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
557257598,MDU6SXNzdWU1NTcyNTc1OTg=,3731,Repeated coordinates leads to unintuitive (broken?) indexing behaviour,8238804,open,0,,,13,2020-01-30T04:21:57Z,2023-11-30T08:58:22Z,,NONE,,,,"#### MCVE Code Sample
<!-- In order for the maintainers to efficiently understand and prioritize issues, we ask you post a ""Minimal, Complete and Verifiable Example"" (MCVE): http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports -->

```python
import xarray as xr
import numpy as np

sample_idx = xr.IndexVariable(""sample_id"", [""a"", ""b"", ""c""])
da = xr.DataArray(np.eye(3), coords=(sample_idx, sample_idx))

da.shape
# (3, 3)
da[1, :].shape
# (3, 3)
da.loc[""a"", :].shape
# (3, 3)
da.loc[:, ""a""].shape
# ()
da[:, 0].shape 
# ()
da[:, 1]
# <xarray.DataArray ()>
# array(1.)
# Coordinates:
#    sample_id  <U1 'b'
```

#### Expected Output

I had expected:

```python
da.shape
# (3, 3)
da[1, :].shape
# (3)
da.loc[""a"", :].shape
# (3)
da.loc[:, ""a""].shape
# (3)
da[:, 1]
# <xarray.DataArray (sample_id: 3)>
# array([0., 1., 0.])
# Coordinates:
#    sample_id  <U1 'a' 'b' 'c'
```

#### Problem Description

When coordinates are shared between dimensions (as would happen if a pairwise measurement is taken) indexing behaves strangely. It looks like indexing into the initial indices doesn't do anything, while indexing into the last index applies the selection across all dimensions.

```python
da3d = xr.DataArray(
    np.arange(27).reshape((3,3,3)),
    coords=(sample_idx, sample_idx, sample_idx)
)

print(da3d.loc[""a""].shape)
print(da3d.loc[""a"", ""a""].shape)
print(da3d.loc[:, :, ""a""].shape)
# (3, 3, 3)
# (3, 3, 3)
# ()
```

#### Output of ``xr.show_versions()``
<details>
# Paste the output here xr.show_versions() here

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.6 (default, Jan  4 2020, 12:18:30) 
[Clang 11.0.0 (clang-1100.0.33.16)]
python-bits: 64
OS: Darwin
OS-release: 19.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.2
libnetcdf: 4.6.3

xarray: 0.14.1+5.gb0064b25
pandas: 0.25.3
numpy: 1.18.1
scipy: 1.4.1
netCDF4: 1.5.2
pydap: None
h5netcdf: 0.7.4
h5py: 2.9.0
Nio: None
zarr: 2.4.0
cftime: 1.0.3.4
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2.9.2
distributed: 2.9.3
matplotlib: 3.0.3
cartopy: None
seaborn: 0.10.0
numbagg: None
setuptools: 44.0.0
pip: 20.0.2
conda: None
pytest: 5.3.4
IPython: 7.11.1
sphinx: 2.3.1
```

</details>


Update, adding link to related issue:

*  https://github.com/pydata/xarray/issues/1378","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3731/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
564555854,MDU6SXNzdWU1NjQ1NTU4NTQ=,3768,Pointwise indexing,8238804,closed,0,,,6,2020-02-13T09:39:27Z,2021-02-16T23:37:29Z,2021-02-16T23:37:29Z,NONE,,,,"#### MCVE Code Sample
<!-- In order for the maintainers to efficiently understand and prioritize issues, we ask you post a ""Minimal, Complete and Verifiable Example"" (MCVE): http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports -->

```python
import xarray as xr
import numpy as np

da = xr.DataArray(
    np.arange(56).reshape((7, 8)),
    coords={
        'x': list('abcdefg'),
        'y': 10 * np.arange(8)
        },
    dims=['x', 'y']
)

# Shouldn't this be (2,)?
assert da.isel(x=[0, 1], y=[0, 1]).shape == (2, 2)
```

#### Expected Output

I had expected `da.isel(x=[0, 1], y=[0, 1])` to have shape `(2,)`. I had generally expected indexing with `isel` to behave more like numpy indexing. It's very possible I'm just missing something, or that this is more of a documentation issue more than a behavior issue.

#### Problem Description

Going off this example in #507:

```python
In [3]: da.isel_points(x=[0, 1, 6], y=[0, 1, 0], dim='points')
Out[3]:
<xray.DataArray (points: 3)>
array([ 0,  9, 48])
Coordinates:
    y        (points) int64 0 10 0
    x        (points) |S1 'a' 'b' 'g'
  * points   (points) int64 0 1 2
```

and the [deprecation of `isel_points` with `isel`](http://xarray.pydata.org/en/stable/whats-new.html#id75), I had expected to get numpy-like coordinate indexing using `isel`.

This was made a little bit more confusing by the documentation for [setting values by index](http://xarray.pydata.org/en/stable/indexing.html#assigning-values-with-indexing). In particular the example:

```python
In [68]: da[ind_x, ind_y] = -2  # assign -2 to (ix, iy) = (0, 0) and (1, 1)

In [69]: da
Out[69]: 
<xarray.DataArray (x: 3, y: 4)>
array([[-2, -2, -1, -1],
       [-2, -2,  6,  7],
       [ 8,  9, 10, 11]])
```

To me, the comment `# assign -2 to (ix, iy) = (0, 0) and (1, 1)` makes it sound like values will be assigned at the coordinates (0, 0) and (1, 1), not (0, 0), (0, 1), (1, 0), and (1, 1).

All in all, I'm not sure if this is a bug, or an issue with documentation. If `isel` is not meant to behave like `isel_points`, it would be nice to see that in the documentation. If it is possible to get and set points by coordinate (without looping over single coordinates) it would be nice to see an example in the documentation where that's shown.

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

<details>
# Paste the output here xr.show_versions() here

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.6 (default, Jan  4 2020, 12:18:30) 
[Clang 11.0.0 (clang-1100.0.33.16)]
python-bits: 64
OS: Darwin
OS-release: 19.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.2
libnetcdf: 4.6.3

xarray: 0.15.0
pandas: 1.0.1
numpy: 1.18.1
scipy: 1.4.1
netCDF4: 1.5.2
pydap: None
h5netcdf: 0.7.4
h5py: 2.10.0
Nio: None
zarr: 2.4.0
cftime: 1.0.3.4
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2.9.2
distributed: 2.9.3
matplotlib: 3.1.3
cartopy: None
seaborn: 0.10.0
numbagg: None
setuptools: 45.2.0
pip: 20.0.2
conda: None
pytest: 5.3.4
IPython: 7.11.1
sphinx: 2.3.1
</details>
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