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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

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781785888632

Length 548 pages

Edition 2nd Edition

Languages

Python

Tools

IPython

Concepts

Data Analysis

Author (1):

Cyrille Rossant

View More author details

Table of Contents (17) Chapters

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

Chapter 5. High-Performance Computing

In this chapter, we will cover the following topics:

Using Python to write faster code
Accelerating pure Python code with Numba and Just-In-Time compilation
Accelerating array computations with NumExpr
Wrapping a C library in Python with ctypes
Accelerating Python code with Cython
Optimizing Cython code by writing less Python and more C
Releasing the GIL to take advantage of multi-core processors with Cython and OpenMP
Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA
Distributing Python code across multiple cores with IPython
Interacting with asynchronous parallel tasks in IPython
Performing out-of-core computations on large arrays with Dask
Trying the Julia programming language in the Jupyter Notebook

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Authors (1)

Cyrille Rossant

Cyrille Rossant

Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College London. He is a graduate of École Normale Supérieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and Collège de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization. He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.

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