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Matplotlib 2.x By Example

Matplotlib 2.x By Example

By : Allen Yu, Claire Chung, Aldrin Yim
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Matplotlib 2.x By Example

Matplotlib 2.x By Example

By: Allen Yu, Claire Chung, Aldrin Yim

Overview of this book

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Table of Contents (9 chapters)
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What this book covers

In the first part of this book, you will learn the basics of creating a Matplotlib plot:

  • Chapter 1, Hello Plotting World!, covers the basic constituents of a Matplotlib figure, as well as the latest features of Matplotlib version 2
  • Chapter 2, Figure Aesthetics, explains how to in customize the style of components in a Matplotlib figure
  • Chapter 3, Figure Layout and Annotations, explains how to add annotations and subplots, which allow more comprehensive representation of the data

Once we have a solid foundation of the basics of Matplotlib, in part two of this book, you will learn how to mix and match different techniques to create increasingly complex visualizations:

  • Chapter 4, Visualizing Online Data, teaches you how to design intuitive infographics for effective storytelling through the use of real-world datasets.
  • Chapter 5, Visualizing Multivariate Data, gives you an overview of the plot types that are suitable for visualizing datasets with multiple features or dimensions.
  • Chapter 6, Adding Interactivity and Animating Plots, shows you that Matplotlib is not limited to creating static plots. You will learn how to create interactive charts and animations.

Finally, in part three of this book, you will learn some practical considerations and data analysis routines that are relevant to scientific plotting:

  • Chapter 7, A Practical Guide to Scientific Plotting, explains that data visualization is an art that's closely coupled with statistics. As a data scientist, you will learn how to create visualizations that are not only understandable by yourself, but legible to your target audiences.
  • Chapter 8, Exploratory Data Analytics and Infographics, guides you through more advanced topics in geographical infographics and exploratory data analytics.

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