Matplotlib for Python Developers电子书

Title Page

Copyright and Credits

Matplotlib for Python Developers Second Edition

Dedication

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the authors

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Introduction to Matplotlib

What is Matplotlib?

Merits of Matplotlib

Easy to use

Diverse plot types

Hackable to the core (only when you want)

Open source and community support

What's new in Matplotlib 2.x?

Improved functionality and performance

Improved color conversion API and RGBA support

Improved image support

Faster text rendering

Change in the default animation codec

Changes in default styles

Matplotlib website and online documentation

Output formats and backends

Static output formats

Raster images

Vector images

Setting up Matplotlib

Installing Python

Python installation for Windows

Python installation for macOS

Python installation for Linux

Installing Matplotlib

About the dependencies

Installing the pip Python package manager

Installing Matplotlib with pip

Setting up Jupyter Notebook

Starting a Jupyter Notebook session

Running Jupyter Notebook on a remote server

Editing and running code

Manipulating notebook kernel and cells

Embed your Matplotlib plots

Documenting in Markdown

Save your hard work!

Summary

Getting Started with Matplotlib

Loading data

List

NumPy array

pandas DataFrame

Our first plots with Matplotlib

Importing the pyplot

Line plot

Scatter plot

Overlaying multiple data series in a plot

Multiline plots

Scatter plot to show clusters

Adding a trendline over a scatter plot

Adjusting axes, grids, labels, titles, and legends

Adjusting axis limits

Adding axis labels

Adding a grid

Titles and legends

Adding a title

Adding a legend

A complete example

Saving plots to a file

Setting the output format

Setting the figure resolution

Jupyter support

Interactive navigation toolbar

Configuring Matplotlib

Configuring within Python code

Reverting to default settings

Global setting via configuration rc file

Finding the rc configuration file

Editing the rc configuration file

Summary

Decorating Graphs with Plot Styles and Types

Controlling the colors

Default color cycle

Single-lettered abbreviations for basic colors

Standard HTML color names

RGB or RGBA color code

Hexadecimal color code

Depth of grayscale

Colormaps

Creating custom colormaps

Line and marker styles

Marker styles

Choosing the shape of markers

Using custom characters as markers

Adjusting marker sizes and colors

Fine-tuning marker styles with keyword arguments

Line styles

Color

Line thickness

Dash patterns

Designing a custom dash style

Cap styles

Spines

More native Matplotlib plot types

Choosing the right plot

Histogram

Bar plot

Setting bar plot properties

Drawing bar plots with error bars using multivariate data

Mean-and-error plots

Pie chart

Polar chart

Controlling radial and angular grids

Text and annotations

Adding text annotations

Font

Mathematical notations

Mathtext

LaTeX support

External text renderer

Arrows

Using style sheets

Applying a style sheet

Creating own style sheet

Resetting to default styles

Aesthetics and readability considerations in styling

Suitable font styles

Effective use of colors

Keeping it simple

Summary

Advanced Matplotlib

Drawing Subplots

Initiating a figure with plt.figure()

Initiating subplots as axes with plt.subplot()

Adding subplots with plt.figure.add_subplot()

Initiating an array of subplots with plt.subplots()

Shared axes

Setting the margin with plt.tight_layout()

Aligning subplots of different dimensions with plt.subplot2grid()

Drawing inset plots with fig.add_axes()

Adjusting subplot dimensions post hoc with plt.subplots_adjust

Adjusting axes and ticks

Customizing tick spacing with locators

Removing ticks with NullLocator

Locating ticks in multiples with MultipleLocator

Locators to display date and time

Customizing tick formats with formatters

Using a non-linear axis scale

More on Pandas-Matplotlib integration

Showing distribution with the KDE plot

Showing the density of bivariate data with hexbin plots

Expanding plot types with Seaborn

Visualizing multivariate data with a heatmap

Showing hierarchy in multivariate data with clustermap

Image plotting

Financial plotting

3D plots with Axes3D

Geographical plotting

Basemap

GeoPandas

Summary

Embedding Matplotlib in GTK+3

Installing and setting up GTK+3

A brief introduction to GTK+3

Introduction to the GTK+3 signal system

Installing Glade

Designing the GUI using Glade

Summary

Embedding Matplotlib in Qt 5

A brief introduction to Qt 5 and PyQt 5

Differences between Qt 4 and PyQt 4

Introducing QT Creator / QT Designer

Summary

Embedding Matplotlib in wxWidgets Using wxPython

A brief introduction to wxWidgets and wxPython

Embedding Matplotlib in a GUI from wxGlade

Summary

Integrating Matplotlib with Web Applications

Installing Docker

Docker for Windows users

Docker for Mac users

More about Django

Django development in Docker containers

Starting a new Django site

Installation of Django dependencies

Django environment setup

Running the development server

Showing Bitcoin prices using Django and Matplotlib

Creating a Django app

Creating a simple Django view

Creating a Bitcoin candlestick view

Integrating more pricing indicators

Integrating the image into a Django template

Summary

Matplotlib in the Real World

Typical API data formats

CSV

JSON

Importing and visualizing data from a JSON API

Using Seaborn to simplify visualization tasks

Scraping information from websites

Matplotlib graphical backends

Non-interactive backends

Interactive backends

Creating animated plot

Summary

Integrating Data Visualization into the Workflow

Getting started

Visualizing sample images from the dataset

Importing the UCI ML handwritten digits dataset

Plotting sample images

Extracting one sample each of digits 0-9

Examining the randomness of the dataset

Plotting the 10 digits in subplots

Exploring the data nature by the t-SNE method

Understanding t-Distributed stochastic neighbor embedding

Importing the t-SNE method from scikit-learn

Drawing a t-SNE plot for our data

Creating a CNN to recognize digits

Evaluating prediction results with visualizations

Examining the prediction performance for each digit

Extracting falsely predicted images

Summary