issue_comments: 418188977

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https://github.com/pydata/xarray/issues/1844#issuecomment-418188977 https://api.github.com/repos/pydata/xarray/issues/1844 418188977 MDEyOklzc3VlQ29tbWVudDQxODE4ODk3Nw== 6628425 2018-09-03T20:30:45Z 2018-09-03T20:30:45Z MEMBER

No worries @chiaral; I agree on the xarray side this isn't so well documented (you have to follow the link to the pandas description of the datetime components).

Unfortunately there is not a simple attribute for grouping by matching month and day. It is possible to define your own vector of integers for this purpose, however. Perhaps you've already found a workaround, but just in case, here is one way to define a "modified ordinal day" that you can use in a groupby call: ``` In [1]: import xarray as xr

In [2]: from datetime import datetime

In [3]: dates = [datetime(1999, 1, 1), datetime(1999, 3, 1), ...: datetime(2000, 1, 1), datetime(2000, 3, 1)] ...:

In [4]: da = xr.DataArray([1, 2, 3, 4], coords=[dates], dims=['time'])

In [5]: not_leap_year = xr.DataArray(~da.indexes['time'].is_leap_year, coords=da.coords)

In [6]: march_or_later = da.time.dt.month >= 3

In [7]: ordinal_day = da.time.dt.dayofyear

In [8]: modified_ordinal_day = ordinal_day + (not_leap_year & march_or_later)

In [9]: modified_ordinal_day = modified_ordinal_day.rename('modified_ordinal_day')

In [10]: modified_ordinal_day Out[10]: <xarray.DataArray 'modified_ordinal_day' (time: 4)> array([ 1, 61, 1, 61]) Coordinates: * time (time) datetime64[ns] 1999-01-01 1999-03-01 2000-01-01 2000-03-01

In [11]: da.groupby(modified_ordinal_day).mean('time') Out[11]: <xarray.DataArray (modified_ordinal_day: 2)> array([2., 3.]) Coordinates: * modified_ordinal_day (modified_ordinal_day) int64 1 61 Note if we use the standard ordinal day we get three groups, because of the difference between non-leap and leap years: In [12]: ordinal_day Out[12]: <xarray.DataArray 'dayofyear' (time: 4)> array([ 1, 60, 1, 61]) Coordinates: * time (time) datetime64[ns] 1999-01-01 1999-03-01 2000-01-01 2000-03-01

In [13]: da.groupby(ordinal_day).mean('time') Out[13]: <xarray.DataArray (dayofyear: 3)> array([2., 2., 4.]) Coordinates: * dayofyear (dayofyear) int64 1 60 61 ```

 
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