issue_comments: 478488200

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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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