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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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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Hello Plotting World!
Hello Plotting World!
Hello Matplotlib!
Setting up the plotting environment
Plotting our first graph
Summary
2
Figure Aesthetics
Figure Aesthetics
Basic structure of a Matplotlib figure
Setting colors in Matplotlib
Adjusting text formats
Customizing lines and markers
Customizing grids, ticks, and axes
Using style sheets
Title and legend
Test your skills
Summary
3
Figure Layout and Annotations
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Figure Layout and Annotations
Adjusting the layout
Annotations
Summary
4
Visualizing Online Data
Visualizing Online Data
Typical API data formats
Introducing pandas
Visualizing the trend of data
Introducing Seaborn
Visualizing univariate distribution
Visualizing a bivariate distribution
Visualizing categorical data
Controlling Seaborn figure aesthetics
Summary
5
Visualizing Multivariate Data
Visualizing Multivariate Data
Getting End-of-Day (EOD) stock data from Quandl
Two-dimensional faceted plots
Other two-dimensional multivariate plots
Three-dimensional (3D) plots
Summary
6
Adding Interactivity and Animating Plots
Adding Interactivity and Animating Plots
Scraping information from websites
Non-interactive backends
Interactive backends
Creating animated plots
Summary
7
A Practical Guide to Scientific Plotting
A Practical Guide to Scientific Plotting
General rules of effective visualization
Visualizing statistical data more intuitively
Summary
8
Exploratory Data Analytics and Infographics
Exploratory Data Analytics and Infographics
Visualizing population health information
Survival data analysis on cancer
Summary

Summary

In this chapter, you learned multiple ways to align subplots to group relevant data, fine-tune the layout, and add extra annotations. Over the session, you equipped yourself with a basic understanding of each component in a common Matplotlib figure, and the know-how needed to adjust them.

Congratulations! You are now all ready to go for more real data exploration!

End of Chapter 3
Next Chapter
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