split pandas row into multiple rows

def split_dataframe_rows(df,column_selectors):
    # we need to keep track of the ordering of the columns
    def _split_list_to_rows(row,row_accumulator,column_selector):
        split_rows = {}
        max_split = 0
        for column_selector in column_selectors:
            split_row = row[column_selector]
            split_rows[column_selector] = split_row
            if len(split_row) > max_split:
                max_split = len(split_row)
            
        for i in range(max_split):
            new_row = row.to_dict()
            for column_selector in column_selectors:
                try:
                    new_row[column_selector] = split_rows[column_selector].pop(0)
                except IndexError:
                    new_row[column_selector] = ''
            row_accumulator.append(new_row)

    new_rows = []
    df.apply(_split_list_to_rows,axis=1,args = (new_rows,column_selectors))
    new_df = pd.DataFrame(new_rows, columns=df.columns)
    return new_df

def explode(df, lst_cols, fill_value='', preserve_index=False):
    # make sure `lst_cols` is list-alike
    if (lst_cols is not None
        and len(lst_cols) > 0
        and not isinstance(lst_cols, (list, tuple, np.ndarray, pd.Series))):
        lst_cols = [lst_cols]
    # all columns except `lst_cols`
    idx_cols = df.columns.difference(lst_cols)
    # calculate lengths of lists
    lens = df[lst_cols[0]].str.len()
    # preserve original index values    
    idx = np.repeat(df.index.values, lens)
    # create "exploded" DF
    res = (pd.DataFrame({
                col:np.repeat(df[col].values, lens)
                for col in idx_cols},
                index=idx)
             .assign(**{col:np.concatenate(df.loc[lens>0, col].values)
                            for col in lst_cols}))
    # append those rows that have empty lists
    if (lens == 0).any():
        # at least one list in cells is empty
        res = (res.append(df.loc[lens==0, idx_cols], sort=False)
                  .fillna(fill_value))
    # revert the original index order
    res = res.sort_index()
    # reset index if requested
    if not preserve_index:        
        res = res.reset_index(drop=True)
    return res