",
268
- " <td>False</td>\n",
269
- " <td>F/1/S</td>\n",
270
- " <td>TRAPPIST-1e</td>\n",
271
- " <td>16.0</td>\n",
272
- " <td>False</td>\n",
273
- " <td>303.0</td>\n",
274
- " <td>70.0</td>\n",
275
- " <td>151.0</td>\n",
276
- " <td>565.0</td>\n",
277
- " <td>2.0</td>\n",
278
- " <td>Willy Santantines</td>\n",
279
- " <td>True</td>\n",
280
- " </tr>\n",
281
- " </tbody>\n",
282
- "</table>\n",
283
- "</div>"
284
- ],
285
- "text/plain": [
286
- " PassengerId HomePlanet CryoSleep Cabin Destination Age VIP \\\n",
287
- "0 0001_01 Europa False B/0/P TRAPPIST-1e 39.0 False \n",
288
- "1 0002_01 Earth False F/0/S TRAPPIST-1e 24.0 False \n",
289
- "2 0003_01 Europa False A/0/S TRAPPIST-1e 58.0 True \n",
290
- "3 0003_02 Europa False A/0/S TRAPPIST-1e 33.0 False \n",
291
- "4 0004_01 Earth False F/1/S TRAPPIST-1e 16.0 False \n",
292
- "\n",
293
- " RoomService FoodCourt ShoppingMall Spa VRDeck Name \\\n",
294
- "0 0.0 0.0 0.0 0.0 0.0 Maham Ofracculy \n",
295
- "1 109.0 9.0 25.0 549.0 44.0 Juanna Vines \n",
296
- "2 43.0 3576.0 0.0 6715.0 49.0 Altark Susent \n",
297
- "3 0.0 1283.0 371.0 3329.0 193.0 Solam Susent \n",
298
- "4 303.0 70.0 151.0 565.0 2.0 Willy Santantines \n",
299
- "\n",
300
- " Transported \n",
301
- "0 False \n",
302
- "1 True \n",
303
- "2 False \n",
304
- "3 False \n",
305
- "4 True "
306
- ]
307
- },
308
- "execution_count": 4,
309
- "metadata": {},
310
- "output_type": "execute_result"
311
- }
312
- ],
313
  "source": [
314
  "# Display the first 5 examples\n",
315
  "df.head(5)"
@@ -341,7 +177,7 @@
341
  },
342
  {
343
  "cell_type": "code",
344
- "execution_count": 5,
345
  "metadata": {
346
  "execution": {
347
  "iopub.execute_input": "2023-06-08T04:31:45.426363Z",
@@ -352,140 +188,7 @@
352
  },
353
  "id": "XjwG5wjfmnjO"
354
  },
355
- "outputs": [
356
- {
357
- "data": {
358
- "text/html": [
359
- "<div>\n",
360
- "<style scoped>\n",
361
- " .dataframe tbody tr th:only-of-type {\n",
362
- " vertical-align: middle;\n",
363
- " }\n",
364
- "\n",
365
- " .dataframe tbody tr th {\n",
366
- " vertical-align: top;\n",
367
- " }\n",
368
- "\n",
369
- " .dataframe thead th {\n",
370
- " text-align: right;\n",
371
- " }\n",
372
- "</style>\n",
373
- "<table border=\"1\" class=\"dataframe\">\n",
374
- " <thead>\n",
375
- " <tr style=\"text-align: right;\">\n",
376
- " <th></th>\n",
377
- " <th>Age</th>\n",
378
- " <th>RoomService</th>\n",
379
- " <th>FoodCourt</th>\n",
380
- " <th>ShoppingMall</th>\n",
381
- " <th>Spa</th>\n",
382
- " <th>VRDeck</th>\n",
383
- " </tr>\n",
384
- " </thead>\n",
385
- " <tbody>\n",
386
- " <tr>\n",
387
- " <th>count</th>\n",
388
- " <td>8514.000000</td>\n",
389
- " <td>8512.000000</td>\n",
390
- " <td>8510.000000</td>\n",
391
- " <td>8485.000000</td>\n",
392
- " <td>8510.000000</td>\n",
393
- " <td>8505.000000</td>\n",
394
- " </tr>\n",
395
- " <tr>\n",
396
- " <th>mean</th>\n",
397
- " <td>28.827930</td>\n",
398
- " <td>224.687617</td>\n",
399
- " <td>458.077203</td>\n",
400
- " <td>173.729169</td>\n",
401
- " <td>311.138778</td>\n",
402
- " <td>304.854791</td>\n",
403
- " </tr>\n",
404
- " <tr>\n",
405
- " <th>std</th>\n",
406
- " <td>14.489021</td>\n",
407
- " <td>666.717663</td>\n",
408
- " <td>1611.489240</td>\n",
409
- " <td>604.696458</td>\n",
410
- " <td>1136.705535</td>\n",
411
- " <td>1145.717189</td>\n",
412
- " </tr>\n",
413
- " <tr>\n",
414
- " <th>min</th>\n",
415
- " <td>0.000000</td>\n",
416
- " <td>0.000000</td>\n",
417
- " <td>0.000000</td>\n",
418
- " <td>0.000000</td>\n",
419
- " <td>0.000000</td>\n",
420
- " <td>0.000000</td>\n",
421
- " </tr>\n",
422
- " <tr>\n",
423
- " <th>25%</th>\n",
424
- " <td>19.000000</td>\n",
425
- " <td>0.000000</td>\n",
426
- " <td>0.000000</td>\n",
427
- " <td>0.000000</td>\n",
428
- " <td>0.000000</td>\n",
429
- " <td>0.000000</td>\n",
430
- " </tr>\n",
431
- " <tr>\n",
432
- " <th>50%</th>\n",
433
- " <td>27.000000</td>\n",
434
- " <td>0.000000</td>\n",
435
- " <td>0.000000</td>\n",
436
- " <td>0.000000</td>\n",
437
- " <td>0.000000</td>\n",
438
- " <td>0.000000</td>\n",
439
- " </tr>\n",
440
- " <tr>\n",
441
- " <th>75%</th>\n",
442
- " <td>38.000000</td>\n",
443
- " <td>47.000000</td>\n",
444
- " <td>76.000000</td>\n",
445
- " <td>27.000000</td>\n",
446
- " <td>59.000000</td>\n",
447
- " <td>46.000000</td>\n",
448
- " </tr>\n",
449
- " <tr>\n",
450
- " <th>max</th>\n",
451
- " <td>79.000000</td>\n",
452
- " <td>14327.000000</td>\n",
453
- " <td>29813.000000</td>\n",
454
- " <td>23492.000000</td>\n",
455
- " <td>22408.000000</td>\n",
456
- " <td>24133.000000</td>\n",
457
- " </tr>\n",
458
- " </tbody>\n",
459
- "</table>\n",
460
- "</div>"
461
- ],
462
- "text/plain": [
463
- " Age RoomService FoodCourt ShoppingMall Spa \\\n",
464
- "count 8514.000000 8512.000000 8510.000000 8485.000000 8510.000000 \n",
465
- "mean 28.827930 224.687617 458.077203 173.729169 311.138778 \n",
466
- "std 14.489021 666.717663 1611.489240 604.696458 1136.705535 \n",
467
- "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
468
- "25% 19.000000 0.000000 0.000000 0.000000 0.000000 \n",
469
- "50% 27.000000 0.000000 0.000000 0.000000 0.000000 \n",
470
- "75% 38.000000 47.000000 76.000000 27.000000 59.000000 \n",
471
- "max 79.000000 14327.000000 29813.000000 23492.000000 22408.000000 \n",
472
- "\n",
473
- " VRDeck \n",
474
- "count 8505.000000 \n",
475
- "mean 304.854791 \n",
476
- "std 1145.717189 \n",
477
- "min 0.000000 \n",
478
- "25% 0.000000 \n",
479
- "50% 0.000000 \n",
480
- "75% 46.000000 \n",
481
- "max 24133.000000 "
482
- ]
483
- },
484
- "execution_count": 5,
485
- "metadata": {},
486
- "output_type": "execute_result"
487
- }
488
- ],
489
  "source": [
490
  "df.describe()"
491
  ]
@@ -502,7 +205,7 @@
502
  },
503
  {
504
  "cell_type": "code",
505
- "execution_count": 6,
506
  "metadata": {
507
  "execution": {
508
  "iopub.execute_input": "2023-06-08T04:31:45.482430Z",
@@ -512,28 +215,7 @@
512
  "shell.execute_reply.started": "2023-06-08T04:31:45.482386Z"
513
  }
514
  },
515
- "outputs": [
516
- {
517
- "name": "stdout",
518
- "output_type": "stream",
519
- "text": [
520
- "Total de valores nulos de PassengerId = 0 equivale al 0.00% del total de la columna.\n",
521
- "Total de valores nulos de HomePlanet = 201 equivale al 2.31% del total de la columna.\n",
522
- "Total de valores nulos de CryoSleep = 217 equivale al 2.50% del total de la columna.\n",
523
- "Total de valores nulos de Cabin = 199 equivale al 2.29% del total de la columna.\n",
524
- "Total de valores nulos de Destination = 182 equivale al 2.09% del total de la columna.\n",
525
- "Total de valores nulos de Age = 179 equivale al 2.06% del total de la columna.\n",
526
- "Total de valores nulos de VIP = 203 equivale al 2.34% del total de la columna.\n",
527
- "Total de valores nulos de RoomService = 181 equivale al 2.08% del total de la columna.\n",
528
- "Total de valores nulos de FoodCourt = 183 equivale al 2.11% del total de la columna.\n",
529
- "Total de valores nulos de ShoppingMall = 208 equivale al 2.39% del total de la columna.\n",
530
- "Total de valores nulos de Spa = 183 equivale al 2.11% del total de la columna.\n",
531
- "Total de valores nulos de VRDeck = 188 equivale al 2.16% del total de la columna.\n",
532
- "Total de valores nulos de Name = 200 equivale al 2.30% del total de la columna.\n",
533
- "Total de valores nulos de Transported = 0 equivale al 0.00% del total de la columna.\n"
534
- ]
535
- }
536
- ],
537
  "source": [
538
  "for feature in df.columns:\n",
539
  " x = df[feature].isna().sum()\n",
@@ -542,7 +224,7 @@
542
  },
543
  {
544
  "cell_type": "code",
545
- "execution_count": 7,
546
  "metadata": {
547
  "execution": {
548
  "iopub.execute_input": "2023-06-08T04:31:45.504577Z",
@@ -553,35 +235,7 @@
553
  },
554
  "id": "UmWpnVxQmnjO"
555
  },
556
- "outputs": [
557
- {
558
- "name": "stdout",
559
- "output_type": "stream",
560
- "text": [
561
- "<class 'pandas.core.frame.DataFrame'>\n",
562
- "RangeIndex: 8693 entries, 0 to 8692\n",
563
- "Data columns (total 14 columns):\n",
564
- " # Column Non-Null Count Dtype \n",
565
- "--- ------ -------------- ----- \n",
566
- " 0 PassengerId 8693 non-null object \n",
567
- " 1 HomePlanet 8492 non-null object \n",
568
- " 2 CryoSleep 8476 non-null object \n",
569
- " 3 Cabin 8494 non-null object \n",
570
- " 4 Destination 8511 non-null object \n",
571
- " 5 Age 8514 non-null float64\n",
572
- " 6 VIP 8490 non-null object \n",
573
- " 7 RoomService 8512 non-null float64\n",
574
- " 8 FoodCourt 8510 non-null float64\n",
575
- " 9 ShoppingMall 8485 non-null float64\n",
576
- " 10 Spa 8510 non-null float64\n",
577
- " 11 VRDeck 8505 non-null float64\n",
578
- " 12 Name 8493 non-null object \n",
579
- " 13 Transported 8693 non-null bool \n",
580
- "dtypes: bool(1), float64(6), object(7)\n",
581
- "memory usage: 891.5+ KB\n"
582
- ]
583
- }
584
- ],
585
  "source": [
586
  "df.info()"
587
  ]
@@ -879,7 +533,7 @@
879
  },
880
  {
881
  "cell_type": "code",
882
- "execution_count": 8,
883
  "metadata": {
884
  "execution": {
885
  "iopub.execute_input": "2023-06-08T04:31:47.665505Z",
@@ -1021,7 +675,7 @@
1021
  "4 70.0 151.0 565.0 2.0 True "
1022
  ]
1023
  },
1024
- "execution_count": 8,
1025
  "metadata": {},
1026
  "output_type": "execute_result"
1027
  }
@@ -1040,7 +694,7 @@
1040
  },
1041
  {
1042
  "cell_type": "code",
1043
- "execution_count": 9,
1044
  "metadata": {
1045
  "execution": {
1046
  "iopub.execute_input": "2023-06-08T04:31:47.695912Z",
@@ -1069,7 +723,7 @@
1069
  "dtype: int64"
1070
  ]
1071
  },
1072
- "execution_count": 9,
1073
  "metadata": {},
1074
  "output_type": "execute_result"
1075
  }
@@ -1081,7 +735,7 @@
1081
  },
1082
  {
1083
  "cell_type": "code",
1084
- "execution_count": 10,
1085
  "metadata": {
1086
  "execution": {
1087
  "iopub.execute_input": "2023-06-08T04:31:47.715118Z",
@@ -1091,20 +745,7 @@
1091
  "shell.execute_reply.started": "2023-06-08T04:31:47.715064Z"
1092
  }
1093
  },
1094
- "outputs": [
1095
- {
1096
- "name": "stdout",
1097
- "output_type": "stream",
1098
- "text": [
1099
- "Columna HomePlanet - ['Europa' 'Earth' 'Mars' nan]\n",
1100
- "Columna Cabin - ['B/0/P' 'F/0/S' 'A/0/S' ... 'G/1499/S' 'G/1500/S' 'E/608/S']\n",
1101
- "Columna Destination - ['TRAPPIST-1e' 'PSO J318.5-22' '55 Cancri e' nan]\n",
1102
- "Columna CryoSleep - [False True nan]\n",
1103
- "Columna VIP - [False True nan]\n",
1104
- "Columna Transported - [False True]\n"
1105
- ]
1106
- }
1107
- ],
1108
  "source": [
1109
  "for column in ('HomePlanet', 'Cabin', 'Destination','CryoSleep','VIP','Transported'):\n",
1110
  " print(\"Columna\",column,\"-\" , df[column].unique())"
@@ -1119,7 +760,7 @@
1119
  },
1120
  {
1121
  "cell_type": "code",
1122
- "execution_count": 11,
1123
  "metadata": {
1124
  "execution": {
1125
  "iopub.execute_input": "2023-06-08T04:31:47.730956Z",
@@ -1181,7 +822,7 @@
1181
  },
1182
  {
1183
  "cell_type": "code",
1184
- "execution_count": 12,
1185
  "metadata": {
1186
  "execution": {
1187
  "iopub.execute_input": "2023-06-08T04:31:47.971942Z",
@@ -1191,37 +832,14 @@
1191
  "shell.execute_reply.started": "2023-06-08T04:31:47.971909Z"
1192
  }
1193
  },
1194
- "outputs": [
1195
- {
1196
- "data": {
1197
- "text/plain": [
1198
- "HomePlanet int64\n",
1199
- "CryoSleep int64\n",
1200
- "Cabin int64\n",
1201
- "Destination int64\n",
1202
- "Age float64\n",
1203
- "VIP int64\n",
1204
- "RoomService float64\n",
1205
- "FoodCourt float64\n",
1206
- "ShoppingMall float64\n",
1207
- "Spa float64\n",
1208
- "VRDeck float64\n",
1209
- "Transported int64\n",
1210
- "dtype: object"
1211
- ]
1212
- },
1213
- "execution_count": 12,
1214
- "metadata": {},
1215
- "output_type": "execute_result"
1216
- }
1217
- ],
1218
  "source": [
1219
  "df.dtypes"
1220
  ]
1221
  },
1222
  {
1223
  "cell_type": "code",
1224
- "execution_count": 13,
1225
  "metadata": {
1226
  "execution": {
1227
  "iopub.execute_input": "2023-06-08T04:31:47.983075Z",
@@ -1250,7 +868,7 @@
1250
  "dtype: object"
1251
  ]
1252
  },
1253
- "execution_count": 13,
1254
  "metadata": {},
1255
  "output_type": "execute_result"
1256
  }
@@ -1610,7 +1228,7 @@
1610
  },
1611
  {
1612
  "cell_type": "code",
1613
- "execution_count": 14,
1614
  "metadata": {
1615
  "execution": {
1616
  "iopub.execute_input": "2023-06-08T04:58:12.183623Z",
@@ -1628,7 +1246,7 @@
1628
  "traceback": [
1629
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1630
  "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
1631
- "\u001b[0;32m<ipython-input-14-6b78e3830b05>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnombres_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnombres_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnombres_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
1632
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 5364\u001b[0m \u001b[0;31m# GH#44051 exclude bool, which would return a 2d ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5365\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5366\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5368\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1633
  "\u001b[0;31mIndexError\u001b[0m: index 6 is out of bounds for axis 0 with size 6"
1634
  ]
 
69
  },
70
  {
71
  "cell_type": "code",
72
+ "execution_count": null,
73
  "metadata": {
74
  "execution": {
75
  "iopub.execute_input": "2023-06-08T04:31:45.299138Z",
 
80
  },
81
  "id": "grAO86gTmnjJ"
82
  },
83
+ "outputs": [],
 
 
 
 
 
 
 
 
 
84
  "source": [
85
  "print(\"TensorFlow v\" + tf.__version__)\n",
86
  "print(\"TensorFlow Decision Forests v\" + tfdf.__version__)"
 
97
  },
98
  {
99
  "cell_type": "code",
100
+ "execution_count": 2,
101
  "metadata": {
102
  "execution": {
103
  "iopub.execute_input": "2023-06-08T04:31:45.313150Z",
 
134
  },
135
  {
136
  "cell_type": "code",
137
+ "execution_count": null,
138
  "metadata": {
139
  "execution": {
140
  "iopub.execute_input": "2023-06-08T04:31:45.382127Z",
 
145
  },
146
  "id": "nCx3PE1xmnjM"
147
  },
148
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
149
  "source": [
150
  "# Display the first 5 examples\n",
151
  "df.head(5)"
 
177
  },
178
  {
179
  "cell_type": "code",
180
+ "execution_count": null,
181
  "metadata": {
182
  "execution": {
183
  "iopub.execute_input": "2023-06-08T04:31:45.426363Z",
 
188
  },
189
  "id": "XjwG5wjfmnjO"
190
  },
191
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
192
  "source": [
193
  "df.describe()"
194
  ]
 
205
  },
206
  {
207
  "cell_type": "code",
208
+ "execution_count": null,
209
  "metadata": {
210
  "execution": {
211
  "iopub.execute_input": "2023-06-08T04:31:45.482430Z",
 
215
  "shell.execute_reply.started": "2023-06-08T04:31:45.482386Z"
216
  }
217
  },
218
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
219
  "source": [
220
  "for feature in df.columns:\n",
221
  " x = df[feature].isna().sum()\n",
 
224
  },
225
  {
226
  "cell_type": "code",
227
+ "execution_count": null,
228
  "metadata": {
229
  "execution": {
230
  "iopub.execute_input": "2023-06-08T04:31:45.504577Z",
 
235
  },
236
  "id": "UmWpnVxQmnjO"
237
  },
238
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
239
  "source": [
240
  "df.info()"
241
  ]
 
533
  },
534
  {
535
  "cell_type": "code",
536
+ "execution_count": 3,
537
  "metadata": {
538
  "execution": {
539
  "iopub.execute_input": "2023-06-08T04:31:47.665505Z",
 
675
  "4 70.0 151.0 565.0 2.0 True "
676
  ]
677
  },
678
+ "execution_count": 3,
679
  "metadata": {},
680
  "output_type": "execute_result"
681
  }
 
694
  },
695
  {
696
  "cell_type": "code",
697
+ "execution_count": 4,
698
  "metadata": {
699
  "execution": {
700
  "iopub.execute_input": "2023-06-08T04:31:47.695912Z",
 
723
  "dtype: int64"
724
  ]
725
  },
726
+ "execution_count": 4,
727
  "metadata": {},
728
  "output_type": "execute_result"
729
  }
 
735
  },
736
  {
737
  "cell_type": "code",
738
+ "execution_count": null,
739
  "metadata": {
740
  "execution": {
741
  "iopub.execute_input": "2023-06-08T04:31:47.715118Z",
 
745
  "shell.execute_reply.started": "2023-06-08T04:31:47.715064Z"
746
  }
747
  },
748
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
749
  "source": [
750
  "for column in ('HomePlanet', 'Cabin', 'Destination','CryoSleep','VIP','Transported'):\n",
751
  " print(\"Columna\",column,\"-\" , df[column].unique())"
 
760
  },
761
  {
762
  "cell_type": "code",
763
+ "execution_count": 5,
764
  "metadata": {
765
  "execution": {
766
  "iopub.execute_input": "2023-06-08T04:31:47.730956Z",
 
822
  },
823
  {
824
  "cell_type": "code",
825
+ "execution_count": null,
826
  "metadata": {
827
  "execution": {
828
  "iopub.execute_input": "2023-06-08T04:31:47.971942Z",
 
832
  "shell.execute_reply.started": "2023-06-08T04:31:47.971909Z"
833
  }
834
  },
835
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
836
  "source": [
837
  "df.dtypes"
838
  ]
839
  },
840
  {
841
  "cell_type": "code",
842
+ "execution_count": 6,
843
  "metadata": {
844
  "execution": {
845
  "iopub.execute_input": "2023-06-08T04:31:47.983075Z",
 
868
  "dtype: object"
869
  ]
870
  },
871
+ "execution_count": 6,
872
  "metadata": {},
873
  "output_type": "execute_result"
874
  }
 
1228
  },
1229
  {
1230
  "cell_type": "code",
1231
+ "execution_count": 7,
1232
  "metadata": {
1233
  "execution": {
1234
  "iopub.execute_input": "2023-06-08T04:58:12.183623Z",
 
1246
  "traceback": [
1247
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1248
  "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
1249
+ "\u001b[0;32m<ipython-input-7-6b78e3830b05>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnombres_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnombres_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnombres_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
1250
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 5364\u001b[0m \u001b[0;31m# GH#44051 exclude bool, which would return a 2d ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5365\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5366\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5368\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1251
  "\u001b[0;31mIndexError\u001b[0m: index 6 is out of bounds for axis 0 with size 6"
1252
  ]
benchmark/pandas_15/pandas_15_fixed.ipynb CHANGED
@@ -51,7 +51,7 @@
51
  },
52
  {
53
  "cell_type": "code",
54
- "execution_count": 3,
55
  "id": "3879efdb",
56
  "metadata": {
57
  "execution": {
@@ -62,207 +62,7 @@
62
  "shell.execute_reply.started": "2023-06-11T14:19:34.854293Z"
63
  }
64
  },
65
- "outputs": [
66
- {
67
- "data": {
68
- "text/html": [
69
- "<div>\n",
70
- "<style scoped>\n",
71
- " .dataframe tbody tr th:only-of-type {\n",
72
- " vertical-align: middle;\n",
73
- " }\n",
74
- "\n",
75
- " .dataframe tbody tr th {\n",
76
- " vertical-align: top;\n",
77
- " }\n",
78
- "\n",
79
- " .dataframe thead th {\n",
80
- " text-align: right;\n",
81
- " }\n",
82
- "</style>\n",
83
- "<table border=\"1\" class=\"dataframe\">\n",
84
- " <thead>\n",
85
- " <tr style=\"text-align: right;\">\n",
86
- " <th></th>\n",
87
- " <th>Id</th>\n",
88
- " <th>MSSubClass</th>\n",
89
- " <th>MSZoning</th>\n",
90
- " <th>LotFrontage</th>\n",
91
- " <th>LotArea</th>\n",
92
- " <th>Street</th>\n",
93
- " <th>Alley</th>\n",
94
- " <th>LotShape</th>\n",
95
- " <th>LandContour</th>\n",
96
- " <th>Utilities</th>\n",
97
- " <th>...</th>\n",
98
- " <th>PoolArea</th>\n",
99
- " <th>PoolQC</th>\n",
100
- " <th>Fence</th>\n",
101
- " <th>MiscFeature</th>\n",
102
- " <th>MiscVal</th>\n",
103
- " <th>MoSold</th>\n",
104
- " <th>YrSold</th>\n",
105
- " <th>SaleType</th>\n",
106
- " <th>SaleCondition</th>\n",
107
- " <th>SalePrice</th>\n",
108
- " </tr>\n",
109
- " </thead>\n",
110
- " <tbody>\n",
111
- " <tr>\n",
112
- " <th>0</th>\n",
113
- " <td>1</td>\n",
114
- " <td>60</td>\n",
115
- " <td>RL</td>\n",
116
- " <td>65.0</td>\n",
117
- " <td>8450</td>\n",
118
- " <td>Pave</td>\n",
119
- " <td>NaN</td>\n",
120
- " <td>Reg</td>\n",
121
- " <td>Lvl</td>\n",
122
- " <td>AllPub</td>\n",
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- " <td>...</td>\n",
124
- " <td>0</td>\n",
125
- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
127
- " <td>NaN</td>\n",
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- " <td>0</td>\n",
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- " <td>2</td>\n",
130
- " <td>2008</td>\n",
131
- " <td>WD</td>\n",
132
- " <td>Normal</td>\n",
133
- " <td>208500</td>\n",
134
- " </tr>\n",
135
- " <tr>\n",
136
- " <th>1</th>\n",
137
- " <td>2</td>\n",
138
- " <td>20</td>\n",
139
- " <td>RL</td>\n",
140
- " <td>80.0</td>\n",
141
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- " <td>2007</td>\n",
155
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156
- " <td>Normal</td>\n",
157
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158
- " </tr>\n",
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160
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162
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163
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164
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165
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166
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167
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168
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169
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- " <td>AllPub</td>\n",
171
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172
- " <td>0</td>\n",
173
- " <td>NaN</td>\n",
174
- " <td>NaN</td>\n",
175
- " <td>NaN</td>\n",
176
- " <td>0</td>\n",
177
- " <td>9</td>\n",
178
- " <td>2008</td>\n",
179
- " <td>WD</td>\n",
180
- " <td>Normal</td>\n",
181
- " <td>223500</td>\n",
182
- " </tr>\n",
183
- " <tr>\n",
184
- " <th>3</th>\n",
185
- " <td>4</td>\n",
186
- " <td>70</td>\n",
187
- " <td>RL</td>\n",
188
- " <td>60.0</td>\n",
189
- " <td>9550</td>\n",
190
- " <td>Pave</td>\n",
191
- " <td>NaN</td>\n",
192
- " <td>IR1</td>\n",
193
- " <td>Lvl</td>\n",
194
- " <td>AllPub</td>\n",
195
- " <td>...</td>\n",
196
- " <td>0</td>\n",
197
- " <td>NaN</td>\n",
198
- " <td>NaN</td>\n",
199
- " <td>NaN</td>\n",
200
- " <td>0</td>\n",
201
- " <td>2</td>\n",
202
- " <td>2006</td>\n",
203
- " <td>WD</td>\n",
204
- " <td>Abnorml</td>\n",
205
- " <td>140000</td>\n",
206
- " </tr>\n",
207
- " <tr>\n",
208
- " <th>4</th>\n",
209
- " <td>5</td>\n",
210
- " <td>60</td>\n",
211
- " <td>RL</td>\n",
212
- " <td>84.0</td>\n",
213
- " <td>14260</td>\n",
214
- " <td>Pave</td>\n",
215
- " <td>NaN</td>\n",
216
- " <td>IR1</td>\n",
217
- " <td>Lvl</td>\n",
218
- " <td>AllPub</td>\n",
219
- " <td>...</td>\n",
220
- " <td>0</td>\n",
221
- " <td>NaN</td>\n",
222
- " <td>NaN</td>\n",
223
- " <td>NaN</td>\n",
224
- " <td>0</td>\n",
225
- " <td>12</td>\n",
226
- " <td>2008</td>\n",
227
- " <td>WD</td>\n",
228
- " <td>Normal</td>\n",
229
- " <td>250000</td>\n",
230
- " </tr>\n",
231
- " </tbody>\n",
232
- "</table>\n",
233
- "<p>5 rows × 81 columns</p>\n",
234
- "</div>"
235
- ],
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- "text/plain": [
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- " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
238
- "0 1 60 RL 65.0 8450 Pave NaN Reg \n",
239
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- "4 5 60 RL 84.0 14260 Pave NaN IR1 \n",
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- "\n",
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- " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold \\\n",
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- "\n",
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- " YrSold SaleType SaleCondition SalePrice \n",
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- "[5 rows x 81 columns]"
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1094
- "Categorical columns: ['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition'] \n",
1095
- "Total Categorical column: 37\n",
1096
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1097
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1098
- "Numerical columns: ['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice'] \n",
1099
- "Total Numerical column: 38\n"
1100
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1101
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1104
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1105
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- " Null count Unique count\n",
1162
- "MSZoning 0 5\n",
1163
- "Street 0 2\n",
1164
- "LotShape 0 4\n",
1165
- "LandContour 0 4\n",
1166
- "Utilities 0 2\n",
1167
- "LotConfig 0 5\n",
1168
- "LandSlope 0 3\n",
1169
- "Neighborhood 0 25\n",
1170
- "Condition1 0 9\n",
1171
- "Condition2 0 8\n",
1172
- "BldgType 0 5\n",
1173
- "HouseStyle 0 8\n",
1174
- "RoofStyle 0 6\n",
1175
- "RoofMatl 0 8\n",
1176
- "Exterior1st 0 15\n",
1177
- "Exterior2nd 0 16\n",
1178
- "ExterQual 0 4\n",
1179
- "ExterCond 0 5\n",
1180
- "Foundation 0 6\n",
1181
- "BsmtQual 37 4\n",
1182
- "BsmtCond 37 4\n",
1183
- "BsmtExposure 38 4\n",
1184
- "BsmtFinType1 37 6\n",
1185
- "BsmtFinType2 38 6\n",
1186
- "Heating 0 6\n",
1187
- "HeatingQC 0 5\n",
1188
- "CentralAir 0 2\n",
1189
- "Electrical 1 5\n",
1190
- "KitchenQual 0 4\n",
1191
- "Functional 0 7\n",
1192
- "GarageType 81 6\n",
1193
- "GarageFinish 81 3\n",
1194
- "GarageQual 81 5\n",
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- "GarageCond 81 5\n",
1196
- "PavedDrive 0 3\n",
1197
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- " <td>Inside</td>\n",
1278
- " <td>Gtl</td>\n",
1279
- " <td>CollgCr</td>\n",
1280
- " <td>Norm</td>\n",
1281
- " <td>Norm</td>\n",
1282
- " <td>...</td>\n",
1283
- " <td>SBrkr</td>\n",
1284
- " <td>Gd</td>\n",
1285
- " <td>Typ</td>\n",
1286
- " <td>Attchd</td>\n",
1287
- " <td>RFn</td>\n",
1288
- " <td>TA</td>\n",
1289
- " <td>TA</td>\n",
1290
- " <td>Y</td>\n",
1291
- " <td>WD</td>\n",
1292
- " <td>Normal</td>\n",
1293
- " </tr>\n",
1294
- " <tr>\n",
1295
- " <th>1</th>\n",
1296
- " <td>RL</td>\n",
1297
- " <td>Pave</td>\n",
1298
- " <td>Reg</td>\n",
1299
- " <td>Lvl</td>\n",
1300
- " <td>AllPub</td>\n",
1301
- " <td>FR2</td>\n",
1302
- " <td>Gtl</td>\n",
1303
- " <td>Veenker</td>\n",
1304
- " <td>Feedr</td>\n",
1305
- " <td>Norm</td>\n",
1306
- " <td>...</td>\n",
1307
- " <td>SBrkr</td>\n",
1308
- " <td>TA</td>\n",
1309
- " <td>Typ</td>\n",
1310
- " <td>Attchd</td>\n",
1311
- " <td>RFn</td>\n",
1312
- " <td>TA</td>\n",
1313
- " <td>TA</td>\n",
1314
- " <td>Y</td>\n",
1315
- " <td>WD</td>\n",
1316
- " <td>Normal</td>\n",
1317
- " </tr>\n",
1318
- " <tr>\n",
1319
- " <th>2</th>\n",
1320
- " <td>RL</td>\n",
1321
- " <td>Pave</td>\n",
1322
- " <td>IR1</td>\n",
1323
- " <td>Lvl</td>\n",
1324
- " <td>AllPub</td>\n",
1325
- " <td>Inside</td>\n",
1326
- " <td>Gtl</td>\n",
1327
- " <td>CollgCr</td>\n",
1328
- " <td>Norm</td>\n",
1329
- " <td>Norm</td>\n",
1330
- " <td>...</td>\n",
1331
- " <td>SBrkr</td>\n",
1332
- " <td>Gd</td>\n",
1333
- " <td>Typ</td>\n",
1334
- " <td>Attchd</td>\n",
1335
- " <td>RFn</td>\n",
1336
- " <td>TA</td>\n",
1337
- " <td>TA</td>\n",
1338
- " <td>Y</td>\n",
1339
- " <td>WD</td>\n",
1340
- " <td>Normal</td>\n",
1341
- " </tr>\n",
1342
- " <tr>\n",
1343
- " <th>3</th>\n",
1344
- " <td>RL</td>\n",
1345
- " <td>Pave</td>\n",
1346
- " <td>IR1</td>\n",
1347
- " <td>Lvl</td>\n",
1348
- " <td>AllPub</td>\n",
1349
- " <td>Corner</td>\n",
1350
- " <td>Gtl</td>\n",
1351
- " <td>Crawfor</td>\n",
1352
- " <td>Norm</td>\n",
1353
- " <td>Norm</td>\n",
1354
- " <td>...</td>\n",
1355
- " <td>SBrkr</td>\n",
1356
- " <td>Gd</td>\n",
1357
- " <td>Typ</td>\n",
1358
- " <td>Detchd</td>\n",
1359
- " <td>Unf</td>\n",
1360
- " <td>TA</td>\n",
1361
- " <td>TA</td>\n",
1362
- " <td>Y</td>\n",
1363
- " <td>WD</td>\n",
1364
- " <td>Abnorml</td>\n",
1365
- " </tr>\n",
1366
- " <tr>\n",
1367
- " <th>4</th>\n",
1368
- " <td>RL</td>\n",
1369
- " <td>Pave</td>\n",
1370
- " <td>IR1</td>\n",
1371
- " <td>Lvl</td>\n",
1372
- " <td>AllPub</td>\n",
1373
- " <td>FR2</td>\n",
1374
- " <td>Gtl</td>\n",
1375
- " <td>NoRidge</td>\n",
1376
- " <td>Norm</td>\n",
1377
- " <td>Norm</td>\n",
1378
- " <td>...</td>\n",
1379
- " <td>SBrkr</td>\n",
1380
- " <td>Gd</td>\n",
1381
- " <td>Typ</td>\n",
1382
- " <td>Attchd</td>\n",
1383
- " <td>RFn</td>\n",
1384
- " <td>TA</td>\n",
1385
- " <td>TA</td>\n",
1386
- " <td>Y</td>\n",
1387
- " <td>WD</td>\n",
1388
- " <td>Normal</td>\n",
1389
- " </tr>\n",
1390
- " </tbody>\n",
1391
- "</table>\n",
1392
- "<p>5 rows × 37 columns</p>\n",
1393
- "</div>"
1394
- ],
1395
- "text/plain": [
1396
- " MSZoning Street LotShape LandContour Utilities LotConfig LandSlope \\\n",
1397
- "0 RL Pave Reg Lvl AllPub Inside Gtl \n",
1398
- "1 RL Pave Reg Lvl AllPub FR2 Gtl \n",
1399
- "2 RL Pave IR1 Lvl AllPub Inside Gtl \n",
1400
- "3 RL Pave IR1 Lvl AllPub Corner Gtl \n",
1401
- "4 RL Pave IR1 Lvl AllPub FR2 Gtl \n",
1402
- "\n",
1403
- " Neighborhood Condition1 Condition2 ... Electrical KitchenQual Functional \\\n",
1404
- "0 CollgCr Norm Norm ... SBrkr Gd Typ \n",
1405
- "1 Veenker Feedr Norm ... SBrkr TA Typ \n",
1406
- "2 CollgCr Norm Norm ... SBrkr Gd Typ \n",
1407
- "3 Crawfor Norm Norm ... SBrkr Gd Typ \n",
1408
- "4 NoRidge Norm Norm ... SBrkr Gd Typ \n",
1409
- "\n",
1410
- " GarageType GarageFinish GarageQual GarageCond PavedDrive SaleType \\\n",
1411
- "0 Attchd RFn TA TA Y WD \n",
1412
- "1 Attchd RFn TA TA Y WD \n",
1413
- "2 Attchd RFn TA TA Y WD \n",
1414
- "3 Detchd Unf TA TA Y WD \n",
1415
- "4 Attchd RFn TA TA Y WD \n",
1416
- "\n",
1417
- " SaleCondition \n",
1418
- "0 Normal \n",
1419
- "1 Normal \n",
1420
- "2 Normal \n",
1421
- "3 Abnorml \n",
1422
- "4 Normal \n",
1423
- "\n",
1424
- "[5 rows x 37 columns]"
1425
- ]
1426
- },
1427
- "execution_count": 12,
1428
- "metadata": {},
1429
- "output_type": "execute_result"
1430
- }
1431
- ],
1432
  "source": [
1433
  "train[categorical_col].head()"
1434
  ]
1435
  },
1436
  {
1437
  "cell_type": "code",
1438
- "execution_count": 13,
1439
  "id": "13215608",
1440
  "metadata": {
1441
  "execution": {
@@ -1454,7 +865,7 @@
1454
  },
1455
  {
1456
  "cell_type": "code",
1457
- "execution_count": 14,
1458
  "id": "e500cc07",
1459
  "metadata": {
1460
  "execution": {
@@ -1466,52 +877,7 @@
1466
  },
1467
  "scrolled": true
1468
  },
1469
- "outputs": [
1470
- {
1471
- "name": "stdout",
1472
- "output_type": "stream",
1473
- "text": [
1474
- " Null count Unique count\n",
1475
- "MSZoning 0 5\n",
1476
- "Street 0 2\n",
1477
- "LotShape 0 4\n",
1478
- "LandContour 0 4\n",
1479
- "Utilities 0 2\n",
1480
- "LotConfig 0 5\n",
1481
- "LandSlope 0 3\n",
1482
- "Neighborhood 0 25\n",
1483
- "Condition1 0 9\n",
1484
- "Condition2 0 8\n",
1485
- "BldgType 0 5\n",
1486
- "HouseStyle 0 8\n",
1487
- "RoofStyle 0 6\n",
1488
- "RoofMatl 0 8\n",
1489
- "Exterior1st 0 15\n",
1490
- "Exterior2nd 0 16\n",
1491
- "ExterQual 0 4\n",
1492
- "ExterCond 0 5\n",
1493
- "Foundation 0 6\n",
1494
- "BsmtQual 37 4\n",
1495
- "BsmtCond 37 4\n",
1496
- "BsmtExposure 38 4\n",
1497
- "BsmtFinType1 37 6\n",
1498
- "BsmtFinType2 38 6\n",
1499
- "Heating 0 6\n",
1500
- "HeatingQC 0 5\n",
1501
- "CentralAir 0 2\n",
1502
- "Electrical 1 5\n",
1503
- "KitchenQual 0 4\n",
1504
- "Functional 0 7\n",
1505
- "GarageType 81 6\n",
1506
- "GarageFinish 81 3\n",
1507
- "GarageQual 81 5\n",
1508
- "GarageCond 81 5\n",
1509
- "PavedDrive 0 3\n",
1510
- "SaleType 0 9\n",
1511
- "SaleCondition 0 6\n"
1512
- ]
1513
- }
1514
- ],
1515
  "source": [
1516
  "data = {\n",
1517
  " 'Null count': null_count,\n",
@@ -1523,7 +889,7 @@
1523
  },
1524
  {
1525
  "cell_type": "code",
1526
- "execution_count": 22,
1527
  "id": "8fe3b97b",
1528
  "metadata": {
1529
  "execution": {
 
51
  },
52
  {
53
  "cell_type": "code",
54
+ "execution_count": null,
55
  "id": "3879efdb",
56
  "metadata": {
57
  "execution": {
 
62
  "shell.execute_reply.started": "2023-06-11T14:19:34.854293Z"
63
  }
64
  },
65
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
66
  "source": [
67
  "train.head()"
68
  ]
 
618
  },
619
  {
620
  "cell_type": "code",
621
+ "execution_count": null,
622
  "id": "9116a799",
623
  "metadata": {
624
  "execution": {
 
629
  "shell.execute_reply.started": "2023-06-11T14:20:06.472701Z"
630
  }
631
  },
632
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
633
  "source": [
634
  "train.shape"
635
  ]
636
  },
637
  {
638
  "cell_type": "code",
639
+ "execution_count": null,
640
  "id": "f23afceb",
641
  "metadata": {
642
  "execution": {
 
647
  "shell.execute_reply.started": "2023-06-11T14:20:07.457995Z"
648
  }
649
  },
650
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
651
  "source": [
652
  "test.shape"
653
  ]
654
  },
655
  {
656
  "cell_type": "code",
657
+ "execution_count": null,
658
  "id": "f9409852",
659
  "metadata": {
660
  "execution": {
 
665
  "shell.execute_reply.started": "2023-06-11T14:20:07.909893Z"
666
  }
667
  },
668
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
669
  "source": [
670
  "submission.shape"
671
  ]
672
  },
673
  {
674
  "cell_type": "code",
675
+ "execution_count": null,
676
  "id": "0eda05c7",
677
  "metadata": {
678
  "execution": {
 
684
  },
685
  "scrolled": true
686
  },
687
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
688
  "source": [
689
  "new=(((train.isnull().sum())/(train.shape[0]))*100)\n",
690
  "new.to_list()"
 
710
  },
711
  {
712
  "cell_type": "code",
713
+ "execution_count": 3,
714
  "id": "a867388f",
715
  "metadata": {
716
  "execution": {
 
751
  },
752
  {
753
  "cell_type": "code",
754
+ "execution_count": null,
755
  "id": "6e6a2825",
756
  "metadata": {
757
  "execution": {
 
762
  "shell.execute_reply.started": "2023-06-11T14:22:15.173686Z"
763
  }
764
  },
765
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
766
  "source": [
767
  "categorical_col = list(train.select_dtypes(include=['object']).columns)\n",
768
  "numerical_col =list(train.select_dtypes(exclude=['object']).columns)\n",
 
785
  },
786
  {
787
  "cell_type": "code",
788
+ "execution_count": null,
789
  "id": "a0ab72f3",
790
  "metadata": {
791
  "execution": {
 
804
  },
805
  {
806
  "cell_type": "code",
807
+ "execution_count": null,
808
  "id": "085c75ed",
809
  "metadata": {
810
  "execution": {
 
816
  },
817
  "scrolled": true
818
  },
819
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
820
  "source": [
821
  "data = {\n",
822
  " 'Null count': null_count,\n",
 
828
  },
829
  {
830
  "cell_type": "code",
831
+ "execution_count": null,
832
  "id": "29ebf8d9",
833
  "metadata": {
834
  "execution": {
 
839
  "shell.execute_reply.started": "2023-06-11T14:20:13.734928Z"
840
  }
841
  },
842
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
843
  "source": [
844
  "train[categorical_col].head()"
845
  ]
846
  },
847
  {
848
  "cell_type": "code",
849
+ "execution_count": null,
850
  "id": "13215608",
851
  "metadata": {
852
  "execution": {
 
865
  },
866
  {
867
  "cell_type": "code",
868
+ "execution_count": null,
869
  "id": "e500cc07",
870
  "metadata": {
871
  "execution": {
 
877
  },
878
  "scrolled": true
879
  },
880
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
881
  "source": [
882
  "data = {\n",
883
  " 'Null count': null_count,\n",
 
889
  },
890
  {
891
  "cell_type": "code",
892
+ "execution_count": 4,
893
  "id": "8fe3b97b",
894
  "metadata": {
895
  "execution": {
benchmark/pandas_15/pandas_15_reproduced.ipynb CHANGED
@@ -51,7 +51,7 @@
51
  },
52
  {
53
  "cell_type": "code",
54
- "execution_count": 3,
55
  "id": "3879efdb",
56
  "metadata": {
57
  "execution": {
@@ -62,207 +62,7 @@
62
  "shell.execute_reply.started": "2023-06-11T14:19:34.854293Z"
63
  }
64
  },
65
- "outputs": [
66
- {
67
- "data": {
68
- "text/html": [
69
- "<div>\n",
70
- "<style scoped>\n",
71
- " .dataframe tbody tr th:only-of-type {\n",
72
- " vertical-align: middle;\n",
73
- " }\n",
74
- "\n",
75
- " .dataframe tbody tr th {\n",
76
- " vertical-align: top;\n",
77
- " }\n",
78
- "\n",
79
- " .dataframe thead th {\n",
80
- " text-align: right;\n",
81
- " }\n",
82
- "</style>\n",
83
- "<table border=\"1\" class=\"dataframe\">\n",
84
- " <thead>\n",
85
- " <tr style=\"text-align: right;\">\n",
86
- " <th></th>\n",
87
- " <th>Id</th>\n",
88
- " <th>MSSubClass</th>\n",
89
- " <th>MSZoning</th>\n",
90
- " <th>LotFrontage</th>\n",
91
- " <th>LotArea</th>\n",
92
- " <th>Street</th>\n",
93
- " <th>Alley</th>\n",
94
- " <th>LotShape</th>\n",
95
- " <th>LandContour</th>\n",
96
- " <th>Utilities</th>\n",
97
- " <th>...</th>\n",
98
- " <th>PoolArea</th>\n",
99
- " <th>PoolQC</th>\n",
100
- " <th>Fence</th>\n",
101
- " <th>MiscFeature</th>\n",
102
- " <th>MiscVal</th>\n",
103
- " <th>MoSold</th>\n",
104
- " <th>YrSold</th>\n",
105
- " <th>SaleType</th>\n",
106
- " <th>SaleCondition</th>\n",
107
- " <th>SalePrice</th>\n",
108
- " </tr>\n",
109
- " </thead>\n",
110
- " <tbody>\n",
111
- " <tr>\n",
112
- " <th>0</th>\n",
113
- " <td>1</td>\n",
114
- " <td>60</td>\n",
115
- " <td>RL</td>\n",
116
- " <td>65.0</td>\n",
117
- " <td>8450</td>\n",
118
- " <td>Pave</td>\n",
119
- " <td>NaN</td>\n",
120
- " <td>Reg</td>\n",
121
- " <td>Lvl</td>\n",
122
- " <td>AllPub</td>\n",
123
- " <td>...</td>\n",
124
- " <td>0</td>\n",
125
- " <td>NaN</td>\n",
126
- " <td>NaN</td>\n",
127
- " <td>NaN</td>\n",
128
- " <td>0</td>\n",
129
- " <td>2</td>\n",
130
- " <td>2008</td>\n",
131
- " <td>WD</td>\n",
132
- " <td>Normal</td>\n",
133
- " <td>208500</td>\n",
134
- " </tr>\n",
135
- " <tr>\n",
136
- " <th>1</th>\n",
137
- " <td>2</td>\n",
138
- " <td>20</td>\n",
139
- " <td>RL</td>\n",
140
- " <td>80.0</td>\n",
141
- " <td>9600</td>\n",
142
- " <td>Pave</td>\n",
143
- " <td>NaN</td>\n",
144
- " <td>Reg</td>\n",
145
- " <td>Lvl</td>\n",
146
- " <td>AllPub</td>\n",
147
- " <td>...</td>\n",
148
- " <td>0</td>\n",
149
- " <td>NaN</td>\n",
150
- " <td>NaN</td>\n",
151
- " <td>NaN</td>\n",
152
- " <td>0</td>\n",
153
- " <td>5</td>\n",
154
- " <td>2007</td>\n",
155
- " <td>WD</td>\n",
156
- " <td>Normal</td>\n",
157
- " <td>181500</td>\n",
158
- " </tr>\n",
159
- " <tr>\n",
160
- " <th>2</th>\n",
161
- " <td>3</td>\n",
162
- " <td>60</td>\n",
163
- " <td>RL</td>\n",
164
- " <td>68.0</td>\n",
165
- " <td>11250</td>\n",
166
- " <td>Pave</td>\n",
167
- " <td>NaN</td>\n",
168
- " <td>IR1</td>\n",
169
- " <td>Lvl</td>\n",
170
- " <td>AllPub</td>\n",
171
- " <td>...</td>\n",
172
- " <td>0</td>\n",
173
- " <td>NaN</td>\n",
174
- " <td>NaN</td>\n",
175
- " <td>NaN</td>\n",
176
- " <td>0</td>\n",
177
- " <td>9</td>\n",
178
- " <td>2008</td>\n",
179
- " <td>WD</td>\n",
180
- " <td>Normal</td>\n",
181
- " <td>223500</td>\n",
182
- " </tr>\n",
183
- " <tr>\n",
184
- " <th>3</th>\n",
185
- " <td>4</td>\n",
186
- " <td>70</td>\n",
187
- " <td>RL</td>\n",
188
- " <td>60.0</td>\n",
189
- " <td>9550</td>\n",
190
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191
- " <td>NaN</td>\n",
192
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193
- " <td>Lvl</td>\n",
194
- " <td>AllPub</td>\n",
195
- " <td>...</td>\n",
196
- " <td>0</td>\n",
197
- " <td>NaN</td>\n",
198
- " <td>NaN</td>\n",
199
- " <td>NaN</td>\n",
200
- " <td>0</td>\n",
201
- " <td>2</td>\n",
202
- " <td>2006</td>\n",
203
- " <td>WD</td>\n",
204
- " <td>Abnorml</td>\n",
205
- " <td>140000</td>\n",
206
- " </tr>\n",
207
- " <tr>\n",
208
- " <th>4</th>\n",
209
- " <td>5</td>\n",
210
- " <td>60</td>\n",
211
- " <td>RL</td>\n",
212
- " <td>84.0</td>\n",
213
- " <td>14260</td>\n",
214
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215
- " <td>NaN</td>\n",
216
- " <td>IR1</td>\n",
217
- " <td>Lvl</td>\n",
218
- " <td>AllPub</td>\n",
219
- " <td>...</td>\n",
220
- " <td>0</td>\n",
221
- " <td>NaN</td>\n",
222
- " <td>NaN</td>\n",
223
- " <td>NaN</td>\n",
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- " <td>0</td>\n",
225
- " <td>12</td>\n",
226
- " <td>2008</td>\n",
227
- " <td>WD</td>\n",
228
- " <td>Normal</td>\n",
229
- " <td>250000</td>\n",
230
- " </tr>\n",
231
- " </tbody>\n",
232
- "</table>\n",
233
- "<p>5 rows × 81 columns</p>\n",
234
- "</div>"
235
- ],
236
- "text/plain": [
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- " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
238
- "0 1 60 RL 65.0 8450 Pave NaN Reg \n",
239
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- "\n",
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- " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold \\\n",
245
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246
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- "3 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n",
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- "4 Lvl AllPub ... 0 NaN NaN NaN 0 12 \n",
250
- "\n",
251
- " YrSold SaleType SaleCondition SalePrice \n",
252
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253
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254
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255
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- "\n",
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- "[5 rows x 81 columns]"
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1034
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1036
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1091
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1092
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1094
- "Categorical columns: ['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition'] \n",
1095
- "Total Categorical column: 37\n",
1096
- "\n",
1097
- "\n",
1098
- "Numerical columns: ['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice'] \n",
1099
- "Total Numerical column: 38\n"
1100
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1101
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1102
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  "source": [
1104
  "categorical_col = list(train.select_dtypes(include=['object']).columns)\n",
1105
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1122
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1124
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1159
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1161
- " Null count Unique count\n",
1162
- "MSZoning 0 5\n",
1163
- "Street 0 2\n",
1164
- "LotShape 0 4\n",
1165
- "LandContour 0 4\n",
1166
- "Utilities 0 2\n",
1167
- "LotConfig 0 5\n",
1168
- "LandSlope 0 3\n",
1169
- "Neighborhood 0 25\n",
1170
- "Condition1 0 9\n",
1171
- "Condition2 0 8\n",
1172
- "BldgType 0 5\n",
1173
- "HouseStyle 0 8\n",
1174
- "RoofStyle 0 6\n",
1175
- "RoofMatl 0 8\n",
1176
- "Exterior1st 0 15\n",
1177
- "Exterior2nd 0 16\n",
1178
- "ExterQual 0 4\n",
1179
- "ExterCond 0 5\n",
1180
- "Foundation 0 6\n",
1181
- "BsmtQual 37 4\n",
1182
- "BsmtCond 37 4\n",
1183
- "BsmtExposure 38 4\n",
1184
- "BsmtFinType1 37 6\n",
1185
- "BsmtFinType2 38 6\n",
1186
- "Heating 0 6\n",
1187
- "HeatingQC 0 5\n",
1188
- "CentralAir 0 2\n",
1189
- "Electrical 1 5\n",
1190
- "KitchenQual 0 4\n",
1191
- "Functional 0 7\n",
1192
- "GarageType 81 6\n",
1193
- "GarageFinish 81 3\n",
1194
- "GarageQual 81 5\n",
1195
- "GarageCond 81 5\n",
1196
- "PavedDrive 0 3\n",
1197
- "SaleType 0 9\n",
1198
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1199
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1203
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1204
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1300
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1301
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1320
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- " <td>TA</td>\n",
1362
- " <td>Y</td>\n",
1363
- " <td>WD</td>\n",
1364
- " <td>Abnorml</td>\n",
1365
- " </tr>\n",
1366
- " <tr>\n",
1367
- " <th>4</th>\n",
1368
- " <td>RL</td>\n",
1369
- " <td>Pave</td>\n",
1370
- " <td>IR1</td>\n",
1371
- " <td>Lvl</td>\n",
1372
- " <td>AllPub</td>\n",
1373
- " <td>FR2</td>\n",
1374
- " <td>Gtl</td>\n",
1375
- " <td>NoRidge</td>\n",
1376
- " <td>Norm</td>\n",
1377
- " <td>Norm</td>\n",
1378
- " <td>...</td>\n",
1379
- " <td>SBrkr</td>\n",
1380
- " <td>Gd</td>\n",
1381
- " <td>Typ</td>\n",
1382
- " <td>Attchd</td>\n",
1383
- " <td>RFn</td>\n",
1384
- " <td>TA</td>\n",
1385
- " <td>TA</td>\n",
1386
- " <td>Y</td>\n",
1387
- " <td>WD</td>\n",
1388
- " <td>Normal</td>\n",
1389
- " </tr>\n",
1390
- " </tbody>\n",
1391
- "</table>\n",
1392
- "<p>5 rows × 37 columns</p>\n",
1393
- "</div>"
1394
- ],
1395
- "text/plain": [
1396
- " MSZoning Street LotShape LandContour Utilities LotConfig LandSlope \\\n",
1397
- "0 RL Pave Reg Lvl AllPub Inside Gtl \n",
1398
- "1 RL Pave Reg Lvl AllPub FR2 Gtl \n",
1399
- "2 RL Pave IR1 Lvl AllPub Inside Gtl \n",
1400
- "3 RL Pave IR1 Lvl AllPub Corner Gtl \n",
1401
- "4 RL Pave IR1 Lvl AllPub FR2 Gtl \n",
1402
- "\n",
1403
- " Neighborhood Condition1 Condition2 ... Electrical KitchenQual Functional \\\n",
1404
- "0 CollgCr Norm Norm ... SBrkr Gd Typ \n",
1405
- "1 Veenker Feedr Norm ... SBrkr TA Typ \n",
1406
- "2 CollgCr Norm Norm ... SBrkr Gd Typ \n",
1407
- "3 Crawfor Norm Norm ... SBrkr Gd Typ \n",
1408
- "4 NoRidge Norm Norm ... SBrkr Gd Typ \n",
1409
- "\n",
1410
- " GarageType GarageFinish GarageQual GarageCond PavedDrive SaleType \\\n",
1411
- "0 Attchd RFn TA TA Y WD \n",
1412
- "1 Attchd RFn TA TA Y WD \n",
1413
- "2 Attchd RFn TA TA Y WD \n",
1414
- "3 Detchd Unf TA TA Y WD \n",
1415
- "4 Attchd RFn TA TA Y WD \n",
1416
- "\n",
1417
- " SaleCondition \n",
1418
- "0 Normal \n",
1419
- "1 Normal \n",
1420
- "2 Normal \n",
1421
- "3 Abnorml \n",
1422
- "4 Normal \n",
1423
- "\n",
1424
- "[5 rows x 37 columns]"
1425
- ]
1426
- },
1427
- "execution_count": 12,
1428
- "metadata": {},
1429
- "output_type": "execute_result"
1430
- }
1431
- ],
1432
  "source": [
1433
  "train[categorical_col].head()"
1434
  ]
1435
  },
1436
  {
1437
  "cell_type": "code",
1438
- "execution_count": 13,
1439
  "id": "13215608",
1440
  "metadata": {
1441
  "execution": {
@@ -1454,7 +865,7 @@
1454
  },
1455
  {
1456
  "cell_type": "code",
1457
- "execution_count": 14,
1458
  "id": "e500cc07",
1459
  "metadata": {
1460
  "execution": {
@@ -1466,52 +877,7 @@
1466
  },
1467
  "scrolled": true
1468
  },
1469
- "outputs": [
1470
- {
1471
- "name": "stdout",
1472
- "output_type": "stream",
1473
- "text": [
1474
- " Null count Unique count\n",
1475
- "MSZoning 0 5\n",
1476
- "Street 0 2\n",
1477
- "LotShape 0 4\n",
1478
- "LandContour 0 4\n",
1479
- "Utilities 0 2\n",
1480
- "LotConfig 0 5\n",
1481
- "LandSlope 0 3\n",
1482
- "Neighborhood 0 25\n",
1483
- "Condition1 0 9\n",
1484
- "Condition2 0 8\n",
1485
- "BldgType 0 5\n",
1486
- "HouseStyle 0 8\n",
1487
- "RoofStyle 0 6\n",
1488
- "RoofMatl 0 8\n",
1489
- "Exterior1st 0 15\n",
1490
- "Exterior2nd 0 16\n",
1491
- "ExterQual 0 4\n",
1492
- "ExterCond 0 5\n",
1493
- "Foundation 0 6\n",
1494
- "BsmtQual 37 4\n",
1495
- "BsmtCond 37 4\n",
1496
- "BsmtExposure 38 4\n",
1497
- "BsmtFinType1 37 6\n",
1498
- "BsmtFinType2 38 6\n",
1499
- "Heating 0 6\n",
1500
- "HeatingQC 0 5\n",
1501
- "CentralAir 0 2\n",
1502
- "Electrical 1 5\n",
1503
- "KitchenQual 0 4\n",
1504
- "Functional 0 7\n",
1505
- "GarageType 81 6\n",
1506
- "GarageFinish 81 3\n",
1507
- "GarageQual 81 5\n",
1508
- "GarageCond 81 5\n",
1509
- "PavedDrive 0 3\n",
1510
- "SaleType 0 9\n",
1511
- "SaleCondition 0 6\n"
1512
- ]
1513
- }
1514
- ],
1515
  "source": [
1516
  "data = {\n",
1517
  " 'Null count': null_count,\n",
@@ -1523,7 +889,7 @@
1523
  },
1524
  {
1525
  "cell_type": "code",
1526
- "execution_count": 15,
1527
  "id": "8fe3b97b",
1528
  "metadata": {
1529
  "execution": {
@@ -1542,7 +908,7 @@
1542
  "traceback": [
1543
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1544
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
1545
- "\u001b[0;32m<ipython-input-15-8d5e312935a0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Alley'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'PoolQC'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Fence'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'MiscFeature'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1546
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 5342\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5343\u001b[0m \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m return super().drop(\n\u001b[0m\u001b[1;32m 5345\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5346\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1547
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4709\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4710\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4711\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4713\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1548
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m 4751\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4752\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4753\u001b[0;31m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4754\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
 
51
  },
52
  {
53
  "cell_type": "code",
54
+ "execution_count": null,
55
  "id": "3879efdb",
56
  "metadata": {
57
  "execution": {
 
62
  "shell.execute_reply.started": "2023-06-11T14:19:34.854293Z"
63
  }
64
  },
65
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
66
  "source": [
67
  "train.head()"
68
  ]
 
618
  },
619
  {
620
  "cell_type": "code",
621
+ "execution_count": null,
622
  "id": "9116a799",
623
  "metadata": {
624
  "execution": {
 
629
  "shell.execute_reply.started": "2023-06-11T14:20:06.472701Z"
630
  }
631
  },
632
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
633
  "source": [
634
  "train.shape"
635
  ]
636
  },
637
  {
638
  "cell_type": "code",
639
+ "execution_count": null,
640
  "id": "f23afceb",
641
  "metadata": {
642
  "execution": {
 
647
  "shell.execute_reply.started": "2023-06-11T14:20:07.457995Z"
648
  }
649
  },
650
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
651
  "source": [
652
  "test.shape"
653
  ]
654
  },
655
  {
656
  "cell_type": "code",
657
+ "execution_count": null,
658
  "id": "f9409852",
659
  "metadata": {
660
  "execution": {
 
665
  "shell.execute_reply.started": "2023-06-11T14:20:07.909893Z"
666
  }
667
  },
668
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
669
  "source": [
670
  "submission.shape"
671
  ]
672
  },
673
  {
674
  "cell_type": "code",
675
+ "execution_count": null,
676
  "id": "0eda05c7",
677
  "metadata": {
678
  "execution": {
 
684
  },
685
  "scrolled": true
686
  },
687
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
688
  "source": [
689
  "new=(((train.isnull().sum())/(train.shape[0]))*100)\n",
690
  "new.to_list()"
 
710
  },
711
  {
712
  "cell_type": "code",
713
+ "execution_count": 3,
714
  "id": "a867388f",
715
  "metadata": {
716
  "execution": {
 
751
  },
752
  {
753
  "cell_type": "code",
754
+ "execution_count": null,
755
  "id": "6e6a2825",
756
  "metadata": {
757
  "execution": {
 
762
  "shell.execute_reply.started": "2023-06-11T14:22:15.173686Z"
763
  }
764
  },
765
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
766
  "source": [
767
  "categorical_col = list(train.select_dtypes(include=['object']).columns)\n",
768
  "numerical_col =list(train.select_dtypes(exclude=['object']).columns)\n",
 
785
  },
786
  {
787
  "cell_type": "code",
788
+ "execution_count": null,
789
  "id": "a0ab72f3",
790
  "metadata": {
791
  "execution": {
 
804
  },
805
  {
806
  "cell_type": "code",
807
+ "execution_count": null,
808
  "id": "085c75ed",
809
  "metadata": {
810
  "execution": {
 
816
  },
817
  "scrolled": true
818
  },
819
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
820
  "source": [
821
  "data = {\n",
822
  " 'Null count': null_count,\n",
 
828
  },
829
  {
830
  "cell_type": "code",
831
+ "execution_count": null,
832
  "id": "29ebf8d9",
833
  "metadata": {
834
  "execution": {
 
839
  "shell.execute_reply.started": "2023-06-11T14:20:13.734928Z"
840
  }
841
  },
842
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
843
  "source": [
844
  "train[categorical_col].head()"
845
  ]
846
  },
847
  {
848
  "cell_type": "code",
849
+ "execution_count": null,
850
  "id": "13215608",
851
  "metadata": {
852
  "execution": {
 
865
  },
866
  {
867
  "cell_type": "code",
868
+ "execution_count": null,
869
  "id": "e500cc07",
870
  "metadata": {
871
  "execution": {
 
877
  },
878
  "scrolled": true
879
  },
880
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
881
  "source": [
882
  "data = {\n",
883
  " 'Null count': null_count,\n",
 
889
  },
890
  {
891
  "cell_type": "code",
892
+ "execution_count": 4,
893
  "id": "8fe3b97b",
894
  "metadata": {
895
  "execution": {
 
908
  "traceback": [
909
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
910
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
911
+ "\u001b[0;32m<ipython-input-4-8d5e312935a0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Alley'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'PoolQC'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Fence'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'MiscFeature'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
912
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 5342\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5343\u001b[0m \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m return super().drop(\n\u001b[0m\u001b[1;32m 5345\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5346\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
913
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4709\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4710\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4711\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4713\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
914
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m 4751\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4752\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4753\u001b[0;31m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4754\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
benchmark/pandas_2/pandas_2_fixed.ipynb CHANGED
@@ -83,7 +83,7 @@
83
  },
84
  {
85
  "cell_type": "code",
86
- "execution_count": 3,
87
  "metadata": {
88
  "execution": {
89
  "iopub.execute_input": "2023-06-02T11:50:31.684884Z",
@@ -93,396 +93,7 @@
93
  "shell.execute_reply.started": "2023-06-02T11:50:31.684847Z"
94
  }
95
  },
96
- "outputs": [
97
- {
98
- "data": {
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176
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203
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205
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206
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207
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208
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209
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210
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212
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213
- " </tr>\n",
214
- " <tr>\n",
215
- " <th>3</th>\n",
216
- " <td>60</td>\n",
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- " <td>RL</td>\n",
218
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219
- " <td>11250</td>\n",
220
- " <td>Pave</td>\n",
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237
- " </tr>\n",
238
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239
- " <th>4</th>\n",
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- " <td>70</td>\n",
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242
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244
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246
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249
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251
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252
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253
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256
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258
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259
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260
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262
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263
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264
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267
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268
- " <td>Pave</td>\n",
269
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272
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280
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281
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282
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283
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284
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285
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286
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288
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289
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304
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307
- " <td>...</td>\n",
308
- " <td>...</td>\n",
309
- " </tr>\n",
310
- " <tr>\n",
311
- " <th>1456</th>\n",
312
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313
- " <td>RL</td>\n",
314
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315
- " <td>7917</td>\n",
316
- " <td>Pave</td>\n",
317
- " <td>NaN</td>\n",
318
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319
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320
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321
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322
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323
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324
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325
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326
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327
- " <td>0</td>\n",
328
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329
- " <td>2007</td>\n",
330
- " <td>WD</td>\n",
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- " <td>Normal</td>\n",
332
- " <td>175000</td>\n",
333
- " </tr>\n",
334
- " <tr>\n",
335
- " <th>1457</th>\n",
336
- " <td>20</td>\n",
337
- " <td>RL</td>\n",
338
- " <td>85.0</td>\n",
339
- " <td>13175</td>\n",
340
- " <td>Pave</td>\n",
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344
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345
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- " <td>...</td>\n",
347
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349
- " <td>MnPrv</td>\n",
350
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351
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352
- " <td>2</td>\n",
353
- " <td>2010</td>\n",
354
- " <td>WD</td>\n",
355
- " <td>Normal</td>\n",
356
- " <td>210000</td>\n",
357
- " </tr>\n",
358
- " <tr>\n",
359
- " <th>1458</th>\n",
360
- " <td>70</td>\n",
361
- " <td>RL</td>\n",
362
- " <td>66.0</td>\n",
363
- " <td>9042</td>\n",
364
- " <td>Pave</td>\n",
365
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367
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370
- " <td>...</td>\n",
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375
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376
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377
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378
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- " <td>Normal</td>\n",
380
- " <td>266500</td>\n",
381
- " </tr>\n",
382
- " <tr>\n",
383
- " <th>1459</th>\n",
384
- " <td>20</td>\n",
385
- " <td>RL</td>\n",
386
- " <td>68.0</td>\n",
387
- " <td>9717</td>\n",
388
- " <td>Pave</td>\n",
389
- " <td>NaN</td>\n",
390
- " <td>Reg</td>\n",
391
- " <td>Lvl</td>\n",
392
- " <td>AllPub</td>\n",
393
- " <td>Inside</td>\n",
394
- " <td>...</td>\n",
395
- " <td>0</td>\n",
396
- " <td>NaN</td>\n",
397
- " <td>NaN</td>\n",
398
- " <td>NaN</td>\n",
399
- " <td>0</td>\n",
400
- " <td>4</td>\n",
401
- " <td>2010</td>\n",
402
- " <td>WD</td>\n",
403
- " <td>Normal</td>\n",
404
- " <td>142125</td>\n",
405
- " </tr>\n",
406
- " <tr>\n",
407
- " <th>1460</th>\n",
408
- " <td>20</td>\n",
409
- " <td>RL</td>\n",
410
- " <td>75.0</td>\n",
411
- " <td>9937</td>\n",
412
- " <td>Pave</td>\n",
413
- " <td>NaN</td>\n",
414
- " <td>Reg</td>\n",
415
- " <td>Lvl</td>\n",
416
- " <td>AllPub</td>\n",
417
- " <td>Inside</td>\n",
418
- " <td>...</td>\n",
419
- " <td>0</td>\n",
420
- " <td>NaN</td>\n",
421
- " <td>NaN</td>\n",
422
- " <td>NaN</td>\n",
423
- " <td>0</td>\n",
424
- " <td>6</td>\n",
425
- " <td>2008</td>\n",
426
- " <td>WD</td>\n",
427
- " <td>Normal</td>\n",
428
- " <td>147500</td>\n",
429
- " </tr>\n",
430
- " </tbody>\n",
431
- "</table>\n",
432
- "<p>1460 rows × 80 columns</p>\n",
433
- "</div>"
434
- ],
435
- "text/plain": [
436
- " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
437
- "Id \n",
438
- "1 60 RL 65.0 8450 Pave NaN Reg \n",
439
- "2 20 RL 80.0 9600 Pave NaN Reg \n",
440
- "3 60 RL 68.0 11250 Pave NaN IR1 \n",
441
- "4 70 RL 60.0 9550 Pave NaN IR1 \n",
442
- "5 60 RL 84.0 14260 Pave NaN IR1 \n",
443
- "... ... ... ... ... ... ... ... \n",
444
- "1456 60 RL 62.0 7917 Pave NaN Reg \n",
445
- "1457 20 RL 85.0 13175 Pave NaN Reg \n",
446
- "1458 70 RL 66.0 9042 Pave NaN Reg \n",
447
- "1459 20 RL 68.0 9717 Pave NaN Reg \n",
448
- "1460 20 RL 75.0 9937 Pave NaN Reg \n",
449
- "\n",
450
- " LandContour Utilities LotConfig ... PoolArea PoolQC Fence MiscFeature \\\n",
451
- "Id ... \n",
452
- "1 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
453
- "2 Lvl AllPub FR2 ... 0 NaN NaN NaN \n",
454
- "3 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
455
- "4 Lvl AllPub Corner ... 0 NaN NaN NaN \n",
456
- "5 Lvl AllPub FR2 ... 0 NaN NaN NaN \n",
457
- "... ... ... ... ... ... ... ... ... \n",
458
- "1456 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
459
- "1457 Lvl AllPub Inside ... 0 NaN MnPrv NaN \n",
460
- "1458 Lvl AllPub Inside ... 0 NaN GdPrv Shed \n",
461
- "1459 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
462
- "1460 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
463
- "\n",
464
- " MiscVal MoSold YrSold SaleType SaleCondition SalePrice \n",
465
- "Id \n",
466
- "1 0 2 2008 WD Normal 208500 \n",
467
- "2 0 5 2007 WD Normal 181500 \n",
468
- "3 0 9 2008 WD Normal 223500 \n",
469
- "4 0 2 2006 WD Abnorml 140000 \n",
470
- "5 0 12 2008 WD Normal 250000 \n",
471
- "... ... ... ... ... ... ... \n",
472
- "1456 0 8 2007 WD Normal 175000 \n",
473
- "1457 0 2 2010 WD Normal 210000 \n",
474
- "1458 2500 5 2010 WD Normal 266500 \n",
475
- "1459 0 4 2010 WD Normal 142125 \n",
476
- "1460 0 6 2008 WD Normal 147500 \n",
477
- "\n",
478
- "[1460 rows x 80 columns]"
479
- ]
480
- },
481
- "execution_count": 3,
482
- "metadata": {},
483
- "output_type": "execute_result"
484
- }
485
- ],
486
  "source": [
487
  "train_df"
488
  ]
@@ -927,7 +538,7 @@
927
  },
928
  {
929
  "cell_type": "code",
930
- "execution_count": 4,
931
  "metadata": {
932
  "execution": {
933
  "iopub.execute_input": "2023-06-02T12:01:20.884031Z",
@@ -1183,7 +794,7 @@
1183
  "SalePrice 1459 49.98"
1184
  ]
1185
  },
1186
- "execution_count": 4,
1187
  "metadata": {},
1188
  "output_type": "execute_result"
1189
  }
@@ -1198,7 +809,7 @@
1198
  },
1199
  {
1200
  "cell_type": "code",
1201
- "execution_count": 5,
1202
  "metadata": {
1203
  "execution": {
1204
  "iopub.execute_input": "2023-06-02T12:02:21.469961Z",
 
83
  },
84
  {
85
  "cell_type": "code",
86
+ "execution_count": null,
87
  "metadata": {
88
  "execution": {
89
  "iopub.execute_input": "2023-06-02T11:50:31.684884Z",
 
93
  "shell.execute_reply.started": "2023-06-02T11:50:31.684847Z"
94
  }
95
  },
96
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
97
  "source": [
98
  "train_df"
99
  ]
 
538
  },
539
  {
540
  "cell_type": "code",
541
+ "execution_count": 3,
542
  "metadata": {
543
  "execution": {
544
  "iopub.execute_input": "2023-06-02T12:01:20.884031Z",
 
794
  "SalePrice 1459 49.98"
795
  ]
796
  },
797
+ "execution_count": 3,
798
  "metadata": {},
799
  "output_type": "execute_result"
800
  }
 
809
  },
810
  {
811
  "cell_type": "code",
812
+ "execution_count": 4,
813
  "metadata": {
814
  "execution": {
815
  "iopub.execute_input": "2023-06-02T12:02:21.469961Z",
benchmark/pandas_2/pandas_2_reproduced.ipynb CHANGED
@@ -83,7 +83,7 @@
83
  },
84
  {
85
  "cell_type": "code",
86
- "execution_count": 3,
87
  "metadata": {
88
  "execution": {
89
  "iopub.execute_input": "2023-06-02T11:50:31.684884Z",
@@ -93,396 +93,7 @@
93
  "shell.execute_reply.started": "2023-06-02T11:50:31.684847Z"
94
  }
95
  },
96
- "outputs": [
97
- {
98
- "data": {
99
- "text/html": [
100
- "<div>\n",
101
- "<style scoped>\n",
102
- " .dataframe tbody tr th:only-of-type {\n",
103
- " vertical-align: middle;\n",
104
- " }\n",
105
- "\n",
106
- " .dataframe tbody tr th {\n",
107
- " vertical-align: top;\n",
108
- " }\n",
109
- "\n",
110
- " .dataframe thead th {\n",
111
- " text-align: right;\n",
112
- " }\n",
113
- "</style>\n",
114
- "<table border=\"1\" class=\"dataframe\">\n",
115
- " <thead>\n",
116
- " <tr style=\"text-align: right;\">\n",
117
- " <th></th>\n",
118
- " <th>MSSubClass</th>\n",
119
- " <th>MSZoning</th>\n",
120
- " <th>LotFrontage</th>\n",
121
- " <th>LotArea</th>\n",
122
- " <th>Street</th>\n",
123
- " <th>Alley</th>\n",
124
- " <th>LotShape</th>\n",
125
- " <th>LandContour</th>\n",
126
- " <th>Utilities</th>\n",
127
- " <th>LotConfig</th>\n",
128
- " <th>...</th>\n",
129
- " <th>PoolArea</th>\n",
130
- " <th>PoolQC</th>\n",
131
- " <th>Fence</th>\n",
132
- " <th>MiscFeature</th>\n",
133
- " <th>MiscVal</th>\n",
134
- " <th>MoSold</th>\n",
135
- " <th>YrSold</th>\n",
136
- " <th>SaleType</th>\n",
137
- " <th>SaleCondition</th>\n",
138
- " <th>SalePrice</th>\n",
139
- " </tr>\n",
140
- " <tr>\n",
141
- " <th>Id</th>\n",
142
- " <th></th>\n",
143
- " <th></th>\n",
144
- " <th></th>\n",
145
- " <th></th>\n",
146
- " <th></th>\n",
147
- " <th></th>\n",
148
- " <th></th>\n",
149
- " <th></th>\n",
150
- " <th></th>\n",
151
- " <th></th>\n",
152
- " <th></th>\n",
153
- " <th></th>\n",
154
- " <th></th>\n",
155
- " <th></th>\n",
156
- " <th></th>\n",
157
- " <th></th>\n",
158
- " <th></th>\n",
159
- " <th></th>\n",
160
- " <th></th>\n",
161
- " <th></th>\n",
162
- " <th></th>\n",
163
- " </tr>\n",
164
- " </thead>\n",
165
- " <tbody>\n",
166
- " <tr>\n",
167
- " <th>1</th>\n",
168
- " <td>60</td>\n",
169
- " <td>RL</td>\n",
170
- " <td>65.0</td>\n",
171
- " <td>8450</td>\n",
172
- " <td>Pave</td>\n",
173
- " <td>NaN</td>\n",
174
- " <td>Reg</td>\n",
175
- " <td>Lvl</td>\n",
176
- " <td>AllPub</td>\n",
177
- " <td>Inside</td>\n",
178
- " <td>...</td>\n",
179
- " <td>0</td>\n",
180
- " <td>NaN</td>\n",
181
- " <td>NaN</td>\n",
182
- " <td>NaN</td>\n",
183
- " <td>0</td>\n",
184
- " <td>2</td>\n",
185
- " <td>2008</td>\n",
186
- " <td>WD</td>\n",
187
- " <td>Normal</td>\n",
188
- " <td>208500</td>\n",
189
- " </tr>\n",
190
- " <tr>\n",
191
- " <th>2</th>\n",
192
- " <td>20</td>\n",
193
- " <td>RL</td>\n",
194
- " <td>80.0</td>\n",
195
- " <td>9600</td>\n",
196
- " <td>Pave</td>\n",
197
- " <td>NaN</td>\n",
198
- " <td>Reg</td>\n",
199
- " <td>Lvl</td>\n",
200
- " <td>AllPub</td>\n",
201
- " <td>FR2</td>\n",
202
- " <td>...</td>\n",
203
- " <td>0</td>\n",
204
- " <td>NaN</td>\n",
205
- " <td>NaN</td>\n",
206
- " <td>NaN</td>\n",
207
- " <td>0</td>\n",
208
- " <td>5</td>\n",
209
- " <td>2007</td>\n",
210
- " <td>WD</td>\n",
211
- " <td>Normal</td>\n",
212
- " <td>181500</td>\n",
213
- " </tr>\n",
214
- " <tr>\n",
215
- " <th>3</th>\n",
216
- " <td>60</td>\n",
217
- " <td>RL</td>\n",
218
- " <td>68.0</td>\n",
219
- " <td>11250</td>\n",
220
- " <td>Pave</td>\n",
221
- " <td>NaN</td>\n",
222
- " <td>IR1</td>\n",
223
- " <td>Lvl</td>\n",
224
- " <td>AllPub</td>\n",
225
- " <td>Inside</td>\n",
226
- " <td>...</td>\n",
227
- " <td>0</td>\n",
228
- " <td>NaN</td>\n",
229
- " <td>NaN</td>\n",
230
- " <td>NaN</td>\n",
231
- " <td>0</td>\n",
232
- " <td>9</td>\n",
233
- " <td>2008</td>\n",
234
- " <td>WD</td>\n",
235
- " <td>Normal</td>\n",
236
- " <td>223500</td>\n",
237
- " </tr>\n",
238
- " <tr>\n",
239
- " <th>4</th>\n",
240
- " <td>70</td>\n",
241
- " <td>RL</td>\n",
242
- " <td>60.0</td>\n",
243
- " <td>9550</td>\n",
244
- " <td>Pave</td>\n",
245
- " <td>NaN</td>\n",
246
- " <td>IR1</td>\n",
247
- " <td>Lvl</td>\n",
248
- " <td>AllPub</td>\n",
249
- " <td>Corner</td>\n",
250
- " <td>...</td>\n",
251
- " <td>0</td>\n",
252
- " <td>NaN</td>\n",
253
- " <td>NaN</td>\n",
254
- " <td>NaN</td>\n",
255
- " <td>0</td>\n",
256
- " <td>2</td>\n",
257
- " <td>2006</td>\n",
258
- " <td>WD</td>\n",
259
- " <td>Abnorml</td>\n",
260
- " <td>140000</td>\n",
261
- " </tr>\n",
262
- " <tr>\n",
263
- " <th>5</th>\n",
264
- " <td>60</td>\n",
265
- " <td>RL</td>\n",
266
- " <td>84.0</td>\n",
267
- " <td>14260</td>\n",
268
- " <td>Pave</td>\n",
269
- " <td>NaN</td>\n",
270
- " <td>IR1</td>\n",
271
- " <td>Lvl</td>\n",
272
- " <td>AllPub</td>\n",
273
- " <td>FR2</td>\n",
274
- " <td>...</td>\n",
275
- " <td>0</td>\n",
276
- " <td>NaN</td>\n",
277
- " <td>NaN</td>\n",
278
- " <td>NaN</td>\n",
279
- " <td>0</td>\n",
280
- " <td>12</td>\n",
281
- " <td>2008</td>\n",
282
- " <td>WD</td>\n",
283
- " <td>Normal</td>\n",
284
- " <td>250000</td>\n",
285
- " </tr>\n",
286
- " <tr>\n",
287
- " <th>...</th>\n",
288
- " <td>...</td>\n",
289
- " <td>...</td>\n",
290
- " <td>...</td>\n",
291
- " <td>...</td>\n",
292
- " <td>...</td>\n",
293
- " <td>...</td>\n",
294
- " <td>...</td>\n",
295
- " <td>...</td>\n",
296
- " <td>...</td>\n",
297
- " <td>...</td>\n",
298
- " <td>...</td>\n",
299
- " <td>...</td>\n",
300
- " <td>...</td>\n",
301
- " <td>...</td>\n",
302
- " <td>...</td>\n",
303
- " <td>...</td>\n",
304
- " <td>...</td>\n",
305
- " <td>...</td>\n",
306
- " <td>...</td>\n",
307
- " <td>...</td>\n",
308
- " <td>...</td>\n",
309
- " </tr>\n",
310
- " <tr>\n",
311
- " <th>1456</th>\n",
312
- " <td>60</td>\n",
313
- " <td>RL</td>\n",
314
- " <td>62.0</td>\n",
315
- " <td>7917</td>\n",
316
- " <td>Pave</td>\n",
317
- " <td>NaN</td>\n",
318
- " <td>Reg</td>\n",
319
- " <td>Lvl</td>\n",
320
- " <td>AllPub</td>\n",
321
- " <td>Inside</td>\n",
322
- " <td>...</td>\n",
323
- " <td>0</td>\n",
324
- " <td>NaN</td>\n",
325
- " <td>NaN</td>\n",
326
- " <td>NaN</td>\n",
327
- " <td>0</td>\n",
328
- " <td>8</td>\n",
329
- " <td>2007</td>\n",
330
- " <td>WD</td>\n",
331
- " <td>Normal</td>\n",
332
- " <td>175000</td>\n",
333
- " </tr>\n",
334
- " <tr>\n",
335
- " <th>1457</th>\n",
336
- " <td>20</td>\n",
337
- " <td>RL</td>\n",
338
- " <td>85.0</td>\n",
339
- " <td>13175</td>\n",
340
- " <td>Pave</td>\n",
341
- " <td>NaN</td>\n",
342
- " <td>Reg</td>\n",
343
- " <td>Lvl</td>\n",
344
- " <td>AllPub</td>\n",
345
- " <td>Inside</td>\n",
346
- " <td>...</td>\n",
347
- " <td>0</td>\n",
348
- " <td>NaN</td>\n",
349
- " <td>MnPrv</td>\n",
350
- " <td>NaN</td>\n",
351
- " <td>0</td>\n",
352
- " <td>2</td>\n",
353
- " <td>2010</td>\n",
354
- " <td>WD</td>\n",
355
- " <td>Normal</td>\n",
356
- " <td>210000</td>\n",
357
- " </tr>\n",
358
- " <tr>\n",
359
- " <th>1458</th>\n",
360
- " <td>70</td>\n",
361
- " <td>RL</td>\n",
362
- " <td>66.0</td>\n",
363
- " <td>9042</td>\n",
364
- " <td>Pave</td>\n",
365
- " <td>NaN</td>\n",
366
- " <td>Reg</td>\n",
367
- " <td>Lvl</td>\n",
368
- " <td>AllPub</td>\n",
369
- " <td>Inside</td>\n",
370
- " <td>...</td>\n",
371
- " <td>0</td>\n",
372
- " <td>NaN</td>\n",
373
- " <td>GdPrv</td>\n",
374
- " <td>Shed</td>\n",
375
- " <td>2500</td>\n",
376
- " <td>5</td>\n",
377
- " <td>2010</td>\n",
378
- " <td>WD</td>\n",
379
- " <td>Normal</td>\n",
380
- " <td>266500</td>\n",
381
- " </tr>\n",
382
- " <tr>\n",
383
- " <th>1459</th>\n",
384
- " <td>20</td>\n",
385
- " <td>RL</td>\n",
386
- " <td>68.0</td>\n",
387
- " <td>9717</td>\n",
388
- " <td>Pave</td>\n",
389
- " <td>NaN</td>\n",
390
- " <td>Reg</td>\n",
391
- " <td>Lvl</td>\n",
392
- " <td>AllPub</td>\n",
393
- " <td>Inside</td>\n",
394
- " <td>...</td>\n",
395
- " <td>0</td>\n",
396
- " <td>NaN</td>\n",
397
- " <td>NaN</td>\n",
398
- " <td>NaN</td>\n",
399
- " <td>0</td>\n",
400
- " <td>4</td>\n",
401
- " <td>2010</td>\n",
402
- " <td>WD</td>\n",
403
- " <td>Normal</td>\n",
404
- " <td>142125</td>\n",
405
- " </tr>\n",
406
- " <tr>\n",
407
- " <th>1460</th>\n",
408
- " <td>20</td>\n",
409
- " <td>RL</td>\n",
410
- " <td>75.0</td>\n",
411
- " <td>9937</td>\n",
412
- " <td>Pave</td>\n",
413
- " <td>NaN</td>\n",
414
- " <td>Reg</td>\n",
415
- " <td>Lvl</td>\n",
416
- " <td>AllPub</td>\n",
417
- " <td>Inside</td>\n",
418
- " <td>...</td>\n",
419
- " <td>0</td>\n",
420
- " <td>NaN</td>\n",
421
- " <td>NaN</td>\n",
422
- " <td>NaN</td>\n",
423
- " <td>0</td>\n",
424
- " <td>6</td>\n",
425
- " <td>2008</td>\n",
426
- " <td>WD</td>\n",
427
- " <td>Normal</td>\n",
428
- " <td>147500</td>\n",
429
- " </tr>\n",
430
- " </tbody>\n",
431
- "</table>\n",
432
- "<p>1460 rows × 80 columns</p>\n",
433
- "</div>"
434
- ],
435
- "text/plain": [
436
- " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
437
- "Id \n",
438
- "1 60 RL 65.0 8450 Pave NaN Reg \n",
439
- "2 20 RL 80.0 9600 Pave NaN Reg \n",
440
- "3 60 RL 68.0 11250 Pave NaN IR1 \n",
441
- "4 70 RL 60.0 9550 Pave NaN IR1 \n",
442
- "5 60 RL 84.0 14260 Pave NaN IR1 \n",
443
- "... ... ... ... ... ... ... ... \n",
444
- "1456 60 RL 62.0 7917 Pave NaN Reg \n",
445
- "1457 20 RL 85.0 13175 Pave NaN Reg \n",
446
- "1458 70 RL 66.0 9042 Pave NaN Reg \n",
447
- "1459 20 RL 68.0 9717 Pave NaN Reg \n",
448
- "1460 20 RL 75.0 9937 Pave NaN Reg \n",
449
- "\n",
450
- " LandContour Utilities LotConfig ... PoolArea PoolQC Fence MiscFeature \\\n",
451
- "Id ... \n",
452
- "1 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
453
- "2 Lvl AllPub FR2 ... 0 NaN NaN NaN \n",
454
- "3 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
455
- "4 Lvl AllPub Corner ... 0 NaN NaN NaN \n",
456
- "5 Lvl AllPub FR2 ... 0 NaN NaN NaN \n",
457
- "... ... ... ... ... ... ... ... ... \n",
458
- "1456 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
459
- "1457 Lvl AllPub Inside ... 0 NaN MnPrv NaN \n",
460
- "1458 Lvl AllPub Inside ... 0 NaN GdPrv Shed \n",
461
- "1459 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
462
- "1460 Lvl AllPub Inside ... 0 NaN NaN NaN \n",
463
- "\n",
464
- " MiscVal MoSold YrSold SaleType SaleCondition SalePrice \n",
465
- "Id \n",
466
- "1 0 2 2008 WD Normal 208500 \n",
467
- "2 0 5 2007 WD Normal 181500 \n",
468
- "3 0 9 2008 WD Normal 223500 \n",
469
- "4 0 2 2006 WD Abnorml 140000 \n",
470
- "5 0 12 2008 WD Normal 250000 \n",
471
- "... ... ... ... ... ... ... \n",
472
- "1456 0 8 2007 WD Normal 175000 \n",
473
- "1457 0 2 2010 WD Normal 210000 \n",
474
- "1458 2500 5 2010 WD Normal 266500 \n",
475
- "1459 0 4 2010 WD Normal 142125 \n",
476
- "1460 0 6 2008 WD Normal 147500 \n",
477
- "\n",
478
- "[1460 rows x 80 columns]"
479
- ]
480
- },
481
- "execution_count": 3,
482
- "metadata": {},
483
- "output_type": "execute_result"
484
- }
485
- ],
486
  "source": [
487
  "train_df"
488
  ]
@@ -927,7 +538,7 @@
927
  },
928
  {
929
  "cell_type": "code",
930
- "execution_count": 4,
931
  "metadata": {
932
  "execution": {
933
  "iopub.execute_input": "2023-06-02T12:01:20.884031Z",
@@ -1183,7 +794,7 @@
1183
  "SalePrice 1459 49.98"
1184
  ]
1185
  },
1186
- "execution_count": 4,
1187
  "metadata": {},
1188
  "output_type": "execute_result"
1189
  }
@@ -1198,7 +809,7 @@
1198
  },
1199
  {
1200
  "cell_type": "code",
1201
- "execution_count": 5,
1202
  "metadata": {
1203
  "execution": {
1204
  "iopub.execute_input": "2023-06-02T12:02:21.469961Z",
@@ -1216,7 +827,7 @@
1216
  "traceback": [
1217
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1218
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
1219
- "\u001b[0;32m<ipython-input-5-e2ddd58cfc83>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LotFrontage'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Alley'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'FireplaceQu'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'PoolQC'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Fence'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MiscFeature'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1220
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 5342\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5343\u001b[0m \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m return super().drop(\n\u001b[0m\u001b[1;32m 5345\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5346\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1221
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4709\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4710\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4711\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4713\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1222
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m 4751\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4752\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4753\u001b[0;31m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4754\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
 
83
  },
84
  {
85
  "cell_type": "code",
86
+ "execution_count": null,
87
  "metadata": {
88
  "execution": {
89
  "iopub.execute_input": "2023-06-02T11:50:31.684884Z",
 
93
  "shell.execute_reply.started": "2023-06-02T11:50:31.684847Z"
94
  }
95
  },
96
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
97
  "source": [
98
  "train_df"
99
  ]
 
538
  },
539
  {
540
  "cell_type": "code",
541
+ "execution_count": 3,
542
  "metadata": {
543
  "execution": {
544
  "iopub.execute_input": "2023-06-02T12:01:20.884031Z",
 
794
  "SalePrice 1459 49.98"
795
  ]
796
  },
797
+ "execution_count": 3,
798
  "metadata": {},
799
  "output_type": "execute_result"
800
  }
 
809
  },
810
  {
811
  "cell_type": "code",
812
+ "execution_count": 4,
813
  "metadata": {
814
  "execution": {
815
  "iopub.execute_input": "2023-06-02T12:02:21.469961Z",
 
827
  "traceback": [
828
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
829
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
830
+ "\u001b[0;32m<ipython-input-4-e2ddd58cfc83>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LotFrontage'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Alley'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'FireplaceQu'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'PoolQC'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Fence'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MiscFeature'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
831
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 5342\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5343\u001b[0m \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m return super().drop(\n\u001b[0m\u001b[1;32m 5345\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5346\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
832
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4709\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4710\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4711\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4713\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
833
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m 4751\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4752\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4753\u001b[0;31m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4754\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
benchmark/pandas_3/pandas_3_reproduced.ipynb CHANGED
@@ -26,7 +26,7 @@
26
  },
27
  {
28
  "cell_type": "code",
29
- "execution_count": 2,
30
  "metadata": {
31
  "execution": {
32
  "iopub.execute_input": "2023-10-10T01:30:04.195992Z",
@@ -67,7 +67,7 @@
67
  },
68
  {
69
  "cell_type": "code",
70
- "execution_count": 3,
71
  "metadata": {
72
  "execution": {
73
  "iopub.execute_input": "2023-10-10T01:30:11.150745Z",
@@ -101,7 +101,7 @@
101
  },
102
  {
103
  "cell_type": "code",
104
- "execution_count": 4,
105
  "metadata": {
106
  "execution": {
107
  "iopub.execute_input": "2023-10-10T01:30:13.553749Z",
@@ -119,8 +119,8 @@
119
  "traceback": [
120
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
121
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
122
- "\u001b[0;32m<ipython-input-4-f917086cb02f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRescuerID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRescuerID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
123
- "\u001b[0;32m<ipython-input-3-3509ebcbd1ba>\u001b[0m in \u001b[0;36mprocData\u001b[0;34m(data, name_count, RescuerID)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"NameLen\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"SinNombre\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"nonameyet\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"name_count\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"name_count\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
124
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort, validate)\u001b[0m\n\u001b[1;32m 10410\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10411\u001b[0m )\n\u001b[0;32m> 10412\u001b[0;31m return merge(\n\u001b[0m\u001b[1;32m 10413\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10414\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
125
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m )\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
126
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self, copy)\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[0mjoin_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_join_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m result = self._reindex_and_concat(\n\u001b[0m\u001b[1;32m 886\u001b[0m \u001b[0mjoin_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 887\u001b[0m )\n",
 
26
  },
27
  {
28
  "cell_type": "code",
29
+ "execution_count": 1,
30
  "metadata": {
31
  "execution": {
32
  "iopub.execute_input": "2023-10-10T01:30:04.195992Z",
 
67
  },
68
  {
69
  "cell_type": "code",
70
+ "execution_count": 2,
71
  "metadata": {
72
  "execution": {
73
  "iopub.execute_input": "2023-10-10T01:30:11.150745Z",
 
101
  },
102
  {
103
  "cell_type": "code",
104
+ "execution_count": 3,
105
  "metadata": {
106
  "execution": {
107
  "iopub.execute_input": "2023-10-10T01:30:13.553749Z",
 
119
  "traceback": [
120
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
121
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
122
+ "\u001b[0;32m<ipython-input-3-f917086cb02f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRescuerID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRescuerID\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
123
+ "\u001b[0;32m<ipython-input-2-3509ebcbd1ba>\u001b[0m in \u001b[0;36mprocData\u001b[0;34m(data, name_count, RescuerID)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"NameLen\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"SinNombre\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"nonameyet\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"name_count\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"name_count\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
124
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort, validate)\u001b[0m\n\u001b[1;32m 10410\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10411\u001b[0m )\n\u001b[0;32m> 10412\u001b[0;31m return merge(\n\u001b[0m\u001b[1;32m 10413\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10414\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
125
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m )\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
126
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self, copy)\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[0mjoin_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_join_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m result = self._reindex_and_concat(\n\u001b[0m\u001b[1;32m 886\u001b[0m \u001b[0mjoin_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 887\u001b[0m )\n",
benchmark/pandas_4/pandas_4_fixed.ipynb CHANGED
@@ -125,7 +125,7 @@
125
  },
126
  {
127
  "cell_type": "code",
128
- "execution_count": 3,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:21:45.091494Z",
@@ -135,82 +135,7 @@
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  "shell.execute_reply.started": "2023-04-25T22:21:45.091436Z"
136
  }
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  },
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- "outputs": [
139
- {
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- "data": {
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- "text/html": [
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- "<div>\n",
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- "<style scoped>\n",
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- " .dataframe tbody tr th:only-of-type {\n",
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- " vertical-align: middle;\n",
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- " }\n",
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- "\n",
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- " .dataframe tbody tr th {\n",
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- " vertical-align: top;\n",
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- " }\n",
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- "\n",
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- " .dataframe thead th {\n",
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- " text-align: right;\n",
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- " }\n",
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- "</style>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
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- " <thead>\n",
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- " <tr style=\"text-align: right;\">\n",
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- " <th></th>\n",
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- " <th>emotion</th>\n",
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- " <th>pixels</th>\n",
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- " <th>Usage</th>\n",
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- " </tr>\n",
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- " </thead>\n",
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- " <tbody>\n",
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- " <tr>\n",
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- " <th>0</th>\n",
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- " <td>0</td>\n",
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- " <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
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- " <td>Training</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>1</th>\n",
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- " <td>0</td>\n",
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- " <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n",
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- " <td>Training</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>2</th>\n",
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- " <td>2</td>\n",
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- " <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n",
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- " <td>Training</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>3</th>\n",
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- " <td>4</td>\n",
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- " <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n",
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- " <td>Training</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>4</th>\n",
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- " <td>6</td>\n",
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- " <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n",
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- " <td>Training</td>\n",
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- " </tr>\n",
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- " </tbody>\n",
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- "</table>\n",
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- "</div>"
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- ],
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- "text/plain": [
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- " emotion pixels Usage\n",
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- "0 0 70 80 82 72 58 58 60 63 54 58 60 48 89 115 121... Training\n",
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- "1 0 151 150 147 155 148 133 111 140 170 174 182 15... Training\n",
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- "2 2 231 212 156 164 174 138 161 173 182 200 106 38... Training\n",
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- "3 4 24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1... Training\n",
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- "4 6 4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84... Training"
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- ]
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- },
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- "execution_count": 3,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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  "source": [
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  "#preview first 5 row of data\n",
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  "data.head(5)"
@@ -218,7 +143,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 4,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:21:52.517159Z",
@@ -228,22 +153,7 @@
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  "shell.execute_reply.started": "2023-04-25T22:21:52.517101Z"
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  }
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  },
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "Usage\n",
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- "Training 28709\n",
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- "PublicTest 3589\n",
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- "PrivateTest 3589\n",
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- "Name: count, dtype: int64"
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- ]
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- },
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- "execution_count": 4,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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  "source": [
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  "#check usage values\n",
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  "#80% training, 10% validation and 10% test\n",
@@ -252,7 +162,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 5,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:21:56.062661Z",
@@ -262,88 +172,7 @@
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  "shell.execute_reply.started": "2023-04-25T22:21:56.062605Z"
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  }
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  },
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- "outputs": [
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- {
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- "data": {
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- "text/html": [
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- "<div>\n",
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- "<style scoped>\n",
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- " .dataframe tbody tr th:only-of-type {\n",
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- " vertical-align: middle;\n",
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- " }\n",
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- "\n",
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- " .dataframe tbody tr th {\n",
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- " vertical-align: top;\n",
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- " text-align: right;\n",
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- " }\n",
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- "</style>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
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- " <thead>\n",
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- " <tr style=\"text-align: right;\">\n",
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- " <th></th>\n",
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- " <th>emotion</th>\n",
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- " <th>number</th>\n",
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- " </tr>\n",
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- " </thead>\n",
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- " <tbody>\n",
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- " <tr>\n",
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- " <th>0</th>\n",
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- " <td>Angry</td>\n",
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- " <td>4953</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>1</th>\n",
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- " <td>Fear</td>\n",
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- " <td>5121</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>2</th>\n",
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- " <td>Sad</td>\n",
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- " <td>6077</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>3</th>\n",
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- " <td>Neutral</td>\n",
310
- " <td>6198</td>\n",
311
- " </tr>\n",
312
- " <tr>\n",
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- " <th>4</th>\n",
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- " <td>Happy</td>\n",
315
- " <td>8989</td>\n",
316
- " </tr>\n",
317
- " <tr>\n",
318
- " <th>5</th>\n",
319
- " <td>Surprise</td>\n",
320
- " <td>4002</td>\n",
321
- " </tr>\n",
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- " <tr>\n",
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- " <th>6</th>\n",
324
- " <td>Digust</td>\n",
325
- " <td>547</td>\n",
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- " </tr>\n",
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- " </tbody>\n",
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- "</table>\n",
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- "</div>"
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- ],
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- "text/plain": [
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- " emotion number\n",
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- "0 Angry 4953\n",
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- "1 Fear 5121\n",
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- "2 Sad 6077\n",
336
- "3 Neutral 6198\n",
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- "4 Happy 8989\n",
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- "5 Surprise 4002\n",
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- "6 Digust 547"
340
- ]
341
- },
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- "execution_count": 5,
343
- "metadata": {},
344
- "output_type": "execute_result"
345
- }
346
- ],
347
  "source": [
348
  "#check target labels\n",
349
  "emotion_map = {0: 'Angry', 1: 'Digust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}\n",
@@ -355,7 +184,7 @@
355
  },
356
  {
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  "cell_type": "code",
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- "execution_count": 6,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:21:59.919001Z",
@@ -393,7 +222,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 7,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:22:02.488962Z",
@@ -474,7 +303,7 @@
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  "5 Surprise 4002"
475
  ]
476
  },
477
- "execution_count": 7,
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  "metadata": {},
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  "output_type": "execute_result"
480
  }
@@ -490,7 +319,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 8,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:22:05.563193Z",
@@ -500,18 +329,7 @@
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  "shell.execute_reply.started": "2023-04-25T22:22:05.563141Z"
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  }
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  },
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- "outputs": [
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- {
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- "data": {
506
- "image/png": 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\n",
507
- "text/plain": [
508
- "<Figure size 600x400 with 1 Axes>"
509
- ]
510
- },
511
- "metadata": {},
512
- "output_type": "display_data"
513
- }
514
- ],
515
  "source": [
516
  "# Plotting a bar graph of the class distributions\n",
517
  "plt.figure(figsize=(6,4))\n",
@@ -531,7 +349,7 @@
531
  },
532
  {
533
  "cell_type": "code",
534
- "execution_count": 9,
535
  "metadata": {
536
  "execution": {
537
  "iopub.execute_input": "2023-04-25T22:22:12.246026Z",
 
125
  },
126
  {
127
  "cell_type": "code",
128
+ "execution_count": null,
129
  "metadata": {
130
  "execution": {
131
  "iopub.execute_input": "2023-04-25T22:21:45.091494Z",
 
135
  "shell.execute_reply.started": "2023-04-25T22:21:45.091436Z"
136
  }
137
  },
138
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139
  "source": [
140
  "#preview first 5 row of data\n",
141
  "data.head(5)"
 
143
  },
144
  {
145
  "cell_type": "code",
146
+ "execution_count": null,
147
  "metadata": {
148
  "execution": {
149
  "iopub.execute_input": "2023-04-25T22:21:52.517159Z",
 
153
  "shell.execute_reply.started": "2023-04-25T22:21:52.517101Z"
154
  }
155
  },
156
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
157
  "source": [
158
  "#check usage values\n",
159
  "#80% training, 10% validation and 10% test\n",
 
162
  },
163
  {
164
  "cell_type": "code",
165
+ "execution_count": null,
166
  "metadata": {
167
  "execution": {
168
  "iopub.execute_input": "2023-04-25T22:21:56.062661Z",
 
172
  "shell.execute_reply.started": "2023-04-25T22:21:56.062605Z"
173
  }
174
  },
175
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
176
  "source": [
177
  "#check target labels\n",
178
  "emotion_map = {0: 'Angry', 1: 'Digust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}\n",
 
184
  },
185
  {
186
  "cell_type": "code",
187
+ "execution_count": 3,
188
  "metadata": {
189
  "execution": {
190
  "iopub.execute_input": "2023-04-25T22:21:59.919001Z",
 
222
  },
223
  {
224
  "cell_type": "code",
225
+ "execution_count": 4,
226
  "metadata": {
227
  "execution": {
228
  "iopub.execute_input": "2023-04-25T22:22:02.488962Z",
 
303
  "5 Surprise 4002"
304
  ]
305
  },
306
+ "execution_count": 4,
307
  "metadata": {},
308
  "output_type": "execute_result"
309
  }
 
319
  },
320
  {
321
  "cell_type": "code",
322
+ "execution_count": null,
323
  "metadata": {
324
  "execution": {
325
  "iopub.execute_input": "2023-04-25T22:22:05.563193Z",
 
329
  "shell.execute_reply.started": "2023-04-25T22:22:05.563141Z"
330
  }
331
  },
332
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
333
  "source": [
334
  "# Plotting a bar graph of the class distributions\n",
335
  "plt.figure(figsize=(6,4))\n",
 
349
  },
350
  {
351
  "cell_type": "code",
352
+ "execution_count": 5,
353
  "metadata": {
354
  "execution": {
355
  "iopub.execute_input": "2023-04-25T22:22:12.246026Z",
benchmark/pandas_4/pandas_4_reproduced.ipynb CHANGED
@@ -125,7 +125,7 @@
125
  },
126
  {
127
  "cell_type": "code",
128
- "execution_count": 3,
129
  "metadata": {
130
  "execution": {
131
  "iopub.execute_input": "2023-04-25T22:21:45.091494Z",
@@ -135,82 +135,7 @@
135
  "shell.execute_reply.started": "2023-04-25T22:21:45.091436Z"
136
  }
137
  },
138
- "outputs": [
139
- {
140
- "data": {
141
- "text/html": [
142
- "<div>\n",
143
- "<style scoped>\n",
144
- " .dataframe tbody tr th:only-of-type {\n",
145
- " vertical-align: middle;\n",
146
- " }\n",
147
- "\n",
148
- " .dataframe tbody tr th {\n",
149
- " vertical-align: top;\n",
150
- " }\n",
151
- "\n",
152
- " .dataframe thead th {\n",
153
- " text-align: right;\n",
154
- " }\n",
155
- "</style>\n",
156
- "<table border=\"1\" class=\"dataframe\">\n",
157
- " <thead>\n",
158
- " <tr style=\"text-align: right;\">\n",
159
- " <th></th>\n",
160
- " <th>emotion</th>\n",
161
- " <th>pixels</th>\n",
162
- " <th>Usage</th>\n",
163
- " </tr>\n",
164
- " </thead>\n",
165
- " <tbody>\n",
166
- " <tr>\n",
167
- " <th>0</th>\n",
168
- " <td>0</td>\n",
169
- " <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
170
- " <td>Training</td>\n",
171
- " </tr>\n",
172
- " <tr>\n",
173
- " <th>1</th>\n",
174
- " <td>0</td>\n",
175
- " <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n",
176
- " <td>Training</td>\n",
177
- " </tr>\n",
178
- " <tr>\n",
179
- " <th>2</th>\n",
180
- " <td>2</td>\n",
181
- " <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n",
182
- " <td>Training</td>\n",
183
- " </tr>\n",
184
- " <tr>\n",
185
- " <th>3</th>\n",
186
- " <td>4</td>\n",
187
- " <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n",
188
- " <td>Training</td>\n",
189
- " </tr>\n",
190
- " <tr>\n",
191
- " <th>4</th>\n",
192
- " <td>6</td>\n",
193
- " <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n",
194
- " <td>Training</td>\n",
195
- " </tr>\n",
196
- " </tbody>\n",
197
- "</table>\n",
198
- "</div>"
199
- ],
200
- "text/plain": [
201
- " emotion pixels Usage\n",
202
- "0 0 70 80 82 72 58 58 60 63 54 58 60 48 89 115 121... Training\n",
203
- "1 0 151 150 147 155 148 133 111 140 170 174 182 15... Training\n",
204
- "2 2 231 212 156 164 174 138 161 173 182 200 106 38... Training\n",
205
- "3 4 24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1... Training\n",
206
- "4 6 4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84... Training"
207
- ]
208
- },
209
- "execution_count": 3,
210
- "metadata": {},
211
- "output_type": "execute_result"
212
- }
213
- ],
214
  "source": [
215
  "#preview first 5 row of data\n",
216
  "data.head(5)"
@@ -218,7 +143,7 @@
218
  },
219
  {
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  "cell_type": "code",
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- "execution_count": 4,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:21:52.517159Z",
@@ -228,22 +153,7 @@
228
  "shell.execute_reply.started": "2023-04-25T22:21:52.517101Z"
229
  }
230
  },
231
- "outputs": [
232
- {
233
- "data": {
234
- "text/plain": [
235
- "Usage\n",
236
- "Training 28709\n",
237
- "PublicTest 3589\n",
238
- "PrivateTest 3589\n",
239
- "Name: count, dtype: int64"
240
- ]
241
- },
242
- "execution_count": 4,
243
- "metadata": {},
244
- "output_type": "execute_result"
245
- }
246
- ],
247
  "source": [
248
  "#check usage values\n",
249
  "#80% training, 10% validation and 10% test\n",
@@ -252,7 +162,7 @@
252
  },
253
  {
254
  "cell_type": "code",
255
- "execution_count": 5,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-04-25T22:21:56.062661Z",
@@ -262,88 +172,7 @@
262
  "shell.execute_reply.started": "2023-04-25T22:21:56.062605Z"
263
  }
264
  },
265
- "outputs": [
266
- {
267
- "data": {
268
- "text/html": [
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- "<div>\n",
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- "<style scoped>\n",
271
- " .dataframe tbody tr th:only-of-type {\n",
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- " vertical-align: middle;\n",
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- " }\n",
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- "\n",
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- " .dataframe tbody tr th {\n",
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- " vertical-align: top;\n",
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- " }\n",
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- "\n",
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- " .dataframe thead th {\n",
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- " text-align: right;\n",
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- " }\n",
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- "</style>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
284
- " <thead>\n",
285
- " <tr style=\"text-align: right;\">\n",
286
- " <th></th>\n",
287
- " <th>emotion</th>\n",
288
- " <th>number</th>\n",
289
- " </tr>\n",
290
- " </thead>\n",
291
- " <tbody>\n",
292
- " <tr>\n",
293
- " <th>0</th>\n",
294
- " <td>Angry</td>\n",
295
- " <td>4953</td>\n",
296
- " </tr>\n",
297
- " <tr>\n",
298
- " <th>1</th>\n",
299
- " <td>Fear</td>\n",
300
- " <td>5121</td>\n",
301
- " </tr>\n",
302
- " <tr>\n",
303
- " <th>2</th>\n",
304
- " <td>Sad</td>\n",
305
- " <td>6077</td>\n",
306
- " </tr>\n",
307
- " <tr>\n",
308
- " <th>3</th>\n",
309
- " <td>Neutral</td>\n",
310
- " <td>6198</td>\n",
311
- " </tr>\n",
312
- " <tr>\n",
313
- " <th>4</th>\n",
314
- " <td>Happy</td>\n",
315
- " <td>8989</td>\n",
316
- " </tr>\n",
317
- " <tr>\n",
318
- " <th>5</th>\n",
319
- " <td>Surprise</td>\n",
320
- " <td>4002</td>\n",
321
- " </tr>\n",
322
- " <tr>\n",
323
- " <th>6</th>\n",
324
- " <td>Digust</td>\n",
325
- " <td>547</td>\n",
326
- " </tr>\n",
327
- " </tbody>\n",
328
- "</table>\n",
329
- "</div>"
330
- ],
331
- "text/plain": [
332
- " emotion number\n",
333
- "0 Angry 4953\n",
334
- "1 Fear 5121\n",
335
- "2 Sad 6077\n",
336
- "3 Neutral 6198\n",
337
- "4 Happy 8989\n",
338
- "5 Surprise 4002\n",
339
- "6 Digust 547"
340
- ]
341
- },
342
- "execution_count": 5,
343
- "metadata": {},
344
- "output_type": "execute_result"
345
- }
346
- ],
347
  "source": [
348
  "#check target labels\n",
349
  "emotion_map = {0: 'Angry', 1: 'Digust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}\n",
@@ -355,7 +184,7 @@
355
  },
356
  {
357
  "cell_type": "code",
358
- "execution_count": 6,
359
  "metadata": {
360
  "execution": {
361
  "iopub.execute_input": "2023-04-25T22:21:59.919001Z",
@@ -393,7 +222,7 @@
393
  },
394
  {
395
  "cell_type": "code",
396
- "execution_count": 7,
397
  "metadata": {
398
  "execution": {
399
  "iopub.execute_input": "2023-04-25T22:22:02.488962Z",
@@ -474,7 +303,7 @@
474
  "5 Surprise 4002"
475
  ]
476
  },
477
- "execution_count": 7,
478
  "metadata": {},
479
  "output_type": "execute_result"
480
  }
@@ -490,7 +319,7 @@
490
  },
491
  {
492
  "cell_type": "code",
493
- "execution_count": 8,
494
  "metadata": {
495
  "execution": {
496
  "iopub.execute_input": "2023-04-25T22:22:05.563193Z",
@@ -500,18 +329,7 @@
500
  "shell.execute_reply.started": "2023-04-25T22:22:05.563141Z"
501
  }
502
  },
503
- "outputs": [
504
- {
505
- "data": {
506
- "image/png": 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\n",
507
- "text/plain": [
508
- "<Figure size 600x400 with 1 Axes>"
509
- ]
510
- },
511
- "metadata": {},
512
- "output_type": "display_data"
513
- }
514
- ],
515
  "source": [
516
  "# Plotting a bar graph of the class distributions\n",
517
  "plt.figure(figsize=(6,4))\n",
@@ -531,7 +349,7 @@
531
  },
532
  {
533
  "cell_type": "code",
534
- "execution_count": 9,
535
  "metadata": {
536
  "execution": {
537
  "iopub.execute_input": "2023-04-25T22:22:12.246026Z",
@@ -549,7 +367,7 @@
549
  "traceback": [
550
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
551
  "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
552
- "\u001b[0;32m<ipython-input-9-48ca96f763ed>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mface\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'emotion'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;31m# fix 1 (corresponding fixes)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow2image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mface\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# img = row2image(face)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
553
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1152\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1155\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
554
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1713\u001b[0m \u001b[0;31m# validate the location\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1714\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1715\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1716\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
555
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1645\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1646\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1647\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1649\u001b[0m \u001b[0;31m# -------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
 
125
  },
126
  {
127
  "cell_type": "code",
128
+ "execution_count": null,
129
  "metadata": {
130
  "execution": {
131
  "iopub.execute_input": "2023-04-25T22:21:45.091494Z",
 
135
  "shell.execute_reply.started": "2023-04-25T22:21:45.091436Z"
136
  }
137
  },
138
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139
  "source": [
140
  "#preview first 5 row of data\n",
141
  "data.head(5)"
 
143
  },
144
  {
145
  "cell_type": "code",
146
+ "execution_count": null,
147
  "metadata": {
148
  "execution": {
149
  "iopub.execute_input": "2023-04-25T22:21:52.517159Z",
 
153
  "shell.execute_reply.started": "2023-04-25T22:21:52.517101Z"
154
  }
155
  },
156
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
157
  "source": [
158
  "#check usage values\n",
159
  "#80% training, 10% validation and 10% test\n",
 
162
  },
163
  {
164
  "cell_type": "code",
165
+ "execution_count": null,
166
  "metadata": {
167
  "execution": {
168
  "iopub.execute_input": "2023-04-25T22:21:56.062661Z",
 
172
  "shell.execute_reply.started": "2023-04-25T22:21:56.062605Z"
173
  }
174
  },
175
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
176
  "source": [
177
  "#check target labels\n",
178
  "emotion_map = {0: 'Angry', 1: 'Digust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}\n",
 
184
  },
185
  {
186
  "cell_type": "code",
187
+ "execution_count": 3,
188
  "metadata": {
189
  "execution": {
190
  "iopub.execute_input": "2023-04-25T22:21:59.919001Z",
 
222
  },
223
  {
224
  "cell_type": "code",
225
+ "execution_count": 5,
226
  "metadata": {
227
  "execution": {
228
  "iopub.execute_input": "2023-04-25T22:22:02.488962Z",
 
303
  "5 Surprise 4002"
304
  ]
305
  },
306
+ "execution_count": 5,
307
  "metadata": {},
308
  "output_type": "execute_result"
309
  }
 
319
  },
320
  {
321
  "cell_type": "code",
322
+ "execution_count": null,
323
  "metadata": {
324
  "execution": {
325
  "iopub.execute_input": "2023-04-25T22:22:05.563193Z",
 
329
  "shell.execute_reply.started": "2023-04-25T22:22:05.563141Z"
330
  }
331
  },
332
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
333
  "source": [
334
  "# Plotting a bar graph of the class distributions\n",
335
  "plt.figure(figsize=(6,4))\n",
 
349
  },
350
  {
351
  "cell_type": "code",
352
+ "execution_count": 6,
353
  "metadata": {
354
  "execution": {
355
  "iopub.execute_input": "2023-04-25T22:22:12.246026Z",
 
367
  "traceback": [
368
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
369
  "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
370
+ "\u001b[0;32m<ipython-input-6-48ca96f763ed>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mface\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'emotion'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;31m# fix 1 (corresponding fixes)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow2image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mface\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# img = row2image(face)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
371
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1152\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1155\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
372
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1713\u001b[0m \u001b[0;31m# validate the location\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1714\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1715\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1716\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
373
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1645\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1646\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1647\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1649\u001b[0m \u001b[0;31m# -------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
benchmark/pandas_5/pandas_5_fixed.ipynb CHANGED
@@ -603,7 +603,7 @@
603
  },
604
  {
605
  "cell_type": "code",
606
- "execution_count": 3,
607
  "metadata": {
608
  "execution": {
609
  "iopub.execute_input": "2023-11-16T05:40:08.587382Z",
@@ -613,150 +613,7 @@
613
  "shell.execute_reply.started": "2023-11-16T05:40:08.587345Z"
614
  }
615
  },
616
- "outputs": [
617
- {
618
- "data": {
619
- "text/html": [
620
- "<div>\n",
621
- "<style scoped>\n",
622
- " .dataframe tbody tr th:only-of-type {\n",
623
- " vertical-align: middle;\n",
624
- " }\n",
625
- "\n",
626
- " .dataframe tbody tr th {\n",
627
- " vertical-align: top;\n",
628
- " }\n",
629
- "\n",
630
- " .dataframe thead th {\n",
631
- " text-align: right;\n",
632
- " }\n",
633
- "</style>\n",
634
- "<table border=\"1\" class=\"dataframe\">\n",
635
- " <thead>\n",
636
- " <tr style=\"text-align: right;\">\n",
637
- " <th></th>\n",
638
- " <th>id</th>\n",
639
- " <th>Gender</th>\n",
640
- " <th>Age</th>\n",
641
- " <th>Driving_License</th>\n",
642
- " <th>Region_Code</th>\n",
643
- " <th>Previously_Insured</th>\n",
644
- " <th>Vehicle_Age</th>\n",
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736
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739
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741
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761
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762
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@@ -764,7 +621,7 @@
764
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766
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767
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@@ -774,157 +631,14 @@
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871
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875
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880
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882
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883
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885
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886
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891
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- " id Gender Age Driving_License Region_Code Previously_Insured \\\n",
895
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896
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897
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899
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901
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922
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923
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926
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928
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935
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950
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951
  "train.shape"
@@ -953,7 +656,7 @@
953
  },
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  {
955
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956
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  "metadata": {
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@@ -963,37 +666,14 @@
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964
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977
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979
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981
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982
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986
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991
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997
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1013
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1054
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1055
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1056
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
- " <td>154.347397</td>\n",
1066
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1067
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1068
- " <th>std</th>\n",
1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1082
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1083
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1085
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1086
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1087
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1089
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1090
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1092
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1093
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1094
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1095
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1096
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1097
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1098
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1100
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1101
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1102
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1103
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1106
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1110
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1112
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1113
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1114
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1115
- "count 381109.000000 381109.000000 381109.000000 381109.000000\n",
1116
- "mean 38.822584 26.388807 30564.389581 154.347397\n",
1117
- "std 15.511611 13.229888 17213.155057 83.671304\n",
1118
- "min 20.000000 0.000000 2630.000000 10.000000\n",
1119
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1120
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1121
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1122
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1128
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1129
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1130
  "source": [
1131
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1132
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1519
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1520
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1521
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@@ -1571,7 +1146,7 @@
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@@ -1591,7 +1166,7 @@
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@@ -1614,7 +1189,7 @@
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  "shell.execute_reply.started": "2023-11-16T05:47:52.984232Z"
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- " <th></th>\n",
1684
- " <th>Gender</th>\n",
1685
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1686
- " <th>Driving_License</th>\n",
1687
- " <th>Region_Code</th>\n",
1688
- " <th>Previously_Insured</th>\n",
1689
- " <th>Annual_Premium</th>\n",
1690
- " <th>Policy_Sales_Channel</th>\n",
1691
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1692
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1820
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1821
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1855
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1856
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1862
- " <td>0</td>\n",
1863
- " </tr>\n",
1864
- " </tbody>\n",
1865
- "</table>\n",
1866
- "<p>381109 rows × 12 columns</p>\n",
1867
- "</div>"
1868
- ],
1869
- "text/plain": [
1870
- " Gender Age Driving_License Region_Code Previously_Insured \\\n",
1871
- "0 1 0.333777 1 28.0 0 \n",
1872
- "1 1 2.396751 1 3.0 0 \n",
1873
- "2 1 0.527181 1 28.0 0 \n",
1874
- "3 1 -1.148985 1 11.0 1 \n",
1875
- "4 0 -0.633242 1 41.0 1 \n",
1876
- "... ... ... ... ... ... \n",
1877
- "381104 1 2.267815 1 26.0 1 \n",
1878
- "381105 1 -0.568774 1 37.0 1 \n",
1879
- "381106 1 -1.148985 1 30.0 1 \n",
1880
- "381107 0 1.881007 1 14.0 0 \n",
1881
- "381108 1 0.462713 1 29.0 0 \n",
1882
- "\n",
1883
- " Annual_Premium Policy_Sales_Channel Vintage Response \\\n",
1884
- "0 0.070366 26.0 0.748795 1 \n",
1885
- "1 0.057496 26.0 0.342443 0 \n",
1886
- "2 0.066347 26.0 -1.521998 1 \n",
1887
- "3 0.048348 152.0 0.581474 0 \n",
1888
- "4 0.046259 152.0 -1.378580 0 \n",
1889
- "... ... ... ... ... \n",
1890
- "381104 0.051234 26.0 -0.792954 0 \n",
1891
- "381105 0.069551 152.0 -0.279037 0 \n",
1892
- "381106 0.060439 160.0 0.079509 0 \n",
1893
- "381107 0.078110 124.0 -0.960275 0 \n",
1894
- "381108 0.072827 26.0 0.987826 0 \n",
1895
- "\n",
1896
- " Vehicle_Age_lt_1_Year Vehicle_Age_gt_2_Years Vehicle_Damage_Yes \n",
1897
- "0 0 1 1 \n",
1898
- "1 0 0 0 \n",
1899
- "2 0 1 1 \n",
1900
- "3 1 0 0 \n",
1901
- "4 1 0 0 \n",
1902
- "... ... ... ... \n",
1903
- "381104 0 0 0 \n",
1904
- "381105 1 0 0 \n",
1905
- "381106 1 0 0 \n",
1906
- "381107 0 1 1 \n",
1907
- "381108 0 0 0 \n",
1908
- "\n",
1909
- "[381109 rows x 12 columns]"
1910
- ]
1911
- },
1912
- "execution_count": 16,
1913
- "metadata": {},
1914
- "output_type": "execute_result"
1915
- }
1916
- ],
1917
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1918
  "train"
1919
  ]
1920
  },
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1922
  "cell_type": "code",
1923
- "execution_count": 17,
1924
  "metadata": {
1925
  "execution": {
1926
  "iopub.execute_input": "2023-11-16T05:48:05.334543Z",
@@ -1942,7 +1263,7 @@
1942
  },
1943
  {
1944
  "cell_type": "code",
1945
- "execution_count": 18,
1946
  "metadata": {
1947
  "execution": {
1948
  "iopub.execute_input": "2023-11-16T05:48:12.294471Z",
@@ -1965,7 +1286,7 @@
1965
  },
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1967
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1968
- "execution_count": 19,
1969
  "metadata": {
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  "execution": {
1971
  "iopub.execute_input": "2023-11-16T05:48:18.989930Z",
@@ -1983,7 +1304,7 @@
1983
  },
1984
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1985
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1986
- "execution_count": 20,
1987
  "metadata": {
1988
  "execution": {
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  "iopub.execute_input": "2023-11-16T05:48:25.677104Z",
@@ -2004,7 +1325,7 @@
2004
  },
2005
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2006
  "cell_type": "code",
2007
- "execution_count": 21,
2008
  "metadata": {
2009
  "execution": {
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  "iopub.execute_input": "2023-11-16T05:48:32.755356Z",
@@ -2021,7 +1342,7 @@
2021
  },
2022
  {
2023
  "cell_type": "code",
2024
- "execution_count": 22,
2025
  "metadata": {
2026
  "execution": {
2027
  "iopub.execute_input": "2023-11-16T05:48:39.182421Z",
@@ -2038,7 +1359,7 @@
2038
  },
2039
  {
2040
  "cell_type": "code",
2041
- "execution_count": 23,
2042
  "metadata": {
2043
  "execution": {
2044
  "iopub.execute_input": "2023-11-16T05:48:46.496489Z",
@@ -2048,22 +1369,7 @@
2048
  "shell.execute_reply.started": "2023-11-16T05:48:46.496460Z"
2049
  }
2050
  },
2051
- "outputs": [
2052
- {
2053
- "data": {
2054
- "text/plain": [
2055
- "Index(['Gender', 'Age', 'Driving_License', 'Region_Code', 'Previously_Insured',\n",
2056
- " 'Annual_Premium', 'Policy_Sales_Channel', 'Vintage',\n",
2057
- " 'Vehicle_Age_lt_1_Year', 'Vehicle_Age_gt_2_Years',\n",
2058
- " 'Vehicle_Damage_Yes'],\n",
2059
- " dtype='object')"
2060
- ]
2061
- },
2062
- "execution_count": 23,
2063
- "metadata": {},
2064
- "output_type": "execute_result"
2065
- }
2066
- ],
2067
  "source": [
2068
  "x_train.columns"
2069
  ]
@@ -2289,7 +1595,7 @@
2289
  },
2290
  {
2291
  "cell_type": "code",
2292
- "execution_count": 25,
2293
  "metadata": {
2294
  "execution": {
2295
  "iopub.execute_input": "2023-11-16T06:08:40.887491Z",
 
603
  },
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  {
605
  "cell_type": "code",
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+ "execution_count": null,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-11-16T05:40:08.587382Z",
 
613
  "shell.execute_reply.started": "2023-11-16T05:40:08.587345Z"
614
  }
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  },
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
617
  "source": [
618
  "# Different id nos from the other notebook\n",
619
  "test.head() "
 
621
  },
622
  {
623
  "cell_type": "code",
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+ "execution_count": null,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-11-16T05:39:42.640987Z",
 
631
  "shell.execute_reply.started": "2023-11-16T05:39:42.640955Z"
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
635
  "source": [
636
  "train.head()"
637
  ]
638
  },
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640
  "cell_type": "code",
641
+ "execution_count": null,
642
  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-11-16T05:40:31.853856Z",
 
648
  "shell.execute_reply.started": "2023-11-16T05:40:31.853824Z"
649
  }
650
  },
651
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
652
  "source": [
653
  "\n",
654
  "train.shape"
 
656
  },
657
  {
658
  "cell_type": "code",
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+ "execution_count": null,
660
  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-11-16T05:40:39.805712Z",
 
666
  "shell.execute_reply.started": "2023-11-16T05:40:39.805681Z"
667
  }
668
  },
669
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
670
  "source": [
671
  "train.isnull().sum()"
672
  ]
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": null,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-11-16T05:40:48.806458Z",
 
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-11-16T05:40:54.938097Z",
 
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  "shell.execute_reply.started": "2023-11-16T05:40:54.938068Z"
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  }
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
705
  "source": [
706
  "train[numerical_columns].describe()"
707
  ]
 
1094
  },
1095
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  "cell_type": "code",
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+ "execution_count": 3,
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  "metadata": {
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- " </tr>\n",
1081
- " <tr>\n",
1082
- " <th>25%</th>\n",
1083
- " <td>25.000000</td>\n",
1084
- " <td>15.000000</td>\n",
1085
- " <td>24405.000000</td>\n",
1086
- " <td>82.000000</td>\n",
1087
- " </tr>\n",
1088
- " <tr>\n",
1089
- " <th>50%</th>\n",
1090
- " <td>36.000000</td>\n",
1091
- " <td>28.000000</td>\n",
1092
- " <td>31669.000000</td>\n",
1093
- " <td>154.000000</td>\n",
1094
- " </tr>\n",
1095
- " <tr>\n",
1096
- " <th>75%</th>\n",
1097
- " <td>49.000000</td>\n",
1098
- " <td>35.000000</td>\n",
1099
- " <td>39400.000000</td>\n",
1100
- " <td>227.000000</td>\n",
1101
- " </tr>\n",
1102
- " <tr>\n",
1103
- " <th>max</th>\n",
1104
- " <td>85.000000</td>\n",
1105
- " <td>52.000000</td>\n",
1106
- " <td>540165.000000</td>\n",
1107
- " <td>299.000000</td>\n",
1108
- " </tr>\n",
1109
- " </tbody>\n",
1110
- "</table>\n",
1111
- "</div>"
1112
- ],
1113
- "text/plain": [
1114
- " Age Region_Code Annual_Premium Vintage\n",
1115
- "count 381109.000000 381109.000000 381109.000000 381109.000000\n",
1116
- "mean 38.822584 26.388807 30564.389581 154.347397\n",
1117
- "std 15.511611 13.229888 17213.155057 83.671304\n",
1118
- "min 20.000000 0.000000 2630.000000 10.000000\n",
1119
- "25% 25.000000 15.000000 24405.000000 82.000000\n",
1120
- "50% 36.000000 28.000000 31669.000000 154.000000\n",
1121
- "75% 49.000000 35.000000 39400.000000 227.000000\n",
1122
- "max 85.000000 52.000000 540165.000000 299.000000"
1123
- ]
1124
- },
1125
- "execution_count": 8,
1126
- "metadata": {},
1127
- "output_type": "execute_result"
1128
- }
1129
- ],
1130
  "source": [
1131
  "train[numerical_columns].describe()"
1132
  ]
@@ -1519,7 +1094,7 @@
1519
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1520
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1521
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1522
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1523
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1524
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1525
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@@ -1537,7 +1112,7 @@
1537
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1538
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1539
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1540
- "execution_count": 10,
1541
  "metadata": {
1542
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1543
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@@ -1554,7 +1129,7 @@
1554
  },
1555
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1556
  "cell_type": "code",
1557
- "execution_count": 11,
1558
  "metadata": {
1559
  "execution": {
1560
  "iopub.execute_input": "2023-11-16T05:47:14.997163Z",
@@ -1571,7 +1146,7 @@
1571
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1572
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1573
  "cell_type": "code",
1574
- "execution_count": 12,
1575
  "metadata": {
1576
  "execution": {
1577
  "iopub.execute_input": "2023-11-16T05:47:22.318839Z",
@@ -1591,7 +1166,7 @@
1591
  },
1592
  {
1593
  "cell_type": "code",
1594
- "execution_count": 13,
1595
  "metadata": {
1596
  "execution": {
1597
  "iopub.execute_input": "2023-11-16T05:47:29.960566Z",
@@ -1614,7 +1189,7 @@
1614
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1615
  {
1616
  "cell_type": "code",
1617
- "execution_count": 14,
1618
  "metadata": {
1619
  "execution": {
1620
  "iopub.execute_input": "2023-11-16T05:47:35.704010Z",
@@ -1631,7 +1206,7 @@
1631
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1632
  {
1633
  "cell_type": "code",
1634
- "execution_count": 15,
1635
  "metadata": {
1636
  "execution": {
1637
  "iopub.execute_input": "2023-11-16T05:47:42.692055Z",
@@ -1649,7 +1224,7 @@
1649
  },
1650
  {
1651
  "cell_type": "code",
1652
- "execution_count": 16,
1653
  "metadata": {
1654
  "execution": {
1655
  "iopub.execute_input": "2023-11-16T05:47:52.984270Z",
@@ -1659,268 +1234,14 @@
1659
  "shell.execute_reply.started": "2023-11-16T05:47:52.984232Z"
1660
  }
1661
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1662
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1663
- {
1664
- "data": {
1665
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1666
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1668
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1680
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1681
- " <thead>\n",
1682
- " <tr style=\"text-align: right;\">\n",
1683
- " <th></th>\n",
1684
- " <th>Gender</th>\n",
1685
- " <th>Age</th>\n",
1686
- " <th>Driving_License</th>\n",
1687
- " <th>Region_Code</th>\n",
1688
- " <th>Previously_Insured</th>\n",
1689
- " <th>Annual_Premium</th>\n",
1690
- " <th>Policy_Sales_Channel</th>\n",
1691
- " <th>Vintage</th>\n",
1692
- " <th>Response</th>\n",
1693
- " <th>Vehicle_Age_lt_1_Year</th>\n",
1694
- " <th>Vehicle_Age_gt_2_Years</th>\n",
1695
- " <th>Vehicle_Damage_Yes</th>\n",
1696
- " </tr>\n",
1697
- " </thead>\n",
1698
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1699
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1700
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1701
- " <td>1</td>\n",
1702
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- " <td>28.0</td>\n",
1705
- " <td>0</td>\n",
1706
- " <td>0.070366</td>\n",
1707
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1709
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1710
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1711
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1712
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1713
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1714
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1715
- " <th>1</th>\n",
1716
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1717
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1718
- " <td>1</td>\n",
1719
- " <td>3.0</td>\n",
1720
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1721
- " <td>0.057496</td>\n",
1722
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1724
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1725
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1727
- " <td>0</td>\n",
1728
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1729
- " <tr>\n",
1730
- " <th>2</th>\n",
1731
- " <td>1</td>\n",
1732
- " <td>0.527181</td>\n",
1733
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1734
- " <td>28.0</td>\n",
1735
- " <td>0</td>\n",
1736
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1737
- " <td>26.0</td>\n",
1738
- " <td>-1.521998</td>\n",
1739
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1740
- " <td>0</td>\n",
1741
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1742
- " <td>1</td>\n",
1743
- " </tr>\n",
1744
- " <tr>\n",
1745
- " <th>3</th>\n",
1746
- " <td>1</td>\n",
1747
- " <td>-1.148985</td>\n",
1748
- " <td>1</td>\n",
1749
- " <td>11.0</td>\n",
1750
- " <td>1</td>\n",
1751
- " <td>0.048348</td>\n",
1752
- " <td>152.0</td>\n",
1753
- " <td>0.581474</td>\n",
1754
- " <td>0</td>\n",
1755
- " <td>1</td>\n",
1756
- " <td>0</td>\n",
1757
- " <td>0</td>\n",
1758
- " </tr>\n",
1759
- " <tr>\n",
1760
- " <th>4</th>\n",
1761
- " <td>0</td>\n",
1762
- " <td>-0.633242</td>\n",
1763
- " <td>1</td>\n",
1764
- " <td>41.0</td>\n",
1765
- " <td>1</td>\n",
1766
- " <td>0.046259</td>\n",
1767
- " <td>152.0</td>\n",
1768
- " <td>-1.378580</td>\n",
1769
- " <td>0</td>\n",
1770
- " <td>1</td>\n",
1771
- " <td>0</td>\n",
1772
- " <td>0</td>\n",
1773
- " </tr>\n",
1774
- " <tr>\n",
1775
- " <th>...</th>\n",
1776
- " <td>...</td>\n",
1777
- " <td>...</td>\n",
1778
- " <td>...</td>\n",
1779
- " <td>...</td>\n",
1780
- " <td>...</td>\n",
1781
- " <td>...</td>\n",
1782
- " <td>...</td>\n",
1783
- " <td>...</td>\n",
1784
- " <td>...</td>\n",
1785
- " <td>...</td>\n",
1786
- " <td>...</td>\n",
1787
- " <td>...</td>\n",
1788
- " </tr>\n",
1789
- " <tr>\n",
1790
- " <th>381104</th>\n",
1791
- " <td>1</td>\n",
1792
- " <td>2.267815</td>\n",
1793
- " <td>1</td>\n",
1794
- " <td>26.0</td>\n",
1795
- " <td>1</td>\n",
1796
- " <td>0.051234</td>\n",
1797
- " <td>26.0</td>\n",
1798
- " <td>-0.792954</td>\n",
1799
- " <td>0</td>\n",
1800
- " <td>0</td>\n",
1801
- " <td>0</td>\n",
1802
- " <td>0</td>\n",
1803
- " </tr>\n",
1804
- " <tr>\n",
1805
- " <th>381105</th>\n",
1806
- " <td>1</td>\n",
1807
- " <td>-0.568774</td>\n",
1808
- " <td>1</td>\n",
1809
- " <td>37.0</td>\n",
1810
- " <td>1</td>\n",
1811
- " <td>0.069551</td>\n",
1812
- " <td>152.0</td>\n",
1813
- " <td>-0.279037</td>\n",
1814
- " <td>0</td>\n",
1815
- " <td>1</td>\n",
1816
- " <td>0</td>\n",
1817
- " <td>0</td>\n",
1818
- " </tr>\n",
1819
- " <tr>\n",
1820
- " <th>381106</th>\n",
1821
- " <td>1</td>\n",
1822
- " <td>-1.148985</td>\n",
1823
- " <td>1</td>\n",
1824
- " <td>30.0</td>\n",
1825
- " <td>1</td>\n",
1826
- " <td>0.060439</td>\n",
1827
- " <td>160.0</td>\n",
1828
- " <td>0.079509</td>\n",
1829
- " <td>0</td>\n",
1830
- " <td>1</td>\n",
1831
- " <td>0</td>\n",
1832
- " <td>0</td>\n",
1833
- " </tr>\n",
1834
- " <tr>\n",
1835
- " <th>381107</th>\n",
1836
- " <td>0</td>\n",
1837
- " <td>1.881007</td>\n",
1838
- " <td>1</td>\n",
1839
- " <td>14.0</td>\n",
1840
- " <td>0</td>\n",
1841
- " <td>0.078110</td>\n",
1842
- " <td>124.0</td>\n",
1843
- " <td>-0.960275</td>\n",
1844
- " <td>0</td>\n",
1845
- " <td>0</td>\n",
1846
- " <td>1</td>\n",
1847
- " <td>1</td>\n",
1848
- " </tr>\n",
1849
- " <tr>\n",
1850
- " <th>381108</th>\n",
1851
- " <td>1</td>\n",
1852
- " <td>0.462713</td>\n",
1853
- " <td>1</td>\n",
1854
- " <td>29.0</td>\n",
1855
- " <td>0</td>\n",
1856
- " <td>0.072827</td>\n",
1857
- " <td>26.0</td>\n",
1858
- " <td>0.987826</td>\n",
1859
- " <td>0</td>\n",
1860
- " <td>0</td>\n",
1861
- " <td>0</td>\n",
1862
- " <td>0</td>\n",
1863
- " </tr>\n",
1864
- " </tbody>\n",
1865
- "</table>\n",
1866
- "<p>381109 rows × 12 columns</p>\n",
1867
- "</div>"
1868
- ],
1869
- "text/plain": [
1870
- " Gender Age Driving_License Region_Code Previously_Insured \\\n",
1871
- "0 1 0.333777 1 28.0 0 \n",
1872
- "1 1 2.396751 1 3.0 0 \n",
1873
- "2 1 0.527181 1 28.0 0 \n",
1874
- "3 1 -1.148985 1 11.0 1 \n",
1875
- "4 0 -0.633242 1 41.0 1 \n",
1876
- "... ... ... ... ... ... \n",
1877
- "381104 1 2.267815 1 26.0 1 \n",
1878
- "381105 1 -0.568774 1 37.0 1 \n",
1879
- "381106 1 -1.148985 1 30.0 1 \n",
1880
- "381107 0 1.881007 1 14.0 0 \n",
1881
- "381108 1 0.462713 1 29.0 0 \n",
1882
- "\n",
1883
- " Annual_Premium Policy_Sales_Channel Vintage Response \\\n",
1884
- "0 0.070366 26.0 0.748795 1 \n",
1885
- "1 0.057496 26.0 0.342443 0 \n",
1886
- "2 0.066347 26.0 -1.521998 1 \n",
1887
- "3 0.048348 152.0 0.581474 0 \n",
1888
- "4 0.046259 152.0 -1.378580 0 \n",
1889
- "... ... ... ... ... \n",
1890
- "381104 0.051234 26.0 -0.792954 0 \n",
1891
- "381105 0.069551 152.0 -0.279037 0 \n",
1892
- "381106 0.060439 160.0 0.079509 0 \n",
1893
- "381107 0.078110 124.0 -0.960275 0 \n",
1894
- "381108 0.072827 26.0 0.987826 0 \n",
1895
- "\n",
1896
- " Vehicle_Age_lt_1_Year Vehicle_Age_gt_2_Years Vehicle_Damage_Yes \n",
1897
- "0 0 1 1 \n",
1898
- "1 0 0 0 \n",
1899
- "2 0 1 1 \n",
1900
- "3 1 0 0 \n",
1901
- "4 1 0 0 \n",
1902
- "... ... ... ... \n",
1903
- "381104 0 0 0 \n",
1904
- "381105 1 0 0 \n",
1905
- "381106 1 0 0 \n",
1906
- "381107 0 1 1 \n",
1907
- "381108 0 0 0 \n",
1908
- "\n",
1909
- "[381109 rows x 12 columns]"
1910
- ]
1911
- },
1912
- "execution_count": 16,
1913
- "metadata": {},
1914
- "output_type": "execute_result"
1915
- }
1916
- ],
1917
  "source": [
1918
  "train"
1919
  ]
1920
  },
1921
  {
1922
  "cell_type": "code",
1923
- "execution_count": 17,
1924
  "metadata": {
1925
  "execution": {
1926
  "iopub.execute_input": "2023-11-16T05:48:05.334543Z",
@@ -1942,7 +1263,7 @@
1942
  },
1943
  {
1944
  "cell_type": "code",
1945
- "execution_count": 18,
1946
  "metadata": {
1947
  "execution": {
1948
  "iopub.execute_input": "2023-11-16T05:48:12.294471Z",
@@ -1965,7 +1286,7 @@
1965
  },
1966
  {
1967
  "cell_type": "code",
1968
- "execution_count": 19,
1969
  "metadata": {
1970
  "execution": {
1971
  "iopub.execute_input": "2023-11-16T05:48:18.989930Z",
@@ -1983,7 +1304,7 @@
1983
  },
1984
  {
1985
  "cell_type": "code",
1986
- "execution_count": 20,
1987
  "metadata": {
1988
  "execution": {
1989
  "iopub.execute_input": "2023-11-16T05:48:25.677104Z",
@@ -2004,7 +1325,7 @@
2004
  },
2005
  {
2006
  "cell_type": "code",
2007
- "execution_count": 21,
2008
  "metadata": {
2009
  "execution": {
2010
  "iopub.execute_input": "2023-11-16T05:48:32.755356Z",
@@ -2021,7 +1342,7 @@
2021
  },
2022
  {
2023
  "cell_type": "code",
2024
- "execution_count": 22,
2025
  "metadata": {
2026
  "execution": {
2027
  "iopub.execute_input": "2023-11-16T05:48:39.182421Z",
@@ -2038,7 +1359,7 @@
2038
  },
2039
  {
2040
  "cell_type": "code",
2041
- "execution_count": 23,
2042
  "metadata": {
2043
  "execution": {
2044
  "iopub.execute_input": "2023-11-16T05:48:46.496489Z",
@@ -2048,22 +1369,7 @@
2048
  "shell.execute_reply.started": "2023-11-16T05:48:46.496460Z"
2049
  }
2050
  },
2051
- "outputs": [
2052
- {
2053
- "data": {
2054
- "text/plain": [
2055
- "Index(['Gender', 'Age', 'Driving_License', 'Region_Code', 'Previously_Insured',\n",
2056
- " 'Annual_Premium', 'Policy_Sales_Channel', 'Vintage',\n",
2057
- " 'Vehicle_Age_lt_1_Year', 'Vehicle_Age_gt_2_Years',\n",
2058
- " 'Vehicle_Damage_Yes'],\n",
2059
- " dtype='object')"
2060
- ]
2061
- },
2062
- "execution_count": 23,
2063
- "metadata": {},
2064
- "output_type": "execute_result"
2065
- }
2066
- ],
2067
  "source": [
2068
  "x_train.columns"
2069
  ]
@@ -2289,7 +1595,7 @@
2289
  },
2290
  {
2291
  "cell_type": "code",
2292
- "execution_count": 24,
2293
  "metadata": {
2294
  "execution": {
2295
  "iopub.execute_input": "2023-11-16T06:08:40.887491Z",
@@ -2307,7 +1613,7 @@
2307
  "traceback": [
2308
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
2309
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
2310
- "\u001b[0;32m<ipython-input-24-81969cd12a4b>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcat_feat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
2311
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
2312
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
2313
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
 
603
  },
604
  {
605
  "cell_type": "code",
606
+ "execution_count": null,
607
  "metadata": {
608
  "execution": {
609
  "iopub.execute_input": "2023-11-16T05:40:08.587382Z",
 
613
  "shell.execute_reply.started": "2023-11-16T05:40:08.587345Z"
614
  }
615
  },
616
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
617
  "source": [
618
  "# Different id nos from the other notebook\n",
619
  "test.head() "
 
621
  },
622
  {
623
  "cell_type": "code",
624
+ "execution_count": null,
625
  "metadata": {
626
  "execution": {
627
  "iopub.execute_input": "2023-11-16T05:39:42.640987Z",
 
631
  "shell.execute_reply.started": "2023-11-16T05:39:42.640955Z"
632
  }
633
  },
634
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
635
  "source": [
636
  "train.head()"
637
  ]
638
  },
639
  {
640
  "cell_type": "code",
641
+ "execution_count": null,
642
  "metadata": {
643
  "execution": {
644
  "iopub.execute_input": "2023-11-16T05:40:31.853856Z",
 
648
  "shell.execute_reply.started": "2023-11-16T05:40:31.853824Z"
649
  }
650
  },
651
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
652
  "source": [
653
  "\n",
654
  "train.shape"
 
656
  },
657
  {
658
  "cell_type": "code",
659
+ "execution_count": null,
660
  "metadata": {
661
  "execution": {
662
  "iopub.execute_input": "2023-11-16T05:40:39.805712Z",
 
666
  "shell.execute_reply.started": "2023-11-16T05:40:39.805681Z"
667
  }
668
  },
669
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
670
  "source": [
671
  "train.isnull().sum()"
672
  ]
673
  },
674
  {
675
  "cell_type": "code",
676
+ "execution_count": null,
677
  "metadata": {
678
  "execution": {
679
  "iopub.execute_input": "2023-11-16T05:40:48.806458Z",
 
691
  },
692
  {
693
  "cell_type": "code",
694
+ "execution_count": null,
695
  "metadata": {
696
  "execution": {
697
  "iopub.execute_input": "2023-11-16T05:40:54.938097Z",
 
701
  "shell.execute_reply.started": "2023-11-16T05:40:54.938068Z"
702
  }
703
  },
704
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
705
  "source": [
706
  "train[numerical_columns].describe()"
707
  ]
 
1094
  },
1095
  {
1096
  "cell_type": "code",
1097
+ "execution_count": 3,
1098
  "metadata": {
1099
  "execution": {
1100
  "iopub.execute_input": "2023-11-16T05:46:58.312193Z",
 
1112
  },
1113
  {
1114
  "cell_type": "code",
1115
+ "execution_count": 4,
1116
  "metadata": {
1117
  "execution": {
1118
  "iopub.execute_input": "2023-11-16T05:47:08.177008Z",
 
1129
  },
1130
  {
1131
  "cell_type": "code",
1132
+ "execution_count": 5,
1133
  "metadata": {
1134
  "execution": {
1135
  "iopub.execute_input": "2023-11-16T05:47:14.997163Z",
 
1146
  },
1147
  {
1148
  "cell_type": "code",
1149
+ "execution_count": 6,
1150
  "metadata": {
1151
  "execution": {
1152
  "iopub.execute_input": "2023-11-16T05:47:22.318839Z",
 
1166
  },
1167
  {
1168
  "cell_type": "code",
1169
+ "execution_count": 7,
1170
  "metadata": {
1171
  "execution": {
1172
  "iopub.execute_input": "2023-11-16T05:47:29.960566Z",
 
1189
  },
1190
  {
1191
  "cell_type": "code",
1192
+ "execution_count": 8,
1193
  "metadata": {
1194
  "execution": {
1195
  "iopub.execute_input": "2023-11-16T05:47:35.704010Z",
 
1206
  },
1207
  {
1208
  "cell_type": "code",
1209
+ "execution_count": 9,
1210
  "metadata": {
1211
  "execution": {
1212
  "iopub.execute_input": "2023-11-16T05:47:42.692055Z",
 
1224
  },
1225
  {
1226
  "cell_type": "code",
1227
+ "execution_count": null,
1228
  "metadata": {
1229
  "execution": {
1230
  "iopub.execute_input": "2023-11-16T05:47:52.984270Z",
 
1234
  "shell.execute_reply.started": "2023-11-16T05:47:52.984232Z"
1235
  }
1236
  },
1237
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1238
  "source": [
1239
  "train"
1240
  ]
1241
  },
1242
  {
1243
  "cell_type": "code",
1244
+ "execution_count": 10,
1245
  "metadata": {
1246
  "execution": {
1247
  "iopub.execute_input": "2023-11-16T05:48:05.334543Z",
 
1263
  },
1264
  {
1265
  "cell_type": "code",
1266
+ "execution_count": 11,
1267
  "metadata": {
1268
  "execution": {
1269
  "iopub.execute_input": "2023-11-16T05:48:12.294471Z",
 
1286
  },
1287
  {
1288
  "cell_type": "code",
1289
+ "execution_count": 12,
1290
  "metadata": {
1291
  "execution": {
1292
  "iopub.execute_input": "2023-11-16T05:48:18.989930Z",
 
1304
  },
1305
  {
1306
  "cell_type": "code",
1307
+ "execution_count": 13,
1308
  "metadata": {
1309
  "execution": {
1310
  "iopub.execute_input": "2023-11-16T05:48:25.677104Z",
 
1325
  },
1326
  {
1327
  "cell_type": "code",
1328
+ "execution_count": null,
1329
  "metadata": {
1330
  "execution": {
1331
  "iopub.execute_input": "2023-11-16T05:48:32.755356Z",
 
1342
  },
1343
  {
1344
  "cell_type": "code",
1345
+ "execution_count": 15,
1346
  "metadata": {
1347
  "execution": {
1348
  "iopub.execute_input": "2023-11-16T05:48:39.182421Z",
 
1359
  },
1360
  {
1361
  "cell_type": "code",
1362
+ "execution_count": null,
1363
  "metadata": {
1364
  "execution": {
1365
  "iopub.execute_input": "2023-11-16T05:48:46.496489Z",
 
1369
  "shell.execute_reply.started": "2023-11-16T05:48:46.496460Z"
1370
  }
1371
  },
1372
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1373
  "source": [
1374
  "x_train.columns"
1375
  ]
 
1595
  },
1596
  {
1597
  "cell_type": "code",
1598
+ "execution_count": 16,
1599
  "metadata": {
1600
  "execution": {
1601
  "iopub.execute_input": "2023-11-16T06:08:40.887491Z",
 
1613
  "traceback": [
1614
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1615
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
1616
+ "\u001b[0;32m<ipython-input-16-81969cd12a4b>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcat_feat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1617
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1618
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
1619
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
benchmark/pandas_6/pandas_6_fixed.ipynb CHANGED
@@ -156,7 +156,7 @@
156
  },
157
  {
158
  "cell_type": "code",
159
- "execution_count": 3,
160
  "metadata": {
161
  "execution": {
162
  "iopub.execute_input": "2023-05-18T11:38:26.877375Z",
@@ -166,34 +166,14 @@
166
  "shell.execute_reply.started": "2023-05-18T11:38:26.877341Z"
167
  }
168
  },
169
- "outputs": [
170
- {
171
- "name": "stdout",
172
- "output_type": "stream",
173
- "text": [
174
- "<class 'pandas.core.frame.DataFrame'>\n",
175
- "RangeIndex: 186850 entries, 0 to 186849\n",
176
- "Data columns (total 6 columns):\n",
177
- " # Column Non-Null Count Dtype \n",
178
- "--- ------ -------------- ----- \n",
179
- " 0 Order ID 186305 non-null object\n",
180
- " 1 Product 186305 non-null object\n",
181
- " 2 Quantity Ordered 186305 non-null object\n",
182
- " 3 Price Each 186305 non-null object\n",
183
- " 4 Order Date 186305 non-null object\n",
184
- " 5 Purchase Address 186305 non-null object\n",
185
- "dtypes: object(6)\n",
186
- "memory usage: 8.6+ MB\n"
187
- ]
188
- }
189
- ],
190
  "source": [
191
  "df.info()"
192
  ]
193
  },
194
  {
195
  "cell_type": "code",
196
- "execution_count": 4,
197
  "metadata": {
198
  "execution": {
199
  "iopub.execute_input": "2023-05-18T11:38:27.249459Z",
@@ -203,24 +183,7 @@
203
  "shell.execute_reply.started": "2023-05-18T11:38:27.249429Z"
204
  }
205
  },
206
- "outputs": [
207
- {
208
- "data": {
209
- "text/plain": [
210
- "Order ID 545\n",
211
- "Product 545\n",
212
- "Quantity Ordered 545\n",
213
- "Price Each 545\n",
214
- "Order Date 545\n",
215
- "Purchase Address 545\n",
216
- "dtype: int64"
217
- ]
218
- },
219
- "execution_count": 4,
220
- "metadata": {},
221
- "output_type": "execute_result"
222
- }
223
- ],
224
  "source": [
225
  "df.isnull().sum()"
226
  ]
@@ -234,7 +197,7 @@
234
  },
235
  {
236
  "cell_type": "code",
237
- "execution_count": 5,
238
  "metadata": {
239
  "execution": {
240
  "iopub.execute_input": "2023-05-18T11:38:27.629044Z",
@@ -409,7 +372,7 @@
409
  "[186305 rows x 6 columns]"
410
  ]
411
  },
412
- "execution_count": 5,
413
  "metadata": {},
414
  "output_type": "execute_result"
415
  }
@@ -421,7 +384,7 @@
421
  },
422
  {
423
  "cell_type": "code",
424
- "execution_count": 6,
425
  "metadata": {
426
  "execution": {
427
  "iopub.execute_input": "2023-05-18T11:38:28.039601Z",
@@ -431,24 +394,7 @@
431
  "shell.execute_reply.started": "2023-05-18T11:38:28.039569Z"
432
  }
433
  },
434
- "outputs": [
435
- {
436
- "data": {
437
- "text/plain": [
438
- "Order ID 0\n",
439
- "Product 0\n",
440
- "Quantity Ordered 0\n",
441
- "Price Each 0\n",
442
- "Order Date 0\n",
443
- "Purchase Address 0\n",
444
- "dtype: int64"
445
- ]
446
- },
447
- "execution_count": 6,
448
- "metadata": {},
449
- "output_type": "execute_result"
450
- }
451
- ],
452
  "source": [
453
  "df.isnull().sum()"
454
  ]
@@ -462,7 +408,7 @@
462
  },
463
  {
464
  "cell_type": "code",
465
- "execution_count": 7,
466
  "metadata": {
467
  "execution": {
468
  "iopub.execute_input": "2023-05-18T11:39:04.344286Z",
@@ -474,7 +420,11 @@
474
  },
475
  "outputs": [],
476
  "source": [
477
- "# df=df['Order ID'].astype('int') # fix ----- they fixed it themselves in the following cells\n",
 
 
 
 
478
  "#This Error is occuring due to some string values in Order ID Column"
479
  ]
480
  },
@@ -487,7 +437,7 @@
487
  },
488
  {
489
  "cell_type": "code",
490
- "execution_count": 8,
491
  "metadata": {
492
  "execution": {
493
  "iopub.execute_input": "2023-05-18T11:39:16.699709Z",
@@ -497,176 +447,7 @@
497
  "shell.execute_reply.started": "2023-05-18T11:39:16.699678Z"
498
  }
499
  },
500
- "outputs": [
501
- {
502
- "data": {
503
- "text/html": [
504
- "<div>\n",
505
- "<style scoped>\n",
506
- " .dataframe tbody tr th:only-of-type {\n",
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- " vertical-align: middle;\n",
508
- " }\n",
509
- "\n",
510
- " .dataframe tbody tr th {\n",
511
- " vertical-align: top;\n",
512
- " }\n",
513
- "\n",
514
- " .dataframe thead th {\n",
515
- " text-align: right;\n",
516
- " }\n",
517
- "</style>\n",
518
- "<table border=\"1\" class=\"dataframe\">\n",
519
- " <thead>\n",
520
- " <tr style=\"text-align: right;\">\n",
521
- " <th></th>\n",
522
- " <th>Order ID</th>\n",
523
- " <th>Product</th>\n",
524
- " <th>Quantity Ordered</th>\n",
525
- " <th>Price Each</th>\n",
526
- " <th>Order Date</th>\n",
527
- " <th>Purchase Address</th>\n",
528
- " </tr>\n",
529
- " </thead>\n",
530
- " <tbody>\n",
531
- " <tr>\n",
532
- " <th>0</th>\n",
533
- " <td>176558</td>\n",
534
- " <td>USB-C Charging Cable</td>\n",
535
- " <td>2</td>\n",
536
- " <td>11.95</td>\n",
537
- " <td>04/19/19 08:46</td>\n",
538
- " <td>917 1st St, Dallas, TX 75001</td>\n",
539
- " </tr>\n",
540
- " <tr>\n",
541
- " <th>2</th>\n",
542
- " <td>176559</td>\n",
543
- " <td>Bose SoundSport Headphones</td>\n",
544
- " <td>1</td>\n",
545
- " <td>99.99</td>\n",
546
- " <td>04/07/19 22:30</td>\n",
547
- " <td>682 Chestnut St, Boston, MA 02215</td>\n",
548
- " </tr>\n",
549
- " <tr>\n",
550
- " <th>3</th>\n",
551
- " <td>176560</td>\n",
552
- " <td>Google Phone</td>\n",
553
- " <td>1</td>\n",
554
- " <td>600</td>\n",
555
- " <td>04/12/19 14:38</td>\n",
556
- " <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
557
- " </tr>\n",
558
- " <tr>\n",
559
- " <th>4</th>\n",
560
- " <td>176560</td>\n",
561
- " <td>Wired Headphones</td>\n",
562
- " <td>1</td>\n",
563
- " <td>11.99</td>\n",
564
- " <td>04/12/19 14:38</td>\n",
565
- " <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
566
- " </tr>\n",
567
- " <tr>\n",
568
- " <th>5</th>\n",
569
- " <td>176561</td>\n",
570
- " <td>Wired Headphones</td>\n",
571
- " <td>1</td>\n",
572
- " <td>11.99</td>\n",
573
- " <td>04/30/19 09:27</td>\n",
574
- " <td>333 8th St, Los Angeles, CA 90001</td>\n",
575
- " </tr>\n",
576
- " <tr>\n",
577
- " <th>...</th>\n",
578
- " <td>...</td>\n",
579
- " <td>...</td>\n",
580
- " <td>...</td>\n",
581
- " <td>...</td>\n",
582
- " <td>...</td>\n",
583
- " <td>...</td>\n",
584
- " </tr>\n",
585
- " <tr>\n",
586
- " <th>186845</th>\n",
587
- " <td>259353</td>\n",
588
- " <td>AAA Batteries (4-pack)</td>\n",
589
- " <td>3</td>\n",
590
- " <td>2.99</td>\n",
591
- " <td>09/17/19 20:56</td>\n",
592
- " <td>840 Highland St, Los Angeles, CA 90001</td>\n",
593
- " </tr>\n",
594
- " <tr>\n",
595
- " <th>186846</th>\n",
596
- " <td>259354</td>\n",
597
- " <td>iPhone</td>\n",
598
- " <td>1</td>\n",
599
- " <td>700</td>\n",
600
- " <td>09/01/19 16:00</td>\n",
601
- " <td>216 Dogwood St, San Francisco, CA 94016</td>\n",
602
- " </tr>\n",
603
- " <tr>\n",
604
- " <th>186847</th>\n",
605
- " <td>259355</td>\n",
606
- " <td>iPhone</td>\n",
607
- " <td>1</td>\n",
608
- " <td>700</td>\n",
609
- " <td>09/23/19 07:39</td>\n",
610
- " <td>220 12th St, San Francisco, CA 94016</td>\n",
611
- " </tr>\n",
612
- " <tr>\n",
613
- " <th>186848</th>\n",
614
- " <td>259356</td>\n",
615
- " <td>34in Ultrawide Monitor</td>\n",
616
- " <td>1</td>\n",
617
- " <td>379.99</td>\n",
618
- " <td>09/19/19 17:30</td>\n",
619
- " <td>511 Forest St, San Francisco, CA 94016</td>\n",
620
- " </tr>\n",
621
- " <tr>\n",
622
- " <th>186849</th>\n",
623
- " <td>259357</td>\n",
624
- " <td>USB-C Charging Cable</td>\n",
625
- " <td>1</td>\n",
626
- " <td>11.95</td>\n",
627
- " <td>09/30/19 00:18</td>\n",
628
- " <td>250 Meadow St, San Francisco, CA 94016</td>\n",
629
- " </tr>\n",
630
- " </tbody>\n",
631
- "</table>\n",
632
- "<p>185950 rows × 6 columns</p>\n",
633
- "</div>"
634
- ],
635
- "text/plain": [
636
- " Order ID Product Quantity Ordered Price Each \\\n",
637
- "0 176558 USB-C Charging Cable 2 11.95 \n",
638
- "2 176559 Bose SoundSport Headphones 1 99.99 \n",
639
- "3 176560 Google Phone 1 600 \n",
640
- "4 176560 Wired Headphones 1 11.99 \n",
641
- "5 176561 Wired Headphones 1 11.99 \n",
642
- "... ... ... ... ... \n",
643
- "186845 259353 AAA Batteries (4-pack) 3 2.99 \n",
644
- "186846 259354 iPhone 1 700 \n",
645
- "186847 259355 iPhone 1 700 \n",
646
- "186848 259356 34in Ultrawide Monitor 1 379.99 \n",
647
- "186849 259357 USB-C Charging Cable 1 11.95 \n",
648
- "\n",
649
- " Order Date Purchase Address \n",
650
- "0 04/19/19 08:46 917 1st St, Dallas, TX 75001 \n",
651
- "2 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 \n",
652
- "3 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 \n",
653
- "4 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 \n",
654
- "5 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 \n",
655
- "... ... ... \n",
656
- "186845 09/17/19 20:56 840 Highland St, Los Angeles, CA 90001 \n",
657
- "186846 09/01/19 16:00 216 Dogwood St, San Francisco, CA 94016 \n",
658
- "186847 09/23/19 07:39 220 12th St, San Francisco, CA 94016 \n",
659
- "186848 09/19/19 17:30 511 Forest St, San Francisco, CA 94016 \n",
660
- "186849 09/30/19 00:18 250 Meadow St, San Francisco, CA 94016 \n",
661
- "\n",
662
- "[185950 rows x 6 columns]"
663
- ]
664
- },
665
- "execution_count": 8,
666
- "metadata": {},
667
- "output_type": "execute_result"
668
- }
669
- ],
670
  "source": [
671
  "df=df.loc[df['Order ID']!='Order ID']\n",
672
  "df"
@@ -674,7 +455,7 @@
674
  },
675
  {
676
  "cell_type": "code",
677
- "execution_count": 9,
678
  "metadata": {
679
  "execution": {
680
  "iopub.execute_input": "2023-05-18T11:39:47.133972Z",
@@ -693,7 +474,7 @@
693
  },
694
  {
695
  "cell_type": "code",
696
- "execution_count": 10,
697
  "metadata": {
698
  "execution": {
699
  "iopub.execute_input": "2023-05-18T11:39:42.972483Z",
@@ -703,27 +484,7 @@
703
  "shell.execute_reply.started": "2023-05-18T11:39:42.972453Z"
704
  }
705
  },
706
- "outputs": [
707
- {
708
- "name": "stdout",
709
- "output_type": "stream",
710
- "text": [
711
- "<class 'pandas.core.frame.DataFrame'>\n",
712
- "Index: 185950 entries, 0 to 186849\n",
713
- "Data columns (total 6 columns):\n",
714
- " # Column Non-Null Count Dtype \n",
715
- "--- ------ -------------- ----- \n",
716
- " 0 Order ID 185950 non-null int64 \n",
717
- " 1 Product 185950 non-null object \n",
718
- " 2 Quantity Ordered 185950 non-null int64 \n",
719
- " 3 Price Each 185950 non-null float64 \n",
720
- " 4 Order Date 185950 non-null datetime64[ns]\n",
721
- " 5 Purchase Address 185950 non-null object \n",
722
- "dtypes: datetime64[ns](1), float64(1), int64(2), object(2)\n",
723
- "memory usage: 9.9+ MB\n"
724
- ]
725
- }
726
- ],
727
  "source": [
728
  "df.info()"
729
  ]
 
156
  },
157
  {
158
  "cell_type": "code",
159
+ "execution_count": null,
160
  "metadata": {
161
  "execution": {
162
  "iopub.execute_input": "2023-05-18T11:38:26.877375Z",
 
166
  "shell.execute_reply.started": "2023-05-18T11:38:26.877341Z"
167
  }
168
  },
169
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
170
  "source": [
171
  "df.info()"
172
  ]
173
  },
174
  {
175
  "cell_type": "code",
176
+ "execution_count": null,
177
  "metadata": {
178
  "execution": {
179
  "iopub.execute_input": "2023-05-18T11:38:27.249459Z",
 
183
  "shell.execute_reply.started": "2023-05-18T11:38:27.249429Z"
184
  }
185
  },
186
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
187
  "source": [
188
  "df.isnull().sum()"
189
  ]
 
197
  },
198
  {
199
  "cell_type": "code",
200
+ "execution_count": 3,
201
  "metadata": {
202
  "execution": {
203
  "iopub.execute_input": "2023-05-18T11:38:27.629044Z",
 
372
  "[186305 rows x 6 columns]"
373
  ]
374
  },
375
+ "execution_count": 3,
376
  "metadata": {},
377
  "output_type": "execute_result"
378
  }
 
384
  },
385
  {
386
  "cell_type": "code",
387
+ "execution_count": null,
388
  "metadata": {
389
  "execution": {
390
  "iopub.execute_input": "2023-05-18T11:38:28.039601Z",
 
394
  "shell.execute_reply.started": "2023-05-18T11:38:28.039569Z"
395
  }
396
  },
397
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
398
  "source": [
399
  "df.isnull().sum()"
400
  ]
 
408
  },
409
  {
410
  "cell_type": "code",
411
+ "execution_count": 4,
412
  "metadata": {
413
  "execution": {
414
  "iopub.execute_input": "2023-05-18T11:39:04.344286Z",
 
420
  },
421
  "outputs": [],
422
  "source": [
423
+ " # fix ----- Convert to numeric, forcing errors to NaN, then drop NaNs\n",
424
+ "# df=df['Order ID'].astype('int')\n",
425
+ "df[\"Order ID\"] = pd.to_numeric(df[\"Order ID\"], errors=\"coerce\")\n",
426
+ "df = df.dropna(subset=[\"Order ID\"])\n",
427
+ "\n",
428
  "#This Error is occuring due to some string values in Order ID Column"
429
  ]
430
  },
 
437
  },
438
  {
439
  "cell_type": "code",
440
+ "execution_count": null,
441
  "metadata": {
442
  "execution": {
443
  "iopub.execute_input": "2023-05-18T11:39:16.699709Z",
 
447
  "shell.execute_reply.started": "2023-05-18T11:39:16.699678Z"
448
  }
449
  },
450
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
451
  "source": [
452
  "df=df.loc[df['Order ID']!='Order ID']\n",
453
  "df"
 
455
  },
456
  {
457
  "cell_type": "code",
458
+ "execution_count": null,
459
  "metadata": {
460
  "execution": {
461
  "iopub.execute_input": "2023-05-18T11:39:47.133972Z",
 
474
  },
475
  {
476
  "cell_type": "code",
477
+ "execution_count": null,
478
  "metadata": {
479
  "execution": {
480
  "iopub.execute_input": "2023-05-18T11:39:42.972483Z",
 
484
  "shell.execute_reply.started": "2023-05-18T11:39:42.972453Z"
485
  }
486
  },
487
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
488
  "source": [
489
  "df.info()"
490
  ]
benchmark/pandas_6/pandas_6_reproduced.ipynb CHANGED
@@ -156,7 +156,7 @@
156
  },
157
  {
158
  "cell_type": "code",
159
- "execution_count": 3,
160
  "metadata": {
161
  "execution": {
162
  "iopub.execute_input": "2023-05-18T11:38:26.877375Z",
@@ -166,34 +166,14 @@
166
  "shell.execute_reply.started": "2023-05-18T11:38:26.877341Z"
167
  }
168
  },
169
- "outputs": [
170
- {
171
- "name": "stdout",
172
- "output_type": "stream",
173
- "text": [
174
- "<class 'pandas.core.frame.DataFrame'>\n",
175
- "RangeIndex: 186850 entries, 0 to 186849\n",
176
- "Data columns (total 6 columns):\n",
177
- " # Column Non-Null Count Dtype \n",
178
- "--- ------ -------------- ----- \n",
179
- " 0 Order ID 186305 non-null object\n",
180
- " 1 Product 186305 non-null object\n",
181
- " 2 Quantity Ordered 186305 non-null object\n",
182
- " 3 Price Each 186305 non-null object\n",
183
- " 4 Order Date 186305 non-null object\n",
184
- " 5 Purchase Address 186305 non-null object\n",
185
- "dtypes: object(6)\n",
186
- "memory usage: 8.6+ MB\n"
187
- ]
188
- }
189
- ],
190
  "source": [
191
  "df.info()"
192
  ]
193
  },
194
  {
195
  "cell_type": "code",
196
- "execution_count": 4,
197
  "metadata": {
198
  "execution": {
199
  "iopub.execute_input": "2023-05-18T11:38:27.249459Z",
@@ -203,24 +183,7 @@
203
  "shell.execute_reply.started": "2023-05-18T11:38:27.249429Z"
204
  }
205
  },
206
- "outputs": [
207
- {
208
- "data": {
209
- "text/plain": [
210
- "Order ID 545\n",
211
- "Product 545\n",
212
- "Quantity Ordered 545\n",
213
- "Price Each 545\n",
214
- "Order Date 545\n",
215
- "Purchase Address 545\n",
216
- "dtype: int64"
217
- ]
218
- },
219
- "execution_count": 4,
220
- "metadata": {},
221
- "output_type": "execute_result"
222
- }
223
- ],
224
  "source": [
225
  "df.isnull().sum()"
226
  ]
@@ -234,7 +197,7 @@
234
  },
235
  {
236
  "cell_type": "code",
237
- "execution_count": 5,
238
  "metadata": {
239
  "execution": {
240
  "iopub.execute_input": "2023-05-18T11:38:27.629044Z",
@@ -409,7 +372,7 @@
409
  "[186305 rows x 6 columns]"
410
  ]
411
  },
412
- "execution_count": 5,
413
  "metadata": {},
414
  "output_type": "execute_result"
415
  }
@@ -421,7 +384,7 @@
421
  },
422
  {
423
  "cell_type": "code",
424
- "execution_count": 6,
425
  "metadata": {
426
  "execution": {
427
  "iopub.execute_input": "2023-05-18T11:38:28.039601Z",
@@ -431,24 +394,7 @@
431
  "shell.execute_reply.started": "2023-05-18T11:38:28.039569Z"
432
  }
433
  },
434
- "outputs": [
435
- {
436
- "data": {
437
- "text/plain": [
438
- "Order ID 0\n",
439
- "Product 0\n",
440
- "Quantity Ordered 0\n",
441
- "Price Each 0\n",
442
- "Order Date 0\n",
443
- "Purchase Address 0\n",
444
- "dtype: int64"
445
- ]
446
- },
447
- "execution_count": 6,
448
- "metadata": {},
449
- "output_type": "execute_result"
450
- }
451
- ],
452
  "source": [
453
  "df.isnull().sum()"
454
  ]
@@ -462,7 +408,7 @@
462
  },
463
  {
464
  "cell_type": "code",
465
- "execution_count": 7,
466
  "metadata": {
467
  "execution": {
468
  "iopub.execute_input": "2023-05-18T11:39:04.344286Z",
@@ -480,7 +426,7 @@
480
  "traceback": [
481
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
482
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
483
- "\u001b[0;32m<ipython-input-7-63a46ab18be1>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m#This Error is occuring due to some string values in Order ID Column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
484
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
485
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
486
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
 
156
  },
157
  {
158
  "cell_type": "code",
159
+ "execution_count": null,
160
  "metadata": {
161
  "execution": {
162
  "iopub.execute_input": "2023-05-18T11:38:26.877375Z",
 
166
  "shell.execute_reply.started": "2023-05-18T11:38:26.877341Z"
167
  }
168
  },
169
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
170
  "source": [
171
  "df.info()"
172
  ]
173
  },
174
  {
175
  "cell_type": "code",
176
+ "execution_count": null,
177
  "metadata": {
178
  "execution": {
179
  "iopub.execute_input": "2023-05-18T11:38:27.249459Z",
 
183
  "shell.execute_reply.started": "2023-05-18T11:38:27.249429Z"
184
  }
185
  },
186
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
187
  "source": [
188
  "df.isnull().sum()"
189
  ]
 
197
  },
198
  {
199
  "cell_type": "code",
200
+ "execution_count": 3,
201
  "metadata": {
202
  "execution": {
203
  "iopub.execute_input": "2023-05-18T11:38:27.629044Z",
 
372
  "[186305 rows x 6 columns]"
373
  ]
374
  },
375
+ "execution_count": 3,
376
  "metadata": {},
377
  "output_type": "execute_result"
378
  }
 
384
  },
385
  {
386
  "cell_type": "code",
387
+ "execution_count": null,
388
  "metadata": {
389
  "execution": {
390
  "iopub.execute_input": "2023-05-18T11:38:28.039601Z",
 
394
  "shell.execute_reply.started": "2023-05-18T11:38:28.039569Z"
395
  }
396
  },
397
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
398
  "source": [
399
  "df.isnull().sum()"
400
  ]
 
408
  },
409
  {
410
  "cell_type": "code",
411
+ "execution_count": 4,
412
  "metadata": {
413
  "execution": {
414
  "iopub.execute_input": "2023-05-18T11:39:04.344286Z",
 
426
  "traceback": [
427
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
428
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
429
+ "\u001b[0;32m<ipython-input-4-63a46ab18be1>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m#This Error is occuring due to some string values in Order ID Column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
430
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
431
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
432
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
benchmark/pandas_7/pandas_7_fixed.ipynb CHANGED
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  "source": [
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  "percent_missing = game_df.isnull().sum() * 100 / len(game_df)\n",
402
  "missing_value_df = pd.DataFrame({'column_name': game_df.columns,\n",
@@ -420,7 +351,7 @@
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758
  "outputs": [],
759
  "source": [
760
  "# fix ----- nan values cannot be converted to int, one can first convert to float then convert to int64\n",
 
761
  "game_df['released'] = game_df['released'].apply(lambda x: str(x).split('-')[0]).astype('float').astype('Int64')"
762
  ]
763
  },
 
317
  },
318
  {
319
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320
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327
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328
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331
  "source": [
332
  "percent_missing = game_df.isnull().sum() * 100 / len(game_df)\n",
333
  "missing_value_df = pd.DataFrame({'column_name': game_df.columns,\n",
 
351
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352
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353
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  "iopub.execute_input": "2023-06-03T13:43:24.922281Z",
 
689
  "outputs": [],
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  "source": [
691
  "# fix ----- nan values cannot be converted to int, one can first convert to float then convert to int64\n",
692
+ "# game_df['released'] = game_df['released'].apply(lambda x: str(x).split('-')[0]).astype('int')\n",
693
  "game_df['released'] = game_df['released'].apply(lambda x: str(x).split('-')[0]).astype('float').astype('Int64')"
694
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695
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benchmark/pandas_7/pandas_7_reproduced.ipynb CHANGED
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  "percent_missing = game_df.isnull().sum() * 100 / len(game_df)\n",
402
  "missing_value_df = pd.DataFrame({'column_name': game_df.columns,\n",
@@ -420,7 +351,7 @@
420
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421
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  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
766
- "\u001b[0;32m<ipython-input-6-4958b6271f98>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgame_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'released'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgame_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'released'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
767
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
768
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
769
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
 
317
  },
318
  {
319
  "cell_type": "code",
320
+ "execution_count": null,
321
  "metadata": {
322
  "execution": {
323
  "iopub.execute_input": "2023-06-03T13:42:42.716831Z",
 
327
  "shell.execute_reply.started": "2023-06-03T13:42:42.716765Z"
328
  }
329
  },
330
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
331
  "source": [
332
  "percent_missing = game_df.isnull().sum() * 100 / len(game_df)\n",
333
  "missing_value_df = pd.DataFrame({'column_name': game_df.columns,\n",
 
351
  },
352
  {
353
  "cell_type": "code",
354
+ "execution_count": 3,
355
  "metadata": {
356
  "execution": {
357
  "iopub.execute_input": "2023-06-03T13:53:31.662229Z",
 
368
  "0"
369
  ]
370
  },
371
+ "execution_count": 3,
372
  "metadata": {},
373
  "output_type": "execute_result"
374
  }
 
385
  },
386
  {
387
  "cell_type": "code",
388
+ "execution_count": 4,
389
  "metadata": {
390
  "execution": {
391
  "iopub.execute_input": "2023-06-03T13:43:14.289756Z",
 
612
  "[5 rows x 21 columns]"
613
  ]
614
  },
615
+ "execution_count": 4,
616
  "metadata": {},
617
  "output_type": "execute_result"
618
  }
 
676
  },
677
  {
678
  "cell_type": "code",
679
+ "execution_count": 5,
680
  "metadata": {
681
  "execution": {
682
  "iopub.execute_input": "2023-06-03T13:43:24.922281Z",
 
694
  "traceback": [
695
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
696
  "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
697
+ "\u001b[0;32m<ipython-input-5-4958b6271f98>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgame_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'released'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgame_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'released'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
698
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6532\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6533\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6534\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6535\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6536\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
699
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m return self.apply(\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
700
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
benchmark/pandas_8/pandas_8_fixed.ipynb CHANGED
@@ -39,7 +39,7 @@
39
  },
40
  {
41
  "cell_type": "code",
42
- "execution_count": 3,
43
  "metadata": {
44
  "execution": {
45
  "iopub.execute_input": "2023-03-09T09:44:46.807959Z",
@@ -49,175 +49,14 @@
49
  "shell.execute_reply.started": "2023-03-09T09:44:46.807913Z"
50
  }
51
  },
52
- "outputs": [
53
- {
54
- "data": {
55
- "text/html": [
56
- "<div>\n",
57
- "<style scoped>\n",
58
- " .dataframe tbody tr th:only-of-type {\n",
59
- " vertical-align: middle;\n",
60
- " }\n",
61
- "\n",
62
- " .dataframe tbody tr th {\n",
63
- " vertical-align: top;\n",
64
- " }\n",
65
- "\n",
66
- " .dataframe thead th {\n",
67
- " text-align: right;\n",
68
- " }\n",
69
- "</style>\n",
70
- "<table border=\"1\" class=\"dataframe\">\n",
71
- " <thead>\n",
72
- " <tr style=\"text-align: right;\">\n",
73
- " <th></th>\n",
74
- " <th>Make</th>\n",
75
- " <th>Model</th>\n",
76
- " <th>Type</th>\n",
77
- " <th>Origin</th>\n",
78
- " <th>DriveTrain</th>\n",
79
- " <th>MSRP</th>\n",
80
- " <th>Invoice</th>\n",
81
- " <th>EngineSize</th>\n",
82
- " <th>Cylinders</th>\n",
83
- " <th>Horsepower</th>\n",
84
- " <th>MPG_City</th>\n",
85
- " <th>MPG_Highway</th>\n",
86
- " <th>Weight</th>\n",
87
- " <th>Wheelbase</th>\n",
88
- " <th>Length</th>\n",
89
- " </tr>\n",
90
- " </thead>\n",
91
- " <tbody>\n",
92
- " <tr>\n",
93
- " <th>0</th>\n",
94
- " <td>Acura</td>\n",
95
- " <td>MDX</td>\n",
96
- " <td>SUV</td>\n",
97
- " <td>Asia</td>\n",
98
- " <td>All</td>\n",
99
- " <td>$36,945</td>\n",
100
- " <td>$33,337</td>\n",
101
- " <td>3.5</td>\n",
102
- " <td>6.0</td>\n",
103
- " <td>265.0</td>\n",
104
- " <td>17.0</td>\n",
105
- " <td>23.0</td>\n",
106
- " <td>4451.0</td>\n",
107
- " <td>106.0</td>\n",
108
- " <td>189.0</td>\n",
109
- " </tr>\n",
110
- " <tr>\n",
111
- " <th>1</th>\n",
112
- " <td>Acura</td>\n",
113
- " <td>RSX Type S 2dr</td>\n",
114
- " <td>Sedan</td>\n",
115
- " <td>Asia</td>\n",
116
- " <td>Front</td>\n",
117
- " <td>$23,820</td>\n",
118
- " <td>$21,761</td>\n",
119
- " <td>2.0</td>\n",
120
- " <td>4.0</td>\n",
121
- " <td>200.0</td>\n",
122
- " <td>24.0</td>\n",
123
- " <td>31.0</td>\n",
124
- " <td>2778.0</td>\n",
125
- " <td>101.0</td>\n",
126
- " <td>172.0</td>\n",
127
- " </tr>\n",
128
- " <tr>\n",
129
- " <th>2</th>\n",
130
- " <td>Acura</td>\n",
131
- " <td>TSX 4dr</td>\n",
132
- " <td>Sedan</td>\n",
133
- " <td>Asia</td>\n",
134
- " <td>Front</td>\n",
135
- " <td>$26,990</td>\n",
136
- " <td>$24,647</td>\n",
137
- " <td>2.4</td>\n",
138
- " <td>4.0</td>\n",
139
- " <td>200.0</td>\n",
140
- " <td>22.0</td>\n",
141
- " <td>29.0</td>\n",
142
- " <td>3230.0</td>\n",
143
- " <td>105.0</td>\n",
144
- " <td>183.0</td>\n",
145
- " </tr>\n",
146
- " <tr>\n",
147
- " <th>3</th>\n",
148
- " <td>Acura</td>\n",
149
- " <td>TL 4dr</td>\n",
150
- " <td>Sedan</td>\n",
151
- " <td>Asia</td>\n",
152
- " <td>Front</td>\n",
153
- " <td>$33,195</td>\n",
154
- " <td>$30,299</td>\n",
155
- " <td>3.2</td>\n",
156
- " <td>6.0</td>\n",
157
- " <td>270.0</td>\n",
158
- " <td>20.0</td>\n",
159
- " <td>28.0</td>\n",
160
- " <td>3575.0</td>\n",
161
- " <td>108.0</td>\n",
162
- " <td>186.0</td>\n",
163
- " </tr>\n",
164
- " <tr>\n",
165
- " <th>4</th>\n",
166
- " <td>Acura</td>\n",
167
- " <td>3.5 RL 4dr</td>\n",
168
- " <td>Sedan</td>\n",
169
- " <td>Asia</td>\n",
170
- " <td>Front</td>\n",
171
- " <td>$43,755</td>\n",
172
- " <td>$39,014</td>\n",
173
- " <td>3.5</td>\n",
174
- " <td>6.0</td>\n",
175
- " <td>225.0</td>\n",
176
- " <td>18.0</td>\n",
177
- " <td>24.0</td>\n",
178
- " <td>3880.0</td>\n",
179
- " <td>115.0</td>\n",
180
- " <td>197.0</td>\n",
181
- " </tr>\n",
182
- " </tbody>\n",
183
- "</table>\n",
184
- "</div>"
185
- ],
186
- "text/plain": [
187
- " Make Model Type Origin DriveTrain MSRP Invoice \\\n",
188
- "0 Acura MDX SUV Asia All $36,945 $33,337 \n",
189
- "1 Acura RSX Type S 2dr Sedan Asia Front $23,820 $21,761 \n",
190
- "2 Acura TSX 4dr Sedan Asia Front $26,990 $24,647 \n",
191
- "3 Acura TL 4dr Sedan Asia Front $33,195 $30,299 \n",
192
- "4 Acura 3.5 RL 4dr Sedan Asia Front $43,755 $39,014 \n",
193
- "\n",
194
- " EngineSize Cylinders Horsepower MPG_City MPG_Highway Weight \\\n",
195
- "0 3.5 6.0 265.0 17.0 23.0 4451.0 \n",
196
- "1 2.0 4.0 200.0 24.0 31.0 2778.0 \n",
197
- "2 2.4 4.0 200.0 22.0 29.0 3230.0 \n",
198
- "3 3.2 6.0 270.0 20.0 28.0 3575.0 \n",
199
- "4 3.5 6.0 225.0 18.0 24.0 3880.0 \n",
200
- "\n",
201
- " Wheelbase Length \n",
202
- "0 106.0 189.0 \n",
203
- "1 101.0 172.0 \n",
204
- "2 105.0 183.0 \n",
205
- "3 108.0 186.0 \n",
206
- "4 115.0 197.0 "
207
- ]
208
- },
209
- "execution_count": 3,
210
- "metadata": {},
211
- "output_type": "execute_result"
212
- }
213
- ],
214
  "source": [
215
  "df.head()"
216
  ]
217
  },
218
  {
219
  "cell_type": "code",
220
- "execution_count": 4,
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  "metadata": {
222
  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.836990Z",
@@ -227,21 +66,7 @@
227
  "shell.execute_reply.started": "2023-03-09T09:44:46.836928Z"
228
  }
229
  },
230
- "outputs": [
231
- {
232
- "data": {
233
- "text/plain": [
234
- "Index(['Make', 'Model', 'Type', 'Origin', 'DriveTrain', 'MSRP', 'Invoice',\n",
235
- " 'EngineSize', 'Cylinders', 'Horsepower', 'MPG_City', 'MPG_Highway',\n",
236
- " 'Weight', 'Wheelbase', 'Length'],\n",
237
- " dtype='object')"
238
- ]
239
- },
240
- "execution_count": 4,
241
- "metadata": {},
242
- "output_type": "execute_result"
243
- }
244
- ],
245
  "source": [
246
  "df.columns"
247
  ]
@@ -257,7 +82,7 @@
257
  },
258
  {
259
  "cell_type": "code",
260
- "execution_count": 5,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.852024Z",
@@ -267,36 +92,7 @@
267
  "shell.execute_reply.started": "2023-03-09T09:44:46.851967Z"
268
  }
269
  },
270
- "outputs": [
271
- {
272
- "name": "stdout",
273
- "output_type": "stream",
274
- "text": [
275
- "<class 'pandas.core.frame.DataFrame'>\n",
276
- "RangeIndex: 432 entries, 0 to 431\n",
277
- "Data columns (total 15 columns):\n",
278
- " # Column Non-Null Count Dtype \n",
279
- "--- ------ -------------- ----- \n",
280
- " 0 Make 428 non-null object \n",
281
- " 1 Model 428 non-null object \n",
282
- " 2 Type 428 non-null object \n",
283
- " 3 Origin 428 non-null object \n",
284
- " 4 DriveTrain 428 non-null object \n",
285
- " 5 MSRP 428 non-null object \n",
286
- " 6 Invoice 428 non-null object \n",
287
- " 7 EngineSize 428 non-null float64\n",
288
- " 8 Cylinders 426 non-null float64\n",
289
- " 9 Horsepower 428 non-null float64\n",
290
- " 10 MPG_City 428 non-null float64\n",
291
- " 11 MPG_Highway 428 non-null float64\n",
292
- " 12 Weight 428 non-null float64\n",
293
- " 13 Wheelbase 428 non-null float64\n",
294
- " 14 Length 428 non-null float64\n",
295
- "dtypes: float64(8), object(7)\n",
296
- "memory usage: 50.8+ KB\n"
297
- ]
298
- }
299
- ],
300
  "source": [
301
  "df.info() #memory usage: 50.8+ KB before converting datatypes"
302
  ]
@@ -310,7 +106,7 @@
310
  },
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  {
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  "cell_type": "code",
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- "execution_count": 6,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.878059Z",
@@ -320,25 +116,14 @@
320
  "shell.execute_reply.started": "2023-03-09T09:44:46.878007Z"
321
  }
322
  },
323
- "outputs": [
324
- {
325
- "data": {
326
- "text/plain": [
327
- "array(['All', 'Front', 'Rear', nan], dtype=object)"
328
- ]
329
- },
330
- "execution_count": 6,
331
- "metadata": {},
332
- "output_type": "execute_result"
333
- }
334
- ],
335
  "source": [
336
  "df[\"DriveTrain\"].unique()"
337
  ]
338
  },
339
  {
340
  "cell_type": "code",
341
- "execution_count": 7,
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  "metadata": {
343
  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.892292Z",
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 8,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.908213Z",
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  "shell.execute_reply.started": "2023-03-09T09:44:46.908179Z"
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  }
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  },
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
373
- "<class 'pandas.core.frame.DataFrame'>\n",
374
- "RangeIndex: 432 entries, 0 to 431\n",
375
- "Data columns (total 15 columns):\n",
376
- " # Column Non-Null Count Dtype \n",
377
- "--- ------ -------------- ----- \n",
378
- " 0 Make 428 non-null object \n",
379
- " 1 Model 428 non-null object \n",
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- " 2 Type 428 non-null object \n",
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- " 3 Origin 428 non-null object \n",
382
- " 4 DriveTrain 428 non-null category\n",
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- " 5 MSRP 428 non-null object \n",
384
- " 6 Invoice 428 non-null object \n",
385
- " 7 EngineSize 428 non-null float64 \n",
386
- " 8 Cylinders 426 non-null float64 \n",
387
- " 9 Horsepower 428 non-null float64 \n",
388
- " 10 MPG_City 428 non-null float64 \n",
389
- " 11 MPG_Highway 428 non-null float64 \n",
390
- " 12 Weight 428 non-null float64 \n",
391
- " 13 Wheelbase 428 non-null float64 \n",
392
- " 14 Length 428 non-null float64 \n",
393
- "dtypes: category(1), float64(8), object(6)\n",
394
- "memory usage: 47.9+ KB\n"
395
- ]
396
- }
397
- ],
398
  "source": [
399
  "df.info()"
400
  ]
401
  },
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  {
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- "execution_count": 9,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.943561Z",
@@ -568,15 +324,14 @@
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  "4 115.0 197.0 "
569
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- "execution_count": 9,
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  "metadata": {},
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  "output_type": "execute_result"
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  }
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  ],
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  "source": [
577
- "# df[\"MSRP\"]=pd.to_numeric(df[\"MSRP\"]) \n",
578
- "\n",
579
  "# fix --- preprocess needed: \"$36,945 \"=>36945\n",
 
580
  "df[\"MSRP\"] = df[\"MSRP\"].str.replace(r\"[\\$,]\", \"\", regex=True).astype(float).astype(\"Int64\")\n",
581
  "df.head()"
582
  ]
 
39
  },
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  {
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  "cell_type": "code",
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+ "execution_count": null,
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.807959Z",
 
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  },
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "source": [
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  "df.head()"
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  ]
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  },
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  {
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  "cell_type": "code",
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.836990Z",
 
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  },
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "source": [
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  "df.columns"
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  ]
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": null,
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.852024Z",
 
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  "shell.execute_reply.started": "2023-03-09T09:44:46.851967Z"
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  }
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  },
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "source": [
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  "df.info() #memory usage: 50.8+ KB before converting datatypes"
98
  ]
 
106
  },
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  {
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  "cell_type": "code",
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+ "execution_count": null,
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  "source": [
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  "df[\"DriveTrain\"].unique()"
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  ]
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  },
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  {
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  "metadata": {
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  "execution": {
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  },
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "source": [
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  "df.info()"
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  ]
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  },
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  {
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  "cell_type": "code",
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  "metadata": {
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  "execution": {
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  "iopub.execute_input": "2023-03-09T09:44:46.943561Z",
 
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  "4 115.0 197.0 "
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  ]
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  "metadata": {},
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  "output_type": "execute_result"
330
  }
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  ],
332
  "source": [
 
 
333
  "# fix --- preprocess needed: \"$36,945 \"=>36945\n",
334
+ "# df[\"MSRP\"]=pd.to_numeric(df[\"MSRP\"]) \n",
335
  "df[\"MSRP\"] = df[\"MSRP\"].str.replace(r\"[\\$,]\", \"\", regex=True).astype(float).astype(\"Int64\")\n",
336
  "df.head()"
337
  ]
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89
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90
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- " <thead>\n",
123
- " <tr style=\"text-align: right;\">\n",
124
- " <th></th>\n",
125
- " <th>Movie_Title</th>\n",
126
- " <th>Year</th>\n",
127
- " <th>Director</th>\n",
128
- " <th>Actors</th>\n",
129
- " <th>Rating</th>\n",
130
- " <th>Runtime(Mins)</th>\n",
131
- " <th>Censor</th>\n",
132
- " <th>Total_Gross</th>\n",
133
- " <th>main_genre</th>\n",
134
- " <th>side_genre</th>\n",
135
- " </tr>\n",
136
- " </thead>\n",
137
- " <tbody>\n",
138
- " <tr>\n",
139
- " <th>0</th>\n",
140
- " <td>Kantara</td>\n",
141
- " <td>2022</td>\n",
142
- " <td>Rishab Shetty</td>\n",
143
- " <td>Rishab Shetty, Sapthami Gowda, Kishore Kumar G...</td>\n",
144
- " <td>9.3</td>\n",
145
- " <td>148</td>\n",
146
- " <td>UA</td>\n",
147
- " <td>Gross Unkown</td>\n",
148
- " <td>Action</td>\n",
149
- " <td>Adventure, Drama</td>\n",
150
- " </tr>\n",
151
- " <tr>\n",
152
- " <th>1</th>\n",
153
- " <td>The Dark Knight</td>\n",
154
- " <td>2008</td>\n",
155
- " <td>Christopher Nolan</td>\n",
156
- " <td>Christian Bale, Heath Ledger, Aaron Eckhart, M...</td>\n",
157
- " <td>9.0</td>\n",
158
- " <td>152</td>\n",
159
- " <td>UA</td>\n",
160
- " <td>$534.86M</td>\n",
161
- " <td>Action</td>\n",
162
- " <td>Crime, Drama</td>\n",
163
- " </tr>\n",
164
- " <tr>\n",
165
- " <th>2</th>\n",
166
- " <td>The Lord of the Rings: The Return of the King</td>\n",
167
- " <td>2003</td>\n",
168
- " <td>Peter Jackson</td>\n",
169
- " <td>Elijah Wood, Viggo Mortensen, Ian McKellen, Or...</td>\n",
170
- " <td>9.0</td>\n",
171
- " <td>201</td>\n",
172
- " <td>U</td>\n",
173
- " <td>$377.85M</td>\n",
174
- " <td>Action</td>\n",
175
- " <td>Adventure, Drama</td>\n",
176
- " </tr>\n",
177
- " <tr>\n",
178
- " <th>3</th>\n",
179
- " <td>Inception</td>\n",
180
- " <td>2010</td>\n",
181
- " <td>Christopher Nolan</td>\n",
182
- " <td>Leonardo DiCaprio, Joseph Gordon-Levitt, Ellio...</td>\n",
183
- " <td>8.8</td>\n",
184
- " <td>148</td>\n",
185
- " <td>UA</td>\n",
186
- " <td>$292.58M</td>\n",
187
- " <td>Action</td>\n",
188
- " <td>Adventure, Sci-Fi</td>\n",
189
- " </tr>\n",
190
- " <tr>\n",
191
- " <th>4</th>\n",
192
- " <td>The Lord of the Rings: The Two Towers</td>\n",
193
- " <td>2002</td>\n",
194
- " <td>Peter Jackson</td>\n",
195
- " <td>Elijah Wood, Ian McKellen, Viggo Mortensen, Or...</td>\n",
196
- " <td>8.8</td>\n",
197
- " <td>179</td>\n",
198
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199
- " <td>$342.55M</td>\n",
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204
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206
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212
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213
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214
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215
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216
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217
- " <th>5557</th>\n",
218
- " <td>Disaster Movie</td>\n",
219
- " <td>2008</td>\n",
220
- " <td>Directors:Jason Friedberg, Aaron Seltzer</td>\n",
221
- " <td>Carmen Electra, Vanessa Lachey, Nicole Parker,...</td>\n",
222
- " <td>1.9</td>\n",
223
- " <td>87</td>\n",
224
- " <td>PG-13</td>\n",
225
- " <td>$14.19M</td>\n",
226
- " <td>Comedy</td>\n",
227
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228
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229
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230
- " <th>5558</th>\n",
231
- " <td>The Hottie &amp; the Nottie</td>\n",
232
- " <td>2008</td>\n",
233
- " <td>Tom Putnam</td>\n",
234
- " <td>Paris Hilton, Joel David Moore, Christine Laki...</td>\n",
235
- " <td>1.9</td>\n",
236
- " <td>91</td>\n",
237
- " <td>PG-13</td>\n",
238
- " <td>$0.03M</td>\n",
239
- " <td>Comedy</td>\n",
240
- " <td>Romance</td>\n",
241
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242
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243
- " <th>5559</th>\n",
244
- " <td>From Justin to Kelly</td>\n",
245
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246
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247
- " <td>Kelly Clarkson, Justin Guarini, Katherine Bail...</td>\n",
248
- " <td>1.9</td>\n",
249
- " <td>81</td>\n",
250
- " <td>PG</td>\n",
251
- " <td>$4.92M</td>\n",
252
- " <td>Comedy</td>\n",
253
- " <td>Musical, Romance</td>\n",
254
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255
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256
- " <th>5560</th>\n",
257
- " <td>Superbabies: Baby Geniuses 2</td>\n",
258
- " <td>2004</td>\n",
259
- " <td>Bob Clark</td>\n",
260
- " <td>Jon Voight, Scott Baio, Vanessa Angel, Skyler ...</td>\n",
261
- " <td>1.5</td>\n",
262
- " <td>88</td>\n",
263
- " <td>PG</td>\n",
264
- " <td>$9.11M</td>\n",
265
- " <td>Comedy</td>\n",
266
- " <td>Family, Sci-Fi</td>\n",
267
- " </tr>\n",
268
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269
- " <th>5561</th>\n",
270
- " <td>Cumali Ceber: Allah Seni Alsin</td>\n",
271
- " <td>2017</td>\n",
272
- " <td>Gökhan Gök</td>\n",
273
- " <td>Halil Söyletmez, Doga Konakoglu, Emre Keskin, ...</td>\n",
274
- " <td>1.0</td>\n",
275
- " <td>100</td>\n",
276
- " <td>Not Rated</td>\n",
277
- " <td>Gross Unkown</td>\n",
278
- " <td>Comedy</td>\n",
279
- " <td>Comedy</td>\n",
280
- " </tr>\n",
281
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282
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283
- "<p>5562 rows × 10 columns</p>\n",
284
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285
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287
- " Movie_Title Year \\\n",
288
- "0 Kantara 2022 \n",
289
- "1 The Dark Knight 2008 \n",
290
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291
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292
- "4 The Lord of the Rings: The Two Towers 2002 \n",
293
- "... ... ... \n",
294
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295
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296
- "5559 From Justin to Kelly 2003 \n",
297
- "5560 Superbabies: Baby Geniuses 2 2004 \n",
298
- "5561 Cumali Ceber: Allah Seni Alsin 2017 \n",
299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
- "5557 Carmen Electra, Vanessa Lachey, Nicole Parker,... 1.9 \n",
321
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322
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323
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324
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325
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326
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327
- "0 148 UA Gross Unkown Action Adventure, Drama \n",
328
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330
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331
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332
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333
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334
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335
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336
- "5560 88 PG $9.11M Comedy Family, Sci-Fi \n",
337
- "5561 100 Not Rated Gross Unkown Comedy Comedy \n",
338
- "\n",
339
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421
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422
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423
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432
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433
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434
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435
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436
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437
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438
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440
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
- " <td>148</td>\n",
452
- " <td>UA</td>\n",
453
- " <td>$292.58M</td>\n",
454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
- " <td>$342.55M</td>\n",
467
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469
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470
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471
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472
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473
- " <td>2001</td>\n",
474
- " <td>Peter Jackson</td>\n",
475
- " <td>Elijah Wood, Ian McKellen, Orlando Bloom, Sean...</td>\n",
476
- " <td>8.8</td>\n",
477
- " <td>178</td>\n",
478
- " <td>U</td>\n",
479
- " <td>$315.54M</td>\n",
480
- " <td>Action</td>\n",
481
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482
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483
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484
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485
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486
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487
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488
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489
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491
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492
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493
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494
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495
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496
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497
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498
- " <td>Son of the Mask</td>\n",
499
- " <td>2005</td>\n",
500
- " <td>Lawrence Guterman</td>\n",
501
- " <td>Jamie Kennedy, Traylor Howard, Alan Cumming, L...</td>\n",
502
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503
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504
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505
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506
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507
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508
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509
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510
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511
- " <td>Disaster Movie</td>\n",
512
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513
- " <td>Directors:Jason Friedberg, Aaron Seltzer</td>\n",
514
- " <td>Carmen Electra, Vanessa Lachey, Nicole Parker,...</td>\n",
515
- " <td>1.9</td>\n",
516
- " <td>87</td>\n",
517
- " <td>PG-13</td>\n",
518
- " <td>$14.19M</td>\n",
519
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520
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521
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522
- " <tr>\n",
523
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524
- " <td>The Hottie &amp; the Nottie</td>\n",
525
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526
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527
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528
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529
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530
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531
- " <td>$0.03M</td>\n",
532
- " <td>Comedy</td>\n",
533
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534
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535
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536
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537
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538
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539
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540
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541
- " <td>1.9</td>\n",
542
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543
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544
- " <td>$4.92M</td>\n",
545
- " <td>Comedy</td>\n",
546
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547
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548
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549
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550
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551
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552
- " <td>Bob Clark</td>\n",
553
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554
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555
- " <td>88</td>\n",
556
- " <td>PG</td>\n",
557
- " <td>$9.11M</td>\n",
558
- " <td>Comedy</td>\n",
559
- " <td>Family, Sci-Fi</td>\n",
560
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561
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562
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563
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564
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565
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- " Movie_Title Year \\\n",
568
- "1 The Dark Knight 2008 \n",
569
- "2 The Lord of the Rings: The Return of the King 2003 \n",
570
- "3 Inception 2010 \n",
571
- "4 The Lord of the Rings: The Two Towers 2002 \n",
572
- "5 The Lord of the Rings: The Fellowship of the Ring 2001 \n",
573
- "... ... ... \n",
574
- "5555 Son of the Mask 2005 \n",
575
- "5557 Disaster Movie 2008 \n",
576
- "5558 The Hottie & the Nottie 2008 \n",
577
- "5559 From Justin to Kelly 2003 \n",
578
- "5560 Superbabies: Baby Geniuses 2 2004 \n",
579
- "\n",
580
- " Director \\\n",
581
- "1 Christopher Nolan \n",
582
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583
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584
- "4 Peter Jackson \n",
585
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586
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587
- "5555 Lawrence Guterman \n",
588
- "5557 Directors:Jason Friedberg, Aaron Seltzer \n",
589
- "5558 Tom Putnam \n",
590
- "5559 Robert Iscove \n",
591
- "5560 Bob Clark \n",
592
- "\n",
593
- " Actors Rating \\\n",
594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
- "\n",
606
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607
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608
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609
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610
- "4 179 UA $342.55M Action Adventure, Drama \n",
611
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612
- "... ... ... ... ... ... \n",
613
- "5555 94 U $17.02M Comedy Family, Fantasy \n",
614
- "5557 87 PG-13 $14.19M Comedy Sci-Fi \n",
615
- "5558 91 PG-13 $0.03M Comedy Romance \n",
616
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617
- "5560 88 PG $9.11M Comedy Family, Sci-Fi \n",
618
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
130
  "source": [
131
  "clean_df"
132
  ]
benchmark/pandas_9/pandas_9_reproduced.ipynb CHANGED
@@ -62,7 +62,7 @@
62
  },
63
  {
64
  "cell_type": "code",
65
- "execution_count": 3,
66
  "metadata": {
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  "execution": {
68
  "iopub.execute_input": "2023-12-03T16:02:04.302761Z",
@@ -72,25 +72,14 @@
72
  "shell.execute_reply.started": "2023-12-03T16:02:04.302721Z"
73
  }
74
  },
75
- "outputs": [
76
- {
77
- "data": {
78
- "text/plain": [
79
- "(5562, 10)"
80
- ]
81
- },
82
- "execution_count": 3,
83
- "metadata": {},
84
- "output_type": "execute_result"
85
- }
86
- ],
87
  "source": [
88
  "df.shape"
89
  ]
90
  },
91
  {
92
  "cell_type": "code",
93
- "execution_count": 4,
94
  "metadata": {
95
  "execution": {
96
  "iopub.execute_input": "2023-12-03T16:02:04.321548Z",
@@ -100,257 +89,14 @@
100
  "shell.execute_reply.started": "2023-12-03T16:02:04.321518Z"
101
  }
102
  },
103
- "outputs": [
104
- {
105
- "data": {
106
- "text/html": [
107
- "<div>\n",
108
- "<style scoped>\n",
109
- " .dataframe tbody tr th:only-of-type {\n",
110
- " vertical-align: middle;\n",
111
- " }\n",
112
- "\n",
113
- " .dataframe tbody tr th {\n",
114
- " vertical-align: top;\n",
115
- " }\n",
116
- "\n",
117
- " .dataframe thead th {\n",
118
- " text-align: right;\n",
119
- " }\n",
120
- "</style>\n",
121
- "<table border=\"1\" class=\"dataframe\">\n",
122
- " <thead>\n",
123
- " <tr style=\"text-align: right;\">\n",
124
- " <th></th>\n",
125
- " <th>Movie_Title</th>\n",
126
- " <th>Year</th>\n",
127
- " <th>Director</th>\n",
128
- " <th>Actors</th>\n",
129
- " <th>Rating</th>\n",
130
- " <th>Runtime(Mins)</th>\n",
131
- " <th>Censor</th>\n",
132
- " <th>Total_Gross</th>\n",
133
- " <th>main_genre</th>\n",
134
- " <th>side_genre</th>\n",
135
- " </tr>\n",
136
- " </thead>\n",
137
- " <tbody>\n",
138
- " <tr>\n",
139
- " <th>0</th>\n",
140
- " <td>Kantara</td>\n",
141
- " <td>2022</td>\n",
142
- " <td>Rishab Shetty</td>\n",
143
- " <td>Rishab Shetty, Sapthami Gowda, Kishore Kumar G...</td>\n",
144
- " <td>9.3</td>\n",
145
- " <td>148</td>\n",
146
- " <td>UA</td>\n",
147
- " <td>Gross Unkown</td>\n",
148
- " <td>Action</td>\n",
149
- " <td>Adventure, Drama</td>\n",
150
- " </tr>\n",
151
- " <tr>\n",
152
- " <th>1</th>\n",
153
- " <td>The Dark Knight</td>\n",
154
- " <td>2008</td>\n",
155
- " <td>Christopher Nolan</td>\n",
156
- " <td>Christian Bale, Heath Ledger, Aaron Eckhart, M...</td>\n",
157
- " <td>9.0</td>\n",
158
- " <td>152</td>\n",
159
- " <td>UA</td>\n",
160
- " <td>$534.86M</td>\n",
161
- " <td>Action</td>\n",
162
- " <td>Crime, Drama</td>\n",
163
- " </tr>\n",
164
- " <tr>\n",
165
- " <th>2</th>\n",
166
- " <td>The Lord of the Rings: The Return of the King</td>\n",
167
- " <td>2003</td>\n",
168
- " <td>Peter Jackson</td>\n",
169
- " <td>Elijah Wood, Viggo Mortensen, Ian McKellen, Or...</td>\n",
170
- " <td>9.0</td>\n",
171
- " <td>201</td>\n",
172
- " <td>U</td>\n",
173
- " <td>$377.85M</td>\n",
174
- " <td>Action</td>\n",
175
- " <td>Adventure, Drama</td>\n",
176
- " </tr>\n",
177
- " <tr>\n",
178
- " <th>3</th>\n",
179
- " <td>Inception</td>\n",
180
- " <td>2010</td>\n",
181
- " <td>Christopher Nolan</td>\n",
182
- " <td>Leonardo DiCaprio, Joseph Gordon-Levitt, Ellio...</td>\n",
183
- " <td>8.8</td>\n",
184
- " <td>148</td>\n",
185
- " <td>UA</td>\n",
186
- " <td>$292.58M</td>\n",
187
- " <td>Action</td>\n",
188
- " <td>Adventure, Sci-Fi</td>\n",
189
- " </tr>\n",
190
- " <tr>\n",
191
- " <th>4</th>\n",
192
- " <td>The Lord of the Rings: The Two Towers</td>\n",
193
- " <td>2002</td>\n",
194
- " <td>Peter Jackson</td>\n",
195
- " <td>Elijah Wood, Ian McKellen, Viggo Mortensen, Or...</td>\n",
196
- " <td>8.8</td>\n",
197
- " <td>179</td>\n",
198
- " <td>UA</td>\n",
199
- " <td>$342.55M</td>\n",
200
- " <td>Action</td>\n",
201
- " <td>Adventure, Drama</td>\n",
202
- " </tr>\n",
203
- " <tr>\n",
204
- " <th>...</th>\n",
205
- " <td>...</td>\n",
206
- " <td>...</td>\n",
207
- " <td>...</td>\n",
208
- " <td>...</td>\n",
209
- " <td>...</td>\n",
210
- " <td>...</td>\n",
211
- " <td>...</td>\n",
212
- " <td>...</td>\n",
213
- " <td>...</td>\n",
214
- " <td>...</td>\n",
215
- " </tr>\n",
216
- " <tr>\n",
217
- " <th>5557</th>\n",
218
- " <td>Disaster Movie</td>\n",
219
- " <td>2008</td>\n",
220
- " <td>Directors:Jason Friedberg, Aaron Seltzer</td>\n",
221
- " <td>Carmen Electra, Vanessa Lachey, Nicole Parker,...</td>\n",
222
- " <td>1.9</td>\n",
223
- " <td>87</td>\n",
224
- " <td>PG-13</td>\n",
225
- " <td>$14.19M</td>\n",
226
- " <td>Comedy</td>\n",
227
- " <td>Sci-Fi</td>\n",
228
- " </tr>\n",
229
- " <tr>\n",
230
- " <th>5558</th>\n",
231
- " <td>The Hottie &amp; the Nottie</td>\n",
232
- " <td>2008</td>\n",
233
- " <td>Tom Putnam</td>\n",
234
- " <td>Paris Hilton, Joel David Moore, Christine Laki...</td>\n",
235
- " <td>1.9</td>\n",
236
- " <td>91</td>\n",
237
- " <td>PG-13</td>\n",
238
- " <td>$0.03M</td>\n",
239
- " <td>Comedy</td>\n",
240
- " <td>Romance</td>\n",
241
- " </tr>\n",
242
- " <tr>\n",
243
- " <th>5559</th>\n",
244
- " <td>From Justin to Kelly</td>\n",
245
- " <td>2003</td>\n",
246
- " <td>Robert Iscove</td>\n",
247
- " <td>Kelly Clarkson, Justin Guarini, Katherine Bail...</td>\n",
248
- " <td>1.9</td>\n",
249
- " <td>81</td>\n",
250
- " <td>PG</td>\n",
251
- " <td>$4.92M</td>\n",
252
- " <td>Comedy</td>\n",
253
- " <td>Musical, Romance</td>\n",
254
- " </tr>\n",
255
- " <tr>\n",
256
- " <th>5560</th>\n",
257
- " <td>Superbabies: Baby Geniuses 2</td>\n",
258
- " <td>2004</td>\n",
259
- " <td>Bob Clark</td>\n",
260
- " <td>Jon Voight, Scott Baio, Vanessa Angel, Skyler ...</td>\n",
261
- " <td>1.5</td>\n",
262
- " <td>88</td>\n",
263
- " <td>PG</td>\n",
264
- " <td>$9.11M</td>\n",
265
- " <td>Comedy</td>\n",
266
- " <td>Family, Sci-Fi</td>\n",
267
- " </tr>\n",
268
- " <tr>\n",
269
- " <th>5561</th>\n",
270
- " <td>Cumali Ceber: Allah Seni Alsin</td>\n",
271
- " <td>2017</td>\n",
272
- " <td>Gökhan Gök</td>\n",
273
- " <td>Halil Söyletmez, Doga Konakoglu, Emre Keskin, ...</td>\n",
274
- " <td>1.0</td>\n",
275
- " <td>100</td>\n",
276
- " <td>Not Rated</td>\n",
277
- " <td>Gross Unkown</td>\n",
278
- " <td>Comedy</td>\n",
279
- " <td>Comedy</td>\n",
280
- " </tr>\n",
281
- " </tbody>\n",
282
- "</table>\n",
283
- "<p>5562 rows × 10 columns</p>\n",
284
- "</div>"
285
- ],
286
- "text/plain": [
287
- " Movie_Title Year \\\n",
288
- "0 Kantara 2022 \n",
289
- "1 The Dark Knight 2008 \n",
290
- "2 The Lord of the Rings: The Return of the King 2003 \n",
291
- "3 Inception 2010 \n",
292
- "4 The Lord of the Rings: The Two Towers 2002 \n",
293
- "... ... ... \n",
294
- "5557 Disaster Movie 2008 \n",
295
- "5558 The Hottie & the Nottie 2008 \n",
296
- "5559 From Justin to Kelly 2003 \n",
297
- "5560 Superbabies: Baby Geniuses 2 2004 \n",
298
- "5561 Cumali Ceber: Allah Seni Alsin 2017 \n",
299
- "\n",
300
- " Director \\\n",
301
- "0 Rishab Shetty \n",
302
- "1 Christopher Nolan \n",
303
- "2 Peter Jackson \n",
304
- "3 Christopher Nolan \n",
305
- "4 Peter Jackson \n",
306
- "... ... \n",
307
- "5557 Directors:Jason Friedberg, Aaron Seltzer \n",
308
- "5558 Tom Putnam \n",
309
- "5559 Robert Iscove \n",
310
- "5560 Bob Clark \n",
311
- "5561 Gökhan Gök \n",
312
- "\n",
313
- " Actors Rating \\\n",
314
- "0 Rishab Shetty, Sapthami Gowda, Kishore Kumar G... 9.3 \n",
315
- "1 Christian Bale, Heath Ledger, Aaron Eckhart, M... 9.0 \n",
316
- "2 Elijah Wood, Viggo Mortensen, Ian McKellen, Or... 9.0 \n",
317
- "3 Leonardo DiCaprio, Joseph Gordon-Levitt, Ellio... 8.8 \n",
318
- "4 Elijah Wood, Ian McKellen, Viggo Mortensen, Or... 8.8 \n",
319
- "... ... ... \n",
320
- "5557 Carmen Electra, Vanessa Lachey, Nicole Parker,... 1.9 \n",
321
- "5558 Paris Hilton, Joel David Moore, Christine Laki... 1.9 \n",
322
- "5559 Kelly Clarkson, Justin Guarini, Katherine Bail... 1.9 \n",
323
- "5560 Jon Voight, Scott Baio, Vanessa Angel, Skyler ... 1.5 \n",
324
- "5561 Halil Söyletmez, Doga Konakoglu, Emre Keskin, ... 1.0 \n",
325
- "\n",
326
- " Runtime(Mins) Censor Total_Gross main_genre side_genre \n",
327
- "0 148 UA Gross Unkown Action Adventure, Drama \n",
328
- "1 152 UA $534.86M Action Crime, Drama \n",
329
- "2 201 U $377.85M Action Adventure, Drama \n",
330
- "3 148 UA $292.58M Action Adventure, Sci-Fi \n",
331
- "4 179 UA $342.55M Action Adventure, Drama \n",
332
- "... ... ... ... ... ... \n",
333
- "5557 87 PG-13 $14.19M Comedy Sci-Fi \n",
334
- "5558 91 PG-13 $0.03M Comedy Romance \n",
335
- "5559 81 PG $4.92M Comedy Musical, Romance \n",
336
- "5560 88 PG $9.11M Comedy Family, Sci-Fi \n",
337
- "5561 100 Not Rated Gross Unkown Comedy Comedy \n",
338
- "\n",
339
- "[5562 rows x 10 columns]"
340
- ]
341
- },
342
- "execution_count": 4,
343
- "metadata": {},
344
- "output_type": "execute_result"
345
- }
346
- ],
347
  "source": [
348
  "df"
349
  ]
350
  },
351
  {
352
  "cell_type": "code",
353
- "execution_count": 5,
354
  "metadata": {
355
  "execution": {
356
  "iopub.execute_input": "2023-12-03T16:21:48.290752Z",
@@ -376,7 +122,7 @@
376
  "\u001b[0;31mKeyError\u001b[0m: 'Total Gross (millions)'",
377
  "\nThe above exception was the direct cause of the following exception:\n",
378
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
379
- "\u001b[0;32m<ipython-input-5-e8d3ce81bdde>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclean_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total Gross (millions)\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m\"$0.00M\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total Gross (millions)\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m\"Gross Unkown\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mclean_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclean_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclean_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Censor'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m\"(Banned)\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
380
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3891\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3892\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3893\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3894\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3895\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
381
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3796\u001b[0m ):\n\u001b[1;32m 3797\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3798\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3799\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3800\u001b[0m \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
382
  "\u001b[0;31mKeyError\u001b[0m: 'Total Gross (millions)'"
 
62
  },
63
  {
64
  "cell_type": "code",
65
+ "execution_count": null,
66
  "metadata": {
67
  "execution": {
68
  "iopub.execute_input": "2023-12-03T16:02:04.302761Z",
 
72
  "shell.execute_reply.started": "2023-12-03T16:02:04.302721Z"
73
  }
74
  },
75
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
76
  "source": [
77
  "df.shape"
78
  ]
79
  },
80
  {
81
  "cell_type": "code",
82
+ "execution_count": null,
83
  "metadata": {
84
  "execution": {
85
  "iopub.execute_input": "2023-12-03T16:02:04.321548Z",
 
89
  "shell.execute_reply.started": "2023-12-03T16:02:04.321518Z"
90
  }
91
  },
92
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
93
  "source": [
94
  "df"
95
  ]
96
  },
97
  {
98
  "cell_type": "code",
99
+ "execution_count": 3,
100
  "metadata": {
101
  "execution": {
102
  "iopub.execute_input": "2023-12-03T16:21:48.290752Z",
 
122
  "\u001b[0;31mKeyError\u001b[0m: 'Total Gross (millions)'",
123
  "\nThe above exception was the direct cause of the following exception:\n",
124
  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
125
+ "\u001b[0;32m<ipython-input-3-e8d3ce81bdde>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclean_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total Gross (millions)\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m\"$0.00M\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total Gross (millions)\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m\"Gross Unkown\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mclean_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclean_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclean_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Censor'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m\"(Banned)\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
126
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3891\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3892\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3893\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3894\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3895\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
127
  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3796\u001b[0m ):\n\u001b[1;32m 3797\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3798\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3799\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3800\u001b[0m \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
128
  "\u001b[0;31mKeyError\u001b[0m: 'Total Gross (millions)'"