CSV Validator
Validate CSV column counts and quoting
CSV Input
Validation Result
What is CSV Validation?
A CSV validator checks if your CSV (Comma-Separated Values) file follows proper formatting rules. It catches the problems that make imports fail: a quote that never closes, a quote in the middle of an unquoted value, rows with more or fewer fields than the header, and blank or duplicate column names.
According to RFC 4180, the official CSV specification, valid CSV files must have consistent column counts per row and properly escaped special characters. IANA defines CSV as a registered MIME type (text/csv), making validation crucial for data exchange across systems.
The validator reads the file the way RFC 4180 describes, one character at a time, so a quoted note that spans three lines counts as one field instead of three broken rows. It works out the delimiter (comma, semicolon, tab or pipe) from the first rows and shows which one it used. It checks structure only: it won't tell you that an email column holds a phone number. The check happens locally in the page, so the file never leaves your machine.
Why Validate CSV Files?
Prevent Import Errors
Invalid CSV causes database imports to fail, Excel errors, and broken data pipelines. Validation catches these issues before they break your workflow.
Check Column Consistency
Every row must have the same number of columns. Validation detects missing or extra fields that corrupt data imports.
Detect Structure Issues
Unclosed quotes, stray quotes inside unquoted values, and text after a closing quote are each reported with the line they start on.
Real-Time Feedback
Results update as you type or upload. Each problem names the line it's on, plus the detected delimiter, data row count and column count.
Standards Compliant
Quoting and line-break rules follow RFC 4180, the spec most CSV libraries and database importers implement. A header-only file is valid, as the spec allows.
Sample Data Included
Test with valid and invalid CSV examples. Learn what passes validation and what fails, with clear explanations.
How to Validate CSV
Input Your CSV
Paste CSV data, upload a file, or click "Valid Sample" or "Invalid Sample" to see examples. Comma, semicolon, tab and pipe delimiters are detected automatically.
Instant Validation
Validation runs automatically. The right panel shows whether your CSV is valid or invalid, with a detailed breakdown of any errors found.
Fix Issues
Review error messages, fix problems in your CSV, and revalidate. Continue until your CSV passes validation and is ready for import.
Common Questions
What makes a CSV invalid?
The usual suspects: a row with more or fewer fields than the header, a quoted value whose closing quote is missing, a bare quote inside an unquoted value (`5" screen` instead of `"5"" screen"`), and blank or duplicate header names, often from a trailing comma. The validator reports each of these with its line number.
Does this support different delimiters?
Yes. The validator handles commas, semicolons, tabs and pipes, and picks the one that splits the first rows into a consistent number of columns. The result shows which delimiter it used, so if it guessed wrong you'll see it straight away. There is no manual override yet.
Can I validate large CSV files?
Files of a few MB validate in well under a second. Validation runs in your browser in JavaScript and the whole file sits in the editor, so very large exports (hundreds of MB) are better checked with a command-line tool like csvkit.
How do I fix invalid CSV?
The validator shows specific error messages with line numbers. Fix those in the editor here and the result updates as you type. The CSV Formatter can tidy spacing and quoting afterwards, but it can't guess where a missing quote should go.
Is validation the same as formatting?
No. Validation checks if CSV is correct. Formatting fixes structure and makes it readable. Use validation first to find errors, then formatting to clean them up.
Where can I learn more about CSV standards?
Read RFC 4180 for official CSV specifications. The W3C Model for Tabular Data and Metadata goes further, covering headers, column types and metadata for CSV on the web.
Related CSV Tools
Validation is one step in the CSV workflow. Explore related tools: