Book Image

Python Data Visualization Cookbook

By : Igor Milovanovic
Book Image

Python Data Visualization Cookbook

By: Igor Milovanovic

Overview of this book

Today, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries. 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 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 you 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. During the book, we go from simple plots and charts to more advanced ones, thoroughly explaining why we used them and how not to use them. As we go through the book, we will also discuss 3D diagrams. We will peep into animations just to show you what it takes to go into that area. Maps are irreplaceable for displaying geo-spatial data, so we also show you how to build them. In the last chapter, we show you how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. This book will help those who already know how to program in Python to explore a new field – one of data visualization. As this book is all about recipes that explain how to do something, code samples are abundant, and they are followed by visual diagrams and charts to help you understand the logic and compare your own results with what is explained in the book.
Table of Contents (15 chapters)
Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Defining plot types – bar, line, and stacked charts


In this recipe, we will present different basic plots and what are they used for. Most of the plots described here are used daily, and some of them present the basis for understanding more advanced concepts in data visualization.

Getting ready

We start with some common charts from the matplotlib.pyplot library with just sample datasets to get started with basic charting and lay down the foundations of the following recipes.

How to do it...

We start by creating a simple plot in IPython. IPython is great because it allows us to interactively change plots and see the results immediately.

  1. Start IPython by typing the following at the command prompt:

    $ ipython --pylab
    
  2. Then type the matplotlib plot code:

    In [1]: plot([1,2,3,2,3,2,2,1])
    Out[1]: [<matplotlib.lines.Line2D at 0x412fb50>]

The plot should open in a new window displaying the default look of the plot and some supporting information:

The basic plot in matplotlib contains the following elements...