Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook

Arrow left icon
Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785888632
Length 548 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with Jupyter and IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Jupyter Notebook 4. Profiling and Optimization 5. High-Performance Computing 6. Data Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Wrapping a C library in Python with ctypes


Wrapping a C library in Python allows us to leverage existing C code or to implement a critical part of the code in a fast language such as C.

It is relatively easy to use externally-compiled libraries with Python. The first possibility is to call a command-line executable with the os.system() command, but this method does not extend to compiled libraries.

A more powerful method consists of using a native Python module called ctypes. This module allows us to call functions defined in a compiled library (written in C) from Python. The ctypes module takes care of data type conversions between C and Python. In addition, the numpy.ctypeslib module provides facilities to use NumPy arrays wherever data buffers are used in the external library.

In this example, we will rewrite the code of the Mandelbrot fractal in C, compile it in a shared library, and call it from Python.

Getting ready

The code in this recipe is written for Unix systems and has been tested...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime