Cython: A Guide for Python Programmers

Smith, Kurt W.

ISBN 10: 1491901551 / ISBN 13: 9781491901557

Published by O'Reilly Media (edition 1), 2015

About this Item

It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

Seller Inventory # 1491901551-11-1

Bibliographic Details

• Title: Cython: A Guide for Python Programmers
• Publisher: O'Reilly Media (edition 1)
• Publication Date: 2015
• Language: English
• ISBN 10: 1491901551
• ISBN 13: 9781491901557
• Binding: Paperback
• Edition: 1.
• Condition: Good

About this title

Synopsis

Build software that combines Python’s expressivity with the performance 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’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease.

Author Kurt Smith takes you through Cython’s capabilities, with sample code and in-depth practice exercises. If you’re just starting with Cython, or want to go deeper, you’ll learn how this language is an essential part of any performance-oriented Python programmer’s arsenal.

• Use Cython’s static typing to speed up Python code
• Gain hands-on experience using Cython features to boost your numeric-heavy Python
• Create new types with Cython—and see how fast object-oriented programming in Python can be
• Effectively organize Cython code into separate modules and packages without sacrificing performance
• Use Cython to give Pythonic interfaces to C and C++ libraries
• Optimize code with Cython’s runtime and compile-time profiling tools
• Use Cython’s prange function to parallelize loops transparently with OpenMP

About the Author

Kurt Smith has been using Python in scientific computing ever since his college days, looking for any opportunity to incorporate it into his computational physics classes. He has contributed to the Cython project as part of the 2009 Google Summer of Code, implementing the initial version of typed memoryviews and native cython arrays. He uses Cython extensively in his consulting work at Enthought, training hundreds of scientists, engineers, and researchers in Python, NumPy, Cython, and parallel and high-performance computing.