Avoiding iteration in pandas when I want to update the value in a column x when a condition is true where x is given by another column

I have the following pandas dataframe:

Where bool==1, I would like to update the value in the column given by the col_name column to be 100.

Expected output:

I can do this by iterating through the table, but from what I’ve read this is never best practice. What would be the most efficient way of doing this?

Cheers!

>Solution :

Build a boolean mask with numpy and update:

# identify cells for which the col_name matches the column name
# only keep those that have a bool of 1 in the row
m = ((df['col_name'].to_numpy()[:, None] == df.columns.to_numpy())
     & df['bool'].eq(1).to_numpy()[:, None]
    )

df[m] = 100

Output:

  key1 key2 col_name  bool  col_1  col_2  col_3
0   a1   a2    col_1     0      5     10     20
1   b1   b2    col_3     1     10     10    100
2   c1   c2    col_1     1    100     15      5

Intermediate m:

array([[False, False, False, False, False, False, False],
       [False, False, False, False, False, False,  True],
       [False, False, False, False,  True, False, False]])