[Python] When I create an array of Numpy floats, I get an array of Python floats

The code:

import sys
import numpy as np

print(f"We are using Python {sys.version}", file=sys.stderr)
print(f"We are using numpy version {np.__version__}", file=sys.stderr) # 2.2.1

def find_non_numpy_floats(x: any) -> bool:
if not (isinstance(x, np.float64)):
print(f"Found non-numpy.float64: {x} of type {type(x)}", file=sys.stderr)
return False
else:
return True


w: np.ndarray = np.zeros((2, 2), dtype=np.float64)

np.vectorize(lambda x: find_non_numpy_floats(x))(w)

assert (np.all(np.vectorize(lambda x: isinstance(x, np.float64))(w))), "try to keep using the numpy floats"


I'm expecting Numpy.zeros to generate an array of Numpy float64, which are not the same as Python float if I understand correctly (IEEE 64-bit floats vs something Python specific?)

However the above results in:

We are using Python 3.13.1 (main, Dec 9 2024, 00:00:00) [GCC 14.2.1 20240912 (Red Hat 14.2.1-3)]
We are using numpy version 2.2.1
Found non-numpy.float64: 0.0 of type <class 'float'>
Found non-numpy.float64: 0.0 of type <class 'float'>
Found non-numpy.float64: 0.0 of type <class 'float'>
Found non-numpy.float64: 0.0 of type <class 'float'>


and an assertion error.

Why is that and how can I fix this (and should I want to?)

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