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

Understanding logarithmic plots


More often than not, while reading daily newspapers and similar articles, one can find charts that are used by media organizations to misrepresent the facts. One usual example is using linear scales to create, so called, panic charts where constantly growing value is followed for long period of time (years) and starting values are smaller from latest one by several magnitudes. These values when visualized correctly, would (and usually should), produce linear or almost linear charts. This takes some panic out of the articles they illustrate.

Getting ready

With the logarithmic scale, the ratio of consecutive values is constant. This is important when we are trying to read log plots. With linear (arithmetic) scales, the constant is the distance between consecutive values. In other words, logarithmic plots have constant distance in orders of magnitude. We will see this illustrated on the following plots. The code used to produce this figure is explained here.

As...