html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/6434#issuecomment-1086751973,https://api.github.com/repos/pydata/xarray/issues/6434,1086751973,IC_kwDOAMm_X85AxoTl,2448579,2022-04-03T01:00:48Z,2022-04-03T02:02:34Z,MEMBER,"> which doesn't seem to create the right kind of index given the value type:

There's a typo in the first line, we need `da.time`, this does actually work
```
array = da.isel(time=0).time.values
value = array.item()
seq = np.array([value], dtype=array.dtype)
pd.Index(seq, dtype=array.dtype)
# DatetimeIndex(['2013-01-01'], dtype='datetime64[ns]', freq=None)
```

The issue is that the `.item()` converts datetime64 to int and so `safe_cast_to_index` creates  a IntIndex because we don't pass `dtype` to the `pd.Index` constructor (so that's one possible fix):
https://github.com/pydata/xarray/blob/3ead17ea9e99283e2511b65b9d864d1c7b10b3c4/xarray/core/utils.py#L130-L133

Alternatively, we could loop over datasets and call `expand_dims` if `dim` is present and is a scalar. We already do something similar here:
https://github.com/pydata/xarray/blob/305533d585389f7240ae2383a323337d4761d33a/xarray/core/concat.py#L470-L472","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1189140909