CSV Validator
Validate CSV structure and check for errors
CSV Input
Validation Result
What Is the CSV Validator?
The CSV Validator checks CSV structure and reports errors. Paste or upload CSV; the tool validates quoting, delimiter consistency, and row length. The RFC 4180 defines the CSV format.
This tool runs entirely in your browser. Nothing is sent to a server. Use Valid Sample or Invalid Sample to see examples. For formatting valid CSV, use CSV Formatter. For viewing as a table, use CSV Viewer. For conversion, use CSV to JSON.
CSV Validation Examples
Valid CSV has consistent quoting. Invalid CSV often has unclosed quotes. Telecom-themed examples:
Example 1: Valid CSV
Example 2: Invalid CSV (unclosed quote)
Use Valid Sample or Invalid Sample above to load examples into the editor.
How to Use This Tool
Paste or Upload
Paste CSV into the left panel or upload a .csv or .txt file. Use Valid Sample or Invalid Sample to load examples. Use Clear to reset.
Check the Result
The right panel shows Valid or Invalid and lists any errors with line numbers and descriptions.
Fix and Revalidate
Fix errors in the left panel. The result updates. For formatting after validation, use CSV Formatter.
Common CSV Errors
Unclosed quotes are the most frequent issue. A field starting with " must end with "; otherwise the parser treats subsequent commas as part of the field. Inconsistent column counts—rows with different numbers of columns—often indicate a quoting or delimiter error. The RFC 4180 spec defines the rules. Encoding problems (e.g., UTF-8 BOM or mixed encodings) can also cause validation failures. For a deeper look at CSV parsing, see MDN and common parsing libraries.
When the CSV Validator Helps
Before importing CSV into databases like PostgreSQL or MySQL, validate the structure. Invalid CSV causes import failures or corrupted data. APIs that accept CSV uploads may reject malformed input—running it through here first saves debugging time. Before converting to JSON, YAML, or XML, validation ensures the conversion won't fail or produce wrong results.
ETL pipelines and data workflows often break on bad CSV. Catching errors early with this validator prevents downstream failures. For viewing and editing CSV as a table, use CSV Viewer. For cleaning and normalizing valid CSV, use CSV Formatter. For conversion, use CSV to JSON or CSV to XML.
Frequently Asked Questions
Is my data private?
Yes. Validation runs entirely in your browser. No data is sent to any server.
Validator vs Formatter?
Validator checks structure and reports errors. Formatter cleans and normalizes valid CSV. Use both for quality.
What does it check?
Typical checks include quoting rules, delimiter consistency, row length, and RFC 4180 compliance.
Does it support different delimiters?
The validator may auto-detect or support comma, semicolon, and tab. Check the implementation for details.
Can it fix errors?
No. The validator reports issues; you fix them manually. For cleaning and reformatting, use CSV Formatter.
Related Tools
For the CSV specification, see RFC 4180. MDN's JSON guide covers related formats. json.org defines JSON. YAML spec for YAML conversion.