Question

I am baffled by this

def main():
    for i in xrange(2560000):
        a = [0.0, 0.0, 0.0]

main()

$ time python test.py

real     0m0.793s

Let’s now see with numpy:

import numpy

def main():
    for i in xrange(2560000):
        a = numpy.array([0.0, 0.0, 0.0])

main()

$ time python test.py

real    0m39.338s

Holy CPU cycles batman!

Using numpy.zeros(3) improves, but still not enough IMHO

$ time python test.py

real    0m5.610s
user    0m5.449s
sys 0m0.070s

numpy.version.version = ‘1.5.1’

If you are wondering if the list creation is skipped for optimization in the first example, it is not:

  5          19 LOAD_CONST               2 (0.0)
             22 LOAD_CONST               2 (0.0)
             25 LOAD_CONST               2 (0.0)
             28 BUILD_LIST               3
             31 STORE_FAST               1 (a)

Answer 1

Numpy is optimised for large amounts of data. Give it a tiny 3 length array and, unsurprisingly, it performs poorly.

Consider a separate test

import timeit

reps = 100

pythonTest = timeit.Timer('a = [0.] * 1000000')
numpyTest = timeit.Timer('a = numpy.zeros(1000000)', setup='import numpy')
uninitialised = timeit.Timer('a = numpy.empty(1000000)', setup='import numpy')
# empty simply allocates the memory. Thus the initial contents of the array 
# is random noise

print 'python list:', pythonTest.timeit(reps), 'seconds'
print 'numpy array:', numpyTest.timeit(reps), 'seconds'
print 'uninitialised array:', uninitialised.timeit(reps), 'seconds'

And the output is

python list: 1.22042918205 seconds
numpy array: 1.05412316322 seconds
uninitialised array: 0.0016028881073 seconds

It would seem that it is the zeroing of the array that is taking all the time for numpy. So unless you need the array to be initialised then try using empty.