Pandas [0.19dev] Time-series aware rolling window with MultiIndex with a time-frequency index fails

Code Sample, a copy-pastable example if possible

dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
  index=pd.date_range('20130101 09:00:00', periods=5, freq='s'))
dft['another'] = ['a', 'a', 'c', 'd', 'e']
dft.set_index([dft.index, 'another'])
dft.rolling('2s')

Expected Output

Expected to be able to use a time-series aware rolling window, instead got ValueError: window must be an integer

output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.0rc1
nose: None
pip: 6.0.8
setuptools: 27.2.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
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

Comment From: jreback

pls make sure that the code you are showing is from the version where you are running. you might have from another session. IOW, you are picking up a different version from which you are showing pd.show_versions()

In [1]: pd.__version__
Out[1]: '0.19.0rc1+0.g497a3bc.dirty'

In [2]: dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
   ...:   index=pd.date_range('20130101 09:00:00', periods=5, freq='s'))
   ...: dft['another'] = ['a', 'a', 'c', 'd', 'e']
   ...: dft.set_index([dft.index, 'another'])
   ...: dft.rolling('2s')
   ...:
Out[2]: Rolling [window=2000000000,min_periods=1,center=False,win_type=freq,axis=0]

In [3]: dft.rolling('2s').B.sum()
Out[3]:
2013-01-01 09:00:00    0.0
2013-01-01 09:00:01    1.0
2013-01-01 09:00:02    3.0
2013-01-01 09:00:03    2.0
2013-01-01 09:00:04    4.0
Freq: S, Name: B, dtype: float64

Comment From: nfcampos

@jreback I'm indeed running this in the version I quoted above. This was run in a fresh virtual environment created just for this.

However, I did forget to write inplace=True in the call to set_index in the snippet above, which is why you were unable to reproduce it.

Please try again with this snippet:

dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
  index=pd.date_range('20130101 09:00:00', periods=5, freq='s'))
dft['another'] = ['a', 'a', 'c', 'd', 'e']
dft.set_index([dft.index, 'another'], inplace=True)
dft.rolling('2s')

Comment From: jreback

of course that doesn't work, you have a multi-index.

.rolling does not support a level arg, we recently added to .resample here you can create an issue to add it to .rolling if you'd like

Comment From: nfcampos

@jreback thanks for your reply. The docs aren't at all clear that time-series support for rolling applies only if you have a single index. ~~By the way, the PR you link to actually mentions an on keyword for resample that can be used to select the level of a multi-index. So if this were supported for rolling, the on argument could be used for that as well, instead of a separate level argument.~~ (edit: nevermind, I didn't read that right)

Comment From: jreback

well the doc-string is pretty clear. as I said you can put forth an issue for an enhancement.