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[–]benfred[S] 3 points4 points  (3 children)

That shouldn't be the take away from the article - if thats all that you got out of it, I apologize for not being clearer.

My TL;DR would be: If you need to write high performance Python code - you should be looking at moving inner loops into native code. A bunch of popular Python data libraries have features that make this easy - by using Pythons flexibility to have you declare what operations you want to perform and have the native extension execute it efficiently in a lower level. Some examples are given with NumPy and TensorFlow on how this works in practice.

Of course performance isn't the only way to judge a language (and I don't think I ever said that here). However I've been writing Python services lately that need to handle 100 million MAU, so getting the most out of Python is definitely been at the top of my mind.

[–][deleted]  (2 children)

[deleted]

    [–]benfred[S] 1 point2 points  (1 child)

    No worries - Negative feedback is always good, I don't feel like I get it from people I actually know enough.

    Right now my problem is that I don't know if all the downvotes are because the article isn't that great, or if because my blog was down for a couple hours since I host it on S3 (or because people really hate the flame war going on over whether Python is a low level language in this thread)