Lesson 21 of 40 Data Science Advanced ⏱ 35 min

NumPy — Arrays & Numerical Computing

Master NumPy arrays — vectorised operations, broadcasting, fancy indexing, linear algebra, and performance vs plain Python loops.

Part 1: Introduction to NumPy — Arrays & Numerical Computing

Master NumPy arrays — vectorised operations, broadcasting, fancy indexing, linear algebra, and performance vs plain Python loops.

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

# NumPy — Arrays & Numerical Computing — Python 3.13 Example
from typing import Any

def main() -> None:
    """Entry point demonstrating lesson 21 concepts."""
    print(f"Lesson 21: NumPy — Arrays & Numerical Computing")

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 22. Return to Python Tutorial Home to see the full curriculum, or visit VisualStudioTutor.com for Visual Studio 2026 guides.

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