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/2852#issuecomment-478624700,https://api.github.com/repos/pydata/xarray/issues/2852,478624700,MDEyOklzc3VlQ29tbWVudDQ3ODYyNDcwMA==,10595679,2019-04-01T15:23:35Z,2019-04-01T15:23:35Z,CONTRIBUTOR,"That's a tough question ;) In the current dataset I have 950 unique labels, but in my use cases it can be be a lot more (e.g. agricultaral crops) or a lot less (adminstrative boundaries or regions). ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,425320466
https://github.com/pydata/xarray/issues/2852#issuecomment-478488200,https://api.github.com/repos/pydata/xarray/issues/2852,478488200,MDEyOklzc3VlQ29tbWVudDQ3ODQ4ODIwMA==,10595679,2019-04-01T08:37:42Z,2019-04-01T08:37:42Z,CONTRIBUTOR,"Many thanks for your answers @shoyer and @rabernat .

I am relatively new to `xarray` and `dask`, I am trying to determine if it can fit our need for analysis of large stacks of Sentinel data on our cluster.

I will give a try to `dask.array.histogram` ass @rabernat suggested.

I also had the following idea. Given that:
* I know exactly beforehand which labels (or groups) I want to analyse,
*  `.where(label=xxx).mean('variable')` does the job perfectly for one label,

I do not actually need the discovery of unique labels that `groupby()` performs, what I really need is an efficient way to perform multiple `where()` aggregate operations at once, to avoid traversing the data multiple time.

Maybe there is already something like that in xarray, or maybe this is something I can derive from the implementation of `where()` ?


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