Pandas data frame new column as sum of multiple columns time scalar decremental

user_14200651 0
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I have a data frame with 10 columns of integer. I would like to create new column like this:

df["newcol"] = df[col1]*(11-1)+df["col2"]*(11-2)+..+df[col10]*(11-10)

I've already done the above and it works but I just wonder if there is a pythonic way to do it. Not writing it all down like that because sometime my data frame would have over 100 columns.

pandas python
2 Answers

Easy way

df['newcol'] = df.mul(11 - np.r_[1: df.shape[1] + 1]).sum(1)

Example

>>> df

     0    1    2    3    4    5    6    7    8    9
0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0
1  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0

>>> df.mul(11 - np.r_[1: df.shape[1] + 1])

      0    1    2    3    4    5    6    7    8    9
0  10.0  9.0  8.0  7.0  6.0  5.0  4.0  3.0  2.0  1.0
1  10.0  9.0  8.0  7.0  6.0  5.0  4.0  3.0  2.0  1.0

>>> df.mul(11 - np.r_[1: df.shape[1] + 1]).sum(1)

0    55.0
1    55.0
dtype: float64
user_128331660

I got it working with this:

df[col_list].mul(afloat-np.r_[1:df[col_list].shape[1]+1]).sum(1)
user_142006510
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