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)

Geographical plotting using geopandas

In this recipe, we will learn how to plot geographical maps using the geopandas package that comes packaged with Matplotlib. There are third party packages supported by Matplotlib for advanced geographical maps, such as Basemap (being sunset in 2020) and Cartopy (replacing Basemap). We will learn to use Cartopy in Chapter 14, Plotting Geographical Maps Using Cartopy.

Getting ready

Import the required libraries:

import geopandas as gpd
import matplotlib.pyplot as plt

How to do it...

The following code block highlights the locations...