python - Why is numpy.array so slow? -

i baffled this

def main():     in xrange(2560000):         = [0.0, 0.0, 0.0]  main()  $ time python test.py  real     0m0.793s 

let's see numpy:

import numpy  def main():     in xrange(2560000):         = 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, still not enough imho

$ time python test.py  real    0m5.610s user    0m5.449s sys 0m0.070s 

numpy.version.version = '1.5.1'

if wondering if list creation skipped optimization in first example, 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) 

numpy optimised large amounts of data. give tiny 3 length array and, unsurprisingly, performs poorly.

consider 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 allocates memory. initial contents of array  # random noise  print 'python list:', pythontest.timeit(reps), 'seconds' print 'numpy array:', numpytest.timeit(reps), 'seconds' print 'uninitialised array:', uninitialised.timeit(reps), 'seconds' 

and output is

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

it seem zeroing of array taking time numpy. unless need array initialised try using empty.