Book Image

Matplotlib 3.0 Cookbook

By : Srinivasa Rao Poladi, Nikhil Borkar
Book Image

Matplotlib 3.0 Cookbook

By: Srinivasa Rao Poladi, Nikhil Borkar

Overview of this book

Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Matplotlib 3.0 Cookbook is your hands-on guide to exploring the world of Matplotlib, and covers the most effective plotting packages for Python 3.7. With the help of this cookbook, you'll be able to tackle any problem you might come across while designing attractive, insightful data visualizations. With the help of over 150 recipes, you'll learn how to develop plots related to business intelligence, data science, and engineering disciplines with highly detailed visualizations. Once you've familiarized yourself with the fundamentals, you'll move on to developing professional dashboards with a wide variety of graphs and sophisticated grid layouts in 2D and 3D. You'll annotate and add rich text to the plots, enabling the creation of a business storyline. In addition to this, you'll learn how to save figures and animations in various formats for downstream deployment, followed by extending the functionality offered by various internal and third-party toolkits, such as axisartist, axes_grid, Cartopy, and Seaborn. By the end of this book, you'll be able to create high-quality customized plots and deploy them on the web and on supported GUI applications such as Tkinter, Qt 5, and wxPython by implementing real-world use cases and examples.
Table of Contents (17 chapters)

Relational plots

Relational plots depict the relationship between two continuous variables. There is one common API in seaborn, relplot(), which is used for two types of plots: line and scatter plots. However, there are separate functions for each of these two types as well: lineplot() and scatterplot(). We can either use relplot() with an argument to plot a line plot or scatter plot, or directly use the lineplot() and scatterplot() functions.

Line plots with one-to-one and one-to-many relationships

In line plots, observations are connected to a line whose style can be customized. The relationship between x and y variables could be one-to-one, or at times it could be one-to-many. In our Snack Sales dataset, if we plot each...