issue_comments: 557895005

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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/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?	{ "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }		525685973