best practice for combining partial string indexing + regular mask?

Consider the simple example as follows

import pandas as pd

dataframe = pd.DataFrame({'time' : [pd.to_datetime('2016-06-06'),
                                    pd.to_datetime('2016-06-06'),
                                    pd.to_datetime('2016-06-07'),
                                    pd.to_datetime('2016-06-08')],
                            'value' : [1,2,3,4],
                            'group' : ['A','B','A','B']})

dataframe.set_index('time', inplace = True)

dataframe
Out[13]: 
           group  value
time                   
2016-06-06     A      1
2016-06-06     B      2
2016-06-07     A      3
2016-06-08     B      4

Now I want to use all the cool partial string indexing functions, but also be able to filter on other variables. The solution I come up with is the syntax

dataframe.loc[dataframe['group'] == 'A'].loc['2016-06-06']

which looks really horrible with the two loc combined. Is that the correct pandonic way to do so (while keeping the index?) I have read and read again the documentation but I cannot find the answer.

Thanks!

[gfyoung]

Right, but you get a Series returned from that boolean test. The indices in that Series won’t match those of the dataframe.

[jreback]

We don’t directly have an exposed partial string indexer. Instead .get_loc will do this, but it can return various things (e.g. a single index, array of booleans, or slice), so it is not directly user facing.

In [15]: dfi = dataframe.index

In [16]: dataframe.loc[(dataframe['group'] == 'A') & dfi.isin(dfi[dfi.get_loc('2016-06-06')])]
Out[16]: 
           group  value
time                   
2016-06-06     A      1