Column operations in Pandas

Question

Say I have a dataframe:

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(4,5), columns = list('abcde'))

I would like to substract the entries in column df.a from all other columns. In other words, I would like to get a dataframe that holds as columns the following columns:

|col_b - col_a | col_c - col_a | col_d - col_a|

I have tried df - df.a but this yields something odd:

  0   1   2   3   a   b   c   d   e
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN NaN NaN

How can I do this type of columnwise operations in Pandas? Also, just wondering, what does df -df.a do?

Answer 1

You probably want

>>> df.sub(df.a, axis=0)
   a         b         c         d         e
0  0  0.112285  0.267105  0.365407 -0.159907
1  0  0.380421  0.119536  0.356203  0.096637
2  0 -0.100310 -0.180927  0.112677  0.260202
3  0  0.653642  0.566408  0.086720  0.256536

df-df.a is basically trying to do the subtraction along the other axis, so the indices don't match, and when using binary operators like subtraction "mismatched indices will be unioned together" (as the docs say). Since the indices don't match, you wind up with 0 1 2 3 a b c d e.

For example, you could get to the same destination more indirectly by transposing things, (df.T - df.a).T, which by flipping df means that the default axis is now the right one.