Python Data Visualization Cookbook

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Python Data Visualization Cookbook

Second Edition

Over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries

Igor Milovanovi Giuseppe Vettigli Dimitry Foures

In this package, you will find:

? The authors biography ? A preview chapter from the book, Chapter 1 'Preparing Your Working

Environment' ? A synopsis of the book's content ? More information on Python Data Visualization Cookbook Second Edition

About the Authors

Igor Milovanovi is an experienced developer, with strong background in Linux system

knowledge and software engineering education, he is skilled in building scalable data-driven distributed software rich systems.

Evangelist for high-quality systems design who holds strong interests in software architecture and development methodologies, Igor is always persistent on advocating methodologies which promote high-quality software, such as test-driven development, one-step builds and continuous integration.

He also possesses solid knowledge of product development. Having field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa.

Igor is most grateful to his girlfriend for letting him spent hours on the work instead with her and being avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is thankful to his parents to let him develop in various ways and become a person he is today.

Dimitry Foures is a data scientist with a background in applied mathematics and

theoretical physics. After completing his undergraduate studies in physics at ENS Lyon (France), he studied fluid mechanics at ?cole Polytechnique in Paris where he obtained a first class master's. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data scientist for a smart-energy startup in Cambridge, in close collaboration with the university.

Giuseppe Vettigli is a data scientist who has worked in the research industry and

academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualization in Python on his blog at .

Preface

The best data is the data that we can see and understand. As developers and data scientists, we want to create and build the most comprehensive and understandable visualizations. It is not always simple; we need to find the data, read it, clean it, filter it, and then use the right tool to visualize it. This book explains the process of how to read, clean, and visualize the data into information with straight and simple (and sometimes not so simple) recipes.

How to read local data, remote data, CSV, JSON, and data from relational databases are all explained in this book.

Some simple plots can be plotted with one simple line in Python using matplotlib, but performing more advanced charting requires knowledge of more than just Python. We need to understand information theory and human perception aesthetics to produce the most appealing visualizations.

This book will explain some practices behind plotting with matplotlib in Python, statistics used, and usage examples for different charting features that we should use in an optimal way.

What this book covers

Chapter 1, Preparing Your Working Environment, covers a set of installation recipes and advice on how to install the required Python packages and libraries on your platform.

Chapter 2, Knowing Your Data, introduces you to common data formats and how to read and write them, be it CSV, JSON, XSL, or relational databases.

Chapter 3, Drawing Your First Plots and Customizing Them, starts with drawing simple plots and covers some customization.

Chapter 4, More Plots and Customizations, follows up from the previous chapter and covers more advanced charts and grid customization.

Chapter 5, Making 3D Visualizations, covers three-dimensional data visualizations such as 3D bars, 3D histograms, and also matplotlib animations.

Preface

Chapter 6, Plotting Charts with Images and Maps, deals with image processing, projecting data onto maps, and creating CAPTCHA test images.

Chapter 7, Using Right Plots to Understand Data, covers explanations and recipes on some more advanced plotting techniques such as spectrograms and correlations.

Chapter 8, More on matplotlib Gems, covers a set of charts such as Gantt charts, box plots, and whisker plots, and it also explains how to use LaTeX for rendering text in matplotlib.

Chapter 9, Visualizations on the Clouds with Plot.ly, introduces how to use Plot.ly to create and share your visualizations on its cloud environment.

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