Re: Introduce `__numpy_dtype__` protocol

On Sat, Nov 8, 2025 at 2:25 PM sebastian <[email protected]> wrote:

> Hi all,
>
> A heads up, that I have a PR to introduce `__numpy_dtype__` as a
> protocol.
> Right now when NumPy coerces to a dtype (e.g. via `np.dtype()` or
> `dtype=...`)
> it checks for a `.dtype` attribute on the object and if that is a NumPy
> dtype
> the conversion succeeds.
>
> The problem with this two-fold:
> 1. Not all dtypes are NumPy dtypes.  Right now a non-NumPy dtype can
> interoperate
>     with NumPy by having a `dtype.dtype` attribute that is a NumPy dtype.
>   But that
>     seems awkward and may in some cases prevent the use of the dtype
> attribute for
>     something more to the point.
> 2. While numpy prevents `np.dtype(numpy_array)`, some other array-like
> objects will
>     be converted to a dtype, e.g.:
>         np.array([1, 2, 3], dtype=pandas.Series([1, 2, 3]))
>     works but should not.  Switching to `__numpy_dtype__` allows us to
> disable this trap.
>
> Right now there is no semantic change, besides that downstream would be
> encouraged to
> implement `__numpy_dtype__` if they currently have a `dtype` attribute
> that interoperates
> with NumPy.
> (In the case of a pandas Series that might be to raise an error or just
> set it to `None` so
> that NumPy raises one.)
>
> To me this seems be uncontroversial, since using `.dtype` has problems
> and this gives us a
> way to move away from it and disambiguate these situations.
>

Thanks for working on this Sebastian. After reading the discussion in
https://github.com/numpy/numpy/issues/25306, I agree that this is a useful
thing to add and I don't see any problems.

Cheers,
Ralf

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