Question

I am trying to do a function iteratively to an array, and make a matrix composed of what it returns. If this was native python, what I would do is:

[func(x, y) for y in xrange(Y)]

but if I do that, I need to wrap it with numpy.matrix() to vectorize it. What is the numpy way of doing this? Right now I am initializing a zeros matrix and then populating it with elements I get from a for loop, but that seems inefficient.

Answer 1

Take a look at the numpy tutorial, especially the part about Universal Functions or ufuncs. A ufunc is:

Functions that operate element by element on whole arrays.

which sounds like what you’re asking for. Keep in mind that you probably don’t need to write your own ufunc, but just write func in terms of existing ufuncs. For example:

def hypot(a, b):
    return np.sqrt(a**2 + b**2)

>>> a = np.array([3., 5., 10.])
>>> b = np.array([4., 12., 24.,])
>>> hypot(a, b)
array([  5.,  13.,  26.])