Different operation per each row on multiple columns in python Pandas

Question

I am having pandas dataframe like this

A    B    D    E
2    12   1    2
3    54   1    4
10   2    1    1

I would like to multiply values in C and D column by the value in A, but keep values in B column. So from the dataframe above, the result I would get would be :

A    B    D    E
2    12   2    4
3    54   3    12
10   2    10   10

However, I have many columns I want to modify, not just 2 like in the example. Therefore I would like to know what is the most efficient way to do this.

I was looking at DataFrame.assign function but with that, it seems like I can only modify 1 column at the time.

Thanks !

Inspired by DSM's solution I tried a little modification fo yours as df.loc[:,col_list] = df[col_list] * df['A'] and surprisingly it got me NaNs in all the col_list columns. I wonder why, the datatype in both col_list collumns and "A" columns are numpy.float64 — Patrik Stas
– Patrik Stas, Commented Aug 11, 2015 at 18:15
Hmm, I think there is some issue with the broadcasting here, try this: df[col_list] = df[col_list].mul(df['A'], axis=0) — EdChum
– EdChum, Commented Aug 11, 2015 at 18:18
Basically it looks like the broadcasting is happening along the wrong axes resulting in some funny df filled with NaN values returned, if you did this then it would work: (df[col_list].T * df['A']).T but this isn't so pretty — EdChum
– EdChum, Commented Aug 11, 2015 at 18:32

DSM · Accepted Answer · 2015-08-11 16:09:01Z

1

I never know what do with "most efficient" questions. But if you want that output, you could use mul:

>>> df.loc[:,["D","E"]] = df[["D","E"]].mul(df["A"], axis=0)
>>> df
    A   B   D   E
0   2  12   2   4
1   3  54   3  12
2  10   2  10  10

where you could replace ["D","E"] by list_of_columns so you wouldn't have to repeat the column names.

answered Aug 11, 2015 at 16:09

DSM

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