Implemented entirely in C with micro-optimized code, including a significant rewrite of Python's abstract object API, xrand gives Python the ability to compete with compiled languages for developing software which makes heavy use of random number generation.
Includes all features from the standard library module random.py and more.
Here's a quick script comparing the difference in function calls per second xrand is capable of versus the equivalent random.py function. (ran on Ivy Bridge Core i7 on Windows 32-bit Python 3.6.0)
import random
from functools import partial
from itertools import product, repeat, starmap
import xrand
from tabulate import tabulate
from xrand import *
def compare(f, xrv, pyv, *args, xargs=None, rep='', **kws):
xargs = kws.pop('xargs', args)
x = perf(getattr(xrand, xrv), xargs)
y = perf(getattr(random, pyv), args)
z = x/y-1.0
data.append([f'{f}({rep})', x, y, z])
cards = [f'{a}{b}' for a,b in product('HSDC',(*'23456789','10',*'JKQA'))]
loaded_dice = choices({1:1, 2:1, 3:1, 4:1, 5:1, 6:5})
py_pop, py_weights = loaded_dice.population
py_dice = partial(random.choices,py_pop, cum_weights=py_weights)
data = [('f', 'xrand', 'random', 'dif')]
a = perf(iter_beta(None, 3.0, 3.0).take)
b = perf(random.betavariate, (3.0, 3.0))
data.append(['betavariate(3.0, 3.0)', a, b, a/b])
compare('choice', 'select', 'choice', cards, rep='cards')
a = perf(loaded_dice.next_n, (10,))
b = perf(py_dice, (), {'k':10})
data.append(['choices(cards, k=10)', a, b, a/b])
compare('shuffle', 'shuffle', 'shuffle', cards, rep='cards')
compare('sample', 'sample', 'sample', cards, 20, rep='cards, 20')
compare('randint', 'rand_int', 'randint', 0, 385, xargs=(385,), rep='385')
compare('random', 'rand_float', 'random')
compare('randrange', 'rand_index', 'randrange', 76000, rep='76000')
for i, (a, b, c, d) in enumerate(data[1:], 1):
data[i] = [a, f'{round(b):_}', f'{round(c):_}', f'{d:+,.0%}']
>>> print(tabulate(data))
--------------------- ---------- ---------- -------
f xrand random dif
betavariate(3.0, 3.0) 4_181_041 163_915 +2,551%
choice(cards) 18_632_245 759_560 +2,353%
choices(cards, k=10) 2_618_083 126_883 +2,063%
shuffle(cards) 1_480_818 16_220 +9,029%
sample(cards, 20) 1_329_424 30_537 +4,253%
randint(385) 11_644_439 455_809 +2,455%
random() 33_648_747 27_141_605 +24%
randrange(76000) 14_391_397 588_787 +2,344%
--------------------- ---------- ---------- -------