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Hands-On Data Visualization with Bokeh
Hands-On Data Visualization with Bokeh

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

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Hands-On Data Visualization with Bokeh

Plotting using Glyphs

Glyphs are the fundamental building blocks of Bokeh and are used to create a wide variety of plots. In fact, every single plot that you build while working with Bokeh has a glyph mechanism attached to it, such as lines, rectangles, circles, and any other object that makes up the plot.

In this chapter, you will learn about the following topics:

  • What glyphs are
  • How you can plot with glyphs
  • How to create scatter plots using glyphs
  • How you can customize glyphs

Technical requirements

What are glyphs?

When you see a plot, interactive or not, it is usually composed of geometric shapes that make up every element in that plot. In Bokeh, these geometric shapes are called glyphs. If we wanted to create a line plot, we would have to use a line to represent information on the plot, and if we wanted to create a scatter plot with a circle as our marker, we would use a circle to represent that information.

These geometric shapes that are used to convey visual information to readers about a piece of data are called glyphs.

In this book, we will help you plot the four types of plot where above using glyphs, and we will also give you information on the various types of plot that can be plotted using glyphs.

Plotting with glyphs

In this section, we will learn how to plot the following types of plot using glyphs:

  • Line plots: Line plots offer a way of visualizing the movements of points along the x-and y-axes in the form of a line. These plots are useful for performing time series analytics.
  • Bar plots: Bar plots are useful for indicating the count of each category of a particular column or field in your dataset.
  • Patch plots: Patch plots are used to indicate a region of points in a particular shade of color. Such plots can be used to distinguish different groups within the same dataset.
  • Scatter plots: Scatter plots are used to visualize the relationship between two variables and to indicate the strength of correlation between them.

Creating line plots

...

Customizing glyphs

When creating the preceding scatter plot, we used the circle marker to indicate the points on the plot. Bokeh provides us with a wide variety of markers that you can use instead of the circle, and they are as follows:

- cross()
- x()
- diamond()
- diamond_cross()
- circle_x()
- circle_cross()
- triangle()
- inverted_triangle()
- square()
- square_x()
- square_cross()
- asterisk()

You can also add labels to the x-and y-axes by using the following code:

plot.figure(x_axis_label = "Label name of x axis", y_axis_label = "Label name of y axis")

You can customize the transparency of the points on a scatter plot by using the following code:

plot.circle(x, y, alpha = 0.5)

The alpha argument takes in a range of values between 0 to 1, with 0 being completely transparent and 1 being opaque.

Summary

This chapter has given you an introduction to what glyphs are and how you can use them to create fundamental plots using Bokeh. We also looked at how to customize these plots further.

Glyphs are the fundamental building blocks of Bokeh and are required in order to create more complex, and statistically significant, plots in the future.

In this chapter, you learned how to create four different plots using glyphs. Line plots are commonly used in time series analytics, bar plots are commonly used to compare counts between different categories, patch plots are commonly used to highlight an area of points, and scatter plots, are commonly used to map a relationship between two or more variables.

In the upcoming chapter, we will take these concepts and use them to plot diagrams using NumPy arrays and Pandas DataFrames.

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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.

Who is this book 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 15, 2018
Length: 174 pages
Edition : 1st
Language : English
ISBN-13 : 9781789135404
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Product Details

Publication date : Jun 15, 2018
Length: 174 pages
Edition : 1st
Language : English
ISBN-13 : 9781789135404
Category :
Languages :
Tools :

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Frequently bought together


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Total AU$ 145.97
Hands-On Data Science with Anaconda
AU$53.99
Hands-On Data Visualization with Bokeh
AU$45.99
Hands-On Data Analysis with NumPy and pandas
AU$45.99
Total AU$ 145.97 Stars icon

Table of Contents

9 Chapters
Bokeh Installation and Key Concepts Chevron down icon Chevron up icon
Plotting using Glyphs Chevron down icon Chevron up icon
Plotting with different Data Structures Chevron down icon Chevron up icon
Using Layouts for Effective Presentation Chevron down icon Chevron up icon
Using Annotations, Widgets, and Visual Attributes for Visual Enhancement Chevron down icon Chevron up icon
Building and Hosting Applications Using the Bokeh Server Chevron down icon Chevron up icon
Advanced Plotting with Networks, Geo Data, WebGL, and Exporting Plots Chevron down icon Chevron up icon
The Bokeh Workflow – A Case Study Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(6 Ratings)
5 star 50%
4 star 0%
3 star 33.3%
2 star 0%
1 star 16.7%
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jose Aug 05, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Easy to follow
Amazon Verified review Amazon
R. Millikan Sep 26, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Amazon Verified review Amazon
Hernán Díaz Dec 28, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Amazon Verified review Amazon
John Coleman May 06, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
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.
Amazon Verified review Amazon
SJ Oct 10, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
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.
Amazon Verified review Amazon
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