python-design-pattern

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
• Reviewing code before merge (anti-patterns checklist)
• Debugging issues that might stem from known bad practices

Core Concepts

1. KISS — simplest solution that works; complexity must be justified
2. Single Responsibility — each unit has one reason to change
3. Composition over Inheritance — combine objects, don't extend classes
4. High Cohesion, Low Coupling — modules do one thing well; changes don't ripple
5. Rule of Three — wait for three instances before abstracting

Fundamental Patterns

Pattern 1: KISS - Keep It Simple

Before adding complexity, ask: does a simpler solution work?

# Over-engineered: Factory with registration
class OutputFormatterFactory:
    _formatters: dict[str, type[Formatter]] = {}

    @classmethod
    def register(cls, name: str):
        def decorator(formatter_cls):
            cls._formatters[name] = formatter_cls
            return formatter_cls
        return decorator

    @classmethod
    def create(cls, name: str) -> Formatter:
        return cls._formatters[name]()

@OutputFormatterFactory.register("json")
class JsonFormatter(Formatter):
    ...

# Simple: Just use a dictionary
FORMATTERS = {
    "json": JsonFormatter,
    "csv": CsvFormatter,
    "xml": XmlFormatter,
}

def get_formatter(name: str) -> Formatter:
    """Get formatter by name."""
    if name not in FORMATTERS:
        raise ValueError(f"Unknown format: {name}")
    return FORMATTERS[name]()

The factory pattern adds code without adding value here. Save patterns for when they solve real problems.