Upload clean.py

clean.py

@@ -155,11 +155,11 @@ def clean_column(df, column_name):
 
     # Convert column to determined data type
     if data_type == "float":
-        df.loc[:, column_name] = pd.to_numeric(df[column_name], errors='coerce')
+        df[column_name] = pd.to_numeric(df[column_name], errors='coerce')
     elif data_type == "integer":
-        df.loc[:, column_name] = pd.to_numeric(df[column_name], errors='coerce').astype('Int64')
+        df[column_name] = pd.to_numeric(df[column_name], errors='coerce').astype('Int64')
     elif data_type == "date":
-        df[column_name] = pd.to_datetime(df[column_name], errors='coerce')
+        df[column_name] = pd.to_datetime(df[column_name], errors='coerce', dayfirst=True)
     elif data_type == "string" or data_type == "object":
         # Transform string values
         transform_result = transform_string_column(column_data, column_name)
@@ -178,8 +178,10 @@ def clean_column(df, column_name):
             print(f"  Potential typos found: {typo_result['typos']}")
 
     # Set empty and invalid cells to NaN
-    df.loc[empty_indices + invalid_indices, column_name] = np.nan
-    nonconforming_cells = len(empty_indices) + len(invalid_indices)
+    indices_to_set_nan = set(empty_indices + invalid_indices)
+    existing_indices = df.index.intersection(indices_to_set_nan)
+    df.loc[existing_indices, column_name] = np.nan
+    nonconforming_cells = len(existing_indices)
 
     return df, nonconforming_cells