CSV Validator

How to use the csv validator

1. Paste your CSV, or let the delimiter be detected.
2. Read the issues, which name the row numbers.
3. Check the column summary for unexpected types.

About this tool

A CSV import that fails usually fails for one of a very small number of reasons, and this checks for all of them: rows with a different field count from the header, duplicated or empty column names, and a delimiter that is not what you assumed.

Ragged rows are reported by line number, because that is the only useful form. A file of ten thousand rows with one bad line is a five-second fix when you know which line and a miserable afternoon when you do not.

The column summary infers a type for each column and counts how many values are filled and how many are distinct. That is often where the real problem shows itself — a column you expected to be numeric reported as text usually means a stray value somewhere, and a "unique" count of 1 on a column that should vary means something upstream is writing a constant.

Parsing handles quoted fields properly, so a comma, quote or line break inside a value does not split it. That is the difference between this and splitting on commas, and it is why exports containing addresses or free-text notes so often arrive mangled.

Everything runs in the page. Spreadsheets are usually the most sensitive files anyone handles — customer lists, payroll, financial records — so a tool that works on them without uploading them is doing the one thing that matters most here.

Common uses

• Finding the row that broke an import.
• Checking a file before sending it to a client or a system.
• Confirming an export has the columns you expected.

Frequently asked questions

Why does my file show as one column?

The delimiter is not what was assumed. Many European exports use semicolons. Set it manually rather than relying on detection.

What is a ragged row?

A row with a different number of fields from the header, almost always an unquoted delimiter inside a value. It is the most common cause of a failed import.

Does this validate against a schema?

No. It checks structure — field counts, headers, types — rather than whether values satisfy business rules.