Qfyilyi

I have a pandas dataframe with multiple columns. I would like to calculate the sum of various subsets of this columns and assign a name to each group of columns.

Is it possible to achieve this using groupby or other pandas methods?

Setup:

import numpy as np; np.random.seed(1)

import pandas as pd



df = pd.DataFrame(np.random.randint(0, 10, (3, 5)), columns=['A', 'B', 'C', 'D', 'E'])



columns_groups = {'First': ['A', 'B', 'C'],

                  'Second': ['D', 'E'],

                  'Some': ['A', 'C', 'D'],

                  'All': ['A', 'B', 'C', 'D', 'E']}

Desired output: (is there a more elegant solution?)

out = {}

for name, group in columns_groups.items():

    out[name] = df[group].sum(axis=1)



out = pd.DataFrame(out)



out

Out[22]: 

   All  First  Second  Some

0   27     22       5    19

1   23      8      15    13

2   17     11       6     9

My attempt:

df.groupby(columns_groups, axis=1).sum(axis=1)



Out[21]: 

Empty DataFrame

Columns: 

Index: [0, 1, 2]

Just a different and fun way , using reindex with MultiIndex

df=df.reindex(columns=sum(columns_groups.values(),))

t=[(x,z ) for x , y in columns_groups.items() for z in y]

df.columns=pd.MultiIndex.from_tuples(t)

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

   First  Second  Some  All

0     22       8    18   30

1     17       9    16   26

2      6      15    14   21

Is this okay with you:

pd.DataFrame({k: df[v].sum(axis=1) for k, v in columns_groups.items()})



   All  First  Second  Some

0   27     22       5    19

1   23      8      15    13

2   17     11       6     9

It's same what you did, only in comprehension.