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Why Is Python So Popular in 2025? (blog.jetbrains.com)
submitted 1 year ago by to c/programming@programming.dev
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[–] 3 points 1 year ago* (4 children)

Depends on what you mean by compiled. Python typically translates to meta code and that is interpreted. True compilation to binary runs about 100x faster. Python is slow though there are faster versions and ways around it.

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  • [–] 2 points 1 year ago* (3 children)

    I mean that there are successive steps to transform the entire code into tokens, the tokens into an AST, and the AST into some intermediary or final form.

    True compilation to binary runs about 100x faster.

    No, it doesn't. Take a look at any of the number of projects that have attempted to compile Java to native code over the years. You'd be lucky to see any substantive gain at all. They sometimes have a use for packaging everything up in a single distributed binary, but you don't do it for speed.

    Things like C and Rust are fast because the language semantics can be compiled in a fast way.

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  • [–] 2 points 1 year ago* (2 children)

    We will have to disagree on that. This is all problem spectific, but I have found C code integrated via ctypes, cffi, or by a C extension is over 100x Python alone. Interestingly Python, Numba, and Numpy together which is a more pythonic solution can get to those speeds too.

    All of the other approaches I have tried are much slower: Nuitka, Cython, Numpy alone, PyPy, etc.

    To get best speeds one has to compile for your specific architecture and enable things like vectorization, auto parallel, and fast math. Most default builds including libraries do not do that.

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  • [–] 0 points 1 year ago* (1 child)

    This is all problem spectific, but I have found C code integrated via ctypes, cffi, or by a C extension is over 100x Python alone. Interestingly Python, Numba, and Numpy together which is a more pythonic solution can get to those speeds too.

    Of course you did. Those are changing the semantics of the language. For example, things like Numpy store arrays more like how C does it than Python. That makes all the difference, not merely compiling to native code.

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  • [–] 3 points 1 year ago* (last edited 1 year ago)

    You can get about 10x by compiling Python using PyPy. So compiling is not nothing. Using Numpy alone is about 5x which surprised me. There is a lot of missleading stuff out there about how to make Python fast. Lot of people say CPython is pretty fast or that using a binary library like numpy is fast. No CPython is very slow and libraries are not always that fast.

    Edit: Another compiler is Numba which is more specialized. It can get 30x on some code without numpy. Again compiling can help.

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