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/324#issuecomment-531964854,https://api.github.com/repos/pydata/xarray/issues/324,531964854,MDEyOklzc3VlQ29tbWVudDUzMTk2NDg1NA==,1217238,2019-09-16T21:26:21Z,2019-09-16T21:26:21Z,MEMBER,Still relevant.,"{""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58117200
https://github.com/pydata/xarray/issues/324#issuecomment-336983333,https://api.github.com/repos/pydata/xarray/issues/324,336983333,MDEyOklzc3VlQ29tbWVudDMzNjk4MzMzMw==,1217238,2017-10-16T18:24:33Z,2017-10-16T18:24:33Z,MEMBER,"> Is use case 1 (Multiple groupby arguments along a single dimension) being held back for use case 2 (Multiple groupby arguments along different dimensions)? Use case 1 would be very useful by itself.

No, I think the biggest issue is that grouping variables into a `MultiIndex` on the result sort of works (with the current PR https://github.com/pydata/xarray/pull/924), but it's very easy to end up with weird conflicts between coordinates / MultiIndex levels that are hard to resolve right now within the xarray data model. Probably it would be best to resolve https://github.com/pydata/xarray/issues/1603 first, which will make this much easier.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58117200
https://github.com/pydata/xarray/issues/324#issuecomment-131644079,https://api.github.com/repos/pydata/xarray/issues/324,131644079,MDEyOklzc3VlQ29tbWVudDEzMTY0NDA3OQ==,1217238,2015-08-17T00:13:47Z,2015-08-17T00:13:47Z,MEMBER,"@jhamman For your use case, both hour and dayofyear are along the time dimension, so arguably the result should be 1D with a MultiIndex instead of 2D. So it might make more sense to start with that, and then layer on stack/unstack or pivot functionality.

I guess there are two related use cases here:
1. Multiple groupby arguments along a single dimension (pandas does this one already)
2. Multiple groupby arguments along different dimensions (pandas doesn't do this one).
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,58117200