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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
923355397 MDExOlB1bGxSZXF1ZXN0NjcyMTI5NzY4 5480 Implement weighted groupby dcherian 2448579 open 0     1 2021-06-17T02:57:17Z 2023-07-27T18:09:55Z   MEMBER   1 pydata/xarray/pulls/5480
  • xref #3937
  • [ ] Tests added
  • [ ] Passes pre-commit run --all-files
  • [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
  • [ ] New functions/methods are listed in api.rst

Initial proof-of-concept. Suggestions to improve this are very welcome.

Here's some convenient testing code ``` python
import xarray as xr

ds = xr.tutorial.open_dataset('rasm').load() month_length = ds.time.dt.days_in_month weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()

actual = ds.weighted(month_length).groupby("time.season").mean() expected = (ds * weights).groupby('time.season').sum(skipna=False) xr.testing.assert_allclose(actual, expected) ```

I've added info to the repr python ds.weighted(month_length).groupby("time.season") WeightedDatasetGroupBy, grouped over 'season' 4 groups with labels 'DJF', 'JJA', 'MAM', 'SON'. weighted along dimensions: time by 'days_in_month'

 
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     xarray 13221727 pull

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