issue_comments

So does this mean that the following line in the docs is now false: "If a dimension name is given as an argument to drop, it also drops all variables that use that dimension"

Oops -- yes, that line in the docs / example is broken!

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.

Yes, this was a useful feature that we lost.

Note that in general we try to encourage using methods to create new Datasets rather than modifying existing ones inplace. So it might also make sense to add a drop_dims() method that return a Dataset with the given dimensions removed.

If you're OK creating a new Dataset, it works to remove any variables using a dimension, e.g., ``` In [25]: test_dataset = xr.Dataset(dict( ...: empty_array=xr.DataArray([], dims='a'), ...: populated_array=xr.DataArray([1], {'b':['1']}, 'b') ...: )) ...:

In [26]: test_dataset Out[26]: <xarray.Dataset> Dimensions: (a: 0, b: 1) Coordinates: * b (b) <U1 '1' Dimensions without coordinates: a Data variables: empty_array (a) float64 populated_array (b) int64 1

In [27]: test_dataset.drop('empty_array') Out[27]: <xarray.Dataset> Dimensions: (b: 1) Coordinates: * b (b) <U1 '1' Data variables: populated_array (b) int64 1 ```

You're right that this doesn't work to remove dimensions from existing datasets (e.g., with del). But this isn't specific to dimensions of length 0 -- you can't delete any dimensions. At best, you can delete a coordinate variable corresponding to a dimension: ``` In [46]: test_dataset Out[46]: <xarray.Dataset> Dimensions: (a: 0, b: 1) Coordinates: * b (b) <U1 '1' Dimensions without coordinates: a Data variables: empty_array (a) float64 populated_array (b) int64 1

In [47]: del test_dataset['b']

In [48]: test_dataset Out[48]: <xarray.Dataset> Dimensions: (a: 0, b: 1) Dimensions without coordinates: a, b Data variables: empty_array (a) float64 populated_array (b) int64 1

In [49]: del test_dataset['populated_array']

In [50]: test_dataset Out[50]: <xarray.Dataset> Dimensions: (a: 0, b: 1) Dimensions without coordinates: a, b Data variables: empty_array (a) float64 ```

Is there a way to remove a dimension (and possibly then all data variables which depend on it)?

This used to be possible in the xarray data model prior to v0.9.0. del should to delete a dimension corresponding to a coordinate variable and all other associated variables.

When we made coordinates optional, I updated del to only delete data/coordinate variables. This made sense, but meant there is now no way to get rid of dimensions.

I'd like to suggest two possible fixes: 1. Update del dataset[key] to remove all "orphaned" dimensions that don't correspond to a dimension on any variable. This would enforce the invariant "all dimensions on a dataset correspond to a dimension on one of its variables." If there are other places where we violate this invariant those should be fixed, too. 2. Update Dataset.dims to allow deleting elements. Deleting a dimension on a dataset means all associated variables are also deleted.

Hmmm, this is harder than I originally expected. I imagine someone will comment with an easy solution, otherwise I'll have another look

I think SO is the best place for user Qs, so the answers can be searchable for future generations.

To respond immediately though, have you tried .squeeze? Or, to confirm, you actually want to remove a rather than b here?

```python In [1]: import xarray as xr

In [2]: test_dataset = xr.Dataset(dict( ...: empty_array=xr.DataArray([], dims='a'), ...: populated_array=xr.DataArray([1], {'b':['1']}, 'b') ...: ))

In [3]: test_dataset Out[3]: <xarray.Dataset> Dimensions: (a: 0, b: 1) Coordinates: * b (b) <U1 '1' Dimensions without coordinates: a Data variables: empty_array (a) float64 populated_array (b) int64 1

In [4]: test_dataset.squeeze() Out[4]: <xarray.Dataset> Dimensions: (a: 0) Coordinates: b <U1 '1' Dimensions without coordinates: a Data variables: empty_array (a) float64 populated_array int64 1 ```