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/4498#issuecomment-706640332,https://api.github.com/repos/pydata/xarray/issues/4498,706640332,MDEyOklzc3VlQ29tbWVudDcwNjY0MDMzMg==,1217238,2020-10-11T02:34:47Z,2020-10-11T02:34:47Z,MEMBER,"I might add that this is somewhat I've wanted to speed-up in xarray since the *very* early days. But until I noticed the numpy-groupies package, it seemed like a pretty challenging task.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,718436141
https://github.com/pydata/xarray/issues/4498#issuecomment-706640151,https://api.github.com/repos/pydata/xarray/issues/4498,706640151,MDEyOklzc3VlQ29tbWVudDcwNjY0MDE1MQ==,1217238,2020-10-11T02:32:25Z,2020-10-11T02:32:36Z,MEMBER,"`resample` uses the same machinery in xarray as other grouped aggregations.

Right now, grouped aggregations are very slow when there are many groups (like in resample) because we use a Python loop over groups.

Probably the most obvious way to speed this up would be to wrap the ""numpy-groupies"" package in xarray: https://github.com/pydata/xarray/issues/4473","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,718436141
https://github.com/pydata/xarray/issues/4498#issuecomment-706435642,https://api.github.com/repos/pydata/xarray/issues/4498,706435642,MDEyOklzc3VlQ29tbWVudDcwNjQzNTY0Mg==,2448579,2020-10-09T22:57:48Z,2020-10-09T22:57:55Z,MEMBER,"@mankoff (hi!) This is interesting. If I comment out `.mean()` I get
```
1H xr 0.003030538558959961
1H pd 0.0014064311981201172 

1D xr 0.0026717185974121094
1D pd 0.0013244152069091797
```

i.e. we are 2x slower just on factorizing.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,718436141