issue_comments: 557874527

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https://github.com/pydata/xarray/pull/3550#issuecomment-557874527 https://api.github.com/repos/pydata/xarray/issues/3550 557874527 MDEyOklzc3VlQ29tbWVudDU1Nzg3NDUyNw== 45787861 2019-11-24T10:11:31Z 2019-11-24T10:15:19Z NONE

For xarray.corr(array, array, dim='y'), I think we'd expect an array of the same dimensionality as below, with one point as the correlation of 1.0 (because {1,2} is 100% correlated to {1,2}) and another NaN. Does that make sense?

On the one side, I am with you in terms of "What You Put In Is What You Get Out", on the other hand pandas.Series.cov and np.cov have other behaviors that both seem plausible:

![grafik](https://user-images.githubusercontent.com/45787861/69493085-a1342600-0eaa-11ea-991a-f23f4ae348e3.png) ![grafik](https://user-images.githubusercontent.com/45787861/69493094-b3ae5f80-0eaa-11ea-8984-6710e2331882.png)

So for the moment, we might stick with pandas.Series.cov? Or (future PR?) we rather want the np.cov behavior, this would require more effort and the repr should probably be given as xarray instead of np.array, right? 

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