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Matplotlib 2.x By Example

By : Allen Yu, Claire Chung, Aldrin Yim

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Hello Plotting World!

Hello Plotting World!

Hello Matplotlib!

Setting up the plotting environment

Plotting our first graph

Summary

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

Figure Layout and Annotations

Figure Layout and Annotations

Adjusting the layout

Annotations

Summary

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

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

Adding Interactivity and Animating Plots

Adding Interactivity and Animating Plots

Scraping information from websites

Non-interactive backends

Interactive backends

Creating animated plots

Summary

A Practical Guide to Scientific Plotting

A Practical Guide to Scientific Plotting

General rules of effective visualization

Visualizing statistical data more intuitively

Summary

Exploratory Data Analytics and Infographics

Exploratory Data Analytics and Infographics

Visualizing population health information

Survival data analysis on cancer

Summary

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What you need for this book

These are the prerequisites for this book:

Basic Python knowledge is expected. Interested readers can refer to Learning Python by Fabrizio Romano if they are relatively new to Python programming.
A working installation of Python 3.4 or later is required. The default Python distribution can be obtained from https://www.python.org/download/. Readers may also explore other Python distributions, such as Anaconda (https://www.continuum.io/downloads), which provides better package dependency management.
A Windows 7+, macOS 10.10+, or Linux-based computer with 4 GB RAM or above is recommended.
The code examples are based on Matplotlib 2.x, Seaborn 0.8.0, Pandas 0.20.3, Numpy 1.13.1, SciPy 0.19.1, pycountry 17.5.14, stockstats 0.2.0, BeautifulSoup4 4.6.0, requests 2.18.4, plotly 2.0.14, scikit-learn 0.19.0, GeoPandas 0.2.1, PIL 1.1.6, and lifelines 0.11.1. Brief instructions for installing these packages are included in the chapters, but readers can refer to the official documentation pages for more details.

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