What happened?

After upgrading numpy from 1.23.5 to 1.24.1, calling the unstack method on an xarray.DataArray with integer data produces the warning <__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast. I think this relates to "ongoing work to improve the handling and promotion of dtypes" (Numpy 1.24.0 Release Notes), and is catching the fact that the method attempts to provide nan as a fill value on an integer array.

What did you expect to happen?

In the case below, where there is no need for a fill value, I do not expect to get a warning.

Minimal Complete Verifiable Example

```Python import xarray as xr import numpy as np

np.seterr(all='raise') # uncomment to convert warning to error

da = xr.DataArray( data=np.array([[0]], dtype=int), coords={'x': [0], 'y': [1]}, ) da = da.stack({'z': ['x', 'y']}) da.unstack() ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

Python <__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast <xarray.DataArray (x: 1, y: 1)> array([[0]]) Coordinates: * x (x) int64 0 * y (y) int64 1

Anything else we need to know?

No response

Environment

Is your feature request related to a problem?

A scalar coordinate has the counter-intuitive property of being able to hop from one data variable to another. ``` import xarray as xr

a = xr.Dataset( data_vars={"a": (("x",), [0, 0])}, coords={ "x": [0.1, 2.3], "y": 42, }, ) b = xr.Dataset( data_vars={"b": ("x", [1, 1])}, coords={ "x": [0.1, 2.3], }, ) c = xr.merge((a, b)) Only `a` had the scalar coordinate `y` before merging, but now `c["b"]` has caught it: <xarray.DataArray 'b' (x: 2)> array([1, 1]) Coordinates: * x (x) float64 0.1 2.3 y int64 42 I think this is a bug in a way, because it does not reflect the NetCDF4 data model's ability to keep `y` as a coordinate on `a` alone. Note the "coordinates" attributes in the result of `c.to_netcdf`: netcdf c { dimensions: x = 2 ; variables: int64 a(x) ; a:coordinates = "y" ; double x(x) ; x:_FillValue = NaN ; int64 y ; int64 b(x) ; b:coordinates = "y" ; <---- Says who!? ```

Describe the solution you'd like

I would like each data variable in a dataset to keep track of its own scalar coordinates (as they can, of course and absolutely essentially, do for dimension coordinates). To continue the example above, I think c should have a representation that would lead to the following serialization: netcdf c { dimensions: x = 2 ; variables: int64 a(x) ; a:coordinates = "y" ; double x(x) ; x:_FillValue = NaN ; int64 y ; int64 b(x) ;

Describe alternatives you've considered

No response

Additional context

I think this feature could also help with #4501, wherein squeezing demotes a length-one non-dimensional coordinate to a scalar coordinate without tracking its own scalar coordinate. Egads, that's an ugly sentence. I'll elaborate over there.

Most importantly, this feature would solve a real problem I've encountered: model outputs, one for each combination of model parameters, that record parameters as a scalar coordinate only on the data variables the parameter affects. If you want to concatenate these together with XArray, you invariably get a lot of unncecessary data duplication. A contrived example with two outputs, in which the "temp" variable depends on parameter "time" but the "pressure" variable does not: output_42 = xr.Dataset({ "temp": xr.DataArray( data=[10, 9, 8], dims=("depth",), coords={ "depth": [0, 5, 10], "time": 42, }, ), "pressure": xr.DataArray( data=[0, 7, 14], coords={"depth": [0, 5, 10]}, ) }) output_88 = xr.Dataset({ "temp": xr.DataArray( data=[11, 10, 10], dims=("depth",), coords={ "depth": [0, 5, 10], "time": 88, }, ), "pressure": xr.DataArray( data=[0, 7, 14], coords={"depth": [0, 5, 10]}, ) }) I think it should be possible to concatenate these datasets without duplicating "pressure", like so: <xarray.Dataset> Dimensions: (depth: 3, time: 2) Coordinates: * depth (depth) int64 0 5 10 * time (time) int64 42 88 Data variables: temp (time, depth) int64 10 9 8 11 10 10 pressure (depth) int64 0 7 14 I can't get to there with any variation on xr.concat((output_42, output_88), dim="time", data_vars="minimal"), which I guess can be explained by the fact that "time" is associated with both "temp" and "pressure" in XArray's internal representation.

What is your issue?

Under the assumption that deterministic output is preferred whenever feasible, I'd like to point out that the variable names written into "coordinates" attributes with .to_netcdf are not ordered deterministically. For pipelines that depend on file hashes to validate dependencies, this can be a real headache.

Consider the dataset xarray.Dataset({"x": ((), 0)}, coords={"a": 0, "b": 0}). The NetCDF file XArray writes will include either: variables: int64 x ; x:coordinates = "a b" ; int64 a ; int64 b ; or variables: int64 x ; x:coordinates = "b a" ; int64 a ; int64 b ;

My review of _encode_coordinates leads me to think the behavior results from collecting names in a set. I'd be happy to offer a PR to make the coordinates attribute deterministic. I am not aware of a CF convention regarding any ordering, but would research and follow if it exists. If not, then I would probably sort at L701 and L722.

What happened?

The code example below results in False, because the data type of the a coordinate is changed from 'i4' to 'i8'.

What did you expect to happen?

I expect the result to be True. Creating a MultiIndex should not change the data type of the Indexes from which it is built.

Minimal Complete Verifiable Example

```Python import xarray as xr import numpy as np

ds = xr.Dataset(coords={'a': np.array([0], dtype='i4')}) ds['a'].values.dtype == ds.stack(b=('a',))['a'].values.dtype ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment