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
272004812,MDU6SXNzdWUyNzIwMDQ4MTI=,1699,apply_ufunc(dask='parallelized') output_dtypes for datasets,6213168,open,0,,,8,2017-11-07T22:18:23Z,2020-04-06T15:31:17Z,,MEMBER,,,,"When a Dataset has variables with different dtypes, there's no way to tell apply_ufunc that the same function applied to different variables will produce different dtypes:

```
ds1 = xarray.Dataset(data_vars={'a': ('x', [1, 2]), 'b': ('x', [3.0, 4.5])}).chunk()
ds2 = xarray.apply_ufunc(lambda x: x + 1, ds1, dask='parallelized', output_dtypes=[float])
ds2

<xarray.Dataset>
Dimensions:  (x: 2)
Dimensions without coordinates: x
Data variables:
    a        (x) float64 dask.array<shape=(2,), chunksize=(2,)>
    b        (x) float64 dask.array<shape=(2,), chunksize=(2,)>

ds2.compute()

<xarray.Dataset>
Dimensions:  (x: 2)
Dimensions without coordinates: x
Data variables:
    a        (x) int64 2 3
    b        (x) float64 4.0 5.5
```

### Proposed solution
When the output is a dataset, apply_ufunc could accept either ``output_dtypes=[t]`` (if all output variables will have the same dtype) or ``output_dtypes=[{var1: t1, var2: t2, ...}]``. In the example above, it would be ``output_dtypes=[{'a': int, 'b': float}]``.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1699/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue