Multiplying pandas dataframe and series, element wise

问题

Lets say I have a pandas series:

import pandas as pd
x = pd.DataFrame({0: [1,2,3], 1: [4,5,6], 2: [7,8,9] })
y = pd.Series([-1, 1, -1])

I want to multiply x and y in such a way that I get z:

z = pd.DataFrame({0: [-1,2,-3], 1: [-4,5,-6], 2: [-7,8,-9] })

In other words, if element j of the series is -1, then all elements of the j-th row of x get multiplied by -1. If element k of the series is 1, then all elements of the j-th row of x get multiplied by 1.

How do I do this?

回答1:

You can do that:

>>> new_x = x.mul(y, axis=0)
>>> new_x
   0  1  2
0 -1 -4 -7
1  2  5  8
2 -3 -6 -9

回答2:

You can multiply the dataframes directly.

x * y

回答3:

Although this question is old, I'll add that if the function returns a bunch of nonsensical NaNs, you should multiply by the values of the series in question like so:

new_x = df.mul(s.values, axis=0)

回答4:

As Abdou points out, the answer is

z = x.apply(lambda col: col*y)

Moreover, if you instead have a DataFrame, e.g.

 y = pandas.DataFrame({"colname": [1,-1,-1]})

Then you can do

 z = x.apply(lambda z: z*y["colname"])

来源：https://stackoverflow.com/questions/39948935/multiplying-pandas-dataframe-and-series-element-wise