NumPy: Alternative to `vectorize` that lets me access the array

Question

I have this code:

output_array = np.vectorize(f, otypes='d')(input_array)

And I'd like to replace it with this code, which is supposed to give the same output:

output_array = np.ndarray(input_array.shape, dtype='d')
for i, item in enumerate(input_array):
    output_array[i] = f(item)

The reason I want the second version is that I can then start iterating on output_array in a separate thread, while it's being calculated. (Yes, I know about the GIL, that part is taken care of.)

Unfortunately, the for loop is very slow, even when I'm not processing the data on separate thread. I benchmarked it on both CPython and PyPy3, which is my target platform. On CPython it's 3 times slower than vectorize, and on PyPy3 it's 67 times slower than vectorize!

That's despite the fact that the Numpy documentation says "The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop."

Any idea why my implementation is slow, and how to make a fast implementation that still allows me to use output_array before it's finished?

Answer 1

Sebastian Berg gave me a solution. When iterating over items from the input array, use item.item() rather than just item. This turns the numpy.float64 objects to normal Python floats, making everything much faster and solving my particular problem :)