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
247697176,MDU6SXNzdWUyNDc2OTcxNzY=,1499,Reusing coordinate doesn't show in the dimensions,2941720,closed,0,,,10,2017-08-03T12:55:35Z,2023-12-02T02:50:25Z,2023-12-02T02:50:25Z,CONTRIBUTOR,,,,"For a `DataArray`, when reusing a coordinate for multiple dimensions (is this expected usage?), it only shows once in the repr:

```python
>>> x = xr.IndexVariable(data=range(5), dims='x')
>>> da = xr.DataArray(data=np.random.randn(5, 5), coords={'x': x}, dims=('x', 'x'))
>>> da
<xarray.DataArray (x: 5)>
array([[ 0.704139,  0.135638, -0.84717 , -0.580167,  0.95755 ],
       [ 0.966196, -0.126107,  0.547461,  1.075547, -0.477495],
       [-0.507956, -0.671571,  1.271085,  0.007741, -0.37878 ],
       [-0.969021, -0.440854,  0.062914, -0.3337  , -0.775898],
       [ 0.86893 ,  0.227861,  1.831021,  0.702769,  0.868767]])
Coordinates:
  * x        (x) int64 0 1 2 3 4
```

I think it should be 

```python
<xarray.DataArray (x: 5, x: 5)>
array([[ ... ]])
Coordinates:
  * x        (x) int64 0 1 2 3 4
```

Otherwise, everything appears to work exactly as I would expect.

This isn't an issue for `Datasets`:

```python
>>> xr.Dataset({'da': da})
<xarray.Dataset>
Dimensions:  (x: 5)
Coordinates:
  * x        (x) int64 0 1 2 3 4
Data variables:
    da       (x, x) float64 0.08976 0.1049 -1.291 -0.4605 -0.005165 -0.3259 ...
```

Thanks!","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1499/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
247703455,MDU6SXNzdWUyNDc3MDM0NTU=,1500,Support for attributes with different dtypes when serialising to netcdf4,2941720,open,0,,,4,2017-08-03T13:18:12Z,2020-03-17T14:18:39Z,,CONTRIBUTOR,,,,"At the moment, bool and dates aren't supported as attributes when serializing to netcdf4:

```python
>>> da = xr.DataArray(range(5), attrs={'test': True})
>>> da 
<xarray.DataArray (dim_0: 5)>
array([0, 1, 2, 3, 4])
Dimensions without coordinates: dim_0
Attributes:
    test:     True

>>> da.to_netcdf('test_bool.nc')
...
TypeError: illegal data type for attribute, must be one of dict_keys(['S1', 'i1', 'u1', 'i2', 'u2', 'i4', 'u4', 'i8', 'u8', 'f4', 'f8']), got b1

>>> da = xr.DataArray(range(5), attrs={'test': pd.to_datetime('now')})
>>> da
<xarray.DataArray (dim_0: 5)>
array([0, 1, 2, 3, 4])
Dimensions without coordinates: dim_0
Attributes:
    test:     2017-08-03 13:02:29

>>> da.to_netcdf('test_dt.nc')
...
TypeError: Invalid value for attr: 2017-08-03 13:02:29 must be a number string, ndarray or a list/tuple of numbers/strings for serialization to netCDF files
```

I assume bool attributes aren't supported by `netcdf4-python` and dates are difficult (could always just write these as a string), but this would be really nice to have if possible.

As an aside, using `h5netcdf` works for bools, but coerces them to int64.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1500/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
230158616,MDU6SXNzdWUyMzAxNTg2MTY=,1415,Save arbitrary Python objects to netCDF,2941720,open,0,,,5,2017-05-20T14:58:42Z,2019-04-21T05:08:03Z,,CONTRIBUTOR,,,,"I am looking to transition from pandas to xarray, and the only feature that I am really missing is the ability to seamlessly save arrays of python objects to hdf5 (or netCDF).   This might be an issue for the backend netCDF4 libraries instead, but I thought I would post it here first to see what the opinions were about this functionality.

For context, Pandas allows this by using pytables' `ObjectAtom` to serialize the object using pickle, then saves as a variable length bytes data type.  It is already possible to do this using netCDF4, by applying to each object in the array `np.fromstring(pickle.dumps(obj), dtype=np.uint8)`, and saving these using a uint8 VLType. Then retrieving is simply `pickle.reads(obj.tostring())` for each array.

I know pickle can be a security problem, it can cause an problem if you try to save a numerical array that accidently has dtype=object (pandas gives a warning), and that this is probably quite slow (I think pandas pickles a list containing all the objects for speed), but it would be incredibly convenient.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1415/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue