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
306047853,MDU6SXNzdWUzMDYwNDc4NTM=,1996,Issue selecting time slices,6980561,closed,0,,,1,2018-03-16T19:19:05Z,2018-04-14T14:16:00Z,2018-03-17T18:09:30Z,NONE,,,,"#### Selecting time slices of dataset using dataset's own dimension

This might not be the correct place for this issue, but I was somewhat surprised to find that I couldn't use a dataset's own time dimension to index. The `sel` works as expected when explicitly passing a pandas.datetime object but throws an error (reproduced below) when selecting using the dataset's own time dimension.

#### Working example
```python
import xarray as xr
import pandas as pd
import numpy as np

ds = xr.Dataset({'example': (['time'],  np.arange(0, 15))},
                 coords={'time': pd.date_range(pd.datetime(2011, 6, 1),
                                               pd.datetime(2011, 6, 15), freq='1D')})

# This is not an error
ds.sel(time=slice(pd.datetime(2011, 6, 1), pd.datetime(2011, 6, 6)))

# This is an error
ds.sel(time=slice(0, 5))
```
#### Problem description

I expected to be able to index a dataset using it's own time dimension or the time dimension of another dataset. 

I would expect the operation to work like:
#### Expected Output
```python
import xarray as xr
import pandas as pd
import numpy as np

ds = xr.Dataset({'example': (['x'],  np.arange(0, 15))},
                 coords={'x': np.arange(0, 15)})

# Neither of the below fails
ds.sel(x=slice(0, 15))
ds.sel(x=slice(ds.x[0], ds.x[-1]))
```
where both operations are identical. I know I could use the `isel` method here, but the current behavior was very unexpected. 

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

xarray: 0.10.1
pandas: 0.20.3
numpy: 1.13.1
scipy: 0.19.1
netCDF4: 1.2.4
h5netcdf: None
h5py: 2.7.0
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.10.0
distributed: None
matplotlib: 2.0.2
cartopy: None
seaborn: 0.7.0
setuptools: 38.5.2
pip: 9.0.1
conda: 4.4.11
pytest: 2.9.2
IPython: 6.2.1
sphinx: 1.4.1
</details>
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