issue_comments: 544864012

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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:

If I now use sel() with a date string without time component, I get a 3D array with zero time dimension:

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

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()

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 
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