Python Decorators: Practical Patterns I Use Regularly
Decorators confused me for years until I understood what they actually do. Here are the patterns I reach for in real projects. Decorators took me a long time to understand because most tutorials explain them with abstract examples that have nothing to do with real code. Once I started using them for logging, caching, and authentication, the concept clicked. A decorator is a function that takes a function and returns a new function with added behavior. That is it. The syntax with the @ symbol is just syntactic sugar for passing a function through another function. Writing Your First Decorator A basic decorator wraps a function and adds behavior before or after it runs. The functools.wraps decorator preserves the original function metadata, which matters for debugging and documentation tools. I always include it. import functools def log_calls(func): @functools.wraps(func) def wrapper(*args, **kwargs): print(f'Calling {func.__name__} with args={args}') result = func(*args, **kwargs) print(f'{func.__name__} returned {result}') return result return wrapper @log_calls def add(a, b): return a + b The *args, **kwargs pattern ensures the wrapper accepts any arguments the original function takes. This makes decorators reusable across functions with different signatures. Decorators with Arguments Decorators that…