On Google colab

    import numpy as np
    import time


    vals = np.random.randn(1000000, 2)
    point = np.array([.2, .3])
    s = time.time()
    for x in vals:
        np.linalg.norm(x - point) < 3
    a = time.time() - s

    s = time.time()
    np.linalg.norm(vals - point, axis=1) < 3
    b = time.time() - s

    print(a / b)

~296x faster, significantly faster than the solution in the article. And my assertion was supported by nearly 20 years of numpy being a leading tool in various quantitative fields. It’s not hard to imagine that a ubiquitous tool that’s been used and optimized for almost 20 years is actually pretty good if used properly.

Finally some dude knows how to use numpy properly. I wish I can upvote 5 times.

I basically raise the same question somewhere below and got downvoted LOL.

350X Speed Up by rewritten in Numpy but flagged and hated.

https://news.ycombinator.com/item?id=35379811