Efficiently split Pandas Dataframe cells containing lists into multiple rows, duplicating the other column's values.

separator.py

def explode_from_fields(df, target_columns: list, separator: str):
    """ df = dataframe to split,
        target_columns = list of the columns containing the values to split,
                         if the elements returned from the split are not the
                         same for each columns, the shorter ones are extended
                         to the longest.
        separator = the symbol used to perform the split
        returns: A dataframe with each entry for the target column separated, 
                 with each element moved into a new row.
                 The values in the other columns are duplicated across the newly divided rows."""
    def _split_row(row, rows, tcs, sep):
        splits = []
        has_none = False
        for tc in tcs:
            if row[tc]:
                splits.append(row[tc].split(sep))
            else:
                splits.append([None])
                
        max_len = len(max(splits, key=lambda x: len(x) if x else 0))

        for s in splits:
            if s:
                s.extend([None]*(max_len - len(s)))
            else:
                s = [None]*max_len
        
        for group in list(zip(*splits)):
            new_row = row.to_dict()
            for tc, g in zip(tcs, group):
                new_row[tc] = g
            rows.append(new_row)

    new_rows = []
    df.apply(_split_row, axis=1, args=(new_rows, target_columns, separator))
    new_df = pd.DataFrame(new_rows)
    
    return new_df