fastest way to populate a 1D numpy array

Question

I have seen questions similar to this, but not one directly addressing the issue. I have timed the following two ways of populating the array and half the time using np.zeros() is faster and half the time doing it directly is faster. Is there a preferable way? I am quite new to using numpy arrays, and have gotten involved with the aim of speeding up my code rather without too much thought to readability.

import numpy as np
import time

lis = range(100000)

timer = time.time()
list1 = np.array(lis)
print 'normal array creation', time.time() - timer, 'seconds'

timer = time.time()
list2 = np.zeros(len(lis))
list2.fill(lis)
print 'zero, fill - array creation', time.time() - timer, 'seconds'

Thank you

Answer 1

If you have a list of floats a=[x/10. for x in range(100000)], then you can create an array with:

np.array(a) # 9.92ms
np.fromiter(a, dtype=np.float) # 5.19ms

Your approach

list2 = np.zeros(len(lis))
list2.fill(lis)

won't work as expected. The .fill fills the whole array with one value.

Answer 2

np.fromiter will pre-allocate the output array if given the number of elements:

a = [x/10. for x in range(100000)] # 10.3ms
np.fromiter(a, dtype=np.float) # 3.33ms
np.fromiter(a, dtype=np.float, count=100000) # 3.03ms