@@ -364,25 +364,29 @@ Basic examples::
364364
365365Simulations::
366366
367- # Six roulette wheel spins (weighted sampling with replacement)
367+ >>> # Six roulette wheel spins (weighted sampling with replacement)
368368 >>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
369369 ['red', 'green', 'black', 'black', 'red', 'black']
370370
371- # Deal 20 cards without replacement from a deck of 52 playing cards
372- # and determine the proportion of cards with a ten-value (i.e. a ten,
373- # jack, queen, or king).
371+ >>> # Deal 20 cards without replacement from a deck of 52 playing cards
372+ >>> # and determine the proportion of cards with a ten-value
373+ >>> # (a ten, jack, queen, or king).
374374 >>> deck = collections.Counter(tens=16, low_cards=36)
375375 >>> seen = sample(list(deck.elements()), k=20)
376- >>> print( seen.count('tens') / 20)
376+ >>> seen.count('tens') / 20
377377 0.15
378378
379- # Estimate the probability of getting 5 or more heads from 7 spins
380- # of a biased coin that settles on heads 60% of the time.
381- >>> n = 10000
382- >>> cw = [0.60, 1.00]
383- >>> sum(choices('HT', cum_weights=cw, k=7).count('H') >= 5 for i in range(n)) / n
379+ >>> # Estimate the probability of getting 5 or more heads from 7 spins
380+ >>> # of a biased coin that settles on heads 60% of the time.
381+ >>> trial = lambda: choices('HT', cum_weights=(0.60, 1.00), k=7).count('H') >= 5
382+ >>> sum(trial() for i in range(10000)) / 10000
384383 0.4169
385384
385+ >>> # Probability of the median of 5 samples being in middle two quartiles
386+ >>> trial = lambda : 2500 <= sorted(choices(range(10000), k=5))[2] < 7500
387+ >>> sum(trial() for i in range(10000)) / 10000
388+ 0.7958
389+
386390Example of `statistical bootstrapping
387391<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)> `_ using resampling
388392with replacement to estimate a confidence interval for the mean of a sample of
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