Python Bokeh Tutorial: 10 Interactive Charts You Can Build Step-By-Step
In this video, we dive into Python Bokeh, one of the most powerful and interactive visualization libraries in the Python ecosystem. If you want to create beautiful, dynamic, and web-ready charts, this tutorial will guide you step-by-step with 10 practical Bokeh examples.
0:00 Introduction
0:16 Install Bokeh
0:26 Example 1 – Simple Line Plot
0:53 Example 2 – Scatter Plot with Colors
1:15 Example 3 – Bar Chart
1:38 Example 4 – Multiple Lines on One Figure
2:02 Example 5 – Hover Tool Interaction
2:25 Example 6 – Using ColumnDataSource
2:49 Example 7 – Grid Layout of Plots
3:10 Example 8 – Interactive Slider Widget
3:41 Example 9 – Linked Plots (Zoom & Pan Sync)
4:12 Example 10 – Exporting Plot as HTML
Source Code:
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You’ll learn how to build line charts, bar charts, scatter plots, interactive widgets, hover tools, layouts, and much more. Whether you're a beginner or looking to expand your data visualization skills, this video will help you understand how to use Bokeh for real-world Python projects.
We start by briefly discussing what Bokeh is, how it compares to other libraries like Matplotlib and Plotly, and why it’s perfect for dashboards and browser-based visuals. Then we walk through each example as if we are coding together—clear, simple, and practical.
By the end of this tutorial, you’ll be able to:
🔸 Build interactive plots
🔸 Customize visuals with tools and themes
🔸 Combine multiple charts
🔸 Use ColumnDataSource effectively
🔸 Export your visuals as HTML
🔸 Create dashboard-style layouts
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