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 ra

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).