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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
514077742 MDU6SXNzdWU1MTQwNzc3NDI= 3458 Keep index dimension when selecting only a single coord ngreenwald 13770365 open 0     6 2019-10-29T17:02:29Z 2021-03-02T06:48:08Z   NONE      

MCVE Code Sample

```python

Your code here

import numpy as np import xarray as xr

data = np.zeros((10, 4)) example_xr = xr.DataArray(data, coords=[range(10), ["idx0", "idx1", "idx2", "dim3"]], dims=["rows", "cols"])

desired behavior

subset = example_xr[:, 1:2] subset.shape

inclusive indexing means both idx1 and idx2 kept

subset_named1 = example_xr.loc[:, "idx1":"idx2"] subset_named1.shape

slicing behavior means that 2nd dimension is dropped

subset_named2 = example_xr.loc[:, "idx1"] subset_named2.shape ```

Expected Output

I'd like to be able to use named .loc indexing to select only a single named coord from one dimension, but not have that dimension collapse when subsetting.

Problem Description

I looked, but wasn't able to find anything in the documentation about how to perform this same action using named coords. It works with integer-based slicing.

Output of xr.show_versions()

# Paste the output here xr.show_versions() here 
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     xarray 13221727 issue
568705055 MDU6SXNzdWU1Njg3MDUwNTU= 3785 open_dataarray(cache=False) still uses cached version of dataarray ngreenwald 13770365 closed 0     2 2020-02-21T02:58:22Z 2020-03-03T18:29:46Z 2020-03-03T18:29:46Z NONE      

MCVE Code Sample

```python

Your code here

import xarray as xr import numpy as np import os

create two different xarrays with different sizes and coords

test_xr1 = xr.DataArray(np.zeros((5, 5, 3)), coords=[range(5), range(5), ["x1", "y1", "z1"]], dims=["1", "2", "3"]) test_xr2 = xr.DataArray(np.zeros((10, 2, 3)), coords=[range(10), range(2), ["x2", "y2", "z2"]], dims=["1", "2", "3"])

save first xarray, reload it, and inspect coords

test_xr1.to_netcdf("test_xr.xr") loaded_xr = xr.open_dataarray("test_xr.xr", cache=False) loaded_xr.coords

Out[13]: Coordinates: * 1 (1) int64 0 1 2 3 4 * 2 (2) int64 0 1 2 3 4 * 3 (3) object 'x1' 'y1' 'z1'

remove first xarray, save second with the same name, and load it

os.remove("test_xr.xr") test_xr2.to_netcdf("test_xr.xr") loaded_xr = xr.open_dataarray("test_xr.xr", cache=False) loaded_xr.coords

Out[17]: Coordinates: * 1 (1) int64 0 1 2 3 4 * 2 (2) int64 0 1 2 3 4 * 3 (3) object 'x1' 'y1' 'z1' ```

Expected Output

Rather than loading the newly created/updated version of the file on disk, the cached version is used

Problem Description

If a file ever gets updated on disk and needs to be reloaded, this causes very mysterious bugs. Furthermore, the only workaround I found is to restart the python session.

Output of xr.show_versions()

# Paste the output here xr.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.6.5 |Anaconda, Inc.| (default, Apr 26 2018, 08:42:37) [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] python-bits: 64 OS: Darwin OS-release: 18.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: None LOCALE: en_US.UTF-8 libhdf5: 1.10.2 libnetcdf: 4.6.3 xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.3 scipy: 1.2.1 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: None PseudonetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: 2.2.2 cartopy: None seaborn: 0.9.0 setuptools: 39.0.1 pip: 9.0.3 conda: None pytest: 5.3.2 IPython: 7.11.1 sphinx: None 
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   completed xarray 13221727 issue

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