Speed of Python

[Istvan Albert]

On Sep 7, 12:42 pm, "wang frank" <f... at hotmail.co.jp> wrote:

> Is my conclusion correct that Python is slower than matlab?

There are ways to speed up code execution, but to see substantial
improvement you'll need to use numpy and rework the code to operate
on  vectors/matrices rather than building the result one step at the
time. This applies to Octave as well. See the example code at the end
of the message.

With that code computing 1 million logarithms showed the following
tendency

original => 648.972728 msec per pass
optimized => 492.613773 msec per pass
with numpy => 120.578616 msec per pass

The "slowness of python in this example mainly comes from the function
call (math.log) as it seems about 30% of the runtime is spent calling
the function.

import timeit

setup = """
import math
from  numpy import arange, log
size = 1000
"""

code1 = """
#original code
for i in range(size):
    for j in range(size):
        a = math.log(j+1)
"""

code2 = """
# minor improvements lead to 15% faster speed
from math import log
for i in xrange(size):
    for j in xrange(size):
        a = log(j+1)
"""

code3 = """
# applying via a universal function makes it 5 times faster
for i in xrange(size):
    nums = arange( size )
    a = log( nums + 1)
"""

N = 3
codes = [ code1, code2, code3 ]

for stmt in codes:
    timer = timeit.Timer(stmt=stmt, setup=setup)
    msec  = 1000.0 * timer.timeit(number=N)/N
    print "%f msec per pass" % msec