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https://github.com/pydata/xarray/pull/3550#issuecomment-557895005 https://api.github.com/repos/pydata/xarray/issues/3550 557895005 MDEyOklzc3VlQ29tbWVudDU1Nzg5NTAwNQ== 45787861 2019-11-24T14:41:44Z 2019-11-24T14:41:44Z NONE

I have added several test cases, almost all pass. The ones that don't pass are related to dim="time" and incompletely implemented pandas_cov and pandas_corr, that currently don't have a dim parameter.

I had a look at np.cov, np.corrcoef, pd.Series.cov, pd.frame.cov, pd.Series.corr, and pd.frame.corr: Computation of cov and corr is implemented in many different ways.

xr.cov and xr.corr now imitate the behavior of the respective pd.Series functions.

I actually prefer the numpy behavior, resulting in covariance and correlation matrices. However I feel that efficient implementation of this behavior is above my current understanding of xarray. So, I would highly appreciate your support on this implementation!

Otherwise, I added the ddof parameter to xr.cov to imitate the behavior of the respective pd.Series functions. I now get the results that pd.Series produces. However, I had to set ddof=1 for xr.cov and ddof=0 for xr.corr. Does that make sense?

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