Summary  
This chapter covers three methods for generating new data columns: using conditional logic for rule-based categorization, example-driven extraction for pattern recognition, and custom formula columns for calculation-based derivations.

General domain of usage  
Data transformation

Sometimes the data you need doesn't exist in your source files. In this chapter, you'll learn three ways to create new columns in Power Query: Conditional Columns for rule-based categorization, Column from Examples for pattern-based extraction, and Custom Columns for formula-based calculations.

You'll build three columns that stay in your model going forward: a Shipping Status column that labels orders as "Shipped" or "Pending," an Order Month column extracted from the order date, and a Price Gap column showing the difference between list price and cost price.

You want to create a column that labels each order as "High Value" if the total amount is over $500 and "Standard" otherwise. Which Power Query feature is best for this?

Master the skills needed to turn raw data into professional, interactive reports using Power BI. You'll connect to data sources, clean and transform data with Power Query, build data models, write DAX measures, and publish polished dashboards to Power BI Service. No prior experience required.

Adding Columns