Python - DataFrame: Multiply multiple columns by another column and save in new columns

Question

I couldn't find an efficient away of doing that. I have below DataFrame in Python with columns from A to Z

     A    B    C ...   Z
0  2.0  8.0  1.0 ... 5.0
1  3.0  9.0  0.0 ... 4.0
2  4.0  9.0  0.0 ... 3.0
3  5.0  8.0  1.0 ... 2.0
4  6.0  8.0  0.0 ... 1.0
5  7.0  9.0  1.0 ... 0.0

I need to multiply each of the columns from B to Z by A, (B x A, C x A, ..., Z x A), and save the results on new columns (R1, R2 ..., R25). I would have something like this:

     A    B    C ...   Z    R1   R2  ...  R25
0  2.0  8.0  1.0 ... 5.0  16.0  2.0  ... 10.0
1  3.0  9.0  0.0 ... 4.0  27.0  0.0  ... 12.0
2  4.0  9.0  0.0 ... 3.0  36.0  0.0  ... 12.0
3  5.0  8.0  1.0 ... 2.0  40.0  5.0  ... 10.0
4  6.0  8.0  0.0 ... 1.0  48.0  0.0  ... 6.0
5  7.0  9.0  1.0 ... 0.0  63.0  7.0  ... 0.0

I was able to calculate the results using below code, but from here I would need to merge with original df. Doesn't sound efficient. There must be a simple/clean way of doing that.

df.loc[:,'B':'D'].multiply(df['A'], axis="index")

That's an example, my real DataFrame has 160 columns x 16k rows.

jezrael · Accepted Answer · 2018-01-17 11:17:15Z

2

Create new columns names by list comprehension and then join to original:

df1 = df.loc[:,'B':'D'].multiply(df['A'], axis="index")
df1.columns = ['R{}'.format(x) for x in range(1, len(df1.columns) + 1)]

df = df.join(df1)
print (df)
     A    B    C    Z    R1   R2
0  2.0  8.0  1.0  5.0  16.0  2.0
1  3.0  9.0  0.0  4.0  27.0  0.0
2  4.0  9.0  0.0  3.0  36.0  0.0
3  5.0  8.0  1.0  2.0  40.0  5.0
4  6.0  8.0  0.0  1.0  48.0  0.0
5  7.0  9.0  1.0  0.0  63.0  7.0

edited Jan 17, 2018 at 11:17

answered Jan 17, 2018 at 11:11

jezrael

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