Pandas : Sum multiple columns and get results in multiple columns

Question

I have a "sample.txt" like this.

idx A   B   C   D   cat
J   1   2   3   1   x
K   4   5   6   2   x
L   7   8   9   3   y
M   1   2   3   4   y
N   4   5   6   5   z
O   7   8   9   6   z

With this dataset, I want to get sum in row and column. In row, it is not a big deal. I made result like this.

### MY CODE ###
import pandas as pd

df = pd.read_csv('sample.txt',sep="\t",index_col='idx')
df.info()

df2 = df.groupby('cat').sum()
print( df2 )

The result is like this.

      A   B   C   D
cat                
x     5   7   9   3
y     8  10  12   7
z    11  13  15  11

But I don't know how to write a code to get result like this. (simply add values in column A and B as well as column C and D)

    AB  CD
J   3   4
K   9   8
L   15  12
M   3   7
N   9   11
O   15  15

Could anybody help how to write a code?

By the way, I don't want to do like this. (it looks too dull, but if it is the only way, I'll deem it)

df2 = df['A'] + df['B']
df3 = df['C'] + df['D']
df = pd.DataFrame([df2,df3],index=['AB','CD']).transpose()
print( df )

Answer 1

When you pass a dictionary or callable to groupby it gets applied to an axis. I specified axis one which is columns.

d = dict(A='AB', B='AB', C='CD', D='CD')
df.groupby(d, axis=1).sum()

Answer 2

Use concat with sum:

df = df.set_index('idx')
df = pd.concat([df[['A', 'B']].sum(1), df[['C', 'D']].sum(1)], axis=1, keys=['AB','CD'])
print( df)
     AB  CD
idx        
J     3   4
K     9   8
L    15  12
M     3   7
N     9  11
O    15  15

Answer 3

Does this do what you need? By using axis=1 with DataFrame.apply, you can use the data that you want in a row to construct a new column. Then you can drop the columns that you don't want anymore.

In [1]: import pandas as pd
In [5]: df = pd.DataFrame(columns=['A', 'B', 'C', 'D'], data=[[1, 2, 3, 4], [1, 2, 3, 4]])

In [6]: df
Out[6]:
   A  B  C  D
0  1  2  3  4
1  1  2  3  4

In [7]: df['CD'] = df.apply(lambda x: x['C'] + x['D'], axis=1)

In [8]: df
Out[8]:
   A  B  C  D  CD
0  1  2  3  4   7
1  1  2  3  4   7

In [13]: df.drop(['C', 'D'], axis=1)
Out[13]:
   A  B  CD
0  1  2   7
1  1  2   7