Generating Discrete random variables with specified weights using SciPy or NumPy

Question

I am looking for a simple function that can generate an array of specified random values based on their corresponding (also specified) probabilities. I only need it to generate float values, but I don't see why it shouldn't be able to generate any scalar. I can think of many ways of building this from existing functions, but I think I probably just missed an obvious SciPy or NumPy function.

E.g.:

>>> values = [1.1, 2.2, 3.3]
>>> probabilities = [0.2, 0.5, 0.3]
>>> print some_function(values, probabilities, size=10)
(2.2, 1.1, 3.3, 3.3, 2.2, 2.2, 1.1, 2.2, 3.3, 2.2)

Note: I found scipy.stats.rv_discrete but I don't understand how it works. Specifically, I do not understand what this (below) means nor what it should do:

numargs = generic.numargs
[ <shape(s)> ] = ['Replace with resonable value', ]*numargs

If rv_discrete is what I should be using, could you please provide me with a simple example and an explanation of the above "shape" statement?

Answer 1

Drawing from a discrete distribution is directly built into numpy. The function is called random.choice (difficult to find without any reference to discrete distributions in the numpy docs).

elements = [1.1, 2.2, 3.3]
probabilities = [0.2, 0.5, 0.3]
np.random.choice(elements, 10, p=probabilities)