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)

Creating multi-page PDF reports

In this recipe, we will learn how to create a PDF document with multiple pages containing a number of figures. This will be handy if you have to create a bunch of reports for a specific user or department and ship it as one single document.

Getting ready

We will create three figures with two plots each, and place each figure in one page of the PDF document.

Let's import the required libraries:

import datetime
import numpy as np
import pandas as pd
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
import calendar

How to do it...

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