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

Using scatter plots and histograms


Scatter plots are very often encountered, as they are the most common plot to visualize the relation between two variables. If we want to have a quick look at the data of these two variables and see if there is any relation between them (that is, correlation), we will draw a quick scatter plot. For a scatter plot to exist, we must have one variable that can be systematically changed by, for example, experimenter, so we can inspect the possibilities of influencing another variable.

That's why, in this recipe, we will learn how to understand the scatter plots.

Getting ready

We want to see, for example, how two events are affected by each other or if they are affected at all. This visualization is especially useful on large sets of data, where we cannot make any conclusions by looking at the data in the native form, when it is just numbers.

Correlation between values, if there is any, can be positive and negative. Positive correlation is for increasing X values...