All rows in one dataframe substracts another row

Question

I have a dataframe like this:

ID,   A, B, C, D
111   0, 1, 2, 3
222   2, 3, 4, 5
333   3, 4, 5, 6

and a row

A, B, C, D
0, 1, 0, 1

I want every row in the first dataframe minus the row in the above, and do not affect the ID column. The expect result is

ID,   A, B, C, D
111   0, 0, 2, 2
222   2, 2, 4, 4
333   3, 3, 5, 5

I don’t know the best way to implement this. Could anyone help? Thanks.

Asked By: lserlohn

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Source

Answer 1

You can subsetting df1 from df2 columns to apply subtraction:

df1[df2.columns] = df1[df2.columns] - df2.values

# Or suggested by @mozway
df1[df2.columns] = df1[df2.columns].sub(df2.loc[0], axis=1)

print(df1)

# Output
    ID  A  B  C  D
0  111  0  0  2  2
1  222  2  2  4  4
2  333  3  3  5  5

Input:

>>> df1
    ID  A  B  C  D
0  111  0  1  2  3
1  222  2  3  4  5
2  333  3  4  5  6

>>> df2
   A  B  C  D
0  0  1  0  1

Answered By: Corralien

Answer 2

You can do this:

import pandas as pd

# Create the original DataFrame
df = pd.DataFrame({'ID': [111, 222, 333],
                   'A': [0, 2, 3],
                   'B': [1, 3, 4],
                   'C': [2, 4, 5],
                   'D': [3, 5, 6]})

# Create the row to subtract from the DataFrame
row = pd.DataFrame({'A': [0], 'B': [1], 'C': [0], 'D': [1]})

# Subtract the row from the DataFrame and update it
df[['A', 'B', 'C', 'D']] = df[['A', 'B', 'C', 'D']] - row.values

# Print the updated DataFrame
print(df)

Result

   Id  A  B  C  D
 0 111 0  0  2  2
 1 222 2  2  4  4
 2 333 3  3  5  5

Answered By: PforPython

All rows in one dataframe substracts another row

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