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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly Index

Visualizing maps and bubbles


In this recipe, we will see how to visualize a map and place a bubble on each country, in this case some European countries. The size of each bubble will be proportional to the number of total reported crimes in that country.

Getting ready

Here we will again use the crim_gen.tsv file, which comes with this book, assuming that this file is in the same directory as the code using it.

How to do it...

For the following recipe, we will proceed as follows:

  1. Import and query the data.

  2. Define the coordinates of each country.

  3. Create an entry for each country.

  4. Define the layout for the chart.

  5. Invoke plotly.

    import plotly.plotly as py
    from plotly.graph_objs import *
    
    import pandas as pd
    crimes = pd.read_csv('crim_gen.tsv', sep=',|\t', na_values=': ')
    crimes = crimes[crimes.country.isin(['IT','ES','DE','FR','NO','FI'])]
    
    total_crimes = crimes.query('iccs == "TOTAL"')[['country', '2012 ']].sort(columns='2012 ').values
    coords = {'IT': (13.007813, 42.553080), 'ES': (-3.867188, 39.909736...
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