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Jun 5, 2016 · Cython/Jython is just Python and you can use C or C++/Java libraries respectively with just a little bit of Cython/Jython syntax. Pretty much, yes. Jython is Python and uses Java Libraries with basic import statements.
- Overview
- Installation:
- License:
- Contributing:
- Differences to other Python compilers
- Get the full source history:
- The following is from Pyrex:
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
If you already have a C compiler, just run following command:
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.
See LICENSE.txt.
Want to contribute to the Cython project? Here is some help to get you started.
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.
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:
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
and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you’ll learn how to use Cython to improve Python with ease.
While we could name and give special awards to the programmers that optimised C compilers, that's not the point here. The point that matters to users is to evaluate whether Python users can reach good performances within the ecosystem that "everyone uses" while coding in Python.
May 1, 2011 · Given that an easier‐to‐deploy and better‐compatible JIT compiler would benefit more Python users, we have developed comPyler, a simple JIT compiler functioning as a CPython extension and ...
Cython is an extension to the Python language that allows explicit type declarations and is com-piled directly to C. This addresses Python’s large overhead for numerical loops and the difficulty of efficiently making use of existing C and Fortran code, which Cython code can interact with na-tively.
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Here, we present an overview of the Cython language and compiler in several examples. We then offer guidelines as to when Cython can pro-vide significantly higher performance than pure Python and NumPy code, and when NumPy is a good choice in its own right. We further show how the Cython compiler speeds up Python code,
