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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

Creating bar charts


In this recipe, we will focus on how to create a bar chart to compare the occurrences of different crimes in Germany, Italy, and Spain in the year 2012. In particular, we will create a bar chart where we have three bars for each country, one with the number of burglaries, another with the number of robberies, and a third with the number of motor vehicle thefts.

Getting ready

For this recipe, we need the crim_gen.tsv file which comes with this book. This file contains the number of crimes reported to the police by year and by country. This data has been downloaded from the Eurostat website (http://ec.europa.eu/eurostat).

We assume that this file is in the same directory as the code using it.

How to do it...

The following code example demonstrates how to create a bar chart. We will:

  1. Open a tsv (tab separated values) file.

  2. Isolate and organize the data that we want to plot.

  3. Invoke plotly to make the chart.

    # bar charts
    import pandas as pd
    crimes = pd.read_csv('crim_gen.tsv', sep...
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