issue_comments: 361664085

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https://github.com/pydata/xarray/pull/1837#issuecomment-361664085 https://api.github.com/repos/pydata/xarray/issues/1837 361664085 MDEyOklzc3VlQ29tbWVudDM2MTY2NDA4NQ== 1217238 2018-01-30T17:10:44Z 2018-01-30T17:10:44Z MEMBER

I'll take a look, but I'm open to only fixing this nansum for numpy 1.13+ (or even requiring numpy 1.13 for rolling window arrays), as long as we don't break existing behavior on earlier versions of numpy.

On Tue, Jan 30, 2018 at 3:16 AM Keisuke Fujii notifications@github.com wrote:

Hmm... The support of nansum for object-dtype is much more difficult than I expected.

Now, I am stacked in supporting numpy<1.13. With numpy<1.13, nansum for the object type gives just a nan.

nansum scripts defined in numpy==1.13 does not help because != operator used here https://github.com/numpy/numpy/blob/6914bb41f0fb3c1ba500bae4e7d671da9536786f/numpy/lib/nanfunctions.py#L68 behaves differently in numpy<1.13.

I think our best choice is to revert this PR to 4189d71 https://github.com/pydata/xarray/commit/4189d71d9998ff83deb9a5d7035a2edaf628ae25 and left this issue to #1866 https://github.com/pydata/xarray/issues/1866.

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