From: Miss Islington (bot) <31488909+miss-islington@users.noreply.github.com>
Date: Tue, 9 Jan 2024 19:24:55 +0000 (+0100)
Subject: [3.12] Simplify binomial approximation example with random.binomialvariate() (gh... 
X-Git-Tag: v3.12.2~206
X-Git-Url: http://git.ipfire.org/gitweb.cgi?a=commitdiff_plain;h=9a6b99ee8b750c8891b488a6bd60696bc164c6fa;p=thirdparty%2FPython%2Fcpython.git

[3.12] Simplify binomial approximation example with random.binomialvariate() (gh-113871) (gh-113872)
---

diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst
index 318e5d746114..36f47b92ee3d 100644
--- a/Doc/library/statistics.rst
+++ b/Doc/library/statistics.rst
@@ -1016,19 +1016,16 @@ probability that the Python room will stay within its capacity limits?
     >>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4)
     0.8402
 
-    >>> # Solution using the cumulative binomial distribution
+    >>> # Exact solution using the cumulative binomial distribution
     >>> from math import comb, fsum
     >>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4)
     0.8402
 
     >>> # Approximation using a simulation
-    >>> from random import seed, choices
+    >>> from random import seed, binomialvariate
     >>> seed(8675309)
-    >>> def trial():
-    ...     return choices(('Python', 'Ruby'), (p, q), k=n).count('Python')
-    ...
-    >>> mean(trial() <= k for i in range(10_000))
-    0.8398
+    >>> mean(binomialvariate(n, p) <= k for i in range(10_000))
+    0.8406
 
 
 Naive bayesian classifier