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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 (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

Creating a route planner for a road network

In this recipe, we build upon several techniques described in the previous recipes in order to create a simple GPS-like route planner in Python. We will retrieve California's road network data from the United States Census Bureau in order to find shortest paths in the road network graph. This will allow us to display road itineraries between any two locations in California.

Getting ready

You need Smopy for this recipe. You can install it with pip install git+https://github.com/rossant/smopy. In order for NetworkX to read Shapefile datasets, you also need GDAL/OGR. You can install it with conda install gdal.

Tip

At the time of this writing, gdal does not appear to work well with conda and Python 3.6. You may need to downgrade Python to Python 3.5 with conda install python=3.5.

How to do it...

  1. Let's import the packages:
    >>> import io
        import zipfile
        import requests
        import networkx as nx
        import numpy as np
        import pandas...
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