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.

I've tried returning a tuple (I was using functions like scipy.stats.pearsonr which return that kind of structures) but It returned a 1D Series instead of a Dataframe which was I expected. If I created a Series manually the performance was worse, so I fixed It using the result_type as explained in the official API documentation:

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

So you could edit your code this way:

def myfunc(a, b, c):    # do something    return (e, f, g)df.apply(myfunc, axis=1,  result_type='expand')