Introduction

Welcome to the Data Visualization Techniques Demo!

This project showcases a comprehensive set of data visualization techniques using popular Python libraries:

• Matplotlib — static plotting and customization
• Seaborn — statistical visualization with pandas integration
• Plotly — interactive web-based charts
• Datashader — rendering very large datasets efficiently

What's Included

• A Jupyter Notebook (data-visualization-demo.ipynb) with live, runnable examples across all major visualization types.
• Interactive HTML exports for Plotly charts that work without a running Python kernel.
• Automated helpers to export the notebook to PDF using nbconvert or headless Chromium.
• Examples of exporting animations to MP4/GIF and saving static plots as PNG.
• Comprehensive documentation (this site) for quick reference.

Key Features

1. Multi-library coverage — Matplotlib for low-level control, Seaborn for quick statistical plots, Plotly for interactivity.
2. Large dataset handling — Datashader fallback for hexbin aggregation when rendering hundreds of thousands of points.
3. Export flexibility — Three approaches to PDF generation: LaTeX-based nbconvert, browser print, or headless Chromium.
4. Animation support — FuncAnimation examples with MP4/GIF export options.
5. Reproducible environment — Pinned dependencies in requirements.txt for exact reproduction.

Quick Start

1. Clone or download this repository.
2. Set up a Python virtual environment and install dependencies (see Installation).
3. Launch Jupyter and open the notebook (see Running the Notebook).
4. Run cells to explore visualization techniques and export examples.

For detailed instructions, see Getting Started.

Next Steps

• Explore Visualization Techniques for explanations of each chart type.
• Learn about Exporting & PDF Generation to create shareable documents.
• Refer to Troubleshooting if you run into issues.

Happy visualizing!