[Cyphase]

I tried this on my machine, then tried a pure Python version; I only changed three lines, to:

  import random
  ...
  arr = [random.randint(0, 256) for x in range(n)]
  ...
  arr = sorted(arr)

Here are my times:

  $ time python2.7 hackernews_13682929.py 
   Unsorted execution time: 4.33348608017 seconds
   Sorted execution time:   4.09405398369 seconds
  
  $ time python3.5 hackernews_13682929.py 
   Unsorted execution time: 4.4200146198272705 seconds
   Sorted execution time:   4.188237905502319 seconds
  
  $ time python2.7 hackernews_13682929_purepython.py
   Unsorted execution time: 0.981621026993 seconds
   Sorted execution time:   0.832424879074 seconds
  
  $ time python3.5 hackernews_13682929_purepython.py
   Unsorted execution time: 1.3005650043487549 seconds
   Sorted execution time:   1.157465934753418 seconds
  
  $ time pypy hackernews_13682929_purepython.py
   Unsorted execution time: 0.239459037781 seconds
   Sorted execution time:   0.0910339355469 seconds

As you can see, the pure Python version is faster than the Numpy version, and also has a larger margin between unsorted and sorted. PyPy is of course faster than both, and also has an even greater margin between unsorted and sorted (2.63x faster).

[MooMooMilkParty]

Good call on going pure python. To take this a bit further I made your changes and used numba with @jit(nopython=True, cache=True), for some interesting results. If I do include the sorting into the timing:

    Unsorted execution time: 0.2175428867340088 seconds
    Sorted execution time:   1.133354663848877 seconds

And if I don't:

    Unsorted execution time: 0.21171283721923828 seconds
    Sorted execution time:   0.08376479148864746 seconds