Matplotlib for Python Developers: Effective techniques for data visualization with Python, 2nd Edition

Chapter 2: Getting Started with Matplotlib; Loading data; List; NumPy array; pandas DataFrame; Our first plots with Matplotlib; Importing the pyplot; Line plot; Scatter plot; Overlaying multiple data series in a plot; Multiline plots; Scatter plot to show clusters; Adding a trendline over a scatter plot; Adjusting axes, grids, labels, titles, and legends; Adjusting axis limits; Adding axis labels; Adding a grid; Titles and legends; Adding a title; Adding a legend; A complete example; Saving plots to a file; Setting the output format; Setting the figure resolution; Jupyter support.

This book is a useful resource to perform data visualization with Python using the latest version of Matplotlib (2.1.x). You will create a variety of graphs and charts, and embed your plots within different third party tools. By the end of the book, you will build attractive, insightful and powerful visualizations to make better sense of your data.

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Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Matplotlib; What is Matplotlib?; Merits of Matplotlib; Easy to use; Diverse plot types; Hackable to the core (only when you want); Open source and community support; What's new in Matplotlib 2.x?; Improved functionality and performance; Improved color conversion API and RGBA support; Improved image support; Faster text rendering; Change in the default animation codec; Changes in default styles; Matplotlib website and online documentation.

Output formats and backends Static output formats; Raster images; Vector images; Setting up Matplotlib; Installing Python; Python installation for Windows ; Python installation for macOS; Python installation for Linux; Installing Matplotlib; About the dependencies; Installing the pip Python package manager; Installing Matplotlib with pip; Setting up Jupyter Notebook; Starting a Jupyter Notebook session; Running Jupyter Notebook on a remote server; Editing and running code; Manipulating notebook kernel and cells; Embed your Matplotlib plots; Documenting in Markdown; Save your hard work!

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