Differencing all other(not selected) columns in a dataframe by another column

Question

I have a data frame with the following structure.

  Index1 Index2  -2  -1   0   1   2
0      A      W  20  25  28  29  18
1      B      X  21  20  26  21  27
2      C      Y  19  19  20  17  17
3      D      Z  18  17  15  18  18

I want to subtract the ‘0’ column from all columns except for the index columns and create new columns with the result, titled using the column name and a suffix. I successfully managed to do this:

  Index1 Index2  -2  -1   0   1   2  -1 ph  -2 ph  1 ph  2 ph
0      A      W  20  25  28  29  18     -3     -8     1   -10
1      B      X  21  20  26  21  27     -6     -5    -5     1
2      C      Y  19  19  20  17  17     -1     -1    -3    -3
3      D      Z  18  17  15  18  18      2      3     3     3

The code to recreate the example:

import pandas as pd

# Create DataFrame
data = {'Index1': ['A', 'B', 'C', 'D'],
        'Index2': ['W', 'X', 'Y', 'Z'],
        '-2': [20, 21, 19, 18],
        '-1': [25, 20, 19, 17],
        '0': [28, 26, 20, 15],
        '1': [29, 21, 17, 18],
        '2': [18, 27, 17, 18]
        }
df = pd.DataFrame(data)

df = df.assign(**df[['-1','-2', ,'1', '2']].sub(df['0'], axis=0).add_suffix(' ph'))

print(df)

However, the columns (except for the indexes) can vary (so could be -3,-2,1,0,1 etc.) So I want to update my code to say: subtract column ‘0’ from all columns except for the indexes (Index1 and Index2).

I tried this (and a few other alternatives):

df = df.assign(**df[~['Index1','Index2']].sub(df['0'], axis=0).add_suffix(' ph'))

But didn’t seem to figure it out

Asked By: z star

||

Source

Answer 1

Drop the required columns then subtract and join the result back

c = ['Index1', 'Index2', '0']
df.join(df.drop(columns=c).sub(df['0'], axis=0).add_suffix(' ph'))

Result

  Index1 Index2  -2  -1   0   1   2  -2 ph  -1 ph  1 ph  2 ph
0      A      W  20  25  28  29  18     -8     -3     1   -10
1      B      X  21  20  26  21  27     -5     -6    -5     1
2      C      Y  19  19  20  17  17     -1     -1    -3    -3
3      D      Z  18  17  15  18  18      3      2     3     3

Answered By: Shubham Sharma

Differencing all other(not selected) columns in a dataframe by another column

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