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Python Decorators Explained

bookPractical Decorator Use Cases

In real-world Python projects, decorators are widely used to solve practical problems by adding reusable functionality to functions and methods. Some of the most common use cases include logging, which records when and how functions are called; timing, which measures how long functions take to execute; and validation, which checks arguments before a function runs. By using decorators for these tasks, you can keep your code clean and avoid repeating similar logic throughout your project.


              
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import time

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()  # Record the start time
        result = func(*args, **kwargs)
        end_time = time.time()    # Record the end time
        duration = end_time - start_time
        print(f"Function '{func.__name__}' executed in {duration:.4f} seconds.")
        return result
    return wrapper

@timing_decorator
def process_data():
    total = 0
    for i in range(1000000):
        total += i
    return total

process_data()
copy

This timing decorator works by wrapping the target function and recording the time just before and just after the function runs. It then calculates the difference to determine how long the function took to execute, printing the result along with the function's name. Using such a decorator is beneficial because it allows you to measure performance without modifying the original function's code, making it easier to identify bottlenecks and optimize your application.

question-icon

Fill in the blanks to modify the timing_decorator so that it also logs the arguments passed to the function:

def timing_decorator(func): def wrapper(*args, **kwargs): print(f"Calling {func.__name__} with args: , kwargs: }) start_time = time.time() result = func(*args, **kwargs) end_time = time.time() duration = end_time - start_time print(f"Function '{func.__name__}' executed in {duration:.4f} seconds.") return result return wrapper
Calling process_data with args: (), kwargs: {}
Function 'process_data' executed in 0.1234 seconds.

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SectionΒ 3. ChapterΒ 2

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bookPractical Decorator Use Cases

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In real-world Python projects, decorators are widely used to solve practical problems by adding reusable functionality to functions and methods. Some of the most common use cases include logging, which records when and how functions are called; timing, which measures how long functions take to execute; and validation, which checks arguments before a function runs. By using decorators for these tasks, you can keep your code clean and avoid repeating similar logic throughout your project.


              
1234567891011121314151617181920

            
import time

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()  # Record the start time
        result = func(*args, **kwargs)
        end_time = time.time()    # Record the end time
        duration = end_time - start_time
        print(f"Function '{func.__name__}' executed in {duration:.4f} seconds.")
        return result
    return wrapper

@timing_decorator
def process_data():
    total = 0
    for i in range(1000000):
        total += i
    return total

process_data()
copy

This timing decorator works by wrapping the target function and recording the time just before and just after the function runs. It then calculates the difference to determine how long the function took to execute, printing the result along with the function's name. Using such a decorator is beneficial because it allows you to measure performance without modifying the original function's code, making it easier to identify bottlenecks and optimize your application.

question-icon

Fill in the blanks to modify the timing_decorator so that it also logs the arguments passed to the function:

def timing_decorator(func): def wrapper(*args, **kwargs): print(f"Calling {func.__name__} with args: , kwargs: }) start_time = time.time() result = func(*args, **kwargs) end_time = time.time() duration = end_time - start_time print(f"Function '{func.__name__}' executed in {duration:.4f} seconds.") return result return wrapper
Calling process_data with args: (), kwargs: {}
Function 'process_data' executed in 0.1234 seconds.

Click or drag`n`drop items and fill in the blanks

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 2
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