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Matplotlib 3.0 Cookbook

Matplotlib 3.0 Cookbook

By : Poladi, Borkar
3 (5)
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Matplotlib 3.0 Cookbook

Matplotlib 3.0 Cookbook

3 (5)
By: Poladi, 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)
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Bar plot

Bar plots are the graphs that use bars to compare different categories of data. Bars can be shown vertically or horizontally, based on which axis is used for a categorical variable. Let's assume that we have data on the number of ice creams sold every month in an ice cream parlor over a period of one year. We can visualize this using a bar plot.

Getting ready

We will use the Python calendar package to map numeric months (1 to 12) to the corresponding descriptive months (January to December).

Before we plot the graph, we need to import the necessary packages:

import matplotlib.pyplot as plt
import numpy as np
import calendar
...
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