Hello All,
I've to create a Alteryx Analytical app which allows the user to create multiple rows.
My dataset has column names as Test ID, Operational Area Name, Operational area code and Create Date.
For one test ID and create date the analytical app will allow the user to add multiple Operational area name and operational area code (Using Text Box ). Final output should look like this.
test ID | Operational Area Name | Operational Area Code | Create Date |
TI123 | OA1 | OAC1 | 11/22/2022 |
TI123 | OA2 | OAC2 | 11/22/2022 |
TI123 | OA3 | OAC3 | 11/22/2022 |
So basically it should generate rows based on the values entered in Operational Area Name and Operational Area Code. Rest of the values will be same. I tried following the steps mentioned in the below community post by @csmith11
But it's only designed for one column. When I tried implementing the same logic for two columns, it's creating duplicate rows. So for 3 different Operational Area Name and Operational Area code values, it's creating 6 rows, Could you please help.
Due to my company restrictions, I won't be able to upload my workflow. Apologies.
BR,
Solved! Go to Solution.
Hard to say without seeing the workflow. I was able to crate an app to do what you want if you can download? I did a single line text input for the Test ID and calendar input of the create date and had it update a text input. I did multi line inputs for the Operational area cod e and Name. and had these update a separate text input. For the Operational areas I used a select tool and isolated each column in it's own path and Parsed each path to rows with a \n. I added a record ID after each parse and joined the two columns on record ID ( I deselected both record ID columns in the join). I then appended the test ID and create date.
I'm guessing if you you parse the columns back to back that is causing the duplication.
Thank you so much @RogerS
Quick question, Is it possible to add any condition which allow the app not to create any blank rows?
Regards,
Swapsingh2712
You can add a data cleanse after parsing the data to remove any all null columns or rows.