title: Fastest way to replace values in a numpy array with a list
body: <p>I want to read a list into a numpy array. This list is being replaced in every iteration of a loop and further operations are done on the array. These operations include element-wise subtraction from another numpy array for a distance measure, and checking a threshold condition in this distance using the numpy.all() function. Currently I am using <code>np.array( list )</code> each time to convert the list to an array:</p>
  
  <pre><code>#!/usr/bin/python
  import numpy as np
  a = [1.33,2.555,3.444,5.666,4.555,6.777,8.888]
  %timeit b = np.array(a)
  
  100000 loops, best of 3: 4.83 us per loop
  </code></pre>
  
  <p>Is it possible to do anything better than this, if I know the size of the list and it is invariable? Even small improvements are welcome, as I run this a very large number of times.</p>
  
  <p>I've tried <code>%timeit(np.take(a,range(len(a)),out=b))</code> which takes much longer: <code>100000 loops, best of 3: 16.8 us per loop</code></p>