I was really curious to see how that could go. The best I was able to get is the following

# check_mypy.py
from __future__ import annotations

# Third party
import numpy as np
import numpy.typing as npt
from typing import cast


def prop(
    *,
    arr: npt.NDArray[np.float64] | npt.NDArray[np.int64] ,  # numpy array of floats or/and ints
) -> npt.NDArray[np.float64]:
    return cast(npt.NDArray[np.float64], 100 * arr / arr.sum())


def main() -> int:
    # check 1 - ints
    x : npt.NDArray[np.float64] = np.array([1, 2, 3])
    x1 = prop(arr=x)
    print(x1)

    # check 2 - mixed
    y : npt.NDArray[np.float64]  = np.array([1.4, 21, 3.2])
    y1 = prop(arr=y)
    print(y1)

    # check 3 - floats
    z: npt.NDArray[np.int64] = np.array([1.4, 2.1, 3.2])
    z1 = prop(arr=z)
    print(z1)

    return 0


if __name__ == "__main__":
    raise SystemExit(main())

So no mixed types as this doesn't make sense. Numpy arrays are always of a single type, even though you add other types when defining the array.

Using np.float64/np.int64 was actually what made it work in the end. Not sure if that is acceptable for you. Here you might want to define a Union or type var of all floats, but again not sure. Import thing is that it is a subclass of NP.generic.

Type annotating all defined arrays was also needed as mypy cannot determine the type on its own.

Last, returning fixed npt.NDArray[np.float64] as what you are computing there is always of that type.