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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

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Product type Paperback
Published in Sep 2014
Publisher
ISBN-13 9781783284818
Length 512 pages
Edition 1st Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Notebook 4. Profiling and Optimization 5. High-performance Computing 6. Advanced 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

Introduction


Visualization is a central theme of this book. We create graphics in most recipes because that's the most efficient way to communicate quantitative information. In most cases, we use matplotlib to create plots. In this chapter, we will see more advanced visualization features in Python.

First, we will see a few packages that let us improve the default styling of matplotlib figures and the MATLAB-like pyplot interface. There are other high-level visualization programming interfaces that can be more convenient in some situations.

Also, the Web platform is getting closer and closer to Python. The IPython notebook is a good example of this trend. In this chapter, we will see a few techniques and libraries to create interactive Web visualizations in Python. These techniques will let us combine the power of Python for data analysis and the power of the Web for interactivity.

Finally, we will introduce Vispy, a new high-performance interactive visualization library for big data.

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