Using Numpy Vectorize on Functions that Return Vectors

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!