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
_______________________________________________ NumPy-Discussion mailing list -- [email protected] To unsubscribe send an email to [email protected] https://mail.python.org/mailman3//lists/numpy-discussion.python.org Member address: [email protected]
