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  • shoyer · 2 ✖

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  • cov() and corr() · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
452014148 https://github.com/pydata/xarray/pull/2652#issuecomment-452014148 https://api.github.com/repos/pydata/xarray/issues/2652 MDEyOklzc3VlQ29tbWVudDQ1MjAxNDE0OA== shoyer 1217238 2019-01-07T17:29:45Z 2019-01-07T17:29:45Z MEMBER

I agree that the case for DataArray.dot is questionable. It sort of makes sense because numpy and pandas both have it as a method, but the @ operator is a really a cleaner way to express this now that we're Python 3 only. (Speaking of which, why don't we support @ in xarray yet? :).)

On Mon, Jan 7, 2019 at 1:43 AM Keisuke Fujii notifications@github.com wrote:

@max-sixty https://github.com/max-sixty I am not sure whether DataArray.dot is a right choice. But I am wondering for cov case, it sounds like to compute a covariance of the DataArray itself rather than the cross covariance with another DataArray.

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   cov() and corr() 396102183
451703221 https://github.com/pydata/xarray/pull/2652#issuecomment-451703221 https://api.github.com/repos/pydata/xarray/issues/2652 MDEyOklzc3VlQ29tbWVudDQ1MTcwMzIyMQ== shoyer 1217238 2019-01-06T00:05:49Z 2019-01-06T00:05:49Z MEMBER

I also think making this a function is probably a good idea, even though it's different from pandas.

One question: how should these functions align their arguments? Recall that xarray does an inner join for arithmetic (though there's an option to control this), and an outer join in most other cases. It's not entirely obvious to me what the right choice is here (or if it really even matters).

 
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   cov() and corr() 396102183

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