Summarize Grouped Data In Pandas While Filtering After The Group Part

I am stumbling over the exact (or at least most elegant) steps to group and aggregate some data in Pandas. Let's say I have a DataFrame that looks something like this - system

Solution 1:

You can go by using the power of apply function:

def conditional_sum(grp):
    grp['non_bar_total'] = grp[grp.datatype != 'bar']['datacount'].sum()
    return grp

df.groupby(['system', 'sub_system']).apply(conditional_sum)

system  sub_system  datatype    datacount   non_bar_total
0    a   1   foo     111     444
1    a   1   bar     222     444
2    a   1   baz     333     444
3    a   2   foo     444     999
4    a   2   baz     555     999
5    b   1   foo     667     667
6    b   3   baz     777     777