PySpark withColumn() & withColumns() Tutorial | Add & Update Columns in DataFrame | Complete Guide

🚀 In this PySpark tutorial, you'll learn how to use `withColumn()` and `withColumns()` to add, update, and transform columns in a DataFrame. These are some of the most frequently used DataFrame transformations in real-world Data Engineering projects. In this tutorial, you'll learn: ✅ What is withColumn()? ✅ Add a new column ✅ Update an existing column ✅ Create calculated columns ✅ Add multiple columns using withColumns() ✅ Difference between withColumn() and withColumns() ✅ Real-world examples ✅ Best practices ✅ Common mistakes to avoid ✅ Interview tips By the end of this video, you'll understand when to use `withColumn()` and when `withColumns()` is a better choice for creating multiple columns efficiently. If you're learning PySpark from scratch or preparing for Data Engineering interviews, this tutorial is a must-watch. #PySpark #ApacheSpark #BigData #DataEngineering #Python #Spark