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

Creating 3D bars


Although matplotlib is mainly focused on plotting and mainly in two dimensions, there are different extensions that enable us to plot over geographical maps, to integrate more with Excel and plot in 3D. These extensions are called toolkits in the matplotlib world. Toolkit is a collection of specific function that focuses on one topic, such as plotting in 3D.

Popular toolkits are Basemap, GTK Tools, Excel Tools, Natgrid, AxesGrid, and mplot3d.

We will explore more of mplot3d in this recipe. The toolkit mpl_toolkits.mplot3d provides some basic 3D plotting. Plots supported are scatter, surf, line, and mesh. Although this is not the best 3D plotting library, it comes with matplotlib and we are already familiar with the interface.

Getting ready

Basically, we still need to create a figure and add desired axes to it. The difference is that we specify 3D projection for the figure, and the axes we add are Axes3D.

Now, we can use almost the same functions for plotting. Of course, what...