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https://github.com/cython/cython

The most widely used Python to C compiler
https://github.com/cython/cython

big-data c cpp cpython cpython-extensions cython performance python

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The most widely used Python to C compiler

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README 

          
Welcome to Cython!

==================



Cython is an optimising Python compiler that makes writing C extensions for

Python as easy as Python itself.



Cython translates Python code to C/C++ code, but additionally supports calling

C functions and declaring C types on variables and class attributes.

This allows broad to fine-grained manual tuning that lets the compiler

generate very efficient C code from Cython code.



This makes Cython the ideal language for wrapping external C libraries, and

for fast C modules that speed up the execution of Python code.



* Official website: https://cython.org/

* Documentation: https://docs.cython.org/

* Github repository: https://github.com/cython/cython

* Wiki: https://github.com/cython/cython/wiki



Cython has `more than 70 million downloads `_

per month on PyPI.  You can **support the Cython project** via

`Github Sponsors `_ or

`Tidelift `_.



Installation:

-------------



If you already have a C compiler, just run following command::



   pip install Cython



otherwise, see `the installation page `_.



License:

--------



The original Pyrex program, which Cython is based on, was licensed "free of restrictions" (see below).

Cython itself is licensed under the permissive **Apache License**.



See `LICENSE.txt `_.



Contributing:

-------------



Want to contribute to the Cython project?

Here is some `help to get you started `_.



Differences to other Python compilers

-------------------------------------



Started as a project in the early 2000s, Cython has outlived

`most other attempts `_

at producing static compilers for the Python language.



Similar projects that have a relevance today include:



* `PyPy `_, a Python implementation with a JIT compiler.



  * Pros: JIT compilation with runtime optimisations, fully language compliant,

    good integration with external C/C++ code

  * Cons: non-CPython runtime, relatively large resource usage of the runtime,

    limited compatibility with CPython extensions, non-obvious performance results



* `Numba `_, a Python extension that features a

  JIT compiler for a subset of the language, based on the LLVM compiler

  infrastructure (probably best known for its ``clang`` C compiler).

  It mostly targets numerical code that uses NumPy.



  * Pros: JIT compilation with runtime optimisations

  * Cons: limited language support, relatively large runtime dependency (LLVM),

    non-obvious performance results



* `Pythran `_, a static Python-to-C++

  extension compiler for a subset of the language, mostly targeted

  at numerical computation.  Pythran can be (and is probably best) used

  as an additional

  `backend for NumPy code `_

  in Cython.



* `mypyc `_, a static Python-to-C extension

  compiler, based on the `mypy `_ static Python

  analyser.  Like Cython's

  `pure Python mode `_,

  mypyc can make use of PEP-484 type annotations to optimise code for static types.



  * Pros: good support for language and PEP-484 typing, good type inference,

    reasonable performance gains

  * Cons: no support for low-level optimisations and typing,

    opinionated Python type interpretation, reduced Python compatibility

    and introspection after compilation



* `Nuitka `_, a static Python-to-C extension compiler.



  * Pros: highly language compliant, reasonable performance gains,

    support for static application linking (similar to

    `cython_freeze `_

    but with the ability to bundle library dependencies into a self-contained

    executable)

  * Cons: no support for low-level optimisations and typing



In comparison to the above, Cython provides



* fast, efficient and highly compliant support for almost all

  Python language features, including dynamic features and introspection

* full runtime compatibility with all still-in-use and future versions

  of CPython

* "generate once, compile everywhere" C code generation that allows for

  reproducible performance results and testing

* C compile time adaptation to the target platform and Python version

* support for other C-API implementations, including PyPy and Pyston

* seamless integration with C/C++ code

* broad support for manual optimisation and tuning down to the C level

* a large user base with thousands of libraries, packages and tools

* more than two decades of bug fixing and static code optimisations



The following is from Pyrex:

------------------------------------------------------

Cython was originally based on `Pyrex `_

by Greg Ewing, with the following written in the Pyrex readme document:



This is a development version of Pyrex, a language

for writing Python extension modules.



For more info, take a look at:



* Doc/About.html for a description of the language

* INSTALL.txt    for installation instructions

* USAGE.txt      for usage instructions

* Demos          for usage examples



Comments, suggestions, bug reports, etc. are most

welcome!



Copyright stuff: Pyrex is free of restrictions. You

may use, redistribute, modify and distribute modified

versions.



The latest version of Pyrex can be found `here `_.



| Greg Ewing, Computer Science Dept

| University of Canterbury

| Christchurch, New Zealand



 A citizen of NewZealandCorp, a wholly-owned subsidiary of USA Inc.