Using Numpy Vectorize on Functions that Return Vectors

Question

numpy.vectorize takes a function f:a->b and turns it into g:a[]->b[].

This works fine when a and b are scalars, but I can\'t t

Answer 1

You want to vectorize the function

import numpy as np
def f(x):
    return x * np.array([1,1,1,1,1], dtype=np.float32)

Assuming that you want to get single np.float32 arrays as result, you have to specify this as otype. In your question you specified however otypes=[np.ndarray] which means you want every element to be an np.ndarray. Thus, you correctly get a result of dtype=object.

The correct call would be

np.vectorize(f, signature='()->(n)', otypes=[np.float32])

For such a simple function it is however better to leverage numpy's ufunctions; np.vectorize just loops over it. So in your case just rewrite your function as

def f(x):
    return np.multiply.outer(x, np.array([1,1,1,1,1], dtype=np.float32))

This is faster and produces less obscure errors (note however, that the results dtype will depend on x if you pass a complex or quad precision number, so will be the result).