mean calculation benchmark

calc_mean_bench.py

import numpy as np
import random
import time
from contextlib import contextmanager

data = []
for i in xrange(300000):
    data.append(random.randint(1, 100000000))

float_data = []
for i in xrange(300000):
    float_data.append(random.random() * 10000000)

@contextmanager
def measure(label):
    t1 = time.time()
    yield
    t2 = time.time()
    print '%s: %.3fsec' % (label, t2 - t1)

n = 500

with measure('normal-comprehension'):
    for i in xrange(n):
        sum([ float(i) for i in data ]) / len(data)

with measure('normal-map'):
    for i in xrange(n):
        sum(map(float, data)) / len(data)

with measure('normal-last-float-conversoin'):
    for i in xrange(n):
        float(sum(data)) / len(data)

with measure('normal-float'):
    for i in xrange(n):
        sum(float_data) / len(float_data)

with measure('numpy-mean'):
    for i in xrange(n):
        np.mean(data)

with measure('numpy-mean-float'):
    for i in xrange(n):
        np.mean(float_data)


with measure('numpy-mean-with-numpy-array'):
    for i in xrange(n):
        data2 = np.array(data)
        np.mean(data2)

with measure('numpy-mean-with-numpy-array-float'):
    for i in xrange(n):
        data2 = np.array(float_data)
        np.mean(data2)


nparray_data = np.array(data)
with measure('numpy-mean-to-numpy-array'):
    for i in xrange(n):
        np.mean(nparray_data)

nparray_float_data = np.array(data)
with measure('numpy-mean-to-numpy-array-float'):
    for i in xrange(n):
        np.mean(nparray_float_data)

result on my laptop(MacBook Pro 15inch, 2.8Ghz Core i7 4cores)

normal-comprehension: 28.850sec
normal-map: 13.352sec
normal-last-float-conversoin: 0.855sec
normal-float: 0.815sec
numpy-mean: 6.043sec
numpy-mean-float: 3.352sec
numpy-mean-with-numpy-array: 6.065sec
numpy-mean-with-numpy-array-float: 3.382sec
numpy-mean-to-numpy-array: 0.182sec
numpy-mean-to-numpy-array-float: 0.175sec