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)

Using property cycler

We learned in Chapter 4, Developing Visualizations for Publishing Quality, that Matplotlib has a default color cycle that gets repeated as we plot more graphs in a given axis. The property cycler enables us to define such cyclers for multiple attributes in a single function. If you want to plot an axis with repetitive patterns, we can achieve it with the property cycler.

Getting ready

We will use the object-oriented API here. In Chapter 4, Developing Visualizations for Publishing Quality, we used the pyplot API for this example. Import the required libraries:

from cycler import cycler
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import...