This a small snippet of code I written dozens of times.  It would be nice to have this in the library along with the other distributions.

def binomialvariate(n, p):
    ''' Binomial distribution for the number of successes
        in *n* Bernoulli trials each with a probability *p*
        of success.

        Returns an integer in the range:  0 <= X <= n
    '''
    return sum(random() < p for i in range(n))

If you want to be able to switch to something more efficient for large `n`, Knuth describes a simple O(log(n)) algorithm in TAOCP volume 4 (and attributes it to J. H. Ahrens). It's essentially a bisection search on the value, using the fact that we can use the beta distribution to generate order statistics. Here's a (too) simple Python implementation. It still needs thorough testing, and could be optimised in many ways - e.g., using sensible crossover point for n and not recursing all the way to n = 0.


    def binomialvariate(n, p):
        if n == 0:
            return 0
        a, b = (1 + n)//2, 1 + n//2
        x = betavariate(a, b)
        if x < p:
            return a + binomialvariate(b - 1, (p - x)/(1 - x))
        else:
            return binomialvariate(a - 1, p/x)


>>> binomialvariate(10**10, 0.5)
4999944649

@Mark - Newb here and before I could see your reply, I sent a PR cos I thought simple implementation provided by Raymond Hettinger was good enough. Shall I update the PR? 

I will add the tests soon.

@avi That's Raymond's call, but IMO there's absolutely nothing wrong with getting a reference implementation (with tests, documentation, and all the usual trimmings) in first and then making algorithmic improvements later. 

Thanks for the PR!

> [...] and then making algorithmic improvements later.

Though thinking about it, there is *one* catch: for the random module, it's nice if reproducibility isn't broken more often than necessary in released versions, so it would be best to do any algorithmic tweaking *before* 3.9.0 is released. But that still gives us some time ...

[Avi]
> Shall I update the PR? 

Yes, please.

Is adding random.binomialvariate() still under consideration?

I would be willing to work on this addition to Lib/random.py, as well as the accompanying tests and docs.

Should I use the type of algorithm hinted at by Mark on 2019-07-01 17:56?

A presumed optimal version of this is already available in numpy.

https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.binomial.html

https://github.com/numpy/numpy/blob/2232a473f8713f532c8164c8cf616f7bd05f54a7/numpy/random/_generator.pyx#L2805

I think the NumPy implementation may be from here: https://core.ac.uk/download/pdf/11007254.pdf

(though I'm struggling to find a clear citation in the NumPy source)

Nope, that's the wrong paper. It looks as though this is the right one, but it's hidden behind a paywall: https://dl.acm.org/doi/abs/10.1145/42372.42381