Welcome to Cython!
Cython is a language 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.
The Cython language is very close to the Python language, but Cython 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
If you already have a C compiler, just run following command:
pip install Cython
otherwise, see the installation page.
The original Pyrex program was licensed "free of restrictions" (see below). Cython itself is licensed under the permissive Apache License.
Want to contribute to the Cython project? Here is some help to get you started.
We are currently building the next great Cython edition: Cython 3.0. You can help us make the life of Python 3.x users easier.
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
clangC 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:
The following is from Pyrex:
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.
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