Pretty-print, validate, and clean up CSV files
CSV files from different systems often arrive with quirks, mismatched quoting, ragged column counts, extra whitespace, wrong delimiters. Upload the file here and the formatter detects every structural issue, shows you exactly where it is, and lets you fix everything in one click.
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Identify and remove duplicate rows from CSV files
Validate CSV data against custom rules
Transform CSV columns with 25+ operations: text manipulation (uppercase, lowercase, trim), number formatting, date conversion, extraction patterns, and custom pipelines
A hand-edited CSV breaks an import and you need to find the malformed row and normalise the delimiter and quoting.
Input
name,note Dana,"likes, cats" Sam,ok,EXTRA
Result
⚠ Row 3 has 3 fields, expected 2 Reformatted: consistent quoting, comma delimiter · ✓ valid
The validator flags rows whose field count does not match the header, the usual cause of "import failed at line N", and re-emits with RFC-4180-correct quoting so embedded commas survive. Fixing it here is faster than guessing which of 10,000 rows is wrong.
CSV files from different systems often arrive with quirks, mismatched quoting, ragged column counts, extra whitespace, wrong delimiters. Upload the file here and the formatter detects every structural issue, shows you exactly where it is, and lets you fix everything in one click.
The formatter parses your file with a tolerant CSV parser (PapaParse) and reports structural issues: rows with too many or too few columns (it shows the affected row numbers and the min/max column counts) and parser-level quote or delimiter problems. You can then normalize the column count, switch the output delimiter using presets (Standard comma, Excel semicolon, TSV, pipe, or space) or pick from the full list (comma, semicolon, tab, pipe, colon, or space), standardize quoting (none, all fields, minimal, or strings-only) with a custom quote and escape character, and trim whitespace or drop empty lines.
Beyond structural cleanup, optional transforms can rewrite the actual values: change the case of data rows and of the header row independently (UPPERCASE, lowercase, Title Case, or Sentence case), remove exact-duplicate data rows, sort the rows by a chosen column (numeric when both values parse as numbers, otherwise alphabetical), and pad every column to an equal width. Review the formatted output before downloading.
Validate and repair CSVs before loading them into a database so imports don't fail midway.
Bring CSVs from multiple vendors into a single, consistent format for your data pipeline.
Pinpoint the exact row and column causing a parsing failure in your application.
Yes. You set the input delimiter the tool parses with and a separate output delimiter to re-export with, so you can convert between comma, semicolon, tab, pipe, colon, and space.
By default it only fixes structure (quoting, whitespace, column counts) and leaves cell values as-is. If you turn on the optional transforms, it will change values: case conversion rewrites letter case, remove-duplicates drops repeated rows, and sort reorders rows. Leave those options off to keep values untouched.
Inconsistent column counts (with the affected row numbers) and parser-level quote and delimiter problems. It does not detect or convert text encodings.
Rows and columns are parsed and transformed in memory in your browser. No record ever reaches a server.