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Extend and improve the examples for the random module

This commit is contained in:
Raymond Hettinger 2016-11-21 01:59:39 -08:00
parent ac0720eaa4
commit 6befb64172
1 changed files with 29 additions and 5 deletions
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34
Doc/library/random.rst
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@ -345,8 +345,8 @@ Basic examples::
>>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive >>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
26 26
>>> choice('abcdefghij') # Single random element from a sequence >>> choice(['win', 'lose', 'draw']) # Single random element from a sequence
'c' 'draw'
>>> deck = 'ace two three four'.split() >>> deck = 'ace two three four'.split()
>>> shuffle(deck) # Shuffle a list >>> shuffle(deck) # Shuffle a list
@ -370,8 +370,9 @@ Simulations::
>>> print(seen.count('tens') / 20) >>> print(seen.count('tens') / 20)
0.15 0.15
# Estimate the probability of getting 5 or more heads from 7 spins # Estimate the probability of getting 5 or more heads
# of a biased coin that settles on heads 60% of the time. # from 7 spins of a biased coin that settles on heads
# 60% of the time.
>>> n = 10000 >>> n = 10000
>>> cw = [0.60, 1.00] >>> cw = [0.60, 1.00]
>>> sum(choices('HT', cum_weights=cw, k=7).count('H') >= 5 for i in range(n)) / n >>> sum(choices('HT', cum_weights=cw, k=7).count('H') >= 5 for i in range(n)) / n
@ -416,4 +417,27 @@ between the effects of a drug versus a placebo::
print(f'{n} label reshufflings produced only {count} instances with a difference') print(f'{n} label reshufflings produced only {count} instances with a difference')
print(f'at least as extreme as the observed difference of {observed_diff:.1f}.') print(f'at least as extreme as the observed difference of {observed_diff:.1f}.')
print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null') print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null')
print(f'hypothesis that the observed difference occurred due to chance.') print(f'hypothesis that there is no difference between the drug and the placebo.')
Simulation of arrival times and service deliveries in a single server queue::
from random import gauss, expovariate
average_arrival_interval = 5.6
average_service_time = 5.0
stdev_service_time = 0.5
num_waiting = 0
arrival = service_end = 0.0
for i in range(10000):
num_waiting += 1
arrival += expovariate(1.0 / average_arrival_interval)
print(f'{arrival:6.1f} arrived')
while arrival > service_end:
num_waiting -= 1
service_start = service_end if num_waiting else arrival
service_time = gauss(average_service_time, stdev_service_time)
service_end = service_start + service_time
print(f'\t\t{service_start:.1f} to {service_end:.1f} serviced')