Timothy was debugging a Flask application when he encountered something puzzling. "Margaret, what's this @ symbol doing above this function? The code has @app.route('/home') and then @login_required stacked on top of each other. Are these comments? Some kind of metadata?"
Margaret's eyes lit up. "Those are decorators - one of Python's most elegant features. They're not comments or metadata. They're functions that wrap other functions, adding behavior without modifying the original code. Think of them as gift wrap - the present inside stays the same, but you've added something beautiful around it."
"Gift wrap?" Timothy looked skeptical. "That sounds like magic syntax. How does putting an @ symbol above a function change what it does?"
"It's not magic - it's syntactic sugar for a very clever pattern," Margaret said. "Let me show you what's really happening when you use a decorator."
The Puzzle: The @ Symbol Mystery
Timothy showed Margaret the confusing code:
@login_required
@app.route('/home')
def home():
return "Welcome home!"
# What does this actually mean?
# How does @ change the function?
# Why are there two of them?
# What order do they execute in?
"See what I mean?" Timothy said. "There are two @ symbols, and somehow they modify the home() function. But I don't see any function calls, no wrapping code, nothing. Just these mysterious @ decorations."
"Exactly! You've identified the mystery," Margaret said. "Those @ symbols are Python's decorator syntax - a shorthand for function transformation. What you're seeing is actually this:"
def home():
return "Welcome home!"
home = app.route('/home')(home)
home = login_required(home)
"Notice the order," Margaret pointed. "The decorator closest to the function - @app.route - is applied first. Then @login_required wraps that result. It's inside-out application: bottom decorator first, top decorator last."
Timothy's eyes widened. "Wait, so @decorator is just syntactic sugar for reassigning the function to a wrapped version?"
"Precisely! The decorator takes your function, wraps it with new behavior, and replaces it with the enhanced version. Let me show you how decorators actually work, starting from the ground up."
What Are Decorators?
Margaret pulled up a foundational explanation:
"""
DECORATORS: Functions that modify other functions
A decorator is a callable that:
- Takes a function as an argument
- Returns a new function (usually a wrapper)
- The wrapper adds behavior before/after the original function
- The @ syntax is shorthand for function reassignment
DECORATOR PATTERN:
@decorator
def function():
pass
# Is equivalent to:
def function():
pass
function = decorator(function)
DECORATORS ARE USED FOR:
- Adding functionality without modifying original code
- Cross-cutting concerns (logging, timing, authentication)
- Modifying function behavior dynamically
- DRY principle - write once, apply everywhere
"""
def demonstrate_decorator_basics():
"""Show how decorators work fundamentally"""
def simple_decorator(func):
"""A basic decorator that wraps a function"""
def wrapper():
print(" Before function call")
result = func()
print(" After function call")
return result
return wrapper
# Without @ syntax - explicit wrapping
def greet():
print(" Hello!")
return "greeting"
print("Without decorator:")
result = greet()
print(f" Returned: {result}\n")
# Now wrap it manually
greet = simple_decorator(greet)
print("With decorator (manual wrapping):")
result = greet()
print(f" Returned: {result}\n")
# Using @ syntax - same result
@simple_decorator
def farewell():
print(" Goodbye!")
return "farewell"
print("With @ syntax:")
result = farewell()
print(f" Returned: {result}")
demonstrate_decorator_basics()
Output:
Without decorator:
Hello!
Returned: greeting
With decorator (manual wrapping):
Before function call
Hello!
After function call
Returned: greeting
With @ syntax:
Before function call
Goodbye!
After function call
Returned: farewell
Timothy studied the output carefully. "So the decorator wraps the original function in a new function that adds behavior. The @ syntax just makes it cleaner than manually reassigning the function name."
"Exactly," Margaret confirmed. "Notice how 'Before function call' and 'After function call' appear when the function is decorated, but not before. The wrapper function intercepts the call, adds its own behavior, then calls the original function."
"But why would I want to wrap functions like this?" Timothy asked. "What's the practical advantage?"
"Great question. Imagine you have 50 functions and you want to add timing to all of them. Without decorators, you'd modify each function individually. With decorators, you write the timing logic once and apply it everywhere with a single line."
Real-World Use Case 1: Timing Functions
Margaret pulled up a practical example:
import time
import functools
def timer(func):
"""Decorator that times function execution"""
@functools.wraps(func) # Preserve original function metadata
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
def demonstrate_timing():
"""Show timing decorator in action"""
@timer
def slow_function():
"""Simulates slow operation"""
time.sleep(0.1)
return "Done!"
@timer
def fast_function():
"""Simulates fast operation"""
return sum(range(1000))
print("Calling slow_function:")
result1 = slow_function()
print(f" Result: {result1}\n")
print("Calling fast_function:")
result2 = fast_function()
print(f" Result: {result2}")
demonstrate_timing()
Output:
Calling slow_function:
slow_function() took 0.1002 seconds
Result: Done!
Calling fast_function:
fast_function() took 0.0001 seconds
Result: 499500
"That's brilliant!" Timothy exclaimed. "I just add @timer above any function and it automatically times it. No need to add timing code inside each function."
"Exactly. And notice something important - I used @functools.wraps(func). That preserves the original function's metadata like its name and docstring. Without it, the wrapped function would have the wrapper's metadata instead."
"Why does that matter?" Timothy asked.
"Let me show you the problem and solution side by side."
The @wraps Problem and Solution
Margaret demonstrated:
import functools
def demonstrate_wraps_importance():
"""Show why @functools.wraps matters"""
# Without @wraps - loses metadata
def decorator_without_wraps(func):
def wrapper(*args, **kwargs):
"""Wrapper docstring"""
return func(*args, **kwargs)
return wrapper
# With @wraps - preserves metadata
def decorator_with_wraps(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
"""Wrapper docstring"""
return func(*args, **kwargs)
return wrapper
@decorator_without_wraps
def function_a():
"""Function A docstring"""
pass
@decorator_with_wraps
def function_b():
"""Function B docstring"""
pass
print("Without @wraps:")
print(f" Name: {function_a.__name__}")
print(f" Docstring: {function_a.__doc__}\n")
print("With @wraps:")
print(f" Name: {function_b.__name__}")
print(f" Docstring: {function_b.__doc__}")
demonstrate_wraps_importance()
Output:
Without @wraps:
Name: wrapper
Docstring: Wrapper docstring
With @wraps:
Name: function_b
Docstring: Function B docstring
"I see!" Timothy said. "Without @wraps, the decorated function looks like it's called 'wrapper' with the wrapper's docstring. That would break introspection and debugging."
"Exactly. Always use @functools.wraps in your decorators to preserve the original function's identity. Now let me show you something more powerful - decorators that take arguments."
Decorators with Arguments
"Wait," Timothy interrupted, "how can a decorator take arguments? The decorator itself is already taking the function as an argument."
"Excellent observation," Margaret said. "When you want a decorator with arguments, you need one more level of nesting. You create a function that takes the arguments and returns a decorator."
def demonstrate_decorator_with_arguments():
"""Show decorators that accept arguments"""
def repeat(times):
"""Decorator factory - takes arguments, returns decorator"""
def decorator(func):
"""The actual decorator"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
"""The wrapper that does the work"""
for _ in range(times):
result = func(*args, **kwargs)
return result
return wrapper
return decorator
@repeat(times=3)
def greet(name):
print(f" Hello, {name}!")
return name
print("Calling greet('Timothy') with @repeat(times=3):")
result = greet("Timothy")
print(f" Returned: {result}\n")
# What's really happening:
print("What's really happening:")
print(" 1. repeat(times=3) returns a decorator")
print(" 2. That decorator wraps greet()")
print(" 3. The wrapper calls greet() three times")
demonstrate_decorator_with_arguments()
Output:
Calling greet('Timothy') with @repeat(times=3):
Hello, Timothy!
Hello, Timothy!
Hello, Timothy!
Returned: Timothy
What's really happening:
1. repeat(times=3) returns a decorator
2. That decorator wraps greet()
3. The wrapper calls greet() three times
Timothy worked through the mental model. "So it's three levels deep: repeat(times) returns decorator, which returns wrapper, which calls the original function. That's like Russian nesting dolls!"
"Perfect analogy!" Margaret beamed. "The outermost function captures the arguments, the middle function captures the original function, and the innermost function does the actual work. Now let me show you a powerful real-world pattern."
Real-World Use Case 2: Caching/Memoization
Margaret opened a performance example:
import functools
import time
def demonstrate_caching():
"""Show how decorators enable memoization"""
# Manual caching decorator
def cache(func):
"""Cache function results"""
cached_results = {}
@functools.wraps(func)
def wrapper(*args):
if args not in cached_results:
print(f" Computing {func.__name__}{args}...")
cached_results[args] = func(*args)
else:
print(f" Using cached result for {func.__name__}{args}")
return cached_results[args]
return wrapper
@cache
def fibonacci(n):
"""Compute Fibonacci number (slow without cache)"""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
print("Computing Fibonacci numbers with caching:")
print(f" fib(5) = {fibonacci(5)}")
print(f" fib(6) = {fibonacci(6)}") # Reuses cached results!
print(f" fib(5) = {fibonacci(5)}") # Fully cached
print("\n💡 Python's built-in: @functools.lru_cache")
@functools.lru_cache(maxsize=128)
def fibonacci_builtin(n):
"""Using Python's built-in LRU cache"""
if n < 2:
return n
return fibonacci_builtin(n - 1) + fibonacci_builtin(n - 2)
start = time.perf_counter()
result = fibonacci_builtin(30)
elapsed = time.perf_counter() - start
print(f" fib(30) = {result} in {elapsed:.6f} seconds")
demonstrate_caching()
Output:
Computing Fibonacci numbers with caching:
Computing fibonacci(0)...
Computing fibonacci(1)...
Computing fibonacci(2)...
Using cached result for fibonacci(1)
Computing fibonacci(3)...
Using cached result for fibonacci(2)
Computing fibonacci(4)...
Using cached result for fibonacci(3)
Computing fibonacci(5)...
fib(5) = 5
Using cached result for fibonacci(4)
fib(6) = 8
Using cached result for fibonacci(5)
fib(5) = 5
💡 Python's built-in: @functools.lru_cache
fib(30) = 832040 in 0.000023 seconds
"That's incredible!" Timothy said. "Without caching, computing Fibonacci recursively is exponentially slow. With the @cache decorator, it becomes instant because results are reused."
"Right. And Python even provides @functools.lru_cache as a built-in for this exact pattern. The decorator pattern is perfect for cross-cutting concerns like caching, timing, logging - things you want to add to many functions without changing their core logic."
"What about those stacked decorators I saw in the Flask code?" Timothy asked. "How does that work?"
Stacking Decorators
Margaret pulled up an example:
def demonstrate_stacked_decorators():
"""Show how multiple decorators stack"""
def bold(func):
"""Wrap output in <b> tags"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
return f"<b>{result}</b>"
return wrapper
def italic(func):
"""Wrap output in <i> tags"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
return f"<i>{result}</i>"
return wrapper
def uppercase(func):
"""Convert output to uppercase"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
return result.upper()
return wrapper
@bold
@italic
@uppercase
def greet(name):
return f"Hello, {name}"
print("Stacked decorators execution order:")
result = greet("Timothy")
print(f" Result: {result}\n")
print("Order of execution:")
print(" 1. greet('Timothy') returns 'Hello, Timothy'")
print(" 2. @uppercase makes it 'HELLO, TIMOTHY'")
print(" 3. @italic wraps it '<i>HELLO, TIMOTHY</i>'")
print(" 4. @bold wraps it '<b><i>HELLO, TIMOTHY</i></b>'")
print("\n Bottom decorator executes first!")
print(" Top decorator executes last!")
demonstrate_stacked_decorators()
Output:
Stacked decorators execution order:
Result: <b><i>HELLO, TIMOTHY</i></b>
Order of execution:
1. greet('Timothy') returns 'Hello, Timothy'
2. @uppercase makes it 'HELLO, TIMOTHY'
3. @italic wraps it '<i>HELLO, TIMOTHY</i>'
4. @bold wraps it '<b><i>HELLO, TIMOTHY</i></b>'
Bottom decorator executes first!
Top decorator executes last!
"Ah!" Timothy exclaimed. "The decorator closest to the function executes first, then each one above it wraps the result. It's like putting on layers of clothing - socks, then shoes, then coat. The innermost layer goes on first."
"Perfect analogy again! The order matters. In your Flask example, @app.route registers the route first, then @login_required wraps it with authentication. If you reversed them, it wouldn't work correctly."
"This is powerful," Timothy said. "But what about decorating classes? Can decorators work on classes too?"
Class Decorators
"Absolutely," Margaret said. "Decorators can work on anything callable - functions, methods, or entire classes."
def demonstrate_class_decorators():
"""Show decorators on classes"""
def singleton(cls):
"""Decorator that makes a class a singleton"""
instances = {}
@functools.wraps(cls)
def wrapper(*args, **kwargs):
if cls not in instances:
print(f" Creating new {cls.__name__} instance")
instances[cls] = cls(*args, **kwargs)
else:
print(f" Returning existing {cls.__name__} instance")
return instances[cls]
return wrapper
@singleton
class Database:
"""Database connection (should only have one instance)"""
def __init__(self):
self.connection = "Connected to DB"
print("Creating database instances:")
db1 = Database()
print(f" db1.connection: {db1.connection}")
db2 = Database()
print(f" db2.connection: {db2.connection}")
print(f"\n db1 is db2: {db1 is db2}")
print(" ✓ Singleton pattern enforced by decorator!")
demonstrate_class_decorators()
Output:
Creating database instances:
Creating new Database instance
db1.connection: Connected to DB
Returning existing Database instance
db2.connection: Connected to DB
db1 is db2: True
✓ Singleton pattern enforced by decorator!
"That's elegant," Timothy observed. "The decorator replaces the Database class definition with a new callable - the wrapper function. Every time someone tries to create a Database instance, they're actually calling that wrapper, which ensures only one instance exists. No need to implement singleton logic inside the class itself."
"Exactly. The name Database now refers to the wrapper function, not the original class definition. Decorators are perfect for implementing patterns like singleton, adding class-level behavior, or registering classes. Now let me show you decorators as classes themselves."
Decorators as Classes
Margaret typed a new example:
def demonstrate_decorator_classes():
"""Show decorators implemented as classes"""
class CountCalls:
"""Decorator class that counts function calls"""
def __init__(self, func):
functools.update_wrapper(self, func)
self.func = func
self.count = 0
def __call__(self, *args, **kwargs):
self.count += 1
print(f" Call #{self.count} to {self.func.__name__}")
return self.func(*args, **kwargs)
@CountCalls
def greet(name):
return f"Hello, {name}"
print("Calling decorated function multiple times:")
greet("Alice")
greet("Bob")
greet("Charlie")
print(f"\n Total calls: {greet.count}")
print(" ✓ Decorator class maintains state!")
demonstrate_decorator_classes()
Output:
Calling decorated function multiple times:
Call #1 to greet
Call #2 to greet
Call #3 to greet
Total calls: 3
✓ Decorator class maintains state!
"Wait," Timothy said, "how does this work? The decorator is a class, not a function."
"Great question! When you use a class as a decorator, Python calls __init__ with the function, then calls __call__ every time the decorated function is called. The class instance becomes callable and replaces the original function."
"So the class instance has state - the count variable persists between calls," Timothy realized. "That's like how generators maintain state between yields!"
"Exactly! Different mechanism, same concept. Classes give you a clean way to maintain state in decorators. Now let me show you some real-world decorator patterns."
Real-World Use Case 3: Authentication and Authorization
Margaret pulled up a practical web example:
def demonstrate_authentication_decorator():
"""Show authentication/authorization pattern"""
# Simulated user context
current_user = {"name": "Timothy", "role": "admin"}
def require_auth(func):
"""Require user to be authenticated"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
if current_user is None:
return "Error: Not authenticated"
return func(*args, **kwargs)
return wrapper
def require_role(role):
"""Require specific role"""
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if current_user.get("role") != role:
return f"Error: Requires {role} role"
return func(*args, **kwargs)
return wrapper
return decorator
@require_auth
def view_profile():
return f"Profile: {current_user['name']}"
@require_auth
@require_role("admin")
def delete_user(username):
return f"Deleted user: {username}"
print("Calling authenticated endpoints:")
print(f" view_profile(): {view_profile()}")
print(f" delete_user('bob'): {delete_user('bob')}\n")
# Simulate non-admin user
current_user["role"] = "user"
print("After changing role to 'user':")
print(f" view_profile(): {view_profile()}")
print(f" delete_user('bob'): {delete_user('bob')}")
demonstrate_authentication_decorator()
Output:
Calling authenticated endpoints:
view_profile(): Profile: Timothy
delete_user('bob'): Deleted user: bob
After changing role to 'user':
view_profile(): Profile: Timothy
delete_user('bob'): Error: Requires admin role
"This is brilliant for web applications!" Timothy said. "Instead of checking authentication in every function, you just add @require_auth or @require_role('admin'). The security logic is centralized and consistently applied."
"Exactly. This pattern is used in Flask, Django, FastAPI - all major web frameworks. Decorators are perfect for cross-cutting concerns that apply to many endpoints."
"When shouldn't I use decorators?" Timothy asked. "There must be some pitfalls."
Common Pitfalls and When NOT to Use Decorators
Margaret pulled up a warning list:
"""
DECORATOR PITFALLS:
1. Forgetting @functools.wraps
- Loses function metadata
- Breaks introspection and debugging
2. Decorator order matters
- Bottom decorator executes first
- Wrong order can break functionality
3. Overusing decorators
- Too many decorators makes code hard to follow
- Each decorator adds overhead
4. Decorating methods requires care
- Method decorators need to handle 'self'
- Order with @classmethod/@staticmethod matters
5. Debugging becomes harder
- Stack traces go through decorator layers
- Original function is hidden
WHEN NOT TO USE DECORATORS:
❌ When function-specific logic
- Don't force generic decorator pattern
- Sometimes plain code is clearer
❌ When performance critical
- Each decorator adds function call overhead
- For hot paths, inline the logic
❌ When it obscures intent
- Magic @decorators can hide behavior
- Explicit is better than implicit
❌ When behavior varies significantly
- Decorators work best for consistent patterns
- Conditional logic might be clearer inline
"""
def demonstrate_pitfalls():
"""Show common decorator pitfalls"""
# Pitfall 1: Method decorators
def log_call(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
print(f" Calling {func.__name__}")
return func(*args, **kwargs)
return wrapper
class Example:
@log_call
def method(self):
return "method result"
@classmethod
@log_call
def class_method(cls):
return "class method result"
print("Method decorator pitfall:")
obj = Example()
print(f" {obj.method()}")
print(f" {Example.class_method()}\n")
# Pitfall 2: Too many decorators
@timer
@cache
@log_call
@require_auth
def overdecorated():
"""Too many decorators makes code hard to follow"""
return "result"
print("Too many decorators:")
print(" @timer @cache @log_call @require_auth def func():")
print(" ❌ Which decorator does what?")
print(" ❌ What order do they execute?")
print(" ❌ How do I debug this?")
demonstrate_pitfalls()
"So decorators are powerful but can be overused," Timothy summarized. "Keep them simple, don't stack too many, and always use @functools.wraps."
"Exactly. Decorators should make code clearer, not more obscure. If a decorator doesn't add obvious value, don't use it."
"How do I test code that uses decorators?" Timothy asked.
Testing Decorated Functions
Margaret showed testing patterns:
def demonstrate_testing_decorators():
"""Show how to test decorated functions"""
def rate_limit(calls_per_second):
"""Rate limiting decorator"""
def decorator(func):
last_called = [0.0]
@functools.wraps(func)
def wrapper(*args, **kwargs):
now = time.time()
time_since_last = now - last_called[0]
if time_since_last < (1.0 / calls_per_second):
return "Rate limited"
last_called[0] = now
return func(*args, **kwargs)
return wrapper
return decorator
# Testing strategy 1: Test the decorator directly
@rate_limit(calls_per_second=2)
def api_call():
return "success"
print("Testing decorated function:")
print(f" First call: {api_call()}")
print(f" Second call (too fast): {api_call()}")
time.sleep(0.6)
print(f" Third call (after delay): {api_call()}\n")
# Testing strategy 2: Test undecorated version
def compute(x):
return x * 2
decorated_compute = rate_limit(calls_per_second=10)(compute)
print("Testing decorator separately:")
print(f" Undecorated: compute(5) = {compute(5)}")
print(f" Decorated: decorated_compute(5) = {decorated_compute(5)}")
print(" ✓ Test core logic separately from decorator!")
demonstrate_testing_decorators()
"I see two strategies," Timothy noted. "Test the decorated function as a whole, or test the core function and decorator separately. The second approach gives you better isolation."
"Exactly. For unit tests, you often want to test the decorator's logic independently, then test the core function without decorators. Integration tests can verify the decorated version works end-to-end."
The Gift Wrap Metaphor
Margaret brought it back to her original analogy:
"Remember when I said decorators are like gift wrap? Let me expand on that.
"Imagine you have a present (your original function). The present does one thing well - maybe it's a book that tells a story. That's its core purpose.
"Now you want to:
- Wrap it in beautiful paper (add visual appeal = logging)
- Add a bow (make it special = timing)
- Include a gift receipt (add metadata = documentation)
- Put it in a gift bag (make it secure = authentication)
"Each layer of wrapping (decorator) adds something without changing the present inside. The book still tells the same story. But now it has additional features around it.
"The @ syntax is like saying 'wrap this present with...' and Python handles the wrapping for you. You can add multiple layers (@bold @italic @uppercase), and each one wraps the previous layer - just like wrapping paper, then a bow, then a bag.
"The present (function) stays the same. The wrapping (decorators) adds value. And you can reuse the same wrapping (decorator) on many different presents (functions)."
Key Takeaways
Margaret summarized:
"""
DECORATOR KEY TAKEAWAYS:
1. What are decorators:
- Functions that wrap other functions
- Add behavior without modifying original code
- The @ syntax is syntactic sugar for reassignment
- @decorator above function = function = decorator(function)
2. Basic decorator pattern:
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
# Before logic
result = func(*args, **kwargs)
# After logic
return result
return wrapper
3. Decorator with arguments:
def decorator_factory(arg):
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
# Use arg here
return func(*args, **kwargs)
return wrapper
return decorator
4. Always use @functools.wraps:
- Preserves function metadata
- Keeps __name__, __doc__, etc.
- Essential for debugging and introspection
5. Stacking decorators:
- Bottom decorator executes first
- Each decorator wraps the previous result
- Order matters for functionality
6. Real-world uses:
- Timing/profiling (@timer)
- Caching/memoization (@lru_cache)
- Authentication (@login_required)
- Logging (@log_call)
- Rate limiting (@rate_limit)
- Validation (@validate_input)
7. Advanced patterns:
- Class decorators (modify classes)
- Decorator classes (use __call__)
- Parameterized decorators (factory pattern)
- Method decorators (handle self/cls)
8. When to use:
- Cross-cutting concerns (apply to many functions)
- DRY principle (write once, use everywhere)
- Aspect-oriented programming patterns
- Framework/library integration
9. When NOT to use:
- Function-specific logic
- Performance-critical hot paths
- When it obscures intent
- Over-decoration (too many layers)
10. Common pitfalls:
- Forgetting @functools.wraps
- Wrong decorator order
- Debugging through decorator layers
- Method decorator complications
- Over-engineering simple problems
11. Testing strategies:
- Test decorated function end-to-end
- Test decorator logic separately
- Test core function without decorator
- Use dependency injection for testability
"""
Timothy nodded, the pattern clicking into place. "So decorators are functions that wrap other functions to add behavior. The @ syntax makes it clean and declarative. They're perfect for cross-cutting concerns that apply to many functions - timing, logging, caching, authentication. But I should use them judiciously and always include @functools.wraps to preserve metadata."
"Perfect understanding," Margaret confirmed. "Decorators are one of Python's most powerful features for writing clean, reusable code. They let you separate concerns - the core function does its job, and decorators add the extra behavior. Once you start seeing opportunities for decorators, you'll find them everywhere in Python code - from web frameworks to testing libraries to your own applications."
With that knowledge, Timothy could write elegant decorators, understand how frameworks like Flask use them, avoid common pitfalls, and know when decorators add value versus when plain code is clearer.
Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.
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