Python 3 13 Patterns

Python 3.13 Modern Patterns

Core Principles

Python 3.13 represents the culmination of modern Python design. This skill covers essential patterns for writing clean, efficient, and maintainable Python code using the latest language features.

Modern Type Annotations (Built-in Generics)

Python 3.9+ allows using built-in types for generic annotations. This is the required pattern for all new code:

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

def merge_data(data1: dict[str, any], data2: dict[str, any]) -> dict[str, any]:
    return {**data1, **data2}

# ❌ FORBIDDEN: Legacy typing module imports
from typing import List, Dict  # Don't use these!
def process_items(items: List[str]) -> Dict[str, int]:  # Old style
    pass

Union Types with | Operator

Use the pipe operator for union types. This is cleaner and more Pythonic:

# ✅ REQUIRED: Use | for unions
def get_user(user_id: int) -> User | None:
    """Returns User or None if not found."""
    return db.query(User).get(user_id)

def process_value(value: str | int | float) -> str:
    """Handles multiple types."""
    return str(value)

# ✅ REQUIRED: None always comes last in unions
def find_item(query: str) -> Item | None:
    pass

# ❌ FORBIDDEN: Legacy Optional and Union
from typing import Optional, Union
def get_user(user_id: int) -> Optional[User]:  # Old style
    pass
def process_value(value: Union[str, int, float]) -> str:  # Old style
    pass

Structural Pattern Matching (match/case)

Use match/case for complex conditional logic based on structure:

# ✅ REQUIRED: Use match/case for complex conditionals
def handle_command(command: str) -> str:
    match command.split():
        case ["quit"]:
            return "Exiting..."
        case ["load", filename]:
            return f"Loading {filename}"
        case ["save", filename]:
            return f"Saving to {filename}"
        case ["set", key, value]:
            return f"Setting {key} = {value}"
        case _:
            return "Unknown command"

# Pattern matching with type checking
def process_message(message: dict) -> str:
    match message:
        case {"type": "user_created", "user_id": user_id, "email": email}:
            return f"Welcome user {user_id}: {email}"
        case {"type": "user_deleted", "user_id": user_id}:
            return f"User {user_id} deleted"
        case {"type": "error", "code": code, "message": msg}:
            raise ValueError(f"Error {code}: {msg}")
        case _:
            return "Unknown message type"

# Matching with guards
def categorize_number(n: int) -> str:
    match n:
        case 0:
            return "zero"
        case n if n < 0:
            return "negative"
        case n if n > 0 and n < 10:
            return "small positive"
        case n if n >= 10:
            return "large positive"

Async/Await Patterns

Async functions are first-class citizens in Python 3.13:

import asyncio

# ✅ REQUIRED: Comprehensive error handling in async functions
async def fetch_data(url: str) -> dict[str, any]:
    """Fetch data with proper error handling and resource cleanup."""
    async with aiohttp.ClientSession() as session:
        try:
            async with session.get(url, timeout=10) as response:
                response.raise_for_status()
                return await response.json()
        except asyncio.TimeoutError:
            raise TimeoutError(f"Request to {url} timed out")
        except aiohttp.ClientError as e:
            raise ConnectionError(f"Failed to fetch {url}: {e}")

# ✅ REQUIRED: Use asyncio.gather for concurrent operations
async def fetch_multiple(urls: list[str]) -> list[dict[str, any]]:
    """Fetch multiple URLs concurrently."""
    tasks = [fetch_data(url) for url in urls]
    return await asyncio.gather(*tasks, return_exceptions=True)

# ✅ REQUIRED: Use async context managers for resources
class AsyncDatabaseConnection:
    async def __aenter__(self):
        self.conn = await connect_to_db()
        return self.conn

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        await self.conn.close()

async def query_database():
    async with AsyncDatabaseConnection() as conn:
        return await conn.execute("SELECT * FROM users")

Context Managers

Context managers ensure proper resource cleanup:

from contextlib import contextmanager
from typing import Generator

# ✅ REQUIRED: Use context managers for resource management
@contextmanager
def open_file_safely(filename: str, mode: str = 'r') -> Generator[any, None, None]:
    """Context manager for file operations with error handling."""
    file = None
    try:
        file = open(filename, mode)
        yield file
    except IOError as e:
        print(f"Error opening {filename}: {e}")
        raise
    finally:
        if file:
            file.close()

# Class-based context manager
class DatabaseTransaction:
    def __init__(self, connection):
        self.connection = connection

    def __enter__(self):
        self.connection.begin_transaction()
        return self.connection

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is None:
            self.connection.commit()
        else:
            self.connection.rollback()
        return False  # Don't suppress exceptions

# Usage
with DatabaseTransaction(conn) as db:
    db.execute("INSERT INTO users VALUES (...)")

Decorators

Decorators are powerful for cross-cutting concerns:

from functools import wraps
from typing import Callable, Any
import time

# ✅ REQUIRED: Preserve function metadata with @wraps
def timing_decorator(func: Callable) -> Callable:
    """Measure execution time of functions."""
    @wraps(func)
    def wrapper(*args: Any, **kwargs: Any) -> Any:
        start = time.perf_counter()
        result = func(*args, **kwargs)
        end = time.perf_counter()
        print(f"{func.__name__} took {end - start:.4f}s")
        return result
    return wrapper

# Decorator with arguments (decorator factory)
def retry(max_attempts: int = 3, delay: float = 1.0):
    """Retry decorator with configurable attempts and delay."""
    def decorator(func: Callable) -> Callable:
        @wraps(func)
        def wrapper(*args: Any, **kwargs: Any) -> Any:
            for attempt in range(max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_attempts - 1:
                        raise
                    time.sleep(delay)
            return None
        return wrapper
    return decorator

# Usage
@retry(max_attempts=5, delay=2.0)
def fetch_api_data(url: str) -> dict:
    """Fetch data with automatic retries."""
    pass

Generators

Generators enable memory-efficient data processing:

from typing import Generator

# ✅ REQUIRED: Use generators for large datasets
def read_large_file(filename: str) -> Generator[str, None, None]:
    """Read file line by line without loading into memory."""
    with open(filename, 'r') as file:
        for line in file:
            yield line.strip()

def process_data_stream(data: list[int]) -> Generator[int, None, None]:
    """Process data in chunks."""
    for item in data:
        if item > 0:
            yield item * 2

# Generator with send() for coroutine-like behavior
def running_average() -> Generator[float, float, None]:
    """Calculate running average of sent values."""
    total = 0.0
    count = 0
    while True:
        value = yield (total / count) if count > 0 else 0.0
        total += value
        count += 1

# Usage
avg = running_average()
next(avg)  # Prime the generator
print(avg.send(10))  # 10.0
print(avg.send(20))  # 15.0
print(avg.send(30))  # 20.0

Anti-Patterns to Avoid

Using Legacy typing Imports

# BAD
from typing import List, Dict, Optional, Union
def func(items: List[str]) -> Dict[str, int]:
    pass

# GOOD
def func(items: list[str]) -> dict[str, int]:
    pass

Implicit Any Return Types

# BAD: Function implicitly returns Any
def process_data(data):
    return data.upper()

# GOOD: Explicit return type
def process_data(data: str) -> str:
    return data.upper()

Using Any Instead of object

# BAD: Any disables type checking
from typing import Any
def log_value(value: Any) -> None:
    print(str(value))

# GOOD: object is more precise
def log_value(value: object) -> None:
    print(str(value))

Chained if/elif for Structured Data

# BAD: Complex conditional logic
if isinstance(data, dict) and "type" in data:
    if data["type"] == "user_created":
        handle_user_created(data)
    elif data["type"] == "user_deleted":
        handle_user_deleted(data)

# GOOD: Use match/case
match data:
    case {"type": "user_created", **rest}:
        handle_user_created(data)
    case {"type": "user_deleted", **rest}:
        handle_user_deleted(data)

Not Using Context Managers for Resources

# BAD: Manual resource management
file = open('data.txt', 'r')
try:
    data = file.read()
finally:
    file.close()

# GOOD: Context manager
with open('data.txt', 'r') as file:
    data = file.read()

When to Use This Skill

Activate this skill when:

• Writing new Python 3.13 modules
• Refactoring legacy Python code to modern patterns
• Implementing async/concurrent operations
• Using structural pattern matching (match/case)
• Creating decorators or context managers
• Working with generators for data processing
• Applying modern type annotations

Related Resources

For additional advanced patterns, reference:

• Python 3.13 Official Documentation: https://docs.python.org/3/whatsnew/3.13.html
• Type Hints Best Practices: See type-hints-best-practices skill
• Async Patterns: See async-patterns skill