Numpy is a big improvement here. In Py 2.7 I get this output if I run
Steven's benchmark:
2.10364603996
3.68471002579
4.01849389076
7.41974878311
10.4202470779
9.16782712936
3.36137390137
(confirming his results). If I then run the numpy idiom for this:
########################
import random
from timeit import Timer
import numpy
sieve = numpy.array([random.random() < 0.5 for i in range(10**7)],
dtype=bool)
setup = """from __main__ import sieve
from itertools import islice
hi = 7*10**6
"""
t1 = Timer("(True == sieve[:hi]).sum()", setup)
print(min(t1.repeat(number=10)))
###########################
I get :
0.344316959381
It likely consumes less space as well, since it doesn't store Python
objects in the array.
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