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importpandasaspdimportnumpyasnpidx=pd.date_range('2017-01-01', periods=24, freq='1h')
ss=pd.Series(np.arange(len(idx)), index=idx)
(ss.rolling('2h').cov() ==ss.expanding().cov()).iloc[2:].any() # this should be False!!
Problem description
When using Rolling.cov with an offset window like '2h' the window is expanding and not rolling
Expected Output
Rolling should not be equal to expanding cov - but it is equal.
Output of pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
yeah this is a bug. I don't think we are passing the time-aware window parms thru. instead it looks like we are passing the integer (already converted) ns, so it uses the entire window.
Is passing the converted integer instead of the time-aware window parameters through a deliberate design choice (such as for the sake of avoiding recalculation of the indexes)?
Code Sample, a copy-pastable example if possible
Problem description
When using Rolling.cov with an offset window like '2h' the window is expanding and not rolling
Expected Output
Rolling should not be equal to expanding cov - but it is equal.
Output of
pd.show_versions()
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
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