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 histograms


Histograms are simple; yet it's important to get the right data into them. We will cover histograms in 2D for now.

Histograms are used to visualize estimations of distribution of data. Generally, we use a few terms when speaking of histograms. Vertical rectangles represent frequencies of data points within a particular interval called a bin. Bins are created at fixed intervals, so the total area of a histogram sums to the number of data points.

Instead of using absolute values of data, histograms can display relative frequencies of data. When this is the case, the total area equals 1.

Histograms are often used in image manipulation software as a way to visualize image properties such as distribution of light in a particular color channel. Further, these image histograms can be used in computer vision algorithms to detect peaks aiding in edge detections, image segmentation, and so on.

In Chapter 5, Making 3D Visualizations, we have recipes that deal with 3D histograms.

Getting...