id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
454677926,MDU6SXNzdWU0NTQ2Nzc5MjY=,3015,Assigning values to a subset of a dataset,23265127,closed,0,,,2,2019-06-11T13:03:16Z,2021-05-25T08:12:52Z,2021-05-25T08:12:52Z,NONE,,,,"Hi, can somebody tell me what is the ""correct"" way to manipulate a subset of the data contained in a Dataset? 

Consider the following example:
```
import numpy as np
import xarray as xr

shape = (3, 2)
da1 = xr.DataArray(np.zeros(shape), dims=('x', 'y'), coords=dict(x=[1, 2, 3], y=[4, 5]), name='var1')
da2 = xr.DataArray(np.zeros(shape), dims=('x', 'y'), coords=dict(x=[1, 2, 3], y=[4, 5]), name='var2')
```

I can easily change the value of variable 1 at a given coordinate in the first DataArray using the following syntax:
```
da1.loc[dict(x=1, y=4)] = 1
```

However, if I merge both DataArrays into a single Dataset and want to change both variables at the same time, there seems to be no straightforward solution:
```
ds = xr.merge([da1, da2])
ds.loc[dict(x=1, y=4)] =  ... <-- what to write here?
```

The only solution I could come up with is to modify the two values separately, but this is neither very elegant nor scales with the number of variables:
```
ds['var1'].loc[dict(x=1, y=4)] = 2
ds['var2'].loc[dict(x=1, y=4)] = 3
```

All I could find in the docs about this issue is:
> Using indexing to assign values to a subset of dataset (e.g., ds[dict(space=0)] = 1) is not yet supported.

If not by indexing, what other (more compact) way exists? A potential solution might be to create a separate Dataset and then use the `update` method, but this seems overly complicated, too.

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