I'd like to create a random list of integers for testing purposes. The distribution of the numbers is not important. The only thing that is counting is time. I know generating random numbers is a time-consuming task, but there must be a better way.
Here's my current solution:
import random
import timeit
# Random lists from [0-999] interval
print [random.randint(0, 1000) for r in xrange(10)] # v1
print [random.choice([i for i in xrange(1000)]) for r in xrange(10)] # v2
# Measurement:
t1 = timeit.Timer('[random.randint(0, 1000) for r in xrange(10000)]', 'import random') # v1
t2 = timeit.Timer('random.sample(range(1000), 10000)', 'import random') # v2
print t1.timeit(1000)/1000
print t2.timeit(1000)/1000
v2 is faster than v1, but it is not working on such a large scale. It gives the following error:
ValueError: sample larger than population
Is there a fast, efficient solution that works at that scale?
Some results from the answer
Andrew's: 0.000290962934494
gnibbler's: 0.0058455221653
KennyTM's: 0.00219276118279
NumPy came, saw, and conquered.
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