Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By : Cyrille Rossant
Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By: Cyrille Rossant

Overview of this book

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
Table of Contents (19 chapters)
IPython Interactive Computing and Visualization CookbookSecond Edition
Contributors
Preface
Index

Accelerating Python code with Cython


Cython is both a language (a superset of Python) and a Python library. With Cython, we start from a regular Python program and we add annotations about the type of the variables. Then, Cython translates that code to C and compiles the result into a Python extension module. Finally, we can use this compiled module in any Python program.

While dynamic typing comes with a performance cost in Python, statically-typed variables in Cython generally lead to faster code execution.

Performance gains are most significant in CPU-bound programs, notably in tight Python loops. By contrast, I/O bound programs are not expected to benefit much from a Cython implementation.

In this recipe, we will see how to accelerate the Mandelbrot code example with Cython.

Getting ready

A C compiler is required. You will find all compiler-related instructions in the introduction the this chapter.

You will also need Cython, which should be installed by default with Anaconda. If needed, you...