Bokeh is an open source, interactive, data visualization package in Python that allows users to create interactive and beautiful visualizations that are both statistically significant and aesthetically pleasing.
This book aims to provide you with the tools needed to get started with Bokeh and to create plots that can tell a story through interaction.
This book is well suited for data scientists and data analysts who wish to perform interactive data visualization on their web browsers using the Bokeh library.
A basic knowledge of Python is required in order to understand the content of this book.
Chapter 1, Bokeh Installation and Key Concepts, looks at how to install Bokeh on your PC and how to understand the fundamental concepts that are needed to progress with the rest of the book.
Chapter 2, Plotting Using Glyphs, will teach you how to create visualizations using the building block of Bokeh—Glyphs.
Chapter 3, Plotting with Different Data Structures, explains how to create visualizations using data structures that are found ubiquitously, such as the Pandas DataFrame and the NumPy array.
Chapter 4, Using Layouts for Effective Presentation, explores how to use layouts in order to enhance the aesthetic appeal of your visualizations.
Chapter 5, Using Annotations, Widgets, and Visual Attributes for Visual Enhancement, will teach you how to enhance your plot's interactivity as well as its aesthetics.
Chapter 6, Building and Hosting Applications on the Bokeh Server, goes through how to create and deploy applications that can host interactive visualizations.
Chapter 7, Advanced Plotting with Networks, Geo Data, WebGL, and Exporting Plots, dives into the advanced topics of Bokeh and sheds light on some of the ways in which you can enhance your interactive plotting experience.
Chapter 8, The Bokeh Workflow – A Case Study, comprises a case study that will have you explore data and build an interactive visualization by following a workflow that is tailored for Bokeh!
A basic knowledge of Python is essential. Knowledge of importing packages, and experience of working with NumPy, Pandas, and DataFrames, will help the reader get the most out of this book.
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You can download the code files by following these steps:
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-on-Data-Visualization-with-Bokeh. If there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/Hands-onDataVisualizationwithBokeh_ColorImages.pdf.
Visit the following link to check out videos of the code being run:
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We had to set the x-axis type to datetime in the figure function in order to render dates along the x-axis".
A block of code is set as follows:
#Output the plot
output_file('second_plot.html')
show(plot2)
Any command-line input or output is written as follows:
bokeh serve --show bokeh.py
Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text in bold. Here is an example: "We will be working with the S&P 500 stock data found on Kaggle."
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