Python Binomial Coefficient

This question is old but as it comes up high on search results I will point out that scipy has two functions for computing the binomial coefficients:

1. scipy.special.binom()
2. scipy.special.comb()import scipy.special # the two give the same results scipy.special.binom(10, 5) # 252.0 scipy.special.comb(10, 5) # 252.0 scipy.special.binom(300, 150) # 9.375970277281882e+88 scipy.special.comb(300, 150) # 9.375970277281882e+88 # ...but with `exact == True` scipy.special.comb(10, 5, exact=True) # 252 scipy.special.comb(300, 150, exact=True) # 393759702772827452793193754439064084879232655700081358920472352712975170021839591675861424

Note that scipy.special.comb(exact=True) uses Python integers, and therefore it can handle arbitrarily large results!

Speed-wise, the three versions give somewhat different results:

num = 300

%timeit [[scipy.special.binom(n, k) for k in range(n + 1)] for n in range(num)]
# 52.9 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit [[scipy.special.comb(n, k) for k in range(n + 1)] for n in range(num)]
# 183 ms ± 814 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)each)

%timeit [[scipy.special.comb(n, k, exact=True) for k in range(n + 1)] for n in range(num)]
# 180 ms ± 649 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

(and for n = 300, the binomial coefficients are too large to be represented correctly using float64 numbers, as shown above).