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Bokeh Installation and Key Concepts

Welcome to the world of interactive data visualization using the popular Bokeh library in Python. As you go through the chapter, you will learn about the following topics:

What exactly Bokeh is and how it differs from other plotting libraries
How you can install Bokeh on your local machine
How to verify your Bokeh installation
Where you can go for help should things go wrong
Some key concepts regarding Bokeh's internal workings

Bokeh is an interactive data visualization library in Python that helps users across all levels visualize both simple and complex data from datasets ranging from small to big. You can use Bokeh to create both interactive plots and applications that speak to the general public, statisticians, and even business leaders!

Key concepts and the building blocks of Bokeh

While going through this book, you will come across some terms that are fundamental to understanding the Bokeh package. This section will take you through them.

The following are some key definitions related to Bokeh:

Application: The Bokeh application is a rendered Bokeh document that runs in the browser
Glyphs: Glyphs are the building blocks of Bokeh, and they are the lines, circles, rectangles, and other shapes that you see on a Bokeh plot
Server: The Bokeh server is used to share and publish interactive plots and apps to an audience of your choice
Widgets: Widgets in Bokeh are the sliders, drop-down menus, and other small tools that you can embed into your plot to add some interactivity

Plot outputs

There are two methods you can use to render your plot:

output_file: This method is used to output your plot as an HTML file and can be used as illustrated in the following code:

output_file('plot.html')

output_notebook: This is used to output your plot in the Jupyter Notebook you are presently working on and can be used as illustrated in the following code:

output_notebook()

Interfaces:

The first step to understanding interfaces is to understand what a class and a method are. Think of a class as a vessel that holds different types of cookie together. The vessel in this case is the class and the cookies are the methods that give the vessel some functionality, in our case, as a container for the cookies.

Since Python is an object-oriented programming language, it uses classes to group different objects that it creates together.

A class by itself is useless unless it has some functionality associated with it. These functionalities are provided to classes by methods.

Bokeh provides a mid-level plotting interface, similar to that of matplotlib , which is known as bokeh.plotting. The main class in the bokeh.plotting interface is the Figure class, which includes methods for adding different kinds of glyphs to a plot.

A user can create a Figure object by using the figure function, as illustrated in the following code:

from bokeh.plotting import figure

# create a Figure object
p = figure(plot_width=500, plot_height=400, tools="pan,hover")

In Bokeh, the figure function, as illustrated in the preceding code, is used to initialize and store the contents of your plot. The variable p in the preceding code now holds information about the plot, including its height, width, and the kind of tools the plot will use. Since figure is our main class, methods such as line, circle, and so on can be added to our diagram in order to create the plot.

Key benefits

A step by step approach to creating interactive plots with Bokeh

Go from installation 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

Description

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.

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

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jose Aug 05, 2018

Easy to follow

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R. Millikan Sep 26, 2018

This is a really good book for getting someone started with Bokeh. Their is soooo much more to learn, but it is a very good start

Hernán Díaz Dec 28, 2020

Muy buen libro, es un poco corto pero va directo al grano con lo importante y te enseña lo suficiente para que se pueda continuar por uno mismo

John Coleman May 06, 2019

The book is a quick read and is useful for getting up and running with Bokeh, but it is woefully incomplete and has some occasional technical errors.As a glaring example of the incompleteness, the book gives very, very little guidance about the handling of color (which is a major topic in any graphing library). It illustrates uses of color words such as color = 'red' , but has no discussion of issues like: which color words can you use? Where can you find a list? How could you specify a color using RGB or HSL or color hex strings? It illustrates CategoricalColorMapper (applied to a hard-wired two color palette) but has zero discussion or even mention of LinearColorMapper and LogColorMapper for continuous data or other options for color palettes (such as the rich built-in set of palettes). And even though it mentions color mapping, it doesn't say anything at all about color bars which provide a legend for the meaning of colors. These are all things I had to learn on my own from other sources (such as Stack Overflow) while reading this book. There were numerous other cases where relatively basic questions which naturally arose when reading the book were left unanswered in the book itself. I have illustrated the incompleteness with color, but I could also mention e.g. tools. It has a decent but still incomplete discussion of hover tools but its discussion of other tools ranges from nonexistent to cursory.As far as technical errors, there aren't too many but there are some. For example, on page 25 it says that "You can also add labels to the x- and y-axis by using the following code: plot.figure(x_axis_label = ..." but -- you can't. Using the author's code yields AttributeError: 'Figure' object has no attribute 'figure'. The fix isn't hard -- but is to be found on Stack Overflow (or the online documentation) rather than the book itself.On the plus side, the book does a good job in explaining how Bokeh plays nicely with pandas and numpy and is reasonably well written. For this reason, it deserves more than one or two stars. If you want a quick start which will give you a basic orientation to Bokeh in just a few days, I recommend this book. But, if you want to learn to make sophisticated graphs with Bokeh, this isn't your guide.

SJ Oct 10, 2018

This book gives a quick intro to bokeh. It does not go indepth into the more complex uses of bokeh. You should really use this in conjunction with the online bokeh.pydata.org resource to fill out the missing pieces etc. Because I like to know the structure of the data I am using and the data structure required by various 'platforms', it would have been helpful if samples of the data sets used eg the SP500 data were given. This data is given in a separate chapter at the end, but as the tech requirements are always repeated at the beginning of each chapter and section, I dont see why sample data structure cannot also be shown. This would give the reader a better appreciation for the method and arguments used. The book is also a little thin on graphing networks and geo data, hopefully the next edition might beef up this chapter considerably. I give this an average rating.

Hands-On Data Visualization with Bokeh: Interactive web plotting for Python using Bokeh

What do you get with Print?

Hands-On Data Visualization with Bokeh

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

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Hands-On Data Visualization with Bokeh: Interactive web plotting for Python using Bokeh

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