By Kurt W. Smith
Build software program that mixes Python’s expressivity with the functionality and keep an eye on of C (and C++). It’s attainable with Cython, the compiler and hybrid programming language utilized by foundational programs resembling NumPy, and favourite in initiatives together with Pandas, h5py, and scikits-learn. during this useful advisor, you’ll use Cython to enhance Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease.
Author Kurt Smith takes you thru Cython’s services, with pattern code and in-depth perform workouts. If you’re simply beginning with Cython, or are looking to move deeper, you’ll find out how this language is a necessary a part of any performance-oriented Python programmer’s arsenal.
- Use Cython’s static typing to hurry up Python code
- Gain hands-on adventure utilizing Cython positive aspects to spice up your numeric-heavy Python
- Create new kinds with Cython—and see how briskly object-oriented programming in Python can be
- Effectively manage Cython code into separate modules and applications with no sacrificing performance
- Use Cython to provide Pythonic interfaces to C and C++ libraries
- Optimize code with Cython’s runtime and compile-time profiling tools
- Use Cython’s prange functionality to parallelize loops transparently with OpenMP
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Additional info for Cython - A guide for Python programmers
These extra commands are IPython-specific commands called magic commands, and they start with either a single (%) or double (%%) percent sign. They provide functionality beyond what the plain Python interpreter supplies. IPython has several magic com‐ mands to allow dynamic compilation of Cython code, which we cover here. Before we can use these magic Cython commands, we first need to tell IPython to load them. : for i in range(n): 2. If this is the case, an out-of-date IPython is likely the culprit; please update to a more recent version.
Overrides a compiler directive The preceding example includes only the more common options, most of which we will cover in this and future chapters. The arguments most commonly used are --cplus to generate a C++ source file rather than C; -a to generate an annotated HTML version of the source, useful for performance analysis and covered in depth in Chapter 9; and the -2 or -3 arguments to control which major version of the Python language to use and enforce. o into a dynamic library with the right linking flags.
Pyx source file or files, and setup compiles the generated C or C++ code into a Python extension module. py build_ext --inplace The build_ext argument is a command instructing distutils to build the Extension object or objects that the cythonize call created. pyx source file. 1. com/cythonbook/examples. py build_ext --help Other options allow us to control the preprocessor, include directo‐ ries, link directories, and link libraries. The output from this command will look different on Mac OS X, Linux, and Windows— that’s distutils doing its job for us and handling the platform-specific aspects of the compilation.