Question

If I have an 1D numpy.ndarray b and a Python function f that I want to vectorize, this is very easy using the numpy.vectorize function:

c = numpy.vectorize(f)(a).

But if f returns a 1D numpy.ndarray instead of a scalar, how can I build a 2D numpy.ndarray instead? (That is, I want every 1D numpy.ndarray returned from f to become a row in the new 2D numpy.ndarray.)

Example:

def f(x):
    return x * x

a = numpy.array([1,2,3])
c = numpy.vectorize(f)(a)

def f_1d(x):
    return numpy.array([x, x])

a = numpy.ndarray([1,2,3])
d = ???(f_1d)(a)

In the above example c would become array([1, 4, 9]). What should ??? be replaced with if d should become array([[1, 1], [2, 2], [3, 3]])?

Answer 1

Could do this instead:

def f_1d(x):
    return (x,x)
d = numpy.column_stack(numpy.vectorize(f_1d)(a))

will output:

array([[1, 1],
       [2, 2],
       [3, 3]])