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

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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!

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