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Matplotlib 3.0 Cookbook

By : Poladi, Borkar

3 (5)

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.

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

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

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

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

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

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

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

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

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

Developing Interactive Plots

Developing Interactive Plots

Introduction

Events and callbacks

Widgets

Animation

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

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

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

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

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

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

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3D visualization of linearly non-separable data in 2D

In this recipe, we will learn how we can visualize 2D data that is linearly non-separable in 3D. This is typically used to explain the internal workings of the Support Vector Machines algorithm, which takes lower dimensional data to higher dimensional space so that it can find a plane that separates the data into various clusters neatly.

We will plot both 2D and 3D plots with the same data to visualize it better.

Getting ready

Import the required libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

How to do it...

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