Sum columns by level in a pandas MultiIndex DataFrame

I have my df with multi-index columns. All of my values are in float, and I want to merge values with in first level of multi-index. Please see below for detail.

first        bar                 baz                 foo   
second       one       two       one       two       one    
A       0.895717  0.805244  1.206412  2.565646  1.431256    
B       0.410835  0.813850  0.132003  0.827317  0.076467    
C       1.413681  1.607920  1.024180  0.569605  0.875906 

first        bar                 baz                 foo   

A       (0.895717+0.805244) (1.206412+2.565646)  1.431256    
B       (0.410835+0.813850) (0.132003+0.827317)  0.076467    
C       (1.413681+1.607920) (1.024180+0.569605)  0.875906 

The values are actually added (I just didn't feel like doing all this :)). Bottom line is that I just want to level-up(higher level I guess) and within the index, add all the values. Please let me know a good way to do this. Thank you!

I believe you're looking for a groupby along the first axis.

df.groupby(level=0, axis=1).sum()

Or (more succinctly),

df.sum(level=0, axis=1)

The level argument to sum implies grouping.

df

first  bar     baz     foo    
second one two one two one two
A        2   3   3   4  10   8
B       22  16   7   3   2  26
C        4   5   1   9   6   5

df.sum(level=0, axis=1)

first  bar  baz  foo
A        5    7   18
B       38   10   28
C        9   10   11

Performance wise, there's hardly any difference between the two methods outlined above (the latter is a few ticks faster).

Keep in mind that df.sum(level, axis) will only work if you set your columns to the multi-index. Example,

D = {'one': range(6), 
     'two': range(1,7), 
     'CAT1': 'A A A A A A'.split(), 
     'CAT2': 'B B B C C C'.split(), 
     'CAT3': 'D D E E F F'.split()}

df = pd.DataFrame(D)
df = df.set_index('CAT1 CAT2 CAT3'.split())
df
                one  two
CAT1 CAT2 CAT3          
A    B    D       0    1
          D       1    2
          E       2    3
     C    E       3    4
          F       4    5
          F       5    6

If your data is in this form, you will have to use df.groupby(level=n).sum(axis=1)

df.groupby(level = 0).sum(axis=1)

      one  two
CAT1          
A      15   21

df.groupby(level = 1).sum(axis=1)

      one  two
CAT2          
B       3    6
C      12   15

df.groupby(level = 2).sum(axis=1)

      one  two
CAT3          
D       1    3
E       5    7
F       9   11

If you try skipping the groupby,

df.sum(level = 1, axis=1)

ValueError: level > 0 or level < -1 only valid with  MultiIndex

Which is an interesting error since,

df.index

MultiIndex(levels=[[u'A'], [u'B', u'C'], [u'D', u'E', u'F']],
           labels=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 2, 2]],
           names=[u'CAT1', u'CAT2', u'CAT3'])