issues: 957918751

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957918751 MDU6SXNzdWU5NTc5MTg3NTE= 5664 Interpolation behaviour inconsistent with numpy? 7017525 open 0     4 2021-08-02T08:56:28Z 2024-03-12T01:15:46Z   NONE      

Hey all, When running dataset.interp(time=dataset.time) fills with np.nan if one of the neighbor is a np.nan even when interpolation is not actually needed.

Here is the sample code to reproduce the issue : ```python def test_crop_times_nan() : ds = xr.Dataset( data_vars = { "some_variable" : (['x', 'time'], np.array([[np.nan, 0, 1]])) }, coords = { "time" : np.array([0,1,2]) } ) result = ds.interp(time=ds.time)

    # result["some_variable"].value == [nan, nan, 1.0]
    # whereas [nan, 0, 1.0] is EXPECTED
    xr.testing.assert_allclose(ds, result)

Please note that numpy does not have the same behavior :python

import numpy as np np.interp([0,1,2], xp=[0,1,2], fp=[np.nan,0,1]) array([nan, 0., 1.]) ```

Is that an intended behaviour for xarray? If so, does this mean that I first have to check if an interpolation is needed instead of doing it no matter what (and use reindex instead of interp if it is not needed) ?
(this will be kind of tricky if interpolation is needed for certain values and some not...)

Thanks for your help ;)

Environment:

Output of <tt>xr.show_versions()</tt> INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Jul 28 2020, 12:59:40) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 5.8.0-7642-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.0 libnetcdf: 4.7.4 xarray: 0.18.2 pandas: 1.2.4 numpy: 1.19.4 scipy: 1.6.0 netCDF4: 1.5.6 pydap: None h5netcdf: 0.8.1 h5py: 3.1.0 Nio: None zarr: None cftime: 1.3.0 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2021.01.0 distributed: 2021.01.0 matplotlib: 3.4.2 cartopy: None seaborn: None numbagg: None pint: None setuptools: 57.4.0 pip: 20.2.4 conda: None pytest: None IPython: 7.19.0 sphinx: None 
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