What's faster, `filter()` or `reduce()`? Find out using a very primitive Monte Carlo search für π.

__init__.py

from random import random
from datetime import datetime


def simulate_dart(x=None):
    point_x = random()
    point_y = random()
    distance = (point_x**2 + point_y**2)**.5    # x**0.5 == sqrt(x)
    return (distance < 1) and 1 or 0


def run_simulation_filter(steps=1000000):
    hits = filter(simulate_dart,range(steps))
    ratio = len(hits) / float(steps)
    return ratio * 4


def run_simulation_reduce(steps=1000000):
    hits = reduce(lambda acc, n: acc + simulate_dart(), range(steps))
    ratio = hits / float(steps)
    return ratio * 4


if __name__ == "__main__":
    start = datetime.now()
    print "Starte Simulation..."
    print "Ergebnis: ", run_simulation_filter(1000000000)     # filter ist ca. 15% schneller
    print "Zeit: ", datetime.now() - start