Python Decorators and Generators
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Python Decorators and Generators: Simplifying Complex Code
Python is loved for its clean syntax and powerful features, and two such features that often go hand in hand are decorators and generators. Both help simplify code and make it more efficient, reusable, and readable.
🔁 Generators: Efficient Data Iteration
Generators are functions that return an iterator and allow you to iterate over data without loading everything into memory. Instead of returning all items at once, they yield one item at a time using the yield keyword.
python
def countdown(n):
while n > 0:
yield n
n -= 1
When you call countdown(5), it doesn’t execute immediately. Instead, it returns a generator object, which produces values one at a time. This is perfect for handling large datasets or infinite sequences.
🎯 Decorators: Modifying Function Behavior
Decorators are functions that modify the behavior of other functions without changing their code. They use the @decorator_name syntax.
python
def logger(func):
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__}")
return func(*args, **kwargs)
return wrapper
@logger
def greet(name):
print(f"Hello, {name}!")
When you call greet("Alice"), it logs the function call before executing it. Decorators are widely used in logging, authentication, timing functions, and more.
🔄 When Generators and Decorators Combine
Decorators can be used to enhance generator functions as well. For example, you can use a decorator to log the values a generator yields or to track execution time.
Together, decorators and generators help write elegant, memory-efficient, and modular code — making them essential tools in every Python programmer’s toolkit.
Read More
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Python Error Handling and Exceptions
OOP in Python: Classes and Objects
Functions in Python: Basics to Advanced
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