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:
scipy.special.binom()
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).
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