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

Visualizing the filesystem tree using a polar bar


We want to show in this recipe how to solve a "real-world" task—how to use matplotlib to visualize our directory occupancy.

In this recipe we will learn how to visualize a filesystem tree with relative sizes.

Getting ready

We all have big hard drives that sometimes contain stuff that we usually forget about. It would be nice to see what is inside such a directory, and what the biggest file inside is.

Although there are many more sophisticated and elaborate software products for this job, we want to demonstrate how this is achievable using Python and matplotlib.

How to do it...

Let's perform the following steps:

  1. Implement a few helper functions to deal with folder discovery and internal data structures.

  2. Implement the main function, draw(), that does the plotting.

  3. Implement the main program body that verifies the user input arguments:

    import os
    import sys
    
    import matplotlib.pyplot as plt
    import matplotlib.cm as cm
    import numpy as np
    
    def build_folders...