Conditional Sum Pandas Dataframe - pandas

Question

I am trying to aggregate and sum values from a Pandas Dataframe based on the values in the column "Gender". This is a sample of the dataset that I am working on:
df_genders = pd.DataFrame({'Country': ['US','US','US','US','US','India','India','India','UK','UK','UK','UK'],
'Gender': ['Man','Woman', 'Non-Binary,Genderqueer', 'Non-Binary', 'Non-Binary,Genderqueer,Non-Conforming',
'Man','Woman','Non-Binary','Man','Woman', 'Non-Binary,Genderqueer', 'Non-Binary,Genderqueer,Non-Conforming'],
'Count': [7996,915,11,34,153,3857,287,47,2566,272,72,99]})
df_genders
Since the values of Gender are not very consistent, I would like to group them together and sum their Counts in order to obtain for each country the sum of Men, Women and Non-Binary (Non-Binary being nor "Man" nor "Woman").
I wasn't able to write a code for conditional grouping and summing, therefore my approach was to find out the totals per Country and then subtract from the totals the sum of man + woman, being thus left with the sum of non-binary:
df_genders.groupby('Country')['Count'].sum() - df_genders[(df_genders['Gender']=='Man') | (df_genders['Gender']=='Woman')].groupby('Country')['Count'].sum()
Do you know a better way to solve this or in general a way for performing conditional aggregations (group by and sum)?
Thank you!

Answer 1

You could do directly:
res = df_genders[~df_genders['Gender'].isin(('Man', 'Woman'))]['Count'].sum()
print(res)
Output
416
But I think is better if you create a new column with the classification you are seeking, for example, one approach:
df_genders['grouped-genders'] = df_genders['Gender'].map({ 'Man' : 'Man', 'Woman' : 'Woman' }).fillna('Non-Binary')
print(df_genders)
Output
Country Gender Count grouped-genders
0 US Man 7996 Man
1 US Woman 915 Woman
2 US Non-Binary,Genderqueer 11 Non-Binary
3 US Non-Binary 34 Non-Binary
4 US Non-Binary,Genderqueer,Non-Conforming 153 Non-Binary
5 India Man 3857 Man
6 India Woman 287 Woman
7 India Non-Binary 47 Non-Binary
8 UK Man 2566 Man
9 UK Woman 272 Woman
10 UK Non-Binary,Genderqueer 72 Non-Binary
11 UK Non-Binary,Genderqueer,Non-Conforming 99 Non-Binary
and then group by the new column to obtain the count of all the genders:
res = df_genders.groupby('grouped-genders')['Count'].sum().reset_index()
print(res)
Output
grouped-genders Count
0 Man 14419
1 Non-Binary 416
2 Woman 1474