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/7559#issuecomment-1444208978,https://api.github.com/repos/pydata/xarray/issues/7559,1444208978,IC_kwDOAMm_X85WFOFS,2448579,2023-02-24T18:27:18Z,2023-02-25T03:46:49Z,MEMBER,">  is there anywhere else in xarray where we have made some choice about how to let the user choose between specifying via indexes or labels? 

`coarsen` vs `groupby`/`groupby_bins`/`resample`. 

I explored this idea in [this tutorial](https://tutorial.xarray.dev/intermediate/01-high-level-computation-patterns.html#xarray-provides-high-level-patterns-in-both-index-space-and-label-space)

I think it may be a fundamental concept for labelled array analysis. You need to pick whether you're working in ""index space"" like unlabelled arrays, or in ""label space"".  This also came up in [this issue](https://github.com/pydata/xarray/issues/7558) where `shift` (and `roll`) operate in ""index space"".

Another example: Alignment is in ""label space"", broadcasting seems like ""index space"" (you just change shapes, but it does use dimension names to do that so maybe 50/50).","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 1}",,1599056009