Free CSV cleaner: remove duplicates, fix dates, tidy headers

How to clean a messy CSV file

Most CSV problems come from the same few places: a person typed the data, two systems exported it differently, or somebody merged files by hand. This tool fixes the common ones without a spreadsheet and without writing code.

What each option does

• Trim spaces removes the invisible space before or after a value. " Ana" and "Ana" otherwise count as different people, which breaks lookups and pivot tables.
• Remove empty rows deletes rows where every cell is blank, a typical leftover from exports and copy-paste.
• Remove duplicates keeps the first copy of a row that matches another row exactly. Trimming runs first, so rows that only differed by spaces are caught too.
• Normalize headers turns "Order ID" and "Customer Name" into order_id and customer_name, which is what databases, pandas and most import tools expect.
• Normalize dates rewrites dates such as 3/4/24, 13 April 2024 or Apr 16 2024 as 2024-04-16, the one format every tool reads the same way. It only touches columns where most values really are dates.

Why day-first or month-first matters

Is 03/04/2024 the third of April or the fourth of March? Nothing in the text says. The US reads month first, most other countries read day first. Pick the convention your source uses. Values like 25/12/2024 are unambiguous and are always converted correctly.

Delimiters and Excel exports

Excel in many European and Latin American regions saves CSV files with semicolons, because the comma is the decimal mark. The tool detects commas, semicolons, tabs and pipes automatically, and you can write the result back with a different delimiter.

When a one-off cleaner is not enough

If you repeat the same cleanup every week, merge several files, or need rules like "match company names that are spelled differently", a saved script is better than clicking through options each time. That is a small, fixed-price job.