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

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

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

## Fundamental Patterns

### Pattern 1: KISS - Keep It Simple

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

```python
# 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:
    """Ge