Upload segment_notebooks.py

segment_notebooks.py

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+import json
+import itertools
+from datasets import load_dataset
+
+
+def segment_cells(content):
+    
+    # segment notebooks into lists of individual cells
+    cells = []
+    cell_types = []
+    for cell in content['cells']:
+        # select only non-empty cells
+        if len(cell['source']) != 0:
+            cells.append(' '.join(cell['source']))
+            cell_types.append(cell['cell_type'])
+            
+    return cells, cell_types
+
+
+def parse_notebook(batch):
+    try:
+        cells, types = segment_cells(json.loads(batch['content']))
+        
+        # get cell types and group them into lists
+        cell_type_groups = [list(g) for k,g in itertools.groupby(types)]
+        cell_types = [k for k,g in itertools.groupby(types)]
+        cell_groups = []
+        
+        group_start = 0
+        for g in cell_type_groups:
+            cell_groups.append(cells[group_start:group_start+len(g)])
+            group_start += len(g)
+        
+        batch['cells'] = cell_groups
+        batch['cell_types'] = cell_types
+        batch['cell_type_groups'] = cell_type_groups
+        
+    except:
+        # if json.loads() returns error, skip and add a placeholder
+        batch['cells'] = [['empty']]
+        batch['cell_types'] = ['empty']
+        batch['cell_type_groups'] = [['empty']]
+        
+    del batch['content']
+    return batch
+
+
+if __name__ == "__main__":
+    
+    # load dataset
+    dataset = load_dataset("bigcode/the-stack",data_dir="data/jupyter-notebook", split="train",use_auth_token=True)
+    # segment notebooks
+    dataset = dataset.map(segment) 
+    # filter out erronous cells via placeholders
+    dataset = dataset.filter(lambda entry: entry['cell_types']!=['empty'])
+    # push to hub
+    dataset.push_to_hub("bigcode/jupyter-parsed")