10 comments

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Finally. Its only makes sense, as compilers become more efficient.

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Finally. Its only makes sense, as compilers become more efficient.

In this case you mean interpreters, esp PyPy.

I'm still a fan of compiled statically-typed native languages like Nim (which comes close to Python's syntax).

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What is a virtual machine language, but an interpreted language that requires multiple compilations?

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Python is a horrible language. The community forked it and any language that is so loosely defined that text formatting effects execution at run time is a bastion of degeneracy. It also is garbage at concurrency.

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Your criticisms are valid (except the one about the off-side rule).

But it's still better than most other scripting languages (esp PHP, JavaScript, Perl).

And (although they don't compare directly) it's a good thing that it's becoming more popular than Java.

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When you spend hours to fix a bug caused by someone deleting a tab and a space that then isn't caught by brace matching by the compiler because the language is interpreted you wont think the off-side rule is so great. Java is terrible but it's only so high up in the list because its the only language Indians can understand how to program in. I use golang, .net core, and c++ for serious shit and i use javascript or python to piece together little automation scripts when what im doing can be done in less than 50 lines.

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When you spend hours to fix a bug caused by someone deleting a tab and a space that then isn't caught by brace matching by the compiler because the language is interpreted you wont think the off-side rule is so great.

I've been programming Python for 20 years (and now Nim), and this just never happens. Sounds like a made-up rationalization from someone without actual experience.

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Only when I briefly used mod-python's Python Server Pages did Python's blocking scheme prove at all problematic.

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The concurrency problem is by far the top of that list for me, and is why Python can fuck itself. I am ever-amazed that it became this popular for data science with its so-called GIL preventing proper threading, only allowing some emulation of it to help handle blocking I/O. 'Use multiple processes', they say -- so at least double the work for concurrency, and one process per core, each with its own copy of the data set in memory (it doesn't have proper shared memory either). Nope!

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Yeah all the people who tell you to just shell out in python and start another process dont really understand concurrency. Not only that then you have to start trying to track and manage process ID's which is the most fragile shit ever. It's always better to just use a real language instead.