issue_comments

Implemented in https://github.com/pydata/xarray/pull/2593

I never saw clear use-cases for TimeGrouper but I could be convinced. On Thu, Oct 4, 2018 at 2:51 PM David Huard notifications@github.com wrote:

Do you think there would be a benefit to implementing a TimeGrouper class based on panda's ?

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Do you think there would be a benefit to implementing a TimeGrouper class based on panda's ?

My instinct would be to first pursue the simple approach that @shoyer has started here. If it turns out that passing a pandas.Series rather than a pandas.Grouper instance in line 236 of groupby.py prevents us from replicating some important behavior of resample, then it might be something to think about.

As of yet, while there are a few details that need to be added to Stephan's implementation (e.g., as he notes in the to-do comment, proper handling of the closed, label, and base arguments; there is some other complexity regarding how to handle gaps in the time series, etc.), I do not (yet) see any reason why these couldn't be handled with some modifications to the current approach. The logic in TimeGrouper is definitely a good reference for how to handle the different arguments to resample, but if we can, I think it would be nice to avoid the complexity of defining a new Grouper class.

Do you think there would be a benefit to implementing a TimeGrouper class based on panda's ?

Inspired by @spencerkclark's suggestion, I tried another version based on cftime_range and reindex with method='pad'. This one seems to be working in more cases: ```python In [7]: times = xarray.cftime_range('2000', periods=30, freq='MS')

In [8]: da = xarray.DataArray(range(30), [('time', times)])

In [9]: times Out[9]: CFTimeIndex([2000-01-01 00:00:00, 2000-02-01 00:00:00, 2000-03-01 00:00:00, 2000-04-01 00:00:00, 2000-05-01 00:00:00, 2000-06-01 00:00:00, 2000-07-01 00:00:00, 2000-08-01 00:00:00, 2000-09-01 00:00:00, 2000-10-01 00:00:00, 2000-11-01 00:00:00, 2000-12-01 00:00:00, 2001-01-01 00:00:00, 2001-02-01 00:00:00, 2001-03-01 00:00:00, 2001-04-01 00:00:00, 2001-05-01 00:00:00, 2001-06-01 00:00:00, 2001-07-01 00:00:00, 2001-08-01 00:00:00, 2001-09-01 00:00:00, 2001-10-01 00:00:00, 2001-11-01 00:00:00, 2001-12-01 00:00:00, 2002-01-01 00:00:00, 2002-02-01 00:00:00, 2002-03-01 00:00:00, 2002-04-01 00:00:00, 2002-05-01 00:00:00, 2002-06-01 00:00:00], dtype='object')

In [10]: da.resample(time='12MS').mean() Out[10]: <xarray.DataArray (time: 3)> array([ 5.5, 17.5, 26.5]) Coordinates: * time (time) object 2000-01-01 00:00:00 ... 2002-01-01 00:00:00

In [11]: da.resample(time='6MS').mean() Out[11]: <xarray.DataArray (time: 5)> array([ 2.5, 8.5, 14.5, 20.5, 26.5]) Coordinates: * time (time) object 2000-01-01 00:00:00 ... 2002-01-01 00:00:00

In [12]: da.resample(time='3MS').mean() Out[12]: <xarray.DataArray (time: 10)> array([ 1., 4., 7., 10., 13., 16., 19., 22., 25., 28.]) Coordinates: * time (time) object 2000-01-01 00:00:00 ... 2002-04-01 00:00:00 ```