Unpack Pandas series to multiple Pandas series(unpacking cols)

unpack_pandas.py

def unpack_col(col_to_unpack, df_to_append = None, header = 'col', sep=',', na_value=''):
    import pandas as pd
    unpacked_cols = col_to_unpack.fillna(na_value).apply(lambda x: pd.Series(x.split(','))).fillna(na_value)
    #add dynamic columns names based on # of rows and parameter header passed for prefix (header_#)
    col_names = []
    for i in unpacked_cols.columns:
        col_names.append(header + '_' + str(i))
    unpacked_cols.columns = col_names
    if isinstance(df_to_append, pd.DataFrame):
        #return df concatenated with previously unpacked columns
        return pd.concat([df_to_append, unpacked_cols], axis=1)
    else:
        #return df only with unpacked columns
        return unpacked_cols
def unpack_col_normalized(col_to_unpack, df_to_append, sep=',', na_value=''):
    s = df_to_append[col_to_unpack].str.split(sep).apply(pd.Series,1).stack()
    s.index = s.index.droplevel(-1)
    s.name = str(df_to_append[col_to_unpack].name)
    return df_to_append.drop(col_to_unpack,1).join(s)