CSV Cleaner: Prepare Files for Import
Review a CSV, tidy its cells and headers, and remove exact duplicate rows without converting identifiers.
Choose a CSV and review the cleanup rules before importing the output.
Your cleaned CSV
Run the task to review a counted result here.
Load the example to try the complete workflow.Limits and next steps
Header row and rectangular records required. No date guessing, inferred numbers, encoding repair or ID-based record merging. Exact duplicate removal is a chosen destructive transformation of output only; the input stays intact. Download and inspect the result before importing it elsewhere.
Instructions and examples
From input to output
- Choose a UTF-8 CSV or open the optional paste area.
- Review Input delimiter, Output delimiter, Header names and the cell/duplicate/formula options.
- Select Clean CSV, compare before/after previews and change counts, then download the reviewed CSV.
Settings and result behavior
- Header trimming and lowercase underscores are explicit transformations; blank or colliding names are rejected.
- Duplicate removal occurs after selected trimming and compares whole rows, not IDs.
- Formula protection is opt-in and prefixes potentially executable cells with an apostrophe, including negative-looking values. Quoted CSV alone is not protection.
A worked example
- Input
id,name 001, Ada 001, Ada
- Settings
- Keep header names; Trim spaces around cell values on; Remove exact duplicate rows on.
- Expected result
id,name 001,Ada
Supported input and limits
Choose a UTF-8 CSV, TSV or TXT file, or open “Paste CSV instead”. Use comma, semicolon, tab or pipe separators. A header row with unique nonblank names and the same field count on every row is required. The output delimiter can differ from the input.
Up to 10 MB and 50,000 data rows. Rectangular data and unique, nonempty headers required. CSV cannot force spreadsheet apps to keep cell types. No guessed date conversion.
If the result is unexpected
- For a header collision, rename the conflicting source headers; Fixora does not silently invent names.
- If spreadsheet software changes 001 to 1 or guesses dates, import the relevant columns as text. The CSV bytes cannot force cell types.
Check before using the result
Check row count, retained identifiers, renamed headers and protected cells. Import a small sample in the target application before replacing an important dataset.
Report a problem with this tool. Include a nonsensitive example and your browser details.
Questions about this tool
Will leading-zero IDs stay intact?
Values remain strings during cleanup, so 00123 is not converted to 123. CSV cannot set column types for another application: import identifier columns as text in your spreadsheet.
Does removing duplicates merge records with the same ID?
No. This cleaner removes exact duplicate rows after the selected transformations. Different records sharing an ID need a separate decision.
Practical guides
- Compare Two CSV Exports Using a Unique Key
Match CSV records by a stable identifier and distinguish added, removed and changed rows from simple row-order differences.
- Clean a CSV While Preserving Leading-zero Identifiers
Trim cells and headers, remove exact duplicate rows and inspect identifiers before importing a cleaned CSV into a spreadsheet.