Matplotlib for Python developers : effective techniques for data visualization with Python / Aldrin Yim, Claire Chung, Allen Yu.

Format:

Book

Author/Creator:

Yim, Aldrin Kay Yuen, author.

Chung, Claire Yik Lok, author.

Yu, Allen Chi Shing, author.

Language:

English

Subjects (All):

Python (Computer program language).

Information visualization.

Physical Description:

1 online resource (300 pages)

Edition:

Second edition.

Other Title:

Effective techniques for data visualization with Python

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2018.

System Details:

text file

Summary:

Leverage the power of Matplotlib to visualize and understand your data more effectively About This Book Perform effective data visualization with Matplotlib and get actionable insights from your data Design attractive graphs, charts, and 2D plots, and deploy them to the web Get the most out of Matplotlib in this practical guide with updated code and examples Who This Book Is For This book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you're a data scientist or analyst and wish to create attractive visualizations using Python, you'll find this book useful. Some knowledge of Python programming is all you need to get started. What You Will Learn Create 2D and 3D static plots such as bar charts, heat maps, and scatter plots Get acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of Matplotlib Develop advanced static plots with third-party packages such as Pandas, GeoPandas, and Seaborn Create interactive plots with real-time updates Develop web-based, Matplotlib-powered graph visualizations with third-party packages such as Django Write data visualization code that is readily expandable on the cloud platform In Detail Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you'll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You'll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you'll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you'll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to bu...

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: 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

Chapter 2: 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

Chapter 3: 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

Chapter 4: 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

Chapter 5: 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

Chapter 6: 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

Chapter 7: Embedding Matplotlib in wxWidgets Using wxPython

A brief introduction to wxWidgets and wxPython

Embedding Matplotlib in a GUI from wxGlade

Chapter 8: 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

Chapter 9: 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

Chapter 10: 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

Index.

Notes:

Previous edition published: 2009.

Description based on print version record.

OCLC:

1034612438