By Kurt W. Smith
Build software program that mixes Python’s expressivity with the functionality and keep watch over of C (and C++). It’s attainable with Cython, the compiler and hybrid programming language utilized by foundational programs corresponding to NumPy, and sought after in tasks together with Pandas, h5py, and scikits-learn. during this functional advisor, you’ll find out how to 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 cross deeper, you’ll learn the way 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 good points to spice up your numeric-heavy Python
- Create new kinds with Cython—and see how briskly object-oriented programming in Python can be
- Effectively set up Cython code into separate modules and applications with out sacrificing performance
- Use Cython to offer 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 resources for Cython - A guide for Python programmers
How does Cython fit in? As we saw in Chapter 2, we can use the cython and standard C compilers to translate Cython source code into a compiled platform-specific extension module. Whenever Python runs anything inside an extension module, it is running compiled code, so no interpreter overhead can slow things down. How big of a difference does interpretation versus direct execution make? It can vary widely, depending on the Python code in question, but usually we can expect around a 10 to 30 percent speedup from converting Python code into an equivalent extension module.
Pyxbld file: its purpose is to customize pyximport for this and other use cases. pyxbld. pyx file being imported. pyxbld file? One or two Python functions, each optional: make_ext(modname, pyxfilename) If defined, the make_ext function is called with two string arguments before compilation. pyx file being compiled. cythonize. This allows the user to customize the Extension being used. pyx file when creating the extension module. See the fol‐ lowing example. setup. This allows the user to control the setup arguments passed in, which provides full control over distutils.
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. : a, b = a+b, a return a In : The %%cython magic command allows us to write a block of Cython code directly in the IPython interpreter. After exiting the block with two returns, IPython will take the Cython code we defined, paste it into a uniquely named Cython source file, and compile it into an extension module.