issue_comments: 773240852

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https://github.com/pydata/xarray/issues/4858#issuecomment-773240852 https://api.github.com/repos/pydata/xarray/issues/4858 773240852 MDEyOklzc3VlQ29tbWVudDc3MzI0MDg1Mg== 12862013 2021-02-04T11:33:32Z 2021-02-04T11:34:36Z CONTRIBUTOR

Hi, we had a similar discussion in de #3596, xarray makes a distinction between np.nan and xarray.dtypes.NaN. The current behaviour is consistent with that of other xarray functions such as shift. Though, I am personally not a big fan of this distinction.

Check e.g. this comment: https://github.com/pydata/xarray/pull/3596#discussion_r388612638

The example I posted in this comment: ```

da = xr.DataArray(np.arange(9).reshape(3,3), dims=("x", "y")) da.shift(x=1, fill_value=np.nan) array([[-9223372036854775808, -9223372036854775808, -9223372036854775808], [ 0, 1, 2], [ 3, 4, 5]]) Dimensions without coordinates: x, y

da.rolling(x=3).construct("new_axis", stride=3, fill_value=np.nan) <xarray.DataArray (x: 1, y: 3, new_axis: 3)> array([[[-9223372036854775808, -9223372036854775808, 0], [-9223372036854775808, -9223372036854775808, 1], [-9223372036854775808, -9223372036854775808, 2]]]) Dimensions without coordinates: x, y, new_axis ```

Hmm, so numpy changed its behaviour? Then this example, should probably also fail in numpy 1.20.

On a side note: I am not a big fan of the example in the doctest, it displays an edge case, which is not unique to pad.

I think the nicest solution would be to make the usage xarray.dtypes.NaN and np.nan equivalent. But this would require changes in all xarray functions that take some kind of fill_value. 

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