a workaround for achieving this would be to use the apply method of xarray data structures along with the expand_dims method. Here's an example of how we can use it:

``` import xarray as xr dataset = xr.Dataset(data_vars={'foo': 1, 'bar': 2})

Define a function that expands the given variable along a new dimension

def expand_variable(da): if da.name == 'foo': return da.expand_dims('zar') else: return da

Use the apply method to apply the function to only the desired variables

expanded_dataset = dataset.apply(expand_variable, keep_attrs=True)

print(expanded_dataset)

``` Here, the expand_variable function is defined to only expand the variable with the name 'foo' along a new dimension. The apply method is used to apply this function to each data variable in the dataset, but only 'foo' is actually modified.

I think these are good alternatives.

From my experiments (and I'm still trying to create a minimum reproducible code that shows the real problem behind the slowdowns) reindexing can be quite an expensive. We used to have many coordinates (to ensure that critical metdata stays with data_variables) and those coordinates were causing slowdowns on reindexing operations.

Thus the two calls update and expand_dims might cause two reindex merges to occur.

However, for this particular issue, I think that documenting the strategies proposed in the docstring is good enough. I have a feeling if one can get to the bottom of 7224, the performance concerns here will be mitigated too.

We can leave the performance discussion to: https://github.com/pydata/xarray/issues/7224

note that the return value of update is deprecated, since update is one of the few in-place operations xarray has. Going forward it should be: python dataset.update(dataset[[vars_to_expand]].expand_dims(...))

You can also generalize to dataset = dataset.update(dataset[[vars_to_expand]].expand_dims(...))

If it's only a single variable you could do: dataset["foo"] = dataset["foo"].expand_dims("zar")