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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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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

Resolving dependencies in a directed acyclic graph with a topological sort

In this recipe, we will show an application of a well-known graph algorithm: topological sorting. Let's consider a directed graph describing dependencies between items. For example, in a package manager, before we can install a given package P, we may need to install dependent packages.

The set of dependencies forms a directed graph. With topological sorting, the package manager can resolve the dependencies and find the right installation order of the packages.

Topological sorting has many other applications. Here, we will illustrate this notion on real data from the JavaScript package manager npm. We will find the installation order of the required packages for the react JavaScript package.

How to do it...

  1. We import a few packages:
    >>> import io
        import json
        import requests
        import numpy as np
        import networkx as nx
        import matplotlib.pyplot as plt
        %matplotlib inline
  2. We download the dataset...
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