


Splitting Comma-Separated String Entries in a Pandas DataFrame to Create Separate Rows
Problem:
We have a Pandas DataFrame containing strings with comma-separated values in one column. We wish to split each CSV entry and create a new row for each unique value. For instance, "a,b,c" should become "a", "b", "c".
Solution:
Option 1: DataFrame.explode() (Pandas 0.25.0 )
The DataFrame.explode() method is specifically designed for this purpose. It allows us to split a list-like column (in this case, our comma-separated strings) into individual rows.
In [1]: df.explode('var1') Out[1]: var1 var2 var3 0 a 1 XX 1 b 1 XX 2 c 1 XX 3 d 2 ZZ 4 e 2 ZZ 5 f 2 ZZ 6 x 2 ZZ 7 y 2 ZZ
Option 2: Custom Vectorized Function
If DataFrame.explode() is not available or we need more customization, we can create our own vectorized function:
import numpy as np def explode(df, lst_cols, fill_value='', preserve_index=False): # Convert `lst_cols` to a list if it is a string. if isinstance(lst_cols, str): lst_cols = [lst_cols] # Calculate the lengths of each list in `lst_cols`. lens = df[lst_cols[0]].str.len() # Create a new index based on the lengths of the lists. idx = np.repeat(df.index.values, lens) # Create a new DataFrame with the exploded columns. exp_df = pd.DataFrame({ col: np.repeat(df[col].values, lens) for col in df.columns.difference(lst_cols) }, index=idx).assign(**{ col: np.concatenate(df.loc[lens > 0, col].values) for col in lst_cols }) # Append rows with empty lists if necessary. if (lens == 0).any(): exp_df = exp_df.append(df.loc[lens == 0, df.columns.difference(lst_cols)], sort=False).fillna(fill_value) # Revert the original index order and reset the index if requested. exp_df = exp_df.sort_index() if not preserve_index: exp_df = exp_df.reset_index(drop=True) return exp_df
Example usage:
In [2]: explode(df, 'var1') Out[2]: var1 var2 var3 0 a 1 XX 1 b 1 XX 2 c 1 XX 3 d 2 ZZ 4 e 2 ZZ 5 f 2 ZZ 6 x 2 ZZ 7 y 2 ZZ
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