issue_comments: 802058819

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https://github.com/pydata/xarray/issues/4663#issuecomment-802058819 https://api.github.com/repos/pydata/xarray/issues/4663 802058819 MDEyOklzc3VlQ29tbWVudDgwMjA1ODgxOQ== 2448579 2021-03-18T16:15:19Z 2021-03-18T16:15:19Z MEMBER

I would start by trying to fix

``` python import dask.array as da import numpy as np from xarray.tests import raise_if_dask_computes

with raise_if_dask_computes(max_computes=0): ds = xr.Dataset( dict( a=("x", da.from_array(np.random.randint(0, 100, 100))), b=(("x", "y"), da.random.random((100, 10))), ) ) ds.b.sel(x=ds.a.data) ```

specifically this np.asarray call ~/work/python/xarray/xarray/core/dataset.py in _validate_indexers(self, indexers, missing_dims) 1960 yield k, np.empty((0,), dtype="int64") 1961 else: -> 1962 v = np.asarray(v) 1963 1964 if v.dtype.kind in "US":

Then the next issue is the multiple computes that happen when we pass a DataArray instead of a dask.array (ds.b.sel(x=ds.a)) In general, I guess we can only support this for unindexed dimensions since we wouldn't know what index labels to use without computing the indexer array. This seems to be the case in that colab notebook.

 
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