Start with the imperfections.
Spaces, duplicates and inconsistent dates are visible in this illustrative sample.
From a messy sheet to a clearer copy. With a record of every rule.
Scroll illustration uses a separate fictional sample; report counts come from the actual pipeline.Spaces, duplicates and inconsistent dates are visible in this illustrative sample.
A clean copy takes shape. The original is kept beside it for comparison.
Rule counts explain the result. Review the rows before taking the file away.
Turn a messy CSV or Excel sheet into a cleaner copy. Inspect the rules, counts, fields and sample rows before you download.
Explore the sample report, or run the local Python app with your own CSV or Excel file.
Your file stays in memory while the demo processes it.
The report separates automatic rule counts from the sample rows you review yourself.
Rule counts, detected fields and before/after samples appear here.
The two samples are not row-aligned after duplicate removal. Compare patterns and totals, then review the exported file.