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Adding and Updating Columns in Pandas DataFrame | Python Pandas Tutorial for Data Engineering

In this video, we will discuss how to add calculated columns and update values in Pandas DataFrames using Python for enhanced data analysis. This Pandas tutorial will help you unlock the power of data enrichment. Learn how to create new columns with derived metrics (like commission amount and profit margin calculation) and categorize sales data based on custom rules. We'll cover essential Pandas techniques, including using `fillna()` to handle missing data, applying boolean indexing for conditional updates, and leveraging the `apply()` method for complex column transformations. Also, we'll show how to efficiently update existing columns using the `replace()` method. Topics Covered: Adding New Columns in Pandas Calculating Derived Metrics (Commission Amount, Profit Margin) Handling Missing Data (`fillna()`) Conditional Logic & Sales Categorization Using `apply()` for Complex Transformations Updating Existing Columns (`replace()`) Boolean Indexing Continue Your Spark Learning Enroll in our Guided Program to learn Apache Spark and get hands-on experience using Databricks Community Edition: https://forms.gle/3LtJ13iNdDCv7cxY6 Resources: Ready to kickstart your coding journey? Join Python for Beginners: Learn Python with Hands-on Projects and master Python by building real-world projects from day one! https://www.udemy.com/course/python-f... Continue Your Learning Journey with Pandas! 🚀 ✅ Previous Video:    • How to Use Apply Functions for Transf...   ✅ Next Video:    • Chaining Transformations in Pandas | ...   ✅ Full Course:    • Python Pandas for Data Engineers and ...   Connect with Us: Newsletter: http://notifyme.itversity.com LinkedIn:   / itversity   Facebook:   / itversity   Twitter:   / itversity   Instagram:   / itversity   What’s Next? In upcoming videos, we’ll explore additional file formats and advanced data manipulation techniques. Stay tuned to master the full capabilities of Python Pandas! #DataEngineering #Pandas #Python #Analytics #DataAnalysis #programming