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  • shoyer · 2 ✖

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  • Allow skipna in .dot() · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
707270259 https://github.com/pydata/xarray/issues/4482#issuecomment-707270259 https://api.github.com/repos/pydata/xarray/issues/4482 MDEyOklzc3VlQ29tbWVudDcwNzI3MDI1OQ== shoyer 1217238 2020-10-12T18:08:55Z 2020-10-12T18:08:55Z MEMBER

I'm happy to live with a memory copy for now with fillna and notnull, but allocating the full, un-chunked array into memory is a showstopper. Is there a different workaround that I can use in the meantime?

This is surprising behavior, and definitely sounds like a bug!

If you could put together a minimal test case for reproducing the issue, we could look into it. It's hard to say what a work-around would be without knowing the source of the issue.

 
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   Allow skipna in .dot() 713834297
704064370 https://github.com/pydata/xarray/issues/4482#issuecomment-704064370 https://api.github.com/repos/pydata/xarray/issues/4482 MDEyOklzc3VlQ29tbWVudDcwNDA2NDM3MA== shoyer 1217238 2020-10-06T06:38:51Z 2020-10-06T06:38:51Z MEMBER

I agree this would be welcome! Even if it isn't much faster than the options already shown here, at least we could point users to the best option we know of.

I suspect achieving the full speed of dot() with skip-NA support is impossible, but we can probably do much better. I might start by prototyping something in Numba, just to get a sense of what is achievable with a low-level approach. But keep in mind that functions like np.dot and np.einsum ("GEMM") are a few of the most highly optimized routines in numerical computing.

 
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   Allow skipna in .dot() 713834297

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