Python Patterns

# Python Development Patterns

Idiomatic Python patterns and best practices for building robust, efficient, and maintainable applications.

## When to Activate

- Writing new Python code
- Reviewing Python code
- Refactoring existing Python code
- Designing Python packages/modules

## Core Principles

### 1. Readability Counts

Python prioritizes readability. Code should be obvious and easy to understand.

```python
# Good: Clear and readable
def get_active_users(users: list[User]) -> list[User]:
    """Return only active users from the provided list."""
    return [user for user in users if user.is_active]


# Bad: Clever but confusing
def get_active_users(u):
    return [x for x in u if x.a]
```

### 2. Explicit is Better Than Implicit

Avoid magic; be clear about what your code does.

```python
# Good: Explicit configuration
import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Bad: Hidden side effects
import some_module
some_module.setup()  # What does this do?
```

### 3. EAFP - Easier to Ask Forgiveness Than Permission

Python prefers exception handling over checking conditions.

```python
# Good: EAFP style
def get_value(dictionary: dict, key: str) -> Any:
    try:
        return dictionary[key]
    except KeyError:
        return default_value

# Bad: LBYL (Look Before You Leap) style
def get_value(dictionary: dict, key: str) -> Any:
    if key in dictionary:
        return dictionary[key]
    else:
        return default_value
```

## Type Hints

### Basic Type Annotations

```python
from typing import Optional, List, Dict, Any

def process_user(
    user_id: str,
    data: Dict[str, Any],
    active: bool = True
) -> Optional[User]:
    """Process a user and return the updated User or None."""
    if not active:
        return None
    return User(user_id, data)
```

### Modern Type Hints (Python 3.9+)

```python
# Python 3.9+ - Use built-in types
def process_items(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

# Python 3.8 and earlier - Use typing module
from typing import List, Dict

def process_items(items: List[str]) -> Dict[str, int]:
    return {item: len(item) for item in items}
```

### Type Aliases and TypeVar

```python
from typing import TypeVar, Union

# Type alias for complex types
JSON = Union[dict[str, Any], list[Any], str, int, float, bool, None]

def parse_json(data: str) -> JSON:
    return json.loads(data)

# Generic types
T = TypeVar('T')

def first(items: list[T]) -> T | None:
    """Return the first item or None if list is empty."""
    return items[0] if items else None
```

### Protocol-Based Duck Typing

```python
from typing import Protocol

class Renderable(Protocol):
    def render(self) -> str:
        """Render the object to a string."""

def render_all(items: list[Renderable]) -> str:
    """Render all items that implement the Renderable protocol."""
    return "\n".join(item.render() for item in items)
```

## Error Handling Patterns

### Specific Exception Handling

```python
# Good: Catch specific exceptions
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except FileNotFoundError as e:
        raise ConfigError(f"Config file not found: {path}") from e
    except json.JSONDecodeError as e:
        raise ConfigError(f"Invalid JSON in config: {path}") from e

# Bad: Bare except
def load_config(path: str) -> Config:
    try:
        with open(path) as f:
            return Config.from_json(f.read())
    except:
        return None  # Silent failure!
```

### Exception Chaining

```python
def process_data(data: str) -> Result:
    try:
        parsed = json.loads(data)
    except json.JSONDecodeError as e: