Hands-On Data Visualization with Bokeh

图书信息

作者	Kevin Jolly
出版社	Packt Publishing
ISBN	9781789131314
出版时间	2018-06-15
字数	12.4万
分类	进口书,外文原版书,电脑,网络

读书简介

Learn how to create interactive and visually aesthetic plots using the Bokeh package in Python About This Book ? A step by step approach to creating interactive plots with Bokeh ? Go from nstallation all the way to deploying your very own Bokeh application ? Work with a real time datasets to practice and create your very own plots and applications Who This Book Is For This book is well suited for data scientists and data analysts who want to perform interactive data visualization on their web browsers using Bokeh. Some exposure to Python programming will be helpful, but prior experience with Bokeh is not required. What You Will Learn ? Installing Bokeh and understanding its key concepts ? Creating plots using glyphs, the fundamental building blocks of Bokeh ? Creating plots using different data structures like NumPy and Pandas ? Using layouts and widgets to visually enhance your plots and add a layer of interactivity ? Building and hosting applications on the Bokeh server ? Creating advanced plots using spatial data In Detail Adding a layer of interactivity to your plots and converting these plots into applications hold immense value in the field of data science. The standard approach to adding interactivity would be to use paid software such as Tableau, but the Bokeh package in Python offers users a way to create both interactive and visually aesthetic plots for free. This book gets you up to speed with Bokeh - a popular Python library for interactive data visualization. The book starts out by helping you understand how Bokeh works internally and how you can set up and install the package in your local machine. You then use a real world data set which uses stock data from Kaggle to create interactive and visually stunning plots. You will also learn how to leverage Bokeh using some advanced concepts such as plotting with spatial and geo data. Finally you will use all the concepts that you have learned in the previous chapters to create your very own Bokeh application from scratch. By the end of the book you will be able to create your very own Bokeh application. You will have gone through a step by step process that starts with understanding what Bokeh actually is and ends with building your very own Bokeh application filled with interactive and visually aesthetic plots. Style and approach This books take you through a step by step process that starts with understanding what Bokeh actually is and ends with building your very own Bokeh application filled with interactive and visually aesthetic plots that will amaze your users.

目录

Title Page

Copyright and Credits

Hands-On Data Visualization with Bokeh

Dedication

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the author

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

Code in action

Conventions used

Get in touch

Reviews

Bokeh Installation and Key Concepts

Technical requirements

The difference between static and interactive plotting

Installing the Bokeh library

Installing Bokeh using a Python distribution

Verifying your installation

When things go wrong

Key concepts and the building blocks of Bokeh

Plot outputs

Summary

Plotting using Glyphs

Technical requirements

What are glyphs?

Plotting with glyphs

Creating line plots

Creating bar plots

Creating patch plots

Creating scatter plots

Customizing glyphs

Summary

Plotting with different Data Structures

Technical requirements

Creating plots using NumPy arrays

Creating line plots using NumPy arrays

Creating scatter plots using NumPy arrays

Creating plots using pandas DataFrames

Creating a time series plot using a pandas DataFrame

Creating scatter plots using a pandas DataFrame

Creating plots with ColumnDataSource

Creating a time series plot using the ColumnDataSource

Creating a scatter plot using the ColumnDataSource

Summary

Using Layouts for Effective Presentation

Technical requirements

Creating multiple plots along the same row

Creating multiple plots in the same column

Creating multiple plots in a row and column

Creating multiple plots using a tabbed layout

Creating a robust grid layout

Linking multiple plots together

Summary

Using Annotations, Widgets, and Visual Attributes for Visual Enhancement

Technical requirements

Creating annotations to convey supplemental information

Adding titles to plots

Adding legends to plots

Adding color maps to plots

Creating widgets to add interactivity to plots

Creating a button widget

Creating the checkbox widget

Creating a drop-down menu widget

Creating the radio button widget

Creating a slider widget

Creating a text input widget

Creating visual attributes to enhance style and interactivity

Attributes that add interactivity to the plot

Creating a hover tooltip

Creating selections

Attributes that enhance the visual style of the plot

Styling the title

Styling the background

Styling the outline of the plot

Styling the labels

Summary

Building and Hosting Applications Using the Bokeh Server

Technical requirements

Introduction to the Bokeh Server

Building a Bokeh application

Creating a single slider application

Creating a multi-slider application

Combining the slider application with a scatter plot

Combining the slider application with a line plot

Creating an application with the select widget

Creating an application with the button widget

Creating an application to select different columns

Introduction to deploying the Bokeh application

Summary

Advanced Plotting with Networks, Geo Data, WebGL, and Exporting Plots

Technical requirements

Using Bokeh to visualize networks

Visualizing networks with straight paths

Visualizing networks with explicit paths

Visualizing geographic data with Bokeh

Using WebGL to improve performance

Exporting plots as PNG images

Summary

The Bokeh Workflow – A Case Study

Technical requirements

Asking the right question

The exploratory data analysis

Creating an insightful visualization

Creating the base plot

Mapping tech stocks

Adding a hover tool

Improving performance using WebGL

Presenting your results

Summary

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