Practical 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.
1234567891011121314151617181920import 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()
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.
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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.
1234567891011121314151617181920import 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()
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.
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