This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
I have a scenario in which monthly files get loaded from client with the date in few columns, so I want to made them independent of dates. So that the formulas depend on the field names should not be impacted. PFA with the sample data.
Using @mceleavey's regex magic, but without the Transpose/Crosstab combo, which is a very expensive pair of operations if you have lots of data.
This workflow operates only of the field names, so it will always take the same amount of time, no matter how many rows in the dataset. It uses the Field Info tool (one of my favorites) to extract the column metatdata, modifies that and then renames the original columns using a Dynamic Rename.
One thing to note is that after the conversion you have multiple fields with the same name. Alteryx renames them for you, but you might want to look at renaming them to something like [Net Assets (Current)] and [Net Assets (Prev)] to avoid ambiguity in your workflow