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5. Decorators & Higher-Order Functions

5.1 What are Decorators?

A decorator is a function that takes another function (or class) and returns a modified version of it — usually adding behavior before/after the original function runs.

Key points:

• Decorators are higher-order functions (functions that take or return functions).

• They use @ syntax for clean application.

• Common uses: logging, timing, authentication, caching, validation.

Basic mental model:

Python

@decorator def my_function(): pass # is equivalent to: my_function = decorator(my_function)

5.2 Writing Simple Decorators

Step-by-step: Simple logging decorator

Python

def logger(func): def wrapper(*args, **kwargs): print(f"Calling {func.__name__} with args={args}, kwargs={kwargs}") result = func(*args, **kwargs) print(f"{func.__name__} returned: {result}") return result return wrapper @logger def add(a, b): return a + b print(add(5, 3))

Output:

text

Calling add with args=(5, 3), kwargs={} add returned: 8 8

Key parts:

• wrapper is the inner function that adds behavior

• args, *kwargs → accepts any arguments

• Call original func and return its result

Preserve original function metadata (name, docstring, etc.)

Python

from functools import wraps def logger(func): @wraps(func) # Important! def wrapper(*args, **kwargs): print(f"→ {func.__name__} called") return func(*args, **kwargs) return wrapper

5.3 Decorators with Arguments

Sometimes you want to pass parameters to the decorator itself.

Example: Repeat decorator with count

Python

from functools import wraps def repeat(times): def decorator(func): @wraps(func) def wrapper(*args, **kwargs): for in range(times): result = func(*args, **kwargs) return result return wrapper return decorator @repeat(3) def sayhello(name): print(f"Hello, {name}!") say_hello("Anshuman")

Output:

text

Hello, Anshuman! Hello, Anshuman! Hello, Anshuman!

Structure: @repeat(3) → returns decorator → which returns wrapper

5.4 @property, @classmethod, @staticmethod as Decorators

These are built-in decorators that change how methods behave.

Python

class Circle: def init(self, radius): self._radius = radius @property # getter def radius(self): return self._radius @radius.setter # setter def radius(self, value): if value < 0: raise ValueError("Radius cannot be negative") self._radius = value @property def area(self): # computed property return 3.14159 self._radius * 2 @classmethod def from_diameter(cls, diameter): return cls(diameter / 2) # alternative constructor @staticmethod def is_valid_radius(r): return r >= 0 c = Circle(5) print(c.radius) # 5 ← looks like attribute print(c.area) # 78.53975 c.radius = 10 # setter works print(c.area) # 314.159 c2 = Circle.from_diameter(20) # class method print(c2.radius) # 10.0 print(Circle.is_valid_radius(-5)) # False ← static method

5.5 @lru_cache (functools) – Memoization

@lru_cache caches function results — huge performance boost for expensive recursive/ repeated calls.

Python

from functools import lru_cache @lru_cache(maxsize=128) # maxsize=None → unlimited def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2) print(fibonacci(35)) # Very fast now! # Without cache: extremely slow

Use cases:

• Recursive algorithms (Fibonacci, tree traversal)

• API calls with same parameters

• Expensive computations

Clear cache: fibonacci.cache_clear()

5.6 Chaining Decorators

You can stack multiple decorators — they apply from bottom to top.

Python

def uppercase(func): @wraps(func) def wrapper(*args, **kwargs): result = func(*args, **kwargs) return result.upper() return wrapper def add_exclamation(func): @wraps(func) def wrapper(*args, **kwargs): return func(*args, **kwargs) + "!" return wrapper @uppercase @add_exclamation def greet(name): return f"Hello {name}" print(greet("Anshuman")) # HELLO ANSHUMAN!

Order matters: @uppercase applied last → final output is uppercase.

5.7 Class Decorators

Decorators can also modify classes (less common but powerful).

Example: Add timestamp attribute to class

Python

from datetime import datetime from functools import wraps def add_timestamp(cls): original_init = cls.__init__ @wraps(original_init) def new_init(self, args, kwargs): original_init(self, args, kwargs) self.created_at = datetime.now().strftime("%Y-%m-%d %H:%M:%S") cls.__init__ = new_init return cls @add_timestamp class User: def init(self, name): self.name = name u = User("Anshuman") print(u.created_at) # e.g. 2026-03-05 16:45:22

Common real-world class decorators:

• @dataclass

• @singleton (ensure only one instance)

• @register (auto-register classes in a factory)

Mini Project – Timing & Logging Decorator Combo

Python

import time from functools import wraps def timer(func): @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 def logger(func): @wraps(func) def wrapper(*args, **kwargs): print(f"→ Running {func.__name__}...") result = func(*args, **kwargs) print(f"← {func.__name__} done.") return result return wrapper @timer @logger def slow_task(n): time.sleep(n) return f"Task completed after {n} seconds" print(slow_task(2))

Output:

text

→ Running slow_task... ← slow_task done. slow_task took 2.0012 seconds Task completed after 2 seconds

This completes the full Decorators & Higher-Order Functions section — now you can write elegant, reusable, and professional Python code using one of its most loved features!

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