CSV Validator & Fixer: Validate and Clean CSV Files
This CSV Validator & Fixer checks pasted or dropped CSV for inconsistent column counts, unclosed or stray quotes, mixed line endings, a leading BOM, and duplicate or empty headers. It reports each issue with its row and column. The fixer can pad ragged rows, trim whitespace, normalize line endings, remove the BOM, and rename duplicate headers, giving you a cleaner copy to review.
CSV Validator & Fixer is a browser-based CSV checker for finding common problems in CSV content. It checks for ragged rows with inconsistent column counts, unclosed or stray quotes, mixed line endings, a leading BOM, and duplicate or empty headers. Each issue is reported with its row and column, helping you find where to inspect the input.
To clean a file, the fixer can pad ragged rows, trim whitespace, normalize line endings, remove a leading BOM, and rename duplicate headers. These actions address the issues the checker identifies and produce a clean copy for review. The tool reports problems as well as providing fixes, so you can see what needs attention before using the result.
CSV represents tabular data as text, with rows and columns separated by delimiters. That makes it convenient for exchanging data between tools, but inconsistent row lengths, quoting problems, or unexpected headers can complicate processing. Checking CSV before it reaches a parser or another workflow can help you catch these issues earlier.
The CSV Validator & Fixer runs entirely in your browser: your input is processed on your device and is not uploaded, stored, or sent to a server. It is free, and once the page has loaded, it works offline. Developers and DevOps engineers can use it to inspect CSV exports, troubleshoot input, and prepare a cleaner copy without manually searching the file.
Step 1: Paste or drop your CSV into the tool. Use the CSV content you want to check.
Step 2: Run the check to look for common structural and header problems.
Step 3: Review the reported issues and their row and column positions to locate problems in the input.
Step 4: Use the fixer to pad ragged rows, trim whitespace, normalize line endings, remove a leading BOM, or rename duplicate headers.
Step 5: Review the cleaned copy before using it in your workflow.
Use case: Check a CSV file before it reaches a parser and fails at runtime. Finding inconsistent column counts or quote problems early can make troubleshooting easier.
Use case: Locate a syntax problem by its reported row and column instead of bisecting the file by hand.
Use case: Confirm that output from another tool is valid CSV, not merely plausible-looking text.
Use case: Clean an export with mixed line endings or a leading BOM before passing it to another tool.
Use case: Check for duplicate or empty headers when preparing a CSV for a workflow that relies on clear column names.
Use case: Create a cleaner copy of CSV with ragged rows by padding rows and normalizing common formatting issues.