python-design-patterns

Python Design Patterns

Write maintainable Python code using fundamental design principles. These patterns help you build systems that are easy to understand, test, and modify.

When to Use This Skill

• Designing new components or services
• Refactoring complex or tangled code
• Deciding whether to create an abstraction
• Choosing between inheritance and composition
• Evaluating code complexity and coupling
• Planning modular architectures

Core Concepts

1. KISS (Keep It Simple)

Choose the simplest solution that works. Complexity must be justified by concrete requirements.

2. Single Responsibility (SRP)

Each unit should have one reason to change. Separate concerns into focused components.

3. Composition Over Inheritance

Build behavior by combining objects, not extending classes.

4. Rule of Three

Wait until you have three instances before abstracting. Duplication is often better than premature abstraction.

Quick Start

# Simple beats clever
# Instead of a factory/registry pattern:
FORMATTERS = {"json": JsonFormatter, "csv": CsvFormatter}

def get_formatter(name: str) -> Formatter:
    return FORMATTERS[name]()

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices Summary

1. Keep it simple - Choose the simplest solution that works
2. Single responsibility - Each unit has one reason to change
3. Separate concerns - Distinct layers with clear purposes
4. Compose, don't inherit - Combine objects for flexibility
5. Rule of three - Wait before abstracting
6. Keep functions small - 20-50 lines (varies by complexity), one purpose
7. Inject dependencies - Constructor injection for testability
8. Delete before abstracting - Remove dead code, then consider patterns
9. Test each layer - Isolated tests for each concern
10. Explicit over clever - Readable code beats elegant code