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.
Cheers,
Sebastian
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