Post by Prateek Jalgaonkar

#PythonForData | Decorators in Action — Quick Walkthrough Recently explored Python decorators—one of the cleanest and most powerful ways to modify or extend the behavior of functions without changing their code. Here’s what I learned (and loved): -> A decorator is just a function that takes another function as input and returns a new one. -> Perfect for use cases like logging, performance tracking, access control, retries, and more. -> You can use @decorator_name to wrap any function in a cleaner, readable way. In a data-heavy world, decorators can be game-changers for: - Logging SQL query executions - Timing data transformation steps - Validating inputs to ETL steps 📊I’ve put together a short, beginner-friendly Jupyter notebook to document these concepts: 🔗 https://lnkd.in/dHZ8VgD4 Part of learning journey through Complete Python Bootcamp on Udemy — a solid refresher on both fundamentals and practical coding patterns. What are your favorite use cases for decorators? #Python #DataEngineering #GitHub #ETL #CodeCleanliness #DataAnalytics #OpenSourceLearning #DataCommunity