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 think of a reason why it wouldn't work with b as an ndarray or list, i.e. f:a->b[] and g:a[]->b[][]

For example:

import numpy as np
def f(x):
    return x * np.array([1,1,1,1,1], dtype=np.float32)
g = np.vectorize(f, otypes=[np.ndarray])
a = np.arange(4)
print(g(a))

This yields:

array([[ 0.  0.  0.  0.  0.],
       [ 1.  1.  1.  1.  1.],
       [ 2.  2.  2.  2.  2.],
       [ 3.  3.  3.  3.  3.]], dtype=object)

Ok, so that gives the right values, but the wrong dtype. And even worse:

g(a).shape

yields:

(4,)

So this array is pretty much useless. I know I can convert it doing:

np.array(map(list, a), dtype=np.float32)

to give me what I want:

array([[ 0.,  0.,  0.,  0.,  0.],
       [ 1.,  1.,  1.,  1.,  1.],
       [ 2.,  2.,  2.,  2.,  2.],
       [ 3.,  3.,  3.,  3.,  3.]], dtype=float32)

but that is neither efficient nor pythonic. Can any of you guys find a cleaner way to do this?

Thanks in advance!

Answer 1

np.vectorize is just a convenience function. It doesn't actually make code run any faster. If it isn't convenient to use np.vectorize, simply write your own function that works as you wish.

The purpose of np.vectorize is to transform functions which are not numpy-aware (e.g. take floats as input and return floats as output) into functions that can operate on (and return) numpy arrays.

Your function f is already numpy-aware -- it uses a numpy array in its definition and returns a numpy array. So np.vectorize is not a good fit for your use case.

The solution therefore is just to roll your own function f that works the way you desire.