Decorators

Introduction

A decorator is a function that takes a function and returns a new function. Used for logging, timing, caching, auth, retries, route registration.

Understanding the topic

Core concepts to understand:

• @deco = fn = deco(fn)
• Use functools.wraps to preserve metadata.
• Decorators with args = factory returning a decorator.
• Class-based decorators with __call__.

Syntax reference

Visual flow / code:

from functools import wraps
import time

def timed(fn):
    @wraps(fn)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = fn(*args, **kwargs)
        print(f"{fn.__name__}: {time.perf_counter()-start:.3f}s")
        return result
    return wrapper

@timed
def slow():
    time.sleep(0.5)

# Parameterized decorator
def retry(n: int):
    def deco(fn):
        @wraps(fn)
        def wrapper(*a, **kw):
            for i in range(n):
                try: return fn(*a, **kw)
                except Exception: pass
            raise
        return wrapper
    return deco

Execution workflow

Real-world use

FastAPI, Flask, Django, Celery, pytest — all use decorators as their public API. They're a Python superpower.

Best practices

• Always @wraps.
• Keep decorator logic small.
• Parameterized decorators = 3 nested fns.

Hands-on exercise

Interview preparation — practice these questions:

• What's a decorator?
• Why @wraps?
• Walk through a parameterized decorator.

Summary

In summary: Functions transforming functions. Wraps preserves identity.