Pandas - Subtract one column from other columns in same dataframe for multiple files with varying column names and number of columns

Question

I want to subtract the 3rd from the right column from all other columns except for itself and the last column, then create new columns with these values with the header including the original column's header. The column names and the number of columns between each file vary, but the names of the 2 most right columns are always the same.

Example df:

ROI005	ROI008	53141	AVG	ERR
2	5	1	4	1
4	2	2	3	3
3	3	1	5	2

Desired output:

ROI005	ROI008	53141	AVG	ERR	ROI005 - Background	ROI008 - Background	Average - Background
2	5	1	4	1	1	4	3
4	2	2	3	3	2	0	1
3	3	1	5	2	2	2	4

I've been using this to get the difference for the 3rd to last column using the below code. I know that I can use df.iloc[: , : -3] for all but the last 3 columns, but I'm not sure how to combine this with df.iloc[: , -2] for multiple columns.

import pandas as pd
import glob

files = glob.glob(r'C:\Users\Me\1Test' + '/*.txt')
for f in files:
    df = pd.read_table(f)
    df['Average - Background']  = df.iloc[ : , -2] - df.iloc[ : , -3]
    df.to_excel(f.replace('txt', 'xlsx'), 'Sheet1')

Data:

{'ROI005': [2, 4, 3],
 'ROI008': [5, 2, 3],
 '53141': [1, 2, 1],
 'AVG': [4, 3, 5],
 'ERR': [1, 3, 2]}

Answer 1

We could subtract that particular column from the other columns horizontally using sub on axis=0 ; then join it back to df :

out = (df.join(df.drop(df.columns[[-3,-1]], axis=1)
               .sub(df[df.columns[-3]], axis=0)
               .add_suffix(' - Background')))

Output:

   ROI005  ROI008  53141  AVG  ERR  ROI005 - Background  ROI008 - Background   AVG - Background  
0       2       5      1    4    1                    1                    4                  3  
1       4       2      2    3    3                    2                    0                  1
2       3       3      1    5    2                    2                    2                  4