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
1115166039,I_kwDOAMm_X85CeBVX,6196,Wrong list of coordinate when a singleton coordinate exists,32069530,open,0,,,5,2022-01-26T15:41:37Z,2023-03-01T19:55:13Z,,NONE,,,,"### What happened?

Here is some simple code:
````
a = xr.DataArray(np.arange(5), dims='x', coords={'x':np.arange(5)})
a = a.assign_coords({'y':1})
````
Now calling `a['x']` or `a['x'].coords` shows `y` as a coordinate of `x`, which is unexpected for me

### What did you expect to happen?

I expect that a singleton coordinate of a dataset  not to be a coordinate of other coordinates present in the dataset

### Minimal Complete Verifiable Example

````
import xarray as xr
import numpy as np
a = xr.DataArray(np.arange(5), dims='x', coords={'x':np.arange(5)})
a = a.assign_coords({'y':1})
print(a['x'].coords)
````
returns
````
Coordinates:
  * x        (x) int64 0 1 2 3 4
    y        int64 1
````

### Relevant log output

_No response_

### Anything else we need to know?

_No response_

### Environment


INSTALLED VERSIONS
------------------
commit: None
python: 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:57:06) 
[GCC 9.4.0]
python-bits: 64
OS: Linux
OS-release: 3.12.53-60.30-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.1
libnetcdf: 4.8.1

xarray: 0.19.0
pandas: 1.3.5
numpy: 1.20.3
scipy: 1.6.3
netCDF4: 1.5.8
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.5.1.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2021.10.0
distributed: 2021.10.0
matplotlib: 3.2.2
cartopy: None
seaborn: None
numbagg: None
pint: None
setuptools: 60.5.0
pip: 21.3.1
conda: None
pytest: None
IPython: 7.31.0
sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6196/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
911513701,MDU6SXNzdWU5MTE1MTM3MDE=,5436,bug or unclear definition of combine_attrs with xr.merge(),32069530,open,0,,,13,2021-06-04T13:43:39Z,2022-09-22T17:27:13Z,,NONE,,,,"Hi all,
I use the latest version of xarray (0.18) and I have some problems. Here are very simple examples:
```python
velocity = xr.DataArray(7.3, name='velocity', attrs={'units':'m/s'})
elevation = xr.DataArray(3.5, name='elevation', attrs={'units':'m'})
```
1) When using `ds =xr.merge([velocity, elevation], combine_attrs='drop')` I expect ds not have any attrs but I may expect variables velocity and elevation to keep their own attributes. This is not the case. All variables loose their attributes

2) When using  combine_attrs='drop_conflicts', elevation and velocity keeps their own units and ds has no attrs.

3) Then, if we set elevation units to be the same as velocity units and use combine_attrs='drop_conflicts', then ds get a new attibute wich is this common units 

As a conclucion, the combine_attrs flag definition is really not clear because it seems to control the final merged dataset attrs but, in reality, can also affects merged variables attributes. From my point of view, behaviour of combine_attrs is not consistent depending on the chosen option.

I would expect merged variables to be (as much as possible) untouched when merged and the combine_attrs to only control the attributes of the merged dataset.

Thanks
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5436/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
1183777627,I_kwDOAMm_X85GjwNb,6423,interpolation of xarray do not preserve attributes,32069530,open,0,,,1,2022-03-28T17:47:33Z,2022-05-21T20:31:40Z,,NONE,,,,"### What is your issue?

Hi all,
Interpolation over an xarray variable do not preserve its attributes:
Here is a minimal example
````
x=np.arange(100)
x=xr.DataArray(x, dims='x', coords={'x':x})
t=np.arange(40,60,0.5)
t=xr.DataArray(t, dims='t', coords={'t':t}, attrs={'units':'s'})
result = x.interp(x=t)
````
t coordinate in result do not have the t unit anymore","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6423/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
595813283,MDU6SXNzdWU1OTU4MTMyODM=,3946,removing uneccessary dimension,32069530,open,0,,,8,2020-04-07T11:57:36Z,2022-05-02T23:07:33Z,,NONE,,,,"Hi Everyone,

Sometimes I generate  DataArray which are invariant along a dimension. I was not able to find a function or a simple workaround to get rid of these dimensions (unless I already know their names). I would like to use something as a combination of ""reduce"" and ""np.allclose"". For the moment I use the equivalent code below which works but is not efficent.
Could it be a candidate for a native efficent xarray function? This can drastically reduce memory usage if relevant.
Thanks


```python
ds = xr.DataArray([[1.,2.],[1.,2.]], dims=('x','y'))

dims_to_remove=list()
for d in ds.dims:
    if np.all(ds[{d:0}]==ds): dims_to_remove.append(d)
ds = ds[dict.fromkeys(dims_to_remove,0)]
ds =  ds.squeeze()
```

","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3946/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
817885693,MDU6SXNzdWU4MTc4ODU2OTM=,4968,swap_coords() function ?,32069530,open,0,,,0,2021-02-27T10:00:53Z,2021-02-27T10:01:52Z,,NONE,,,,"Hi all,

I have a DataArray with coordinates 'time'. I want to swap the time coordinate with another coordinate which is not in the array.
swap_dims() allows to give a non-existing dimension but I cannot define the coords at the same time. I usually use a workaround as follow.
```
a = xr.DataArray(np.random.rand(10), dims='time', coords={'time':1.e-4*np.arange(10)})
celerity=3e8
space = celerity * a['time']
a = a.swap_dims({'time':'space'})
a = a.assign_coords({'space':space.data})
```

I think it could be interesting to be able to swap_coords which could do the last two lines at the same time. Basically a swap_coords() function
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4968/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue