如何将计算的百分比添加到熊猫数据透视表

Question

I have a pivottable similiar to this question , which doesn't seem to have an answer. I have a pivottable called grouped like this:

grouped = age_gender_bkts.pivot_table('population_in_thousands',index='gender',
columns='country_destination', aggfunc='sum').unstack()

This is taken from the pandas dataframe age_gender_bkts:

age_gender_bkts = pd.read_csv('airbnb/age_gender_bkts.csv')
age_gender_bkts[:10]

  age_bucket country_destination gender  population_in_thousands  year
0       100+                  AU   male                        1  2015
1      95-99                  AU   male                        9  2015
2      90-94                  AU   male                       47  2015
3      85-89                  AU   male                      118  2015
4      80-84                  AU   male                      199  2015
5      75-79                  AU   male                      298  2015
6      70-74                  AU   male                      415  2015
7      65-69                  AU   male                      574  2015
8      60-64                  AU   male                      636  2015
9      55-59                  AU   male                      714  2015

I am looking to get, for each country, the ratio between male and female population_in_thousands as a % for each gender eg 12024/11899+12024 for AU .

I am very new to pandas, numpy, looking for a generic solution to calculate columns based on pivot_table . Also, if the reply has a way for me to have created these groups by gender and country without using pivot_table , eg groupby (I couldn't figure it out), that would really help me in my learning.

Answer 1

You can use groupby , transform and sum . Last you can merge data to original DataFrame :

print age_gender_bkts
  age_bucket country_destination gender  population_in_thousands  year
0       100+                  AU   male                        1  2015
1      95-99                  AU   male                        9  2015
2      90-94                  CA   male                       47  2015
3      85-89                  CA   male                      118  2015
4      80-84                  AU   male                      199  2015
5      75-79                  NL   male                      298  2015
6      70-74                  NL   male                      415  2015
7      65-69                  AU   male                      574  2015
8      60-64                  AU   male                      636  2015
9      55-59                  AU   male                      714  2015

grouped = age_gender_bkts.pivot_table('population_in_thousands',index='gender', columns='country_destination', aggfunc='sum').unstack()
df  = (grouped / grouped.groupby(level=0).transform(sum)).reset_index().rename(columns={0:'prop'})
print df
  country_destination gender  prop
0                  AU   male     1
1                  CA   male     1
2                  NL   male     1

print pd.merge(age_gender_bkts, df, on=['country_destination', 'gender'])
  age_bucket country_destination gender  population_in_thousands  year  prop
0       100+                  AU   male                        1  2015     1
1      95-99                  AU   male                        9  2015     1
2      80-84                  AU   male                      199  2015     1
3      65-69                  AU   male                      574  2015     1
4      60-64                  AU   male                      636  2015     1
5      55-59                  AU   male                      714  2015     1
6      90-94                  CA   male                       47  2015     1
7      85-89                  CA   male                      118  2015     1
8      75-79                  NL   male                      298  2015     1
9      70-74                  NL   male                      415  2015     1