Which transformation should you use to have multiple columns in Power BI representing different categories from an Excel spreadsheet?

Using the Pivot Columns transformation in Power BI is the ideal approach for representing multiple categories from an Excel spreadsheet as distinct columns. When you have a dataset that includes a category column (for example, product categories) and another column with associated values (like sales figures), pivoting allows you to create a new column for each unique category. This transformation reorganizes the data to spread it across multiple columns, where each column represents a unique category, and the corresponding row values can be filled in appropriately.

This method is particularly useful when you want to analyze or visualize data in a way that contrasts the performance or characteristics of different categories side-by-side. By pivoting the columns, it enables easier comparison and clearer visualization in reports and dashboards, enhancing the overall analysis experience.

In contrast, the other transformations serve different purposes. Renaming columns modifies existing column names without changing the data's structure. Transposing a table rearranges rows and columns but doesn't help in creating new category-based columns from existing data. Unpivoting columns, on the other hand, would turn existing columns into rows, which is the opposite of what you would want when seeking to have multiple columns for different categories.