Slicing Datetime MultiIndex with string or datetime.date slices

[kayibal]

Basically Indexing a MultiIndex with a DatetimeIndex seems only to be working if you use slices with datetime.datetime or pandas.Timestamp. One would expect it to work also with strings as well as with 'datetime.date' slices as it does for usual indizes.

This seems to be related to #3843.

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd
import time
import datetime as dt

min_ts = time.mktime(dt.date(2016,10,1).timetuple())
timestamps = [min_ts + offset*86400 + noise*3600 
                  for offset, noise in enumerate(np.random.rand(100))]
time_idx = pd.to_datetime(sorted(timestamps), unit='s')
id_idx = np.random.choice(np.arange(10), 100)
df = pd.DataFrame(np.identity(100),
                 index=pd.MultiIndex.from_arrays(
                     [time_idx, id_idx]
                 ))

df.loc[dt.datetime(2016, 10, 1):]  # works
df.loc[dt.date(2016, 10, 1):]  # fails :/

Problem description

The above code raises the following exception on the last line, which is quite unexpected when one is used to indexing on single index dataframes.

Traceback (most recent call last):
  File "<input>", line 11, in <module>
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/core/indexing.py", line 1312, in __getitem__
    return self._getitem_axis(key, axis=0)
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/core/indexing.py", line 1453, in _getitem_axis
    return self._get_slice_axis(key, axis=axis)
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/core/indexing.py", line 1334, in _get_slice_axis
    slice_obj.step, kind=self.name)
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/indexes/base.py", line 2997, in slice_indexer
    kind=kind)
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/indexes/multi.py", line 1578, in slice_locs
    return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/indexes/base.py", line 3176, in slice_locs
    start_slice = self.get_slice_bound(start, 'left', kind)
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/indexes/multi.py", line 1549, in get_slice_bound
    return self._partial_tup_index(label, side=side)
  File "/Users/kayibal/virtualenvs/traildb-sparse/lib/python3.5/site-packages/pandas/indexes/multi.py", line 1594, in _partial_tup_index
    raise TypeError('Level type mismatch: %s' % lab)
TypeError: Level type mismatch: 2016-10-01

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Darwin OS-release: 16.4.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 34.2.0 Cython: 0.25.2 numpy: 1.12.0 scipy: 0.18.1 statsmodels: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None boto: None pandas_datareader: None

[jreback]

datetime.date is not a first class type, so we barely support it, almost always it is coerced (and we certainly don't support indexing via it).

and strings work fine.

In [10]: df.loc['2016-10-01']
Out[10]: 
                               0    1    2    3    4    5    6    7    8    9    10   11   12   13   14   15   16   17   18   19   20   21   22   23   24 ...    75   76   77   78   79   80   81  \
2016-10-01 04:18:59.654717 4  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 ...   0.0  0.0  0.0  0.0  0.0  0.0  0.0   

                               82   83   84   85   86   87   88   89   90   91   92   93   94   95   96   97   98   99  
2016-10-01 04:18:59.654717 4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  

[1 rows x 100 columns]

[jreback]

all that said, if you want to do a PR to support indexing via datetime.date prob would take it.

[jorisvandenbossche]

we certainly don't support indexing via it

We actually do support it:

In [50]: df = pd.DataFrame({'A': np.random.randn(10)}, index=pd.date_range("2012-01-01", periods=10))

In [51]: df
Out[51]: 
                   A
2012-01-01  0.457670
2012-01-02 -0.871395
2012-01-03  1.878444
2012-01-04  1.106600
2012-01-05 -0.282401
2012-01-06  0.057577
2012-01-07  0.567901
2012-01-08 -0.213132
2012-01-09  0.264840
2012-01-10 -0.994729

In [54]: df.loc['2012-01-03']
Out[54]: 
A    1.878444
Name: 2012-01-03 00:00:00, dtype: float64

In [55]: df.loc[datetime.date(2012, 1, 3)]
Out[55]: 
A    1.878444
Name: 2012-01-03 00:00:00, dtype: float64

Above is on normal DatetimeIndex (the key is just coerced to a Timestamp). MultiIndex has always lagged somewhat behind on that.
So I think having it consistent would be good.

Not that I recommend using a datetime.date!

[jreback]

reopening as doesn't work on MI as indicated above.