My understanding is that Cython does not run unmodified standard Python code.
I loved it, being able to float between Python and C in a program, writing in Python whatever was more convenient in Python, in C whatever was more convenient in C, or anywhere in between.
"While pure Python scripts can be compiled with Cython, it usually results only in a speed gain of about 20%-50%."
https://cython.readthedocs.io/en/latest/src/tutorial/pure.ht...
I've seen 2x improvement on string processing code (cleaning text for input to a ML model) by doing nothing other than sticking `%%cython` at the top of a notebook cell.
I don't know if this technique scales to other applications; probably not. But Cython syntactically is a superset of Python.
https://github.com/jameskmurphy/nes/tree/main/nes/cycore
It's more like a DSL for an FFI.
I think it's far more accurate to say that Cython is a programming language of its own that is a hybrid of Python and C++, that happens to produce CPython extension modules when compiled.
The performance benefits are really achieved by incrementally changing your Python code to something that looks a lot more like C(++).
This is also reflected in the Cython documentation which literally mentions the "Cython language".
Cython is great as an alternative to writing C extension modules for performance reasons or to creating bindings to libraries written in C or C++. It's not so great just to make Python applications faster as it's not fully compatible[1].
[1]: https://cython.readthedocs.io/en/latest/src/userguide/limita...
I completely agree. Cython can become quite attractive if your alternative is "write a python extension library by hand in C / C++". I first started using Cython after doing exactly that, writing my extension library in C, then realising that Cython might save a lot of work in generating the bindings and packaging/distribution -- it did, and it ran at exactly the same speed as my pure C library with hand crafted python bindings. After that I've been pretty excited about Cython.
If you've got a python program that needs to do a core of compute-heavy work, if you were to optimise this by writing a C / C++ library for python to use, the work would be: (i) think hard about how the library design will enable performance, (ii) implement that high performance library in C / C++ , (iii) figure out the interface so that python can call into the library, and (iv) figure out how to package and distribute the library so it can be used by python programs.
Cython doesn't really help with parts (i) designing for performance or (ii) writing that high performance code. But it helps a lot with parts (iii) and (iv), generating Python bindings and producing wheel archives that can be managed by existing python package management tooling.