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 <https://pypistats.org/packages/cython>_ per month on PyPI. You can support the Cython project via Github Sponsors <https://github.com/users/scoder/sponsorship>_ or Tidelift <https://tidelift.com/subscription/pkg/pypi-cython>_.

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

pip install Cython

otherwise, see the installation page <https://docs.cython.org/en/latest/src/quickstart/install.html>_.

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 <https://github.com/cython/cython/blob/master/LICENSE.txt>_.

Want to contribute to the Cython project? Here is some help to get you started <https://github.com/cython/cython/blob/master/docs/CONTRIBUTING.rst>_.

Started as a project in the early 2000s, Cython has outlived most other attempts <https://wiki.python.org/moin/PythonImplementations#Compilers>_ at producing static compilers for the Python language.

Similar projects that have a relevance today include:

• PyPy <https://www.pypy.org/>_, 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 <http://numba.pydata.org/>_, 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 <https://pythran.readthedocs.io/>, 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 <https://cython.readthedocs.io/en/latest/src/userguide/numpy_pythran.html> in Cython.

• mypyc <https://mypyc.readthedocs.io/>, a static Python-to-C extension compiler, based on the mypy <http://www.mypy-lang.org/> static Python analyser. Like Cython's pure Python mode <https://cython.readthedocs.io/en/latest/src/tutorial/pure.html>_, 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 <https://nuitka.net/>_, a static Python-to-C extension compiler.

  • Pros: highly language compliant, reasonable performance gains, support for static application linking (similar to cython_freeze <https://github.com/cython/cython/blob/master/bin/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

Cython was originally based on Pyrex <https://www.cosc.canterbury.ac.nz/~greg/python/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 <https://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/>_.

| Greg Ewing, Computer Science Dept | University of Canterbury | Christchurch, New Zealand

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