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/2213#issuecomment-544864012,https://api.github.com/repos/pydata/xarray/issues/2213,544864012,MDEyOklzc3VlQ29tbWVudDU0NDg2NDAxMg==,206773,2019-10-22T08:46:46Z,2019-10-22T12:09:10Z,NONE,"> @hans-permana your example shows a different issue: indexing with a date string yields a time dimension of length 1, rather than squeezing it out 

Nope, look at the screenshot again, the dimension is zero. The very similar issue (if not same) remains and should be considered a bug:

![image](https://user-images.githubusercontent.com/206773/67270122-475dcc00-f4b8-11e9-938f-350b396904de.png)

If I now use `sel()` with a date string without time component, I get a 3D array with zero time dimension:
 
![image](https://user-images.githubusercontent.com/206773/67270196-796f2e00-f4b8-11e9-9851-768019d7798b.png)

However, if I use `sel()` with a date string *with* time component, I get the expected 2D array:

![image](https://user-images.githubusercontent.com/206773/67270563-3497c700-f4b9-11e9-99b0-f5a2336c56ba.png)

**EDIT**

It seems that if I create the `cube` dataset from above with a `time` coordinate variable whose values don't have a time component (e.g.  `2018-06-26 00:00:00.000000`), then both `sel(time='2018-06-26')` and `sel(time='2018-06-26 10:23:05')` work as expected and only yield 2D results.

**EDIT 2**

Root cause may be related to Pandas indexing using strings that encode different accuracy / resolution: http://pandas-docs.github.io/pandas-docs-travis/user_guide/timeseries.html#slice-vs-exact-match. Very contra-intuitive.

#### Output of ``xr.show_versions()``

<details>
python: 3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 22:01:29) [MSC v.1900 64 bit (AMD64)]
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.14.0
pandas: 0.25.2
numpy: 1.16.4
scipy: 1.2.1
netCDF4: 1.5.0.1
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.3.2
cftime: 1.0.3.4
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.0.22
cfgrib: None
iris: None
bottleneck: None
dask: 2.6.0
distributed: 2.6.0
matplotlib: 3.0.3
cartopy: None
seaborn: None
numbagg: None
setuptools: 41.0.1
pip: 19.0.3
conda: None
pytest: 4.4.2
IPython: 7.4.0
sphinx: 2.0.1
</details>

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