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@ -322,36 +322,49 @@ change across Python versions, but two aspects are guaranteed not to change:
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Examples and Recipes
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--------------------
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Basic usage::
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Basic examples::
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>>> random.random() # Random float x, 0.0 <= x < 1.0
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>>> random() # Random float: 0.0 <= x < 1.0
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0.37444887175646646
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>>> random.uniform(1, 10) # Random float x, 1.0 <= x < 10.0
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1.1800146073117523
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>>> uniform(2, 10) # Random float: 2.0 <= x < 10.0
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3.1800146073117523
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>>> random.randrange(10) # Integer from 0 to 9
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>>> expovariate(1/5) # Interval between arrivals averaging 5 seconds
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5.148957571865031
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>>> randrange(10) # Integer from 0 to 9
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7
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>>> random.randrange(0, 101, 2) # Even integer from 0 to 100
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>>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
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26
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>>> random.choice('abcdefghij') # Single random element
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>>> choice('abcdefghij') # Single random element from a sequence
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'c'
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>>> deck = ['jack', 'queen', 'king', 'ace']
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>>> shuffle(deck)
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>>> deck = 'ace two three four'.split()
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>>> shuffle(deck) # Shuffle a list
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>>> deck
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['king', 'queen', 'ace', 'jack']
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['four', 'two', 'ace', 'three']
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>>> random.sample([1, 2, 3, 4, 5], k=3) # Three samples without replacement
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[4, 1, 5]
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>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
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[40, 10, 50, 30]
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>>> # Six weighted samples with replacement
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Simulations::
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# Six roulette wheel spins (weighted sampling with replacement)
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>>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
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['red', 'green', 'black', 'black', 'red', 'black']
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# Probability of getting 5 or more heads from 7 spins
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# Deal 20 cards without replacement from a deck of 52
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# playing cards and determine the proportion of cards
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# with a ten-value (i.e. a ten, jack, queen, or king).
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>>> deck = collections.Counter(tens=16, low_cards=36)
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>>> seen = sample(list(deck.elements()), k=20)
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>>> print(seen.count('tens') / 20)
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0.15
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# Estimate the probability of getting 5 or more heads from 7 spins
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# of a biased coin that settles on heads 60% of the time.
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>>> n = 10000
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>>> cw = [0.60, 1.00]
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@ -360,8 +373,8 @@ Basic usage::
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Example of `statistical bootstrapping
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<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling
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with replacement to estimate a confidence interval for the mean of a small
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sample of size five::
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with replacement to estimate a confidence interval for the mean of a sample of
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size five::
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# http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm
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from statistics import mean
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