What are decorators and how do you write one?

Question

Accepted Answer

A **decorator** is a function that **wraps another function** to add behavior, without modifying the original's code. It's applied with the `@decorator` syntax above a function — a clean way to add cross-cutting concerns like logging, timing, caching, or access control.

## How decorators work

```python
def my_decorator(func):
    def wrapper(*args, **kwargs):       # wraps the original function
        print("before")
        result = func(*args, **kwargs)  # call the original
        print("after")
        return result
    return wrapper                       # return the wrapped version

@my_decorator
def greet(name):
    print(f"Hello {name}")

greet("Ann")
# Output: before / Hello Ann / after
```

The `@my_decorator` syntax is sugar for `greet = my_decorator(greet)` — it replaces `greet` with the `wrapper`, which runs extra code around the original call. The `*args, **kwargs` makes the wrapper work for any function signature.

## A practical example: timing

```python
import time, functools

def timed(func):
    @functools.wraps(func)        # preserves func's name/docstring (important!)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        print(f"{func.__name__} took {time.perf_counter() - start:.3f}s")
        return result
    return wrapper

@timed
def slow_task():
    time.sleep(1)
```

`@functools.wraps(func)` is important — without it, the wrapped function loses its original name and docstring.

## Decorators with arguments

```python
def repeat(times):               # outer takes the decorator's argument
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for _ in range(times):
                func(*args, **kwargs)
        return wrapper
    return decorator

@repeat(times=3)
def hello(): print("hi")  # prints "hi" three times
```

A parameterized decorator needs an extra layer (a function returning a decorator).

## Common built-in / library decorators

```text
@property            → turn a method into a computed attribute
@staticmethod / @classmethod → method types
@functools.lru_cache → memoize results
@app.route("/")      → web framework routing (Flask)
@pytest.fixture      → test fixtures
```

## Why it matters

Decorators are a powerful, distinctly-Pythonic feature for **adding reusable behavior** (logging, timing, caching, authentication, validation, routing) to functions cleanly and declaratively — separating cross-cutting concerns from core logic.

They're everywhere in real Python: web frameworks (`@app.route`), caching (`@lru_cache`), classes (`@property`), and testing.

Understanding how to write them (a wrapper function, `*args/**kwargs`, `functools.wraps`) and the parameterized form lets you both use the ecosystem's decorators correctly and build your own reusable abstractions.