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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
Free Chapter
1
Anatomy of Matplotlib
Anatomy of Matplotlib
Introduction
Working in interactive mode
Working in non-interactive mode
Reading from external files and plotting
Changing and resetting default environment variables
2
Getting Started with Basic Plots
Getting Started with Basic Plots
Introduction
Line plot
Bar plot
Scatter plot
Bubble plot
Stacked plot
Pie plot
Table chart
Polar plot
Histogram
Box plot
Violin plot
Reading and displaying images
Heatmap
Hinton diagram
Contour plot
Triangulations
Stream plot
Path
3
Plotting Multiple Charts, Subplots, and Figures
Plotting Multiple Charts, Subplots, and Figures
Introduction
Plotting multiple graphs on the same axes
Plotting subplots on the same figure
Plotting multiple figures in a session
Logarithmic scale
Using units of measurement
4
Developing Visualizations for Publishing Quality
Developing Visualizations for Publishing Quality
Introduction
Color, line style, and marker customization
Working with standard colormaps
User-defined colors and colormaps
Working with legend
Customizing labels and titles
Using autoscale and axis limits
Customizing ticks and ticklabels
Customizing spines
Twin axes
Using hatch
Using annotation
Using style sheets
5
Plotting with Object-Oriented API
Plotting with Object-Oriented API
Introduction
Plotting a correlation matrix using pyplot and object-oriented APIs
Plotting patches using object-oriented API
Plotting collections using object-oriented API
6
Plotting with Advanced Features
Plotting with Advanced Features
Using property cycler
Using Path effects
Using transforms
Taking control of axes positions
GridSpec for figure layout
Using origin and extent for image orientation
Geographical plotting using geopandas
7
Embedding Text and Expressions
Embedding Text and Expressions
Introduction
Using mathematical expressions with a font dictionary
Annotating a point on a polar plot
Using ConnectionPatch
Using a text box
Plotting area under an integral curve
Defining custom markers
Fractions, regular mathematical expressions, and symbols
Word embeddings in two dimensions
8
Saving the Figure in Different Formats
Saving the Figure in Different Formats
Introduction
Saving the figure in various formats
Avoiding truncation while saving the figure
Saving partial figures
Managing image resolution
Managing transparency for web applications
Creating multi-page PDF reports
9
Developing Interactive Plots
Developing Interactive Plots
Introduction
Events and callbacks
Widgets
Animation
10
Embedding Plots in a Graphical User Interface
Embedding Plots in a Graphical User Interface
Introduction
Using the Slider and Button Widgets of Matplotlib
Using the Slider and Button widgets of Tkinter GUI
Embedding Matplotlib in a Tkinter GUI application
Using the Slider and Button widgets of WxPython GUI
Embedding Matplotlib in to a wxPython GUI application
Using the Slider and Button widgets of Qt's GUI
Embedding Matplotlib in to a Qt GUI application
11
Plotting 3D Graphs Using the mplot3d Toolkit
Plotting 3D Graphs Using the mplot3d Toolkit
Introduction
Line plot
Scatter plot
Bar plot
Polygon plot
Contour plot
Surface plot
Wireframe plot
Triangular surface plot
Plotting 2D data in 3D
3D visualization of linearly non-separable data in 2D
Word embeddings
12
Using the axisartist Toolkit
Using the axisartist Toolkit
Introduction
Understanding attributes in axisartist
Defining curvilinear grids in rectangular boxes
Defining polar axes in rectangular boxes
Using floating axes for a rectangular plot
Creating polar axes using floating axes
Plotting planetary system data on floating polar axes
13
Using the axes_grid1 Toolkit
Using the axes_grid1 Toolkit
Introduction
Plotting twin axes using the axisartist and axesgrid1 toolkits
Using AxesDivider to plot a scatter plot and associated histograms
Using AxesDivider to plot a colorbar
Using ImageGrid to plot images with a colorbar in a grid
Using inset_locator to zoom in on an image
Using inset_locator to plot inset axes
14
Plotting Geographical Maps Using Cartopy Toolkit
Plotting Geographical Maps Using Cartopy Toolkit
Introduction
Plotting basic map features
Plotting projections
Using grid lines and labels
Plotting locations on the map
Plotting country maps with political boundaries
Plotting country maps using GeoPandas and cartopy
Plotting populated places of the world
Plotting the top five and bottom five populated countries
Plotting temperatures across the globe
Plotting time zones
Plotting an animated map
15
Exploratory Data Analysis Using the Seaborn Toolkit
Exploratory Data Analysis Using the Seaborn Toolkit
Introduction
Relational plots
Categorical plots
Distribution plots
Regression plots
Multi-plot grids
Matrix plots
16
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Categorical plots

A categorical plot is used when one of the two variables being plotted is categorical, instead of both being continuous. Seaborn enhances a few of the categorical plots provided by Matplotlib and also adds a few additional ones. We will cover five such groups of plots in this section.

Seaborn provides one common API, catplot(), to cover all such plots. This makes it easier to familiarize yourself with a common set of arguments that can be passed to plot all types of categorical plots. However, each of the different functions can be used directly, and at times some of them may offer unique features that are not common to all types of plots. Please refer to the documentation on each of the specific plots at https://seaborn.pydata.org/api.html.

Strip and swarm plots

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