What Most Developers Get Wrong About Python Decorators

Most tutorials get decorators technically right — but practically wrong.

What Most Developers Get Wrong About Python Decorators

Decorators are one of Python’s most loved and feared features. Everyone uses them — few really understand them.

If you’ve ever used @staticmethod, @classmethod, or the ever-so-trendy @lru_cache, you've already used decorators.

And if you’re like most developers, you’ve probably tried to write your own at some point — only to end up confused by *args, **kwargs, and what exactly a "wrapper" is doing in your stack trace.

But here’s the thing: The problem isn’t you.

The problem is how decorators are typically taught.

Most resources focus on the syntax — but skip the semantics. They show you how decorators work, but not why they often break, become unreadable, or turn your once-clear functions into a debugging nightmare.

In this article, I’ll break down what most developers get wrong about Python decorators, and show you how to truly master them — by focusing on readability, debuggability, and real-world use.

The Basics: What Decorators Actually Are

Before we get to what goes wrong, let’s make sure we’re speaking the same language.

A decorator in Python is just a function that takes another function as an argument, does something with it (or around it), and returns a function.

Here’s the bare bones:

def my_decorator(func): 
    def wrapper(*args, **kwargs): 
        print("Before function call") 
        result = func(*args, **kwargs) 
        print("After function call") 
        return result 
    return wrapper 
 
@my_decorator 
def say_hello(): 
    print("Hello!") 
 
say_hello()

Output:

Before function call 
Hello! 
After function call

Looks simple, right?

But here’s where most developers start to fall into traps.

Mistake #1: Forgetting to Preserve Function Metadata

Try this:

print(say_hello.__name__)

You’ll get:

wrapper

Uh-oh.

This is a classic mistake: when you wrap a function, you lose its identity unless you explicitly preserve it. This breaks debugging, stack traces, and documentation tools.

The Fix: Use functools.wraps

import functools 
 
def my_decorator(func): 
    @functools.wraps(func) 
    def wrapper(*args, **kwargs): 
        print("Before function call") 
        result = func(*args, **kwargs) 
        print("After function call") 
        return result 
    return wrapper

This one line saves you from hours of future confusion. Always use @functools.wraps when writing decorators.

Mistake #2: Using Decorators When You Don’t Need To

Many developers rush to write custom decorators for everything — logging, timing, input validation, etc.

But decorators add cognitive load and obscure behavior. If the goal can be achieved with a simple helper function or a class, that’s often the better choice.

Ask yourself:

• Does this need to wrap other functions, or could it just be called inside them?
• Will this decorator be reused across different modules, or is it one-off logic?
• Is the added indirection really worth the abstraction?

Bad:

@log_execution 
def add(a, b): 
    return a + b

Better (sometimes):

def add(a, b): 
    log_execution() 
    return a + b

Use decorators for true cross-cutting concerns, not just to look fancy.

Mistake #3: Not Thinking About Arguments and Return Values

Beginners often hardcode decorator behavior without considering that the wrapped function might return something — or take varying parameters.

Bad:

def debug(func): 
    def wrapper(): 
        print(f"Calling {func.__name__}") 
        func() 
    return wrapper

Now try using that with a function that accepts arguments — it’ll break.

The Fix: Use *args and **kwargs religiously

def debug(func): 
    @functools.wraps(func) 
    def wrapper(*args, **kwargs): 
        print(f"Calling {func.__name__}") 
        return func(*args, **kwargs) 
    return wrapper

Mistake #4: Over-Abstracting with Decorator Factories

A decorator factory is a function that returns a decorator — often used when your decorator needs parameters.

def repeat(n): 
    def decorator(func): 
        @functools.wraps(func) 
        def wrapper(*args, **kwargs): 
            for _ in range(n): 
                func(*args, **kwargs) 
        return wrapper 
    return decorator 
 
@repeat(3) 
def greet(): 
    print("Hello!")

This is powerful — but can quickly become unreadable.

Only use decorator factories when necessary. If your decorator doesn’t need parameters, don’t over-engineer it.

Mistake #5: Not Testing Decorators in Isolation

Decorators can introduce side effects and subtle bugs. Yet most developers only test them indirectly — by testing decorated functions.

Better approach:

• Write unit tests for the decorator logic separately.
• Use mocks to check if the underlying function is called.
• Validate side effects, return values, and call count.

Example:

from unittest.mock import Mock 
 
def test_my_decorator(): 
    mock_func = Mock() 
    decorated = my_decorator(mock_func) 
    decorated("arg") 
    mock_func.assert_called_once_with("arg")

Don’t treat decorators as magic. They’re testable — and they should be tested.

Bonus: The Zen of Decorators

Here’s a simple checklist for decorator sanity:

• Use @functools.wraps
• Support *args and **kwargs
• Avoid overuse — especially in single-use scenarios
• Test behavior independently
• Keep them short and readable

A decorator should decorate — not obfuscate.

A Real-World Example: Simple Timing Decorator

Let’s wrap this up with a practical, reusable decorator:

import time 
import functools 
 
def timeit(func): 
    @functools.wraps(func) 
    def wrapper(*args, **kwargs): 
        start = time.perf_counter() 
        result = func(*args, **kwargs) 
        end = time.perf_counter() 
        print(f"{func.__name__} took {end - start:.4f} seconds") 
        return result 
    return wrapper 
 
@timeit 
def slow_func(): 
    time.sleep(1) 
 
slow_func()

Clear, useful, and debug-friendly — just how decorators should be.

Conclusion: Stop Copy-Pasting, Start Understanding

Decorators are one of Python’s most elegant tools — but only if you truly understand how and when to use them.

Next time you reach for @something, ask yourself:

• Am I wrapping responsibly?
• Is this making my code better — or just more magical?

Because when used right, decorators can make your code cleaner, faster, and smarter.

When used wrong? They turn your codebase into an opaque black box.

So slow down, wrap wisely — and write decorators that work with your code, not against it.

The best decorators don’t just add behavior — they add clarity.