A weighted version of random.choice

Question

I needed to write a weighted version of random.choice (each element in the list has a different probability for being selected). This is what I came up with:

Answer 1

1. Arrange the weights into a cumulative distribution.
2. Use random.random() to pick a random float 0.0 <= x < total.
3. Search the distribution using bisect.bisect as shown in the example at http://docs.python.org/dev/library/bisect.html#other-examples.

from random import random
from bisect import bisect

def weighted_choice(choices):
    values, weights = zip(*choices)
    total = 0
    cum_weights = []
    for w in weights:
        total += w
        cum_weights.append(total)
    x = random() * total
    i = bisect(cum_weights, x)
    return values[i]

>>> weighted_choice([("WHITE",90), ("RED",8), ("GREEN",2)])
'WHITE'

If you need to make more than one choice, split this into two functions, one to build the cumulative weights and another to bisect to a random point.