You should get the desired behavior by selecting levels:

In [11]: data['N'].groupby(level=['date', 'name']).sum()
Out[11]:
date        name
2016-02-15  A       5
Name: N, dtype: int64

This should be raising a KeyError as dates and names aren't actually column labels.

PRs to make that happen would certainly be welcome

@WillAyd According to Grouping DataFrame with Index Levels and Columns : "Index level names may be specified as keys directly to groupby" (starting with version 0.20, see In/Out[51] on the page). So, what I did should be correct. Am I misunderstanding anything?

I think, it has something to do with the following selection line in my example:

data = data[ data.index.get_level_values('time')>=pd.to_timedelta('09:30:00') ]

Without it, the code works correctly.

Or it might have something to do with the Series input to groupby having only two rows, since the following example (without any selection) also produces incorrect result:

import pandas as pd
data = pd.DataFrame(data={'date':[pd.Timestamp('2016-02-15')]*2, 
    'time':[pd.Timedelta(x) for x in ('10:10:00','11:10:00')],'name':['A']*2, 
    'N':[2,3]}).set_index(['date','time','name']).sort_index()
dataGB = data['N'].groupby(['date','name'])
print(data)
print('Number of groups:',len(dataGB))
print(dataGB.sum())
print(pd.__version__)

Hmm OK thanks for sharing that. I see this was actually implemented in #14432

Looking at the test coverage there I don't see anything that has multiple index levels without a column selection, so that may be the culprit here. Investigation and PRs would be welcome

I can take this one, I see where the problem is. It appears to be in core/groupby/grouper.py:_get_grouper where all_in_columns_index isn't properly checking for series like it does for DataFrame. And since len(keys) == len(group_axis) in this specific case, it isn't grouping properly.