Chaining Transformations in Pandas | Python Pandas Tutorial for Data Engineering

In this video, we will discuss how to use chaining transformations in Pandas to efficiently process data with a seamless workflow. We'll explore the advantages of method chaining, including improved efficiency, enhanced readability, and increased scalability.

We'll demonstrate how to streamline your data analysis workflows by combining multiple Pandas methods into a single, readable chain. Learn how to use `assign()` to dynamically add new columns, filter data using `query()`, and merge multiple DataFrames for comprehensive analysis. We'll also cover best practices for writing clean, maintainable chained code.

*Topics Covered:*
* Introduction to Chaining Transformations in Pandas
* Benefits of Chaining: Efficiency, Readability, Scalability
* Basic Transformations without Chaining vs. with Chaining
* Using `assign()` for Adding New Columns
* Filtering Data within a Chain (e.g., completed sales)
* Practical Best Practices: Readability, Debugging, Reusability
* Chaining with Multiple DataFrames (`merge()` and `query()`)
* Combining Transformations: Merging, Filtering, and Adding Columns
* Calculating Commission Earned in a Chained Workflow

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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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