BUG: IndexSlice on MultiIndex includes out-of-range rows

[markroth8]

Code Sample, a copy-pastable example if possible

>>> dft = pd.DataFrame(np.random.randn(100000,1),columns=['A'],index=pd.date_range('20130101',periods=100000,freq='T'))
>>> dft2 = pd.DataFrame(np.random.randn(200000,1),columns=['A'],index=pd.MultiIndex.from_product([dft.index, ['a', 'b']]))
>>> dft2.loc[pd.IndexSlice['2013-03':'2013-03',:],:]
                              A
2013-01-01 00:00:00 a  0.563968
2013-01-01 00:01:00 a -0.439376
2013-01-01 00:02:00 a  1.785202
2013-01-01 00:03:00 a  0.376901
2013-01-01 00:04:00 a -0.977926
2013-01-01 00:05:00 a -0.738415
2013-01-01 00:06:00 a -2.156905
2013-01-01 00:07:00 a -0.016085
2013-01-01 00:08:00 a  1.035935
2013-01-01 00:09:00 a -1.198991
2013-01-01 00:10:00 a -0.867717
2013-01-01 00:11:00 a -0.596923
2013-01-01 00:12:00 a -1.256923
2013-01-01 00:13:00 a  1.042369
2013-01-01 00:14:00 a  1.161365
2013-01-01 00:15:00 a  0.853495
2013-01-01 00:16:00 a  1.398594
2013-01-01 00:17:00 a -0.431314
2013-01-01 00:18:00 a  2.630920
2013-01-01 00:19:00 a -1.031731
2013-01-01 00:20:00 a -0.891799
2013-01-01 00:21:00 a  0.075546
2013-01-01 00:22:00 a -0.163999
2013-01-01 00:23:00 a  0.678027
2013-01-01 00:24:00 a  1.427323
2013-01-01 00:25:00 a  1.426418
2013-01-01 00:26:00 a  0.492158
2013-01-01 00:27:00 a  0.477629
2013-01-01 00:28:00 a -0.703225
2013-01-01 00:29:00 a  0.864293
...                         ...
2013-03-11 10:25:00 a  1.585880
                    b -0.595927
2013-03-11 10:26:00 a  0.259137
                    b -0.718385
2013-03-11 10:27:00 a -0.143240
                    b -0.898806
2013-03-11 10:28:00 a -0.293221
                    b -1.645180
2013-03-11 10:29:00 a -0.790069
                    b -1.075649
2013-03-11 10:30:00 a  0.368399
                    b  2.206858
2013-03-11 10:31:00 a  1.125287
                    b -0.834588
2013-03-11 10:32:00 a  0.740703
                    b -0.587527
2013-03-11 10:33:00 a  0.765259
                    b -0.662232
2013-03-11 10:34:00 a  2.138155
                    b -0.030379
2013-03-11 10:35:00 a -1.510801
                    b  1.200521
2013-03-11 10:36:00 a  0.934974
                    b  1.340875
2013-03-11 10:37:00 a -0.251199
                    b  1.728432
2013-03-11 10:38:00 a  1.651664
                    b -1.032225
2013-03-11 10:39:00 a -0.352417
                    b  0.378273

[115040 rows x 1 columns]

Expected Output

Data from 2013-01-01 through 2013-03-03 should not be present. Data from 2013-03-04 onward should not be present, either.

output of pd.show_versions()

INSTALLED VERSIONS

commit: 25cec3a
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.0+31.g25cec3a
nose: 1.3.7
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
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: 2.8
boto: None

[jorisvandenbossche]

This seems to work correctly on master in the meantime:

In [12]: df = pd.DataFrame(np.random.randn(200,1), columns=['A'], index=pd.MultiIndex.from_product([pd.date_range('20130101',periods=100), ['a', 'b']]))

In [13]: df.loc[pd.IndexSlice['2013-03':'2013-03',:],:]
Out[13]:
                     A
2013-03-01 a -0.199156
           b  0.121741
2013-03-02 a  0.142018
           b  0.390804
2013-03-03 a -0.883441
           b  0.303635
2013-03-04 a -0.059659
           b  2.252698
...                ...
2013-03-28 a  1.232271
           b  0.735451
2013-03-29 a  0.519657
           b  0.469528
2013-03-30 a -0.814646
           b  0.149653
2013-03-31 a  0.758980
           b  0.089183

[62 rows x 1 columns]

[jorisvandenbossche]

Possibly fixed by #13117, will add an explicit test for this issue