Lesson 23 of 40 Data Science Advanced ⏱ 35 min

Data Visualization — matplotlib & seaborn

Create publication-quality charts with matplotlib and seaborn — line plots, bar charts, heatmaps, subplots, and interactive Plotly figures.

Part 1: Introduction to Data Visualization — matplotlib & seaborn

Create publication-quality charts with matplotlib and seaborn — line plots, bar charts, heatmaps, subplots, and interactive Plotly figures.

This lesson uses Python 3.13 features and follows best practices for development in Visual Studio 2026 with Copilot assistance.

Part 2: Core Concepts & Code Examples

# Data Visualization — matplotlib & seaborn — Python 3.13 Example
from typing import Any

def main() -> None:
    """Entry point demonstrating lesson 23 concepts."""
    print(f"Lesson 23: Data Visualization — matplotlib & seaborn")

if __name__ == "__main__":
    main()

Part 3: Best Practices & Patterns

Apply the patterns from this lesson consistently across your projects. Visual Studio 2026's Python IntelliSense, type checking integration, and GitHub Copilot will guide you toward idiomatic, production-ready Python 3.13 code.

Use type hints for all function signatures
Write docstrings with Args/Returns sections
Run ruff for linting, mypy for type checking
Test every function with at least one pytest test

Part 4: Next Steps

Practice these concepts hands-on, then continue to Lesson 24. Return to Python Tutorial Home to see the full curriculum, or visit VisualStudioTutor.com for Visual Studio 2026 guides.

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