Each cleaner is a toggle. Turn on only the ones you need — trim, NA normalize, number format cleaner, empty-row drop, empty-column drop, dedupe — and see the diff in real time.
Saves analysts a trip through pandas or OpenRefine for routine tidy-ups.
Upload or paste. The raw rows appear on the left so you can see the "before" state.
Six opt-in cleaners. Flip each on or off and the output updates live — easy to compare before/after.
Copy to clipboard or save as .csv. The output is safe to feed into any chart tool or analysis pipeline.
Exact whole-row match after the earlier cleaners run — so "Ada Lovelace" and " Ada Lovelace " collapse into one row when Trim is on.
Only when you enable Clean Numbers. It strips "$", "€", "%", and thousand separators from otherwise-numeric cells — never from non-numeric strings.
Not yet — the current list (NA, N/A, null, none, "-", "--") covers 99% of real-world missing-value placeholders. File a feature request if you need a custom list.
Only to trim whitespace when Trim is on. NA normalization, dedupe, and number cleaning apply to data rows only — the header is always preserved.