What happened?

I'm testing my code with numpy 2.0 and current main xarray and dask and ran into a change that I guess is expected given the way xarray does things, but want to make sure as it could be unexpected for many users.

Doing DataArray.where with an integer array less than 64-bits and an integer as the new value will upcast the array to 64-bit integers (python's int). With old versions of numpy this would preserve the dtype of the array. As far as I can tell the relevant xarray code hasn't changed so this seems to be more about numpy making things more consistent.

The main problem seems to come down to:

https://github.com/pydata/xarray/blob/d933578ebdc4105a456bada4864f8ffffd7a2ced/xarray/core/duck_array_ops.py#L218

As this converts my scalar input int to a numpy array. If it didn't do this array conversion then numpy works as expected. See the MCVE for the xarray specific example, but here's the numpy equivalent:

```python import numpy as np

a = np.zeros((2, 2), dtype=np.uint16)

what I'm intending to do with my xarray data_arr.where(cond, 2)

np.where(a != 0, a, 2).dtype

dtype('uint16')

equivalent to what xarray does:

np.where(a != 0, a, np.asarray(2)).dtype

dtype('int64')

workaround, cast my scalar to a specific numpy type

np.where(a != 0, a, np.asarray(np.uint16(2))).dtype

dtype('uint16')

```

From a numpy point of view, the second where call makes sense that 2 arrays should be upcast to the same dtype so they can be combined. But from an xarray user point of view, I'm entering a scalar so I expect it to be the same as the first where call above.

What did you expect to happen?

See above.

Minimal Complete Verifiable Example

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

data_arr = xr.DataArray(np.array([1, 2], dtype=np.uint16)) print(data_arr.where(data_arr == 2, 3).dtype)

int64

```

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.
• [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

No response

Anything else we need to know?

Numpy 1.x preserves the dtype.

```python In [1]: import numpy as np

In [2]: np.asarray(2).dtype Out[2]: dtype('int64')

In [3]: a = np.zeros((2, 2), dtype=np.uint16)

In [4]: np.where(a != 0, a, np.asarray(2)).dtype Out[4]: dtype('uint16')

In [5]: np.where(a != 0, a, np.asarray(np.uint16(2))).dtype Out[5]: dtype('uint16') ```

Environment

What happened:

While working on some tests that catch and check for warnings in my library I found that xarray with new versions of Python (I think this is the trigger) causes a ton of DeprecationWarnings on import:

python /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/xarray/core/pycompat.py:22: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. duck_array_version = LooseVersion(duck_array_module.__version__)

What you expected to happen:

No warnings.

Minimal Complete Verifiable Example:

```python import warnings warnings.simplefilter("always")

import xarray as xr ```

Results in:

/home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/xarray/core/pycompat.py:22: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. duck_array_version = LooseVersion(duck_array_module.__version__) /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/xarray/core/pycompat.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. duck_array_version = LooseVersion("0.0.0") /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/xarray/core/pycompat.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. duck_array_version = LooseVersion("0.0.0") /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. other = LooseVersion(other) /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. other = LooseVersion(other) /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/xarray/core/npcompat.py:82: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. if LooseVersion(np.__version__) >= "1.20.0": /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. other = LooseVersion(other) /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/xarray/core/pdcompat.py:45: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. if LooseVersion(pd.__version__) < "0.25.0": /home/davidh/miniconda3/envs/satpy_py39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. other = LooseVersion(other)

Anything else we need to know?:

Environment: