Free · No signup · Browser-based

Validate your CSV.

Drop a CSV and get a quick health check — missing values, type inconsistencies, duplicate rows, blank columns, dodgy headers. All in seconds.

01 · How it works

Three steps, then done.

Before you load a CSV into a database or send it to a colleague, it pays to know what's actually in it. This validator scans for the most common issues — missing cells, columns that don't have the type they claim, duplicate rows, entirely empty columns, blank or auto-named headers. Each issue is flagged with a severity so you can fix the urgent ones first.

i. drop

Drop the CSV

Drag from Finder, click to choose, or paste data directly into the input pane.

ii. set parameters

Pick your options

Tool controls appear above the output. Tune them and watch the result update live.

iii. download

Save or build a dashboard

Download the cleaned-up file, or click 'Build dashboard' to see what's actually in your data.

02 · Why ours

Honest, local, fast.

Every other CSV utility online makes you upload your file and wait. Ours runs in your browser, instantly, and surfaces what's interesting in the data while it's at it.

  • 01

    Nothing uploaded

    Parsed locally — open DevTools → Network and you'll see zero requests when you drop a file.

  • 02

    Instant feedback

    Tool runs as you change parameters. No 'Convert' button to wait on.

  • 03

    Insights, free

    Below the output, a strip tells you what's actually in the data — concentrations, outliers, suggested chart.

  • 04

    Dashboard one click away

    If the data made you curious, hit 'Build full dashboard' and it opens in our visualization tool.

"Cleaned up a 50k-row CSV and built a dashboard from it in one tab. Beats opening Excel."
— anyone with a messy export
03 · FAQ

csv validate questions.

What gets checked?
Missing values (per column and total), type mismatches in numeric or date columns, duplicate rows, columns that are entirely empty, duplicate header names, and blank or auto-generated headers like 'column_3'.
High: data probably can't be loaded as-is (duplicate headers, lots of missing values). Medium: would cause downstream confusion (type mismatches, blank headers). Low: cosmetic or recoverable (a few missing cells, empty columns, mild duplicates).
For columns we detect as dates, we re-parse each value. Anything that doesn't parse is flagged.
Use the related tools: deduplicate, sort+filter, or open the dashboard for calculated columns + role overrides. The validator points at problems; the other tools fix them.