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

Making stacked plots


In this recipe, we will show you how to produce a stacked plot. Stacked plots are used when plotting a quantity which can be represented as the sum of several contributions. A stacked plot will allow us to represent not only the overall trend but also the trend of each individual components contributing to the total quantity.

Getting ready

We will consider the world's energy production as our total quantity and will represent the detailed break down in different energy sourced. We will represent the evolution of energy production type from 1973 to 2014. This data is contained in the file ch03-energy-production.csv. The data has been taken from http://www.eia.gov/totalenergy/data/monthly/ and reshaped for the need of the recipe.

How to do it...

Here is the code to produce the stacked plot displayed further:

import pandas as pd
import matplotlib.pyplot as plt

# We load the data with pandas.
df = pd.read_csv('ch03-energy-production.csv')

# We give names for the columns that...