Python and Bokeh Learning Materials

This repository contains educational materials for learning data visualization in Python using Bokeh. These notebooks are designed to be run in Google Colab, making them easily accessible for students without requiring local installation of Python or any dependencies.

Overview

These materials cover fundamental concepts of interactive data visualization using Python's Bokeh library. The exercises are hands-on and interactive, allowing you to experiment with different visualization techniques and understand their applications in real-world scenarios.

Notebooks

Click on the link below to open the notebook directly in Google Colab:

Chapter 3.3 - Python and Bokeh
Introduction to interactive data visualization with Bokeh

Chapter 3.3.2 - Python and Bokeh Workshop (Super Store)
Interactive data visualization exercises using the (Tableau) Super Store dataset with Bokeh

Chapter 3.3.3 - Python and Bokeh Workshop (Use Cases - Dashboard)
Creating interactive dashboards with Bokeh

Chapter 3.3.3 - Python and Bokeh Workshop (Use Cases - App)
Building interactive applications with Bokeh

Chapter 3.3.3 - Python and Bokeh Workshop (Use Cases - Streaming)
Implementing real-time data visualization with Bokeh

Getting Started

1. Click on the "Open in Colab" badge above to open the notebook in Google Colab
2. If prompted, sign in with your Google account
3. Save a copy of the notebook to your Google Drive to keep your work
4. Install required dependencies:

   bokeh==3.6.2
   bokeh_sampledata==2024.2
   numpy==1.26.4
   yfinance==0.2.52

5. Execute the cells and follow the instructions within the notebook

Prerequisites

• A Google account to access Google Colab
• Basic understanding of Python syntax
• Basic knowledge of data visualization concepts
• No local installation required - everything runs in the cloud!

Topics Covered

• Basic plotting with Bokeh
• Interactive visualization tools:
  • Line plots
  • Scatter plots
  • Bar charts
  • Log plots
• Advanced features:
  • Interactive tools (pan, zoom, select)
  • Layout options (grid, row, column)
  • Linked plots
  • Hover tools
  • Widgets and UI elements
• Specialized visualizations:
  • Network graphs
  • Geographic data
  • Financial data visualization
• Working with real-time data using yfinance
• Integration with other libraries (NetworkX)

How to Use

The notebook contains:

• Theoretical explanations
• Code examples with interactive outputs
• Progressive complexity from basic to advanced features
• Real-world applications using stock market data
• Network visualization examples
• Geographic data plotting

Each section builds upon previous concepts and includes working examples that can be modified and experimented with.

Features Highlighted

• Interactive visualizations
• Real-time data integration
• Multiple plot layouts
• Custom tools and widgets
• Linked selections and axes
• Export capabilities to HTML
• Network graph visualization
• Geographic data plotting

Note

• Remember to save your work periodically in Google Colab
• The runtime may disconnect after periods of inactivity
• Some features (like geographical plotting) require API keys
• Make sure to install all required dependencies at the start of your session

The notebook is designed to provide a comprehensive introduction to Bokeh's capabilities while maintaining an interactive and hands-on learning approach.