Decorators in Python — All You Need to Know

The Problem: Why Do Decorators Exist?

Before we talk about decorators, let’s understand the problem they solve.

Imagine you have a data pipeline with several functions. You want to measure how long each function takes to run. Without decorators, you’d do something like this:

import time

def extract_data():
    start = time.time()              # ← timing logic (not related to extraction)

    data = [1, 2, 3, 4, 5]           # ← actual business logic

    duration = time.time() - start   # ← timing logic again
    print(f"extract_data took {duration:.2f}s")
    return data

def transform_data(data):
    start = time.time()              # ← same timing logic, copy-pasted

    result = [x * 2 for x in data]   # ← actual business logic

    duration = time.time() - start   # ← same timing logic, copy-pasted
    print(f"transform_data took {duration:.2f}s")
    return result

def load_data(data):
    start = time.time()              # ← same timing logic, copy-pasted again

    print(f"Loaded {len(data)} records")

    duration = time.time() - start   # ← same timing logic, copy-pasted again
    print(f"load_data took {duration:.2f}s")

Do you see the problem?

The timing code is duplicated in every function. The same start = time.time() ... duration = time.time() - start ... print(...) block, copied three times. If you have 20 functions, you copy it 20 times.

This creates several issues:

• Repetition — the same code appears in every function. This violates the DRY principle (Don’t Repeat Yourself), which says you should define logic once and reuse it.
• Clutter — timing code is mixed with the actual business logic. It becomes harder to see what a function actually does.
• Hard to maintain — want to switch from print to proper logging? You have to change every function.

What you really want is a way to say: “Add timing to this function” — without touching the function’s code → That’s exactly what decorators do.

First, You Need to Understand: Functions Are Objects

Before decorators can make sense, you need to understand one key fact about Python:

Functions are objects — just like strings, integers, or lists.

In many programming languages, functions are special. In Python, they are not. A function is just another value you can work with. This means three things:

1. You can assign a function to a variable:

def greet(name):
    return f"Hello, {name}!"

# Point the variable 'say_hello' to the same function as 'greet'.
# No parentheses - we are NOT calling greet, we are referencing the function itself.
say_hello = greet

print(say_hello("Alice"))  # Output: Hello, Alice!
print(greet("Alice"))      # Output: Hello, Alice!  (same result - same function)

The distinction matters: greet (no parentheses) is the function object itself. greet("Alice") (with parentheses) calls the function and gives you its return value. Keep this difference in mind — it's important for decorators.

2. You can pass a function as an argument to another function:

def shout(text):
    return text.upper()

def whisper(text):
    return text.lower()

def speak(func, text):
    # 'func' is whatever function was passed in.
    # We call it here with 'text' as the argument.
    return func(text)

print(speak(shout, "hello"))    # Output: HELLO
print(speak(whisper, "HELLO"))  # Output: hello

We pass shout and whisper as arguments to speak — the same way you'd pass a string or a number.

3. A function can create and return another function:

def create_multiplier(factor):
    """Creates and returns a new function that multiplies any number by 'factor'."""

    def multiplier(number):
        return number * factor

    # Return the function itself - not the result of calling it.
    return multiplier

double = create_multiplier(2)  # 'double' is now a function that multiplies by 2
triple = create_multiplier(3)  # 'triple' is now a function that multiplies by 3

print(double(5))   # Output: 10
print(triple(5))   # Output: 15

• create_multiplier builds a new function each time you call it.
• The returned function "remembers" the value of factor even after create_multiplier has finished running. (This is called a closure — the inner function captures variables from the outer function's scope.)

These three properties — assigning, passing, and returning functions — are the building blocks of decorators.

If you understand them, decorators are just one more step.

What Is a Closure? (Quick Detour — Optional)

You might have a question about the example above: how does double remember that factor is 2?

After all, create_multiplier(2) has already finished running. Its local variable factor = 2 should be gone. But double(5) still returns 10. How does it know?

This is called a closure. When a function is defined inside another function and uses a variable from the outer function, Python attaches that variable to the inner function. The inner function carries it, even after the outer function is finished.

def create_multiplier(factor):
    def multiplier(number):
        return number * factor     # 'factor' comes from the outer function
    return multiplier

double = create_multiplier(2)
# create_multiplier is done. But 'double' still carries factor=2.

triple = create_multiplier(3)
# This is a SEPARATE closure. It carries factor=3.

print(double(5))   # Output: 10  - uses factor=2
print(triple(5))   # Output: 15  - uses factor=3

• When the inner function leaves the outer function, it packs the variables it needs and takes them along.
• Each call to create_multiplier creates a separate backpack with different content.

Why does this matter for decorators? Because every decorator is a closure. The wrapper function needs access to func — a variable from the outer timer function. Even after timer finishes running, wrapper still remembers which function to call, because it carries func:

def timer(func):                   # ← outer function; 'func' is a variable here
    def wrapper(*args, **kwargs):  # ← inner function (the closure)
        result = func(...)         # ← 'func' is remembered from the outer scope
        return result
    return wrapper                 # ← wrapper is returned out, but carries 'func' with it

That’s all you need to know for now: an inner function remembers variables from the outer function that created it. That’s a closure. That’s what makes decorators work.

What Is a Decorator?

A decorator is a function that takes another function, adds some behavior to it, and returns a new function — without modifying the original function’s code.

That’s it. No magic. Let’s build one from scratch.

Building a Decorator Step by Step

Let's solve the timing problem from the beginning. We want to build a function that:

1. Takes any function as input.
2. Returns a new function that does the same thing as the original, but also measures how long it takes.

But before we write the decorator, we need to understand one concept that will appear inside it.

Quick Review: `argsand*kwargs` (optional)

In Python, functions usually define exactly what arguments they accept:

def greet(name, age):
    print(f"{name} is {age} years old")

greet("Alice", 30)       # ✔ Works
greet("Alice")           # ✘ Error - missing 'age'
greet("Alice", 30, True) # ✘ Error - too many arguments

But sometimes you want a function that accepts any number of arguments — you don’t want to fix the count in advance. That’s what *args and **kwargs let you do.

**a) *args — captures any number of positional arguments as a tuple**

Positional arguments are values you pass to a function by position (first, second, third…), without using a name:

def show_args(*args):
    print(type(args))       # <class 'tuple'>  — args is always a tuple
    print(args)

show_args(1, 2, 3)          # Output: (1, 2, 3)
show_args("hello")          # Output: ('hello',)
show_args()                 # Output: ()  - empty tuple, no arguments passed
show_args(1, "two", True)   # Output: (1, 'two', True) - any types, any count

• The * in *args tells Python: "Take all positional arguments — however many there are — and pack them into a tuple called args."

The name args is just a convention. You could write *stuff and it would work the same way. But everyone uses args, so stick with it.

**b) **kwargs — captures any number of keyword arguments as a dictionary**

Keyword arguments are values you pass using key=value syntax:

def show_kwargs(**kwargs):
    print(type(kwargs))   # <class 'dict'>  - kwargs is always a dictionary
    print(kwargs)

show_kwargs(name="Alice", age=30)    # Output: {'name': 'Alice', 'age': 30}
show_kwargs(x=1)                     # Output: {'x': 1}
show_kwargs()                        # Output: {}  - empty dict, no keyword arguments

• The ** in **kwargs tells Python: "Take all keyword arguments and pack them into a dictionary called kwargs."

Again, the name kwargs is just a convention.

*c) Together, `argsandkwargs` accept any combination of arguments

def accept_anything(*args, **kwargs):
    print(f"Positional: {args}")
    print(f"Keyword:    {kwargs}")

accept_anything(1, 2, 3, name="Alice", age=30)
# Positional: (1, 2, 3)
# Keyword:    {'name': 'Alice', 'age': 30}

This function accepts literally anything you throw at it — any number of positional arguments, any number of keyword arguments, or none at all.

One rule: positional arguments must come before keyword arguments.

*This rule is *not something special about `argsandkwargs`. It applies to every function call in Python.*

accept_anything(1, 2, name="Alice")     # ✔ positional first, then keyword
accept_anything(1, 2, 3)                # ✔ only positional
accept_anything(name="Alice", age=30)   # ✔ only keyword
accept_anything(1, name="Alice", 2)     # ✘ SyntaxError

This is a general Python rule — not specific to *args/**kwargs.

Once Python sees a key=value argument, it expects everything after it to also be key=value. You can't go back to positional.

d) Unpacking — passing args/kwargs through to another function:

You can also do the reverse: take a tuple or dictionary and unpack it into individual arguments using * and **:

def original(a, b, name="default"):
    print(f"a={a}, b={b}, name={name}")

args = (1, 2)
kwargs = {"name": "Alice"}

original(*args, **kwargs)
# This is the same as calling: original(1, 2, name="Alice")
# Output: a=1, b=2, name=Alice

• *args unpacks the tuple into positional arguments.
• **kwargs unpacks the dictionary into keyword arguments.
• The function receives them as if you typed them out by hand.

Why Does This Matter for Decorators?

When we build a decorator, we create a wrapper function that replaces the original function. This means wrapper will be called instead of the original. So wrapper needs to accept whatever arguments the original function accepts.

The problem: we’re writing a general-purpose decorator. We don’t know in advance what functions it will decorate. It might decorate a function with no arguments, or one with five, or one with keyword arguments. We need wrapper to accept anything and pass it all through unchanged.

That’s exactly what *args and **kwargs give us:

def wrapper(*args, **kwargs):
    # Catches ALL arguments, forwards ALL arguments - unchanged.
    result = original(*args, **kwargs)
    return result

Here’s the flow:

Someone calls:    wrapper(1, 2,            name="Alice")
                          │  │                 │
                          ▼  ▼                 ▼
PACK:            *args = (1, 2)            **kwargs = {"name": "Alice"}
                          │                    │
                          ▼                    ▼
FORWARD:         original(*args,           **kwargs)
                 original(1, 2,            name="Alice")   ← unpacked back to normal

wrapper doesn't need to know what the arguments are. It catches everything and forwards everything. This is what makes the decorator work with any function.

Now Let’s Build the Decorator

With that understood, here’s our timer decorator:

import time

def timer(func):
    """
    A decorator that measures how long a function takes to run.

    - Takes a function ('func') as input.
    - Returns a NEW function ('wrapper') that wraps the original.
    """

    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)    # Call the ORIGINAL function with provided arguments
        duration = time.time() - start
        print(f"{func.__name__} took {duration:.2f}s")
        return result     # Return whatever the original returned

    return wrapper    # Return the new function (not calling it - returning it)

Let’s break down each piece:

• **func** — The original function being decorated. It's passed in as an argument to timer.
• **wrapper(*args, **kwargs)* — A new function created inside timer. This is the "enhanced" version that will replace the original. Thanks to `args, **kwargs`, it accepts whatever arguments the original function accepts.
• **func(*args, **kwargs)** — Calls the original function, forwarding all arguments. Whatever was passed to wrapper is passed through to func unchanged.
• **func.__name__* — Every function in Python has a __name__ attribute containing its name as a string (e.g., "extract_data")*. We use it to print which function was timed.
• **return result** — wrapper returns whatever the original function returned. This way, the caller gets the same result as if they called the original directly.
• **return wrapper — timer returns the new function object. Not the result of calling it — the function itself.**

Here’s the flow visually:

timer(func)   # ← Takes the original function as input
│
│   # Inside timer, a new function is created:
│
├── def wrapper(*args, **kwargs):        ← The NEW function that will replace the original
│       start = time.time()              ← 1. Record start time
│       result = func(*args, **kwargs)   ← 2. Call the ORIGINAL function (func)
│       duration = time.time() - start   ← 3. Calculate how long it took
│       print(f"... took {duration}s")   ← 4. Print the duration
│       return result                    ← 5. Return the ORIGINAL function's result
│
│   # timer doesn't call wrapper — it just returns it:
│
└── return wrapper   # ← Give back the new function to whoever called timer

Now let’s use this decorator manually* *(without `@`):**

def extract_data():
    time.sleep(1)   # Simulate some work (pauses for 1 second)
    return [1, 2, 3, 4, 5]

# Manually "decorate" the function:
extract_data = timer(extract_data)

Let me trace what happens on that last line:

1. timer(extract_data) calls timer, passing in the original function — extract_data as an object.
2. Next timer creates wrapper = “decorated function” (which knows how to call the original function + measure time) and returns it.
3. The returned wrapper is stored under the name extract_data.

That’s it. We replaced the name extract_data with a new, enhanced version:

BEFORE:   extract_data  →  original function (just does the work)
AFTER:    extract_data  →  wrapper (measures time + calls the original function)

The original function didn’t disappear — wrapper still holds a reference to it internally (through func). But from now on, every time you call extract_data(), you're calling wrapper():

data = extract_data()     # You think you're calling extract_data...
                          # ...but you're actually calling wrapper, which:
                          #    1. Records the start time
                          #    2. Calls the ORIGINAL extract_data
                          #    3. Prints how long it took
                          #    4. Returns the original result

# Output: extract_data took 1.00s
# data = [1, 2, 3, 4, 5]

The original function’s code is unchanged. We just wrapped it with extra behavior.

The @ Syntax — A Shortcut

Writing extract_data = timer(extract_data) every time is tedious. Python gives you a shortcut — the @ symbol placed above a function definition:

@timer
def extract_data():
    time.sleep(1)
    return [1, 2, 3, 4, 5]

This is 100% identical to:

def extract_data():
    time.sleep(1)
    return [1, 2, 3, 4, 5]

extract_data = timer(extract_data)

The @timer line is just a cleaner way to write the same thing. There is no difference in behavior. It's called "syntactic sugar" — it makes the code sweeter to read and write, but does the same thing underneath.

Now our original problem is solved cleanly:

@timer
def extract_data():
    data = [1, 2, 3, 4, 5]
    return data

@timer
def transform_data(data):
    return [x * 2 for x in data]

@timer
def load_data(data):
    print(f"Loaded {len(data)} records")

• No duplicated timing code.
• Each function contains only its business logic.
• The timing behavior is defined once in the timer decorator and applied with a single line. Want to remove timing? Delete the @timer line. Want to change how timing works? Update the timer function in one place.

How to Think About Decorators — A Mental Shortcut

At this point, you understand the mechanics. But when writing real code, you need a simple mental model — not theory:

A decorator is a “before and after” wrapper around a function.

Some decorators only use “before” (check permissions, then call the function).

Some only use “after” (call the function, then log the result).

Some use both (record time before, print duration after).

But they all follow this pattern.

When you see @something above a function, just ask yourself: "What does this add before, after, or around my function?" That's all a decorator ever does.

How to Know When You Need a Decorator

A practical rule: if you catch yourself copying the same setup/cleanup code into multiple functions, that’s a decorator waiting to be written.

Ask yourself these two questions:

1. Am I repeating the same "before/after" logic in multiple functions?
   → Yes? Write a decorator (Decorators earn their value through reuse).

2. Is this logic INDEPENDENT of what the function actually does?
   (Would the same wrapping logic work for any function?)
   → Yes? It belongs in a decorator.

The One Thing You Must Always Do: @functools.wraps

There’s a subtle problem with our decorator. Let’s see it:

@timer
def extract_data():
    """Extracts data from the source system."""
    return [1, 2, 3, 4, 5]

print(extract_data.__name__)    # Output: wrapper  
print(extract_data.__doc__)     # Output: None     

Why it happens? Because extract_data now points to wrapper — that's what our decorator returned. So when you ask for its name, you get "wrapper". When you ask for its docstring, you get None (because wrapper has no docstring).

This breaks debugging tools, logging, documentation generators, and anything that inspects function metadata.

The fix is one line — @functools.wraps:

import functools
import time

def timer(func):
    @functools.wraps(func)       # ← Copies func's name, docstring, etc. onto wrapper
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        duration = time.time() - start
        print(f"{func.__name__} took {duration:.2f}s")
        return result
    return wrapper

• @functools.wraps(func) is itself a decorator (yes, decorators can decorate other functions inside decorators). It copies the original function's __name__, __doc__, and other metadata onto wrapper, so wrapper "pretends" to be the original function.

Now:

@timer
def extract_data():
    """Extracts data from the source system."""
    return [1, 2, 3, 4, 5]

print(extract_data.__name__)    # Output: extract_data  ✔
print(extract_data.__doc__)     # Output: Extracts data from the source system.  ✔

Always use @functools.wraps. It's a single line that prevents real problems.

The Decorator Template

Here is the pattern you should use every time you write a decorator. Memorize this structure:

import functools

def my_decorator(func):
    @functools.wraps(func)              # Always include this
    def wrapper(*args, **kwargs):       # Accept any arguments
        # ... do something before ...
        result = func(*args, **kwargs)  # Call the original function
        # ... do something after ...
        return result                   # Return the original result
    return wrapper

Every decorator follows this skeleton. The only thing that changes is what you do before and after the func() call.

Decorators with Arguments

So far, our timer decorator always behaves the same way. You can't configure it. But what if you want to pass settings to it?

For example:

• Sometimes print the function’s arguments, sometimes don’t.
• Sometimes retry 3 times, sometimes 5 times.

You need a way to pass configuration to the decorator itself.

The Problem

Your first instinct might be to add parameters directly:

# ✘ This WON'T work
def timer(func, print_args=False):
    def wrapper(*args, **kwargs):
        ...
    return wrapper

@timer(print_args=True)
def transform_data(data):
    ...

# TypeError: decorator() missing 1 required positional argument: 'func'

Why does this break? Because of how Python processes the @ symbol. As you remember @timer is just a shortcut for:

transform_data = timer(transform_data)

Python takes whatever is after @, and calls it with the function as the argument. So:

@timer(print_args=True)
def transform_data(data): ...

# Means:
transform_data = timer(print_args=True)(transform_data)

• Python first calls timer(print_args=True), and then calls the result with transform_data.
• So timer(print_args=True) must return something callable — something that can accept transform_data as an argument → it must return a decorator.
• But our timer doesn't return a decorator. It returns wrapper. It expects func as its first argument, not print_args. As a result it breaks.

The Solution: A Function That Returns a Decorator

We add one more layer — a function that accepts “configuration” (like print_args=True) and returns a decorator function with that “configuration” baked in.

This follows the factory pattern — a general programming concept where a function creates and returns another function or object, instead of you creating it directly.

import functools
import time

def timer(print_args=False):           # ← FACTORY: accepts settings - returns decorator

    def decorator(func):               # ← DECORATOR: accepts the function - returns wrapper
        @functools.wraps(func)
        def wrapper(*args, **kwargs):  # ← WRAPPER: runs when function is called - replaces the original function
            if print_args:
                print(f"Calling {func.__name__} with args={args}, kwargs={kwargs}")
            start = time.time()
            result = func(*args, **kwargs)
            duration = time.time() - start
            print(f"{func.__name__} took {duration:.2f}s")
            return result
        return wrapper

    return decorator                    # ← Factory returns the decorator

Now let’s trace it again:

@timer(print_args=True)
def transform_data(data): ...

# Expands to:
transform_data = timer(print_args=True)(transform_data)

# Step 1: timer(print_args=True) → returns 'decorator'  ✔ (a callable!)
# Step 2: decorator(transform_data) → returns 'wrapper' ✔
# Result: transform_data = wrapper

• timer(print_args=True) now returns decorator — which is callable and accepts func. Both steps work.

The Parentheses Trap

When your decorator accepts arguments, you must always include parentheses — even with all defaults:

@timer()             # ✔ Correct - calls factory, gets decorator back
def my_func(): ...

@timer               # ✘ Wrong - Python does timer(my_func), passing function as 'print_args'
def my_func(): ...

• Without parentheses, Python treats timer as the decorator itself and passes my_func as its first argument — which lands in print_args. This causes confusing errors.

The rule: if your decorator accepts arguments, always use @decorator(), never @decorator.

Common Real-World Decorators

Now that you understand the mechanics, let’s see decorators you’ll encounter in real code.

1. Retry Decorator

Automatically retry a function when it fails due to temporary errors (network timeout, service unavailable, etc.):

import functools
import time

def retry(max_attempts=3, delay=1.0, exceptions=(Exception,)):
    """
    Retries the decorated function if it raises one of the specified exceptions.

    Args:
        max_attempts: How many times to try before giving up.
        delay: Seconds to wait between retries.
        exceptions: A tuple of exception types to catch and retry on.
    """
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            last_exception = None
            for attempt in range(1, max_attempts + 1):
                try:
                    return func(*args, **kwargs)    # If this succeeds, return immediately
                except exceptions as e:
                    # The function raised one of the exceptions we're watching for.
                    # Save last so we can re-raise it if all attempts fail.
                    last_exception = e
                    print(f"{func.__name__}: attempt {attempt}/{max_attempts} failed: {e}")
                    if attempt < max_attempts:
                        time.sleep(delay)           # Wait before retrying
                    # If this was the last attempt, the loop ends naturally
            raise last_exception   # We only reach this line if every attempt failed.
        return wrapper
    return decorator

@retry(max_attempts=3, delay=2.0, exceptions=(ConnectionError, TimeoutError))
def call_external_api(endpoint: str) -> dict:
    # ... HTTP call logic ...
    pass

Without this decorator, you’d have to write the retry loop inside every function that calls an unreliable service. With it, you write the retry logic once and apply it with a single line.

2. Logging Decorator

In production systems, you often want to know when a function was called and whether it finished successfully — without adding print or logger.info statements inside every function.

A logging decorator adds this visibility automatically. You define the logging logic once, and any function you decorate with @log_call will announce when it starts and when it completes:

import functools
import logging

logger = logging.getLogger(__name__)

def log_call(func):
    """Logs when a function is called and when it finishes."""
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        logger.info(f"Calling {func.__name__}")
        result = func(*args, **kwargs)
        logger.info(f"{func.__name__} finished successfully")
        return result
    return wrapper

@log_call
def process_payment(user_id: int, amount: float) -> bool:
    # ... payment logic ...
    return True

# When process_payment(1, 99.99) runs, you'll see in your logs:
# INFO: Calling process_payment
# INFO: process_payment finished successfully

3. Built-in Decorators You’ll See Everywhere

Python and its libraries come with many ready-made decorators. You don’t need to build these — just recognize and use them.

• **@staticmethod and @classmethod** — change how methods behave in a class:

class MathUtils:

    @staticmethod
    def add(a, b):
        # A regular function that lives inside the class.
        # It doesn't need access to the class or any instance.
        return a + b

    @classmethod
    def from_string(cls, expression: str):
        # Receives the CLASS itself as the first argument ('cls'), not an instance.
        # Useful for creating alternative ways to construct objects.
        a, b = expression.split("+")
        return cls.add(int(a), int(b))

• **@property* — lets you access a method as if it were a simple attribute (no parentheses needed)*:

class Circle:
    def __init__(self, radius):
        self.radius = radius

    @property
    def area(self):
        # Called as circle.area — NOT circle.area()
        return 3.14159 * self.radius ** 2

c = Circle(5)
print(c.area)    # Output: 78.53975 — looks like an attribute, but it's computed

• Framework decorators — you’ll encounter these constantly in web frameworks, Airflow, testing libraries, etc.:

# Flask / FastAPI - register a function as a web endpoint
@app.get("/users/{user_id}")
def get_user(user_id: int):
    return {"user_id": user_id}

# Airflow TaskFlow API - turn a function into an Airflow task
@task
def extract_data():
    return [1, 2, 3]

# pytest - run a test with multiple different inputs
@pytest.mark.parametrize("input,expected", [(1, 2), (2, 4), (3, 6)])
def test_double(input, expected):
    assert input * 2 == expected

They all follow the same principle: take your function and add behavior to it, or register it somewhere.

Stacking Multiple Decorators

You can apply more than one decorator to a single function. They are applied **bottom-up** (the one closest to the function is applied first):

@log_call    #   ← (applied second)
@timer       #   ← (applied first)
def process_data(data):
    return [x * 2 for x in data]

# This is equivalent to:
# process_data = log_call(timer(process_data))
#                   │       └────────── timer wraps process_data first
#                   └─────────────────── then log_call wraps the result

When Should You Use Decorators?

Decorators are best for shared behaviors — logic that applies to many functions but isn’t part of their main purpose.

Good Uses

+------------------------+-----------------------------------------------+
| Use Case               | Why It Fits                                   |
+------------------------+-----------------------------------------------+
| Logging / Timing       | Same pattern across many functions            |
| Retry logic            | Same retry behavior for all API calls         |
| Authentication         | Check permissions before running a function   |
| Caching                | Avoid repeating expensive computations        |
| Input validation       | Validate arguments before the function runs   |
| Framework registration | Register functions as endpoints, tasks, etc.  |
+------------------------+-----------------------------------------------+

The common thread: the behavior is generic (it works the same regardless of the specific function) and reused across many functions.

When NOT to Use Decorators

• Logic specific to one function — just put it inside the function. A decorator is not worth it for a single use.
• Complex behavior that depends on the function’s internals — if the decorator needs to know what the function does, it’s the wrong tool.
• When it hurts readability — if a reader can’t guess what @my_decorator does, the decorator might be making the code harder to understand instead of easier.

THE END

That’s it for this article! I hope this gave you a clearer picture of how decorators work in Python, when to use them, and why they can be so useful in your code.

You now have everything you need to use decorators properly — and take your code to the next level.

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