Question

I’m in the process of working on programming project that involves some pretty extensive Monte Carlo simulation in Python, and as such the generation of a tremendous number of random numbers. Very nearly all of them, if not all of them, will be able to be generated by Python’s built in random module.

I’m something of a coding newbie, and unfamiliar with efficient and inefficient ways to do things. Is it faster to generate say, all the random numbers as a list, and then iterate through that list, or generate a new random number each time a function is called, which will be in a very large loop?

Or some other, undoubtedly more clever method?

Answer 1

Generate a random number each time. Since the inner workings of the loop only care about a single random number, generate and use it inside the loop.

Example:

# do this:
import random

for x in xrange(SOMEVERYLARGENUMBER):
    n = random.randint(1,1000) # whatever your range of random numbers is
    # Do stuff with n

# don't do this:
import random

# This list comprehension generates random numbers in a list
numbers = [random.randint(1,1000) for x in xrange(SOMEVERYLARGENUMBER)]

for n in numbers:
    # Do stuff with n

Obviously, in practical terms it really doesn’t matter, unless you’re dealing with billions and billions of iterations, but why bother generating all those numbers if you’re only going to be using one at a time?