Clean & Deduplicate CSV Online
Remove duplicate rows, drop empty lines, trim whitespace and tidy columns — all in your browser. Your file never leaves your device, so it’s safe for sensitive customer and email data.
Three steps to a clean CSV
Drop your CSV
Drag in or paste a CSV. It’s read locally in your browser — no upload, no signup.
Pick clean-up rules
Dedupe (whole-row or by column), remove empty rows, trim whitespace, standardize columns.
Preview & export
See before/after counts and removed rows, then download a clean CSV generated on your device.
Why local processing matters for your data
Customer lists, email databases and sales exports are exactly the files you should be most careful with. Many “online CSV cleaners” quietly upload your file to their server to process it — so your contacts’ personal data lands on infrastructure you don’t control.
TidyCSV is different by design. Your file is read and cleaned entirely inside your browser using the File API — it is never transmitted, never stored, and never seen by us or any third party. Because the data never leaves your device, the tool isn’t a data processor for the personal data in your CSV.
Don’t take our word for it: open your browser’s developer tools, watch the Network panel, and clean a file. You’ll see no request carrying your file’s contents.
Built for real clean-up jobs
Remove duplicates
Drop duplicate rows from a CSV — by the whole row or by a single key column — entirely in your browser. Nothing is uploaded.
Dedupe email list
Remove duplicate contacts from an email list by deduping on the email column — locally, so subscriber data never leaves your browser.
Remove empty rows
Strip fully blank rows out of a CSV so imports and row counts are accurate — all in your browser.
Trim whitespace
Remove leading and trailing whitespace from every cell (or selected columns) so values match, join, and sort correctly.
Clean before CRM import
Prep a lead or contact CSV — dedupe, trim, drop blanks, standardise columns — before it hits your CRM, all locally.
Standardize columns
Trim and case-normalise column names, then pick which columns to keep and in what order — so files from different sources line up.