Performance of filling a numpy array

I just noticed this interesting piece of trivia. Apparently it's much faster to create a zeros array and filling the individual values, than creating an array from scratch by passing a list. This program

import numpy
import time

before = time.time()
for i in xrange(10000000):
    a=numpy.zeros(6)
    a[0] = 1.0
    a[1] = 2.0
    a[2] = 3.0
    a[3] = 4.0
    a[4] = 5.0
    a[5] = 6.0
print time.time() - before

before = time.time()
b=[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
for i in xrange(10000000):
    a=numpy.array(b)
print time.time() - before

outputs these times on my machine

11.7937290668
49.3275549412

I don't know if this scales up, but for small arrays, creating a zero array and then assigning seems a better strategy.

After comments edit

A reader pointed out a different outcome on his machine. In fact, the first option is faster for him. I retried the code on my MacBook 2008 and I confirm his findings. This counters my previous statements completely. Yet, I perfectly remember the result I gave in the opening of this post to be reliable. I want to point out a few more details: the test was made on a Lenovo T530 with Linux Ubuntu 12.04 LTS. Both python and numpy were compiled locally from sources.

To sum up, don't trust my initial evaluation to hold on your machine and configuration. It may well be that my setup was unusual for some reason.