What happened:

I started with a Dataframe that represented an identity matrix and used reindex with multiple dimensions and a fill_value. The goal was to produce a Dataset from a sparse dataframe and the identity matrix was the simplest example. The fill_value in reindex is defined as "Value to use for newly missing values." What I found was that fill_values were not applied at coordinates that were in the unique set of the any coord. For a pure identity matrix, that means fill_values were not applied anywhere (all rows are present, all cols are present). When I thin the identity matrix (skipping elements), the error is more obvious. On the rows and columns that have valid input data, the fill_value is not applied.

What you expected to happen:

I expected all new nan values to be filled with the fill value.

Minimal Complete Verifiable Example:

``` import numpy as np import pandas as pd

n = 10 thin = 2

df = pd.DataFrame.from_dict([ dict(ROW=v, COL=v, LAND=1) for v in np.arange(0, n, thin) ]).set_index(['ROW', 'COL'])

ds = df.to_xarray() rds = ds.reindex(ROW=np.arange(n), COL=np.arange(n), fill_value=0)

p = rds.LAND.plot()

p.axes.set_facecolor('red')

p.axes.figure.savefig('test.png')

print(rds.LAND[:]) print(rds.LAND[::thin, ::thin]) ```

Output: <xarray.DataArray 'LAND' (ROW: 10, COL: 10)> array([[ 1., 0., nan, 0., nan, 0., nan, 0., nan, 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [nan, 0., 1., 0., nan, 0., nan, 0., nan, 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [nan, 0., nan, 0., 1., 0., nan, 0., nan, 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [nan, 0., nan, 0., nan, 0., 1., 0., nan, 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [nan, 0., nan, 0., nan, 0., nan, 0., 1., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) Coordinates: * ROW (ROW) int64 0 1 2 3 4 5 6 7 8 9 * COL (COL) int64 0 1 2 3 4 5 6 7 8 9 <xarray.DataArray 'LAND' (ROW: 5, COL: 5)> array([[ 1., nan, nan, nan, nan], [nan, 1., nan, nan, nan], [nan, nan, 1., nan, nan], [nan, nan, nan, 1., nan], [nan, nan, nan, nan, 1.]]) Coordinates: * ROW (ROW) int64 0 2 4 6 8 * COL (COL) int64 0 2 4 6 8

Anything else we need to know?:

Environment:

Code Sample, a copy-pastable example if possible

```bash

Your code here

pytest -v xarray/tests/test_backends.py::NetCDF4DataTest ```

Problem description

Instead of passing all tests, this fails on the test_88_character_filename_segmentation_fault function.

E AssertionError: exception ImportWarning("can't resolve package from __spec__ or __package__, falling back on __name__ and __path__",) did not match pattern 'segmentation fault'

It oddly doesn't seem to be related to the 88 character issue. I traced this down to xarray/backends/common.py::get_scheduler where an "ImportWarning" is not being caught on line 49.

Expected Output

Passes all tests. Adding ImportWarning to the tuple of exceptions in get_scheduler fixes the problem.

Output of xr.show_versions()