Well, given that they didn't even use pythonic constructs, I'm not quite sure what to think of the article:

    In [1]: import random
    In [2]: r = [random.randrange(100) for _ in range(100000)]
    In [3]: x, y = random.sample(r, 1000), random.sample(r, 1000)
    In [4]: %timeit z = [x[i] + y[i] for i in range(1000)]
    106 µs ± 1.28 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
    In [5]: %timeit z = [i + j for i, j in zip(x, y)]
    67.3 µs ± 3.38 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

(under python 3.6.3)

For those that "don't see it", what I'm seeing is that, instead of looping over a zip of iterables, like they should be doing, they're using an "index range" to access each element by index -- not the best practice, and also results in a noticeable slowdown.

Edit: And for the curious who might think they're different result:

    In [6]: z1 = [x[i] + y[i] for i in range(1000)]
    In [7]: z2 = [i + j for i, j in zip(x, y)]
    In [8]: z1 == z2
    Out[8]: True

Final edit: all in all, I'd say this is a low effort post, aimed at gathering attetion and showing "look how good we are that we know how to speed up python loops using numpy" (/s)...

And I've successfully been python-nerd-sniped.