parallelized monte carlo integration using python 3

monte_carlo.py

import concurrent.futures
import random


def f(x):
    return x**2

def integrate(func, start, end, num_samples=1000):
    """uses random sampling to integrate func from 'start' to 'end'"""
    with concurrent.futures.ProcessPoolExecutor() as executor:
        random_samples = (random.uniform(start, end) for i in range(num_samples))
        total = sum(executor.map(func, random_samples))
        return (end-start) * total /num_samples

#Integrate f(x) = x^2 from 0 to 100. Exact answer is 333333.33
print(integrate(f, 0, 100, num_samples=100000))

it's a bummer they don't have parallelizable comprehensions

executer.[func(i) for i in random_samples]