🚀 Creating Decorators for Logging in Python

Decorators in Python are powerful tools that allow you to add functionality to existing functions or classes without modifying their source code. When combined with logging, they become a clean way to track function calls, arguments, and execution times.

📌 Why Use Decorators for Logging?

• Reusability: Apply consistent logging behavior across multiple functions
• Maintainability: Centralize logging logic instead of duplicating it
• Flexibility: Easily enable/disable logging for specific functions

🧠 Basic Structure of a Logging Decorator

import logging
from functools import wraps

def log_decorator(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        logging.info(f"Calling function: {func.__name__}")
        result = func(*args, **kwargs)
        logging.info(f"Function {func.__name__} completed")
        return result
    return wrapper

📝 Example Usage

@log_decorator
def add(a, b):
    return a + b

add(3, 5)

📈 Advanced Features

• Parameter Logging:

  def log_params(func):
      @wraps(func)
      def wrapper(*args, **kwargs):
          logging.info(f"Arguments: {args}, {kwargs}")
          return func(*args, **kwargs)
      return wrapper
  

• Timing Execution:

  import time
  
  def log_time(func):
      @wraps(func)
      def wrapper(*args, **kwargs):
          start = time.time()
          result = func(*args, **kwargs)
          logging.info(f"Execution time: {time.time() - start:.4f}s")
          return result
      return wrapper
  

🧩 Combining Decorators

@log_params
@log_time
def multiply(a, b):
    return a * b

📚 Recommended Reading

Learn more about decorators in Python or explore advanced logging techniques.