How to Split Comma-Separated Strings in a Pandas DataFrame into Separate Rows?

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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