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

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:

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

These are the prerequisites for this book:

This book aims to help anyone interested in data visualization to get insights from big data with Python and Matplotlib 2.x. Well-visualized data aids analysis and communication regardless of the field. This book will guide Python novices to quickly pick up Matplotlib plotting skills through step-by-step tutorials. Data scientists will learn to prepare high-quality figures for publications. News editors and copywriters will learn how to create intuitive infographics to make their message crisply understandable.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Navigate to the Applications folder, and then go into the Utilities folder."

A block of code is set as follows:

evens = []
with open as f:
    for line in f.readlines():
        evens.append(line.split()[1])

Any command-line input or output is written as follows:

sudo apt update
sudo apt install Python3 build-essential

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Select Markdown from the drop-down list on the toolbar."

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