html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4473#issuecomment-711461331,https://api.github.com/repos/pydata/xarray/issues/4473,711461331,MDEyOklzc3VlQ29tbWVudDcxMTQ2MTMzMQ==,1217238,2020-10-19T01:30:48Z,2020-10-19T01:30:48Z,MEMBER,"> * What's the best way of reconstituting the coords etc, after npg produces the array?

I think we can reuse the existing logic from the `_combine` method here:
https://github.com/pydata/xarray/blob/97e26257e81b0ba35af4a34be43a3e9cc666b9bc/xarray/core/groupby.py#L830

This just gives us an alternative way to calculate `applied`.

> * Presumably we're going to have a fairly different design for this than the existing groupby operations — that design is very nested — wrapping functions and eventually calling `.map` to loop over each group in python.

Agreed. Hopefully this can live alongside in the GroupBy objects.

> * Presumably we're going to need to keep the existing logic around for dask — is it reasonable for an initial version to defer to the existing logic for all dask arrays? (+ @shoyer 's thoughts above on this)

Yes, I agree that we should do this incrementally.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,711626733
https://github.com/pydata/xarray/issues/4473#issuecomment-711460703,https://api.github.com/repos/pydata/xarray/issues/4473,711460703,MDEyOklzc3VlQ29tbWVudDcxMTQ2MDcwMw==,1217238,2020-10-19T01:27:50Z,2020-10-19T01:27:50Z,MEMBER,Something like the resample test case from https://github.com/pydata/xarray/issues/4498 might be a good example for finding 100x speed-ups. The main feature of that case is that there are a _very_ large number of groups (only slightly fewer groups than original data points).,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,711626733
https://github.com/pydata/xarray/issues/4473#issuecomment-701961598,https://api.github.com/repos/pydata/xarray/issues/4473,701961598,MDEyOklzc3VlQ29tbWVudDcwMTk2MTU5OA==,1217238,2020-10-01T07:57:58Z,2020-10-01T07:57:58Z,MEMBER,"> Highly speculative, but would this also be a faster approach to stacking & unstacking? ""Form ~5~ 4"" in the [readme](https://github.com/ml31415/numpy-groupies).

I'm not entirely sure, but I suspect something like the approach in https://github.com/pydata/xarray/pull/4184 might be more directly relevant for speeding up `unstack` (at least with NumPy arrays).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,711626733
https://github.com/pydata/xarray/issues/4473#issuecomment-701609035,https://api.github.com/repos/pydata/xarray/issues/4473,701609035,MDEyOklzc3VlQ29tbWVudDcwMTYwOTAzNQ==,1217238,2020-09-30T19:52:05Z,2020-09-30T19:52:05Z,MEMBER,A prototype implementation of the core functionality here can be found in: https://nbviewer.jupyter.org/gist/shoyer/6d6c82bbf383fb717cc8631869678737,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,711626733