Book Image

Python Data Visualization Cookbook (Second Edition)

Book Image

Python Data Visualization Cookbook (Second Edition)

Overview of this book

Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
Table of Contents (16 chapters)
Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
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...