Interactive Data Visualization in Python With Bokeh

Christopher Bailey 26 Lessons 2h 7m
data-science intermediate

Bokeh prides itself on being a library for interactive data visualization. The graphics are rendered using HTML and JavaScript, and your visualizations are easy to share as an HTML page. You will create a number of visualizations based on a real-world dataset.

The goal of this course is to get you up and running with Bokeh.

You will learn how to:

Transform your data into visualizations
Customize and organize your visualizations
Add interactivity to your visualizations

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Welcome to Bokeh

9 Lessons 31m

1. Intro and Bokeh Course Overview 03:49

2. From Data to Visualization 02:48

3. Setting Up Your Bokeh Environment 02:55

4. Generating Your First Figure to a Static HTML File 03:15

5. Generating Your First Figure to a Jupyter Notebook 01:59

6. Getting Your Figure Ready for Data 04:46

7. Drawing Data With Glyphs 04:16

8. Multiple Glyphs and Adding a Legend 06:45

9. Bokeh Course: Section 1 Review 01:21

Working With Data and Layouts

8 Lessons 38m

1. Bokeh Course: Section 2 Overview 01:09

2. A Quick Aside About Data 03:59

3. Using the ColumnDataSource Object 09:53

4. Using GroupFilter and CDSView 04:22

5. Multiple Visualizations Using Column Layout 07:59

6. GridPlot Layout 05:23

7. Tabbed Layout With Panels 04:10

8. Bokeh Course: Section 2 Review 01:09

Adding Interaction

9 Lessons 57m

1. Bokeh Course: Section 3 Overview 01:08

2. Configuring the Toolbar 03:19

3. Selecting Data Points 10:45

4. Adding Hover Actions 03:24

5. Emphasizing With Hover Inspections 02:53

6. Linking Axes 12:56

7. Linking Selections 10:45

8. Highlighting Data Using the Legend 08:14

9. Bokeh Course: Recap and Review 03:59

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About Christopher Bailey

Chris is an avid Pythonista and creates video tutorials for Real Python. He is a programmer and data analyst. He creates music under the name Tripnet. Chris lives in Colorado with his wife and two dogs.

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Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. The team members who worked on this tutorial are:

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Participant Comments

Michal on June 15, 2021

Great video course on a cool Python library. Thank you.

Pygator on Aug. 18, 2019

This set of video tutorials are great! I can already dream up some use cases. Some video series about some image manipulation packages would be great.

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