Welcome to Cython!

Cython is a Python compiler that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations.

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 the compiler to 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 about 30 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 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
almost two decades of bug fixing and static code optimisations

Get the full source history:

Note that Cython used to ship the full version control repository in its source distribution, but no longer does so due to space constraints. To get the full source history from a downloaded source archive, make sure you have git installed, then step into the base directory of the Cython source distribution and type: