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.
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.
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.