The author of this text implements a pivot function in Flux to reshape their train data from a single column into multiple columns, making it easier to read and directly use without having to write additional application code. The pivot function allows the user to designate how data should be organized and displayed, creating new rows for each unique value identified in the input and adding value columns to the row for each unique value. By adjusting the rowKey and columnKey parameters, the author is able to customize the pivot function to meet their specific needs, including retaining measurement and tags as row keys while grouping by driver and separating fields into individual columns. The pivot function provides a flexible way to manipulate data in Flux, allowing users to create more readable and usable data sets without modifying underlying data structures.