Monte Carlo Simulation with Python: building a histogram on the fly

Here is a possible solution, with fixed bin size, and bins of the form [k * size, (k + 1) * size[. The function finalizebins returns two lists: one with bin counts (a), and the other (b) with bin lower bounds (the upper bound is deduced by adding binsize).

import math, randomdef updatebins(bins, binsize, x):    i = math.floor(x / binsize)    if i in bins:        bins[i] += 1    else:        bins[i] = 1def finalizebins(bins, binsize):    imin = min(bins.keys())    imax = max(bins.keys())    a = [0] * (imax - imin + 1)    b = [binsize * k for k in range(imin, imax + 1)]    for i in range(imin, imax + 1):        if i in bins:            a[i - imin] = bins[i]    return a, b# A test with a mixture of gaussian distributionsdef check(n):    bins = {}    binsize = 5.0    for i in range(n):        if random.random() > 0.5:            x = random.gauss(100, 50)        else:            x = random.gauss(-200, 150)        updatebins(bins, binsize, x)    return finalizebins(bins, binsize)a, b = check(10000)# This must be 10000sum(a)# Plot the datafrom matplotlib.pyplot import *bar(b,a)show()