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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

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785888632
Length 548 pages
Edition 2nd 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 (19) Chapters Close

IPython Interactive Computing and Visualization CookbookSecond Edition
Contributors
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

Visualizing a NetworkX graph in the Notebook with D3.js


D3.js (http://d3js.org) is a popular interactive visualization framework for the web. Written in JavaScript, it allows us to create data-driven visualizations based on web technologies such as HTML, SVG, and CSS. The official gallery contains many examples (https://github.com/d3/d3/wiki/gallery). There are many other JavaScript visualization and charting libraries, but we will focus on D3.js in this recipe.

Being a pure JavaScript library, D3.js has in principle nothing to do with Python. However, the HTML-based Jupyter Notebook can integrate D3.js visualizations seamlessly.

In this recipe, we will create a graph in Python with NetworkX and visualize it in the Jupyter Notebook with D3.js.

Getting ready

You need to know the basics of HTML, JavaScript, and D3.js for this recipe.

How to do it...

  1. Let's import the packages:

    >>> import json
        import numpy as np
        import networkx as nx
        import matplotlib.pyplot as plt
        %matplotlib...
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