New level in MultiIndex DataFrame based on existing column level values

February 15, 2022

Let’s say I have a DataFrame like this:

df = pd.DataFrame(data = [[1,2,3,4,5,6], [3,4,5,6,7,8]], 
                  columns = pd.MultiIndex.from_product([('A1', 'B1', 'A2'), (10,20)], names=['level_0','level_1']))

Here’s how it looks like:
DataFrame image

I want to add a new level in the columns which contains 1 where level_0 value contains "1" and and 2 where level_0 value contains "2". So, basically:

Where level_0 == "A1" –> new_level = 1
Where level_0 == "B1" –> new_level = 1
Where level_0 == "A2" –> new_level = 2

Any suggestions on how to do it?

>Solution :

Use lsit comprehension for extract number from first level values and create new MultiIndex by MultiIndex.from_tuples:

import re

df.columns = pd.MultiIndex.from_tuples([(re.findall(r'(\d+)$', x[0])[0], *x) 
                                         for x in df.columns.tolist()], 
                                       names=('new_level',*df.columns.names))
print (df)

new_level  1           2   
level_0   A1    B1    A2   
level_1   10 20 10 20 10 20
0          1  2  3  4  5  6
1          3  4  5  6  7  8