Free CSV Validator
Validate CSV structure and catch errors instantly in your browser.
CSV Input
Validation Result
What Is the CSV Validator?
You paste a CSV file into a database import tool and it blows up with a cryptic error. Nine times out of ten, it's a quoting issue or a row with the wrong number of columns — problems that are nearly invisible in a plain text editor. The RFC 4180 specification defines what valid CSV looks like, but most editors won't tell you when you've violated it. This validator checks your CSV against those rules: quoting consistency, delimiter uniformity, row length parity, and unclosed quotes — and pinpoints the exact line where things go wrong.
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
Why is my CSV failing validation even though it looks fine?
The most common culprit is an unclosed quote. A field starting with a double quote must end with one before the delimiter — even a trailing space after the closing quote can break it. Check the exact line number the validator reports.
Does this CSV validator work with semicolon or tab delimiters?
Yes. The tool auto-detects common delimiters including comma, semicolon, and tab. If auto-detection fails, try cleaning the file with CSV Formatter first.
Will my CSV data be sent to a server?
No. Validation runs entirely in your browser using JavaScript. Nothing leaves your machine, so it is safe for production exports, PII data, or anything sensitive.
What RFC does CSV follow?
The most widely followed standard is RFC 4180, published by the IETF. It defines comma as the default delimiter, double-quote as the quoting character, and CRLF as the line ending. Real-world CSVs often bend these rules, which is why a validator is useful.
Can the validator fix my CSV errors automatically?
No — it reports where the problem is so you can fix it. For automatic cleanup and reformatting, use CSV Formatter.
Related Tools
For the CSV specification, see RFC 4180. MDN's JSON guide and json.org cover related formats.