[Python-checkins] cpython (3.6): Neaten-up and extend the examples in the random module docs.
raymond.hettinger
python-checkins at python.org
Sun Dec 4 14:01:04 EST 2016
https://hg.python.org/cpython/rev/b966b3426bd1
changeset: 105456:b966b3426bd1
branch: 3.6
parent: 105454:352915c85e1e
user: Raymond Hettinger <python at rcn.com>
date: Sun Dec 04 11:00:34 2016 -0800
summary:
Neaten-up and extend the examples in the random module docs.
files:
Doc/library/random.rst | 24 ++++++++++++++----------
1 files changed, 14 insertions(+), 10 deletions(-)
diff --git a/Doc/library/random.rst b/Doc/library/random.rst
--- a/Doc/library/random.rst
+++ b/Doc/library/random.rst
@@ -364,25 +364,29 @@
Simulations::
- # Six roulette wheel spins (weighted sampling with replacement)
+ >>> # Six roulette wheel spins (weighted sampling with replacement)
>>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
['red', 'green', 'black', 'black', 'red', 'black']
- # Deal 20 cards without replacement from a deck of 52 playing cards
- # and determine the proportion of cards with a ten-value (i.e. a ten,
- # jack, queen, or king).
+ >>> # Deal 20 cards without replacement from a deck of 52 playing cards
+ >>> # and determine the proportion of cards with a ten-value
+ >>> # (a ten, jack, queen, or king).
>>> deck = collections.Counter(tens=16, low_cards=36)
>>> seen = sample(list(deck.elements()), k=20)
- >>> print(seen.count('tens') / 20)
+ >>> seen.count('tens') / 20
0.15
- # Estimate the probability of getting 5 or more heads from 7 spins
- # of a biased coin that settles on heads 60% of the time.
- >>> n = 10000
- >>> cw = [0.60, 1.00]
- >>> sum(choices('HT', cum_weights=cw, k=7).count('H') >= 5 for i in range(n)) / n
+ >>> # Estimate the probability of getting 5 or more heads from 7 spins
+ >>> # of a biased coin that settles on heads 60% of the time.
+ >>> trial = lambda: choices('HT', cum_weights=(0.60, 1.00), k=7).count('H') >= 5
+ >>> sum(trial() for i in range(10000)) / 10000
0.4169
+ >>> # Probability of the median of 5 samples being in middle two quartiles
+ >>> trial = lambda : 2500 <= sorted(choices(range(10000), k=5))[2] < 7500
+ >>> sum(trial() for i in range(10000)) / 10000
+ 0.7958
+
Example of `statistical bootstrapping
<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling
with replacement to estimate a confidence interval for the mean of a sample of
--
Repository URL: https://hg.python.org/cpython
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