Multi-Index Slicing Comparison

I am working with a baby names data set from: https://raw.githubusercontent.com/hadley/data-baby-names/master/baby-names.csv

Which has data that looks like:

"year","name","percent","sex"
1880,"John",0.081541,"boy"
1880,"William",0.080511,"boy"
1880,"James",0.050057,"boy"
1880,"Charles",0.045167,"boy"
1880,"George",0.043292,"boy"
1880,"Frank",0.02738,"boy"
1880,"Joseph",0.022229,"boy"

I have grouped all the names together and summed the percentages for boy and girl with:

data1.groupby(['name','sex'])[['percent']].sum()

Which creates a multi-indexed data frame:

   Name    Sex  Percent

   Aaron   boy  0.292292
           girl 0.000805
   Abagail girl 0.001326
   Abbie   boy  0.000092
           girl 0.022804

For each name, I want to return the higher percentage sex in a new data frame that would like:

   Name    Sex  Percent

   Aaron   boy  0.292292
   Abagail girl 0.001326
   Abbie   girl 0.022804

I have been looking through the multi-index documentation but have not been able to figure this out. Any help is appreciated.

Solution

You can using a groupby tail after the groupby sum

s=df.groupby(['name','sex'])[['percent']].sum()
s.sort_values('Percent').groupby(level=0).tail(1)