如何在python中乘或除两个系列？

Question

I have a dataset like this. The actual dataset is much larger though.

data1 = pd.DataFrame({'Name':["Tom","Andy","Joseph","Joe","Mary","Alexa","Chris","Jessica","Jimmy","Andrea","George","Bruce","Will","Eric","Leonard","Ryan","Megan","Michael","Sara"],\
                  "City":["NY","DC","LAX","NY","DC","DC","SF","SD","NY","SF","SD","DC","LAX","SF","LAX","NY","SF","PDX","FL"],\
                  'Car':["Ford","Ford","TOYOTA","GM","GM","Honda","GM","Porsche","Tesla","TOYOTA","Tesla","Tesla","Honda","GM","Nissan","Porsche","Nissan","Ford","Tesla"]})

First, I want to calculate the actual frequency of the "City" and "Car" combination and did this.

df_City_Car_actual=data1.groupby(["City","Car"]).size()
df_City_Car_actual

Then I want to calculate the expected frequency of the "City" and "Car" combination. So I did this first.

df_City=data1.groupby("City").size()
df_City
df_Car=data1.groupby("Car").size()
df_Car

Then I want to multiply df_City and df_Car and show the expected frequency of City x Car. For example, "DC" frequency is 4 in the df_City and "Ford" frequency is 3 in the df_Car. Therefore, the DC x Ford expected frequency will be 4x3=12.

I tried this but did not work

df_City_Car_expected=df_City*df_Car
df_City_Car_expected

Finally, I want to divide the df_City_Car_actual by the df_City_Car_expected so that the final data will be normalized. Is there a good way to do this? Thanks for your help.

Answer 1

The simplest way I can think of is by using the numpy "outer product" function, as such:

pd.DataFrame(np.outer(df_City.values, df_Car.values), index=df_City.index, columns=df_Car.index)

Which gives:

Car   Ford  GM  Honda  Nissan  Porsche  TOYOTA  Tesla
City                                                 
DC      12  16      8       8        8       8     16
FL       3   4      2       2        2       2      4
LAX      9  12      6       6        6       6     12
NY      12  16      8       8        8       8     16
PDX      3   4      2       2        2       2      4
SD       6   8      4       4        4       4      8
SF      12  16      8       8        8       8     16