issue_comments: 369315468

The drop technique seems reasonable, if a bit long-winded for the programmatic case (loop over all dimensions, find any that are empty -> loop over all variables, drop any that contain those empty dimensions).

As an addition, if the empty dimension also has an associated empty coordinate then it requires an extra step to get rid of it:

``` python In [21]: test_dataset = xr.Dataset(dict( ...: empty_array=xr.DataArray([], dims='a', coords={'a':[]}), ...: populated_array=xr.DataArray([1], {'b':['1']}, 'b') ...: ))

In [22]: test_dataset Out[22]: <xarray.Dataset> Dimensions: (a: 0, b: 1) Coordinates: * a (a) float64 * b (b) <U1 '1' Data variables: empty_array (a) float64 populated_array (b) int32 1

In [23]: test_dataset.drop('empty_array') Out[23]: <xarray.Dataset> Dimensions: (a: 0, b: 1) Coordinates: * a (a) float64 * b (b) <U1 '1' Data variables: populated_array (b) int32 1

In [24]: del test_dataset['a']

In [25]: test_dataset.drop('empty_array') Out[25]: <xarray.Dataset> Dimensions: (b: 1) Coordinates: * b (b) <U1 '1' Data variables: populated_array (b) int32 1 ```

Fixes seem reasonable, based on how we use xarray over at https://github.com/calliope-project/calliope/. The second one also provides more scope to remove subsets of data (all corresponding dims, coords, vars) if the dimension becomes superfluous for any reason, whether or not the dimension is empty.