Pandas Dataframe, how to group columns together in Python

Question

I have a pandas Dataframe and i want to group some of the columns to build higher levels columns:

Exemple : i have

Index       A       B       C       D
    1    0.25     0.3    0.25    0.66
    2    0.25     0.3    0.25    0.66
    3    0.25     0.3    0.25    0.66

and i want

    Index              AB        ||           CD
    Subindex       A   |      B  ||      C    |      D 
    1            0.25  |    0.3  ||   0.25    |    0.66
    2            0.25  |    0.3  ||   0.25    |    0.66
    3            0.25  |    0.3  ||   0.25    |    0.66

Thank you for your help...

Answer 1

Create a dictionary to define your mapping and use pd.MultiIndex.from_tuples . If needed you can also specify names=['level_0', 'level_1'] to add names.

import pandas as pd

d = {'A': 'AB', 'B': 'AB', 'C': 'CD', 'D': 'CD'}
df.columns = pd.MultiIndex.from_tuples([*zip(map(d.get, df), df)])
# Equivalently
# df.columns = pd.MultiIndex.from_tuples([(d[col], col) for col in df.columns])

Output:

         AB         CD      
          A    B     C     D
Index                       
1      0.25  0.3  0.25  0.66
2      0.25  0.3  0.25  0.66
3      0.25  0.3  0.25  0.66

Answer 2

groupby / concat hack

m = {'A': 'AB', 'B': 'AB', 'C': 'CD', 'D': 'CD'}
pd.concat(dict((*df.groupby(m, 1),)), axis=1)

         AB         CD      
          A    B     C     D
Index                       
1      0.25  0.3  0.25  0.66
2      0.25  0.3  0.25  0.66
3      0.25  0.3  0.25  0.66

Note that with this method it is possible to select an arbitrary subset of the columns in the original DataFrame, whereas the alternative answer appears to require a valid dictionary mapping for all values in the parent DataFrame