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
776595030,MDExOlB1bGxSZXF1ZXN0NTQ3MDUzOTM5,4744,Speed up Dataset._construct_dataarray,2448579,closed,0,,,1,2020-12-30T19:03:05Z,2021-01-05T17:32:16Z,2021-01-05T17:32:13Z,MEMBER,,0,pydata/xarray/pulls/4744,"<!-- Feel free to remove check-list items aren't relevant to your change -->

 - [ ] Tests added
 - [x] Passes `isort . && black . && mypy . && flake8`
 - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst`

Significantly speeds up `_construct_dataarray` by iterating over `._coord_names` instead of `.coords`. This avoids unnecessarily constructing a `DatasetCoordinates` object and massively speeds up repr construction for datasets with large numbers of variables.

Construct a 2000 variable dataset
```python
import numpy as np
import xarray as xr

a = np.arange(0, 2000)
b = np.core.defchararray.add(""long_variable_name"", a.astype(str))
coords = dict(time=np.array([0, 1]))
data_vars = dict()
for v in b:
    data_vars[v] = xr.DataArray(
        name=v,
        data=np.array([3, 4]),
        dims=[""time""],
        coords=coords
    )
ds0 = xr.Dataset(data_vars)
```

Before:
```
%timeit ds0['long_variable_name1999']
%timeit ds0.__repr__()

1.33 ms ± 23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
2.66 s ± 52.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

After:

```
%timeit ds0['long_variable_name1999']
%timeit ds0.__repr__()

10.5 µs ± 203 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
84.2 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
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