Extend and improve the examples for the random module
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@ -345,8 +345,8 @@ Basic examples::
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>>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
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26
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>>> choice('abcdefghij') # Single random element from a sequence
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'c'
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>>> choice(['win', 'lose', 'draw']) # Single random element from a sequence
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'draw'
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>>> deck = 'ace two three four'.split()
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>>> shuffle(deck) # Shuffle a list
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@ -370,8 +370,9 @@ Simulations::
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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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# Estimate the probability of getting 5 or more heads
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# from 7 spins of a biased coin that settles on heads
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# 60% of the time.
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>>> n = 10000
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>>> cw = [0.60, 1.00]
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>>> sum(choices('HT', cum_weights=cw, k=7).count('H') >= 5 for i in range(n)) / n
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@ -416,4 +417,27 @@ between the effects of a drug versus a placebo::
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print(f'{n} label reshufflings produced only {count} instances with a difference')
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print(f'at least as extreme as the observed difference of {observed_diff:.1f}.')
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print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null')
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print(f'hypothesis that the observed difference occurred due to chance.')
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print(f'hypothesis that there is no difference between the drug and the placebo.')
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Simulation of arrival times and service deliveries in a single server queue::
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from random import gauss, expovariate
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average_arrival_interval = 5.6
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average_service_time = 5.0
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stdev_service_time = 0.5
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num_waiting = 0
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arrival = service_end = 0.0
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for i in range(10000):
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num_waiting += 1
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arrival += expovariate(1.0 / average_arrival_interval)
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print(f'{arrival:6.1f} arrived')
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while arrival > service_end:
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num_waiting -= 1
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service_start = service_end if num_waiting else arrival
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service_time = gauss(average_service_time, stdev_service_time)
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service_end = service_start + service_time
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print(f'\t\t{service_start:.1f} to {service_end:.1f} serviced')
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