python-patterns

name	python-patterns
description	Pythonic idioms, PEP 8, type hints, and best practices. Use when writing, reviewing, or refactoring Python code.
origin	MCC

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.

# 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.

# 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.

# 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

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

Type Aliases, TypeVar, and Protocols

from typing import TypeVar, Union, Protocol

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

# Generic types
T = TypeVar('T')

def first(items: list[T]) -> T | None:
    return items[0] if items else None

# Protocol-based duck typing
class Renderable(Protocol):
    def render(self) -> str: ...

def render_all(items: list[Renderable]) -> str:
    return "\n".join(item.render() for item in items)

Error Handling Patterns

Specific Exception Handling

# 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

Custom Exception Hierarchy

class AppError(Exception):
    """Base exception for all application errors."""
    pass

class ValidationError(AppError):
    """Raised when input validation fails."""
    pass

class NotFoundError(AppError):
    """Raised when a requested resource is not found."""
    pass

Quick Reference: Python Idioms

Idiom	Description
EAFP	Easier to Ask Forgiveness than Permission
Context managers	Use with for resource management
List comprehensions	For simple transformations
Generators	For lazy evaluation and large datasets
Type hints	Annotate function signatures
Dataclasses	For data containers with auto-generated methods
__slots__	For memory optimization
f-strings	For string formatting (Python 3.6+)
pathlib.Path	For path operations (Python 3.4+)
enumerate	For index-element pairs in loops

Anti-Patterns to Avoid

# Bad: Mutable default arguments
def append_to(item, items=[]):
    items.append(item)
    return items

# Good: Use None and create new list
def append_to(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items

# Bad: Checking type with type()
if type(obj) == list:
    process(obj)

# Good: Use isinstance
if isinstance(obj, list):
    process(obj)

# Bad: Comparing to None with ==
if value == None:
    process()

# Good: Use is
if value is None:
    process()

# Bad: from module import *
from os.path import *

# Good: Explicit imports
from os.path import join, exists

# Bad: Bare except
try:
    risky_operation()
except:
    pass

# Good: Specific exception
try:
    risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")

Remember: Python code should be readable, explicit, and follow the principle of least surprise. When in doubt, prioritize clarity over cleverness.

Reference Files

• detailed-examples.md -- Context managers, comprehensions, generators, data classes, decorators, concurrency, package organization, memory/performance, and tooling configuration