How to use the CSV Cleaner.
CSV cleanup should follow explicit rules for whitespace, blanks and delimiters. Preserve values whose formatting carries meaning, such as leading-zero identifiers.
Make the workflow fit your task.
Confirm encoding, delimiter, quoting and the columns whose formatting must remain textual. Apply explicit cleanup rules to a copy, retaining original row references. Report changed cells and verify that row counts and identifier values survive the conversion.
- What you provide
- CSV and explicit cleanup rules.
- What you get
- Consistent delimiters, whitespace and missing-value handling.
See the input and the result.
Illustrative input and output · a teaching example, not a live WebAct run
Example input
SKU value is " 0012 "; trim surrounding spaces but preserve leading zeros.
Completed example
Cleaned SKU: 0012 stored as text, not the number 12.
Load this input into the prompt, then copy it to WebAct to try the task. Your result may differ from the illustration.
Decisions and troubleshooting.
Should leading zeros be removed during CSV cleanup?
Keep them when they belong to identifiers, postal codes or fixed-width values. Numeric conversion should follow the column's declared meaning.
Why does opening the cleaned CSV change long identifiers?
The spreadsheet may infer numbers automatically. Import identifier columns as text and compare the parsed values with the saved file.
Try it with your own source.
Replace the example with your material in the task prompt. Keep the requirements you need, then copy the task into WebAct.
Customize and copy the task ↑