Returning multiple columns with pandas apply and user-defined functions

Question

Say I have a function:

def fn(x)
    y = x ** 2
    z = x ** 3
    return y, z

And I want to use df['x'].apply(lambda x: fn(x)) to return both y and z in separate columns. Is there a good way to do this by still using fn(x)? In reality, my function will be much more complicated - so I only want to run it once within the apply and assign output[0], output[1], etc to individual columns.

Answer 1

How about this method? (n.b., I edited this answer in light of the comment below) so the apply step could take a single function with shared calculations and return the required series for the merge step.

data = {'state':['Ohio','Ohio','Ohio','Nevada','Nevada'], 'year':[2000,2001,2002,2001,2002],'pop':[1.5,1.7,3.6,2.4,2.9]}
frame = pd.DataFrame(data, columns = ['year','state','pop'])
def fn(x,head1,head2):
    y = x ** 2
    z = x ** 3
    return pd.Series({head1:y, head2:z}) 
frame = frame.merge(frame['pop'].apply(lambda s: fn(s,'xsqr','xcube')), left_index=True, right_index=True)

Results:

   year   state  pop   xcube   xsqr
0  2000    Ohio  1.5   3.375   2.25
1  2001    Ohio  1.7   4.913   2.89
2  2002    Ohio  3.6  46.656  12.96
3  2001  Nevada  2.4  13.824   5.76
4  2002  Nevada  2.9  24.389   8.41