pandas apply function that returns multiple values to rows in pandas dataframe

Return Series and it will put them in a DataFrame.

def myfunc(a, b, c):    do something    return pd.Series([e, f, g])

This has the bonus that you can give labels to each of the resulting columns. If you return a DataFrame it just inserts multiple rows for the group.

Based on the excellent answer by @U2EF1, I've created a handy function that applies a specified function that returns tuples to a dataframe field, and expands the result back to the dataframe.

def apply_and_concat(dataframe, field, func, column_names):    return pd.concat((        dataframe,        dataframe[field].apply(            lambda cell: pd.Series(func(cell), index=column_names))), axis=1)

Usage:

df = pd.DataFrame([1, 2, 3], index=['a', 'b', 'c'], columns=['A'])print df   Aa  1b  2c  3def func(x):    return x*x, x*x*xprint apply_and_concat(df, 'A', func, ['x^2', 'x^3'])   A  x^2  x^3a  1    1    1b  2    4    8c  3    9   27

Hope it helps someone.

Just return a list instead of tuple.

In [81]: dfOut[81]:                             x         y         zts                                               2014-05-15 10:38:00  0.120117  0.987305  0.1162112014-05-15 10:39:00  0.117188  0.984375  0.1220702014-05-15 10:40:00  0.119141  0.987305  0.1191412014-05-15 10:41:00  0.116211  0.984375  0.1201172014-05-15 10:42:00  0.119141  0.983398  0.118164[5 rows x 3 columns]In [82]: def myfunc(args):   ....:        e=args[0] + 2*args[1]   ....:        f=args[1]*args[2] +1   ....:        g=args[2] + args[0] * args[1]   ....:        return [e,f,g]   ....: In [83]: df.apply(myfunc ,axis=1)Out[83]:                             x         y         zts                                               2014-05-15 10:38:00  2.094727  1.114736  0.2348032014-05-15 10:39:00  2.085938  1.120163  0.2374272014-05-15 10:40:00  2.093751  1.117629  0.2367702014-05-15 10:41:00  2.084961  1.118240  0.2345122014-05-15 10:42:00  2.085937  1.116202  0.235327