I have the following DataFrame with MultiIndex columns (the same applies to MultiIndex rows):
import pandas as pd
df = pd.DataFrame(columns=pd.MultiIndex.from_product([['A','B'],[1,2,3,4]]),
data=[[0,1,2,3,4,5,6,7],[10,11,12,13,14,15,16,17],
[20,21,22,23,24,25,26,27]])
Now I want to select the following columns into a new DataFrame: from the A group, elements at index [1,2,4], AND from the B group, elements at index [1,3]. So my new DataFrame would have 5 columns.
I can easily make any of the two selections separately using .loc:
df_grA = df.loc[:,('A',(1,2,4))]
df_grB = df.loc[:,('B',(1,3))]
But I cannot find a way to achieve what I want.
The only way that I can think of is to concat the two pieces together like that:
df_selection = pd.concat([df_grA,df_grB],axis=1)
This works, but it's clunky. I can't believe there's not a more convenient way.
Another idea with product:
from itertools import product
print (df.loc[:, list(product('A',(1,2,4))) + list(product('B',(1,2,3)))])
A B
1 2 4 1 2 3
0 0 1 3 4 5 6
1 10 11 13 14 15 16
2 20 21 23 24 25 26