Handling a CustomBusinessDay in time-based .rolling()

Starting with 10 business days following Christmas Eve:

from pandas.tseries.offsets import CustomBusinessDay
from pandas.tseries.holiday import USFederalHolidayCalendar

days = CustomBusinessDay(calendar=USFederalHolidayCalendar())

df = pd.DataFrame({'value': np.arange(n)},
                  index=pd.date_range('2015-12-24', periods=n, freq=days))

I can compute the three-day sum of the values with just:

In [21]: df.rolling('3d').sum()
Out[21]:
            value
2015-12-24    0.0
2015-12-28    1.0
2015-12-29    3.0
2015-12-30    6.0
2015-12-31    9.0
2016-01-04    5.0
2016-01-05   11.0
2016-01-06   18.0
2016-01-07   21.0
2016-01-08   24.0

But this is purely in terms of Gregorian calendar days, not the business calendar days that I had created the DataFrame with.

I can easily compute

df.index - 3*days

though I get a performance warning:

Non-vectorized DateOffset being applied to Series or DatetimeIndex

But I can’t just pass this offset directly:

In [23]: df.rolling(3*days).sum()
...
ValueError: <3 * CustomBusinessDays> is a non-fixed frequency

I would like to be able to handle a CustomBusinessDay in .rolling(). (Because the DataFrame may come from any source, it would be easier to just pass the DateOffset object to .rolling() instead of using anything specific to the DataFrame’s index.)

I know that the freq parameter was deprecated in 0.18, though 0.19 kinda brings this back in window. Is there an intrinsic reason window can’t handle a CustomBusinessDay?

[chris-b1]

You could probably build a second version of this class that handled an arbitrary offset object rather than a fixed window, although it wouldn’t be performant, as there would have to be a ton of calls back into python space. Unless there’s some trick I’m not thinking of. https://github.com/pydata/pandas/blob/master/pandas/window.pyx#L252

There was an issue about cythonized offsets, #11214, and I started a proof of concept, but never went any further with it. https://github.com/chris-b1/pandas/tree/cythonize-offset

[mroeschke]

I think this issue can be closed as the VariableOffsetWindowIndexer was added to handle non-fixed offsets. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.indexers.VariableOffsetWindowIndexer.html