Valid: simple table
- name,age
- Ada,30
- Bob,28 -> Valid (3 rows, 2 columns)
format validator
CSV Validator
Validate CSV with PapaParse, catch column mismatches, and keep data local to your browser.
Results
Processing…- Status
- Processing...
- Details
- Processing...
- Rows
- Processing...
- Columns
- Processing...
How to use this validator
- Paste CSV with a header and sample rows.
- Run validate to parse with PapaParse and count columns.
- If mismatches appear, fix the flagged rows (often missing quotes or commas) and re-validate.
Rules & checks
Uses PapaParse client-side to parse CSV with standard comma delimiter and quoted fields.
Skips empty lines and trims whitespace around cells.
Checks every row for a consistent column count based on the header/first row.
Best for small-to-medium CSVs (a few thousand rows) to keep the browser responsive.
Inputs explained
CSV content
Paste comma-separated text. Quote fields that contain commas or line breaks. Keep size modest for browser parsing.
When to use it
- Preflight CSV exports before importing into CRM/BI/warehouse tools
- Validate user-uploaded CSVs in support or admin workflows
- Catch column drift between environments or versioned exports
- Prep CSV before converting to JSON/NDJSON or schema validation
Common errors
- Row has fewer/more columns than the header
- Unterminated or stray quotes around fields
- Unquoted commas inside values causing extra columns
- Blank lines in the middle of data
Limitations
- Structural check only—does not enforce data types or business rules.
- Targets comma-delimited CSV; does not auto-detect alternative delimiters.
- Very large files may be slow in-browser; consider chunked/server validation for multi-MB datasets.
Tips
- Quote any field containing commas, line breaks, or quotes (escape inner quotes as "").
- Remove blank trailing lines to avoid false mismatches.
- Keep files small in-browser; use chunked/server-side validation for very large CSVs.
- If you expect tabs, use a TSV validator instead.
Examples
Valid: quoted commas
- name,notes
- "Ada","Seattle, WA"
- "Bob","NY, NY" -> Valid (commas preserved in quotes)
Invalid: missing column
- name,age
- Ada -> Invalid (row 2 has 1 column; expected 2)
Invalid: stray quote
- name,notes
- "Ada,"Seattle -> Invalid (unterminated quote)
Deep dive
This CSV validator uses PapaParse to spot malformed rows, stray quotes, and column mismatches before you import data.
It runs entirely client-side for privacy; keep CSVs modest in size and quote any fields that contain commas or line breaks.
FAQs
- Is my CSV uploaded or stored?
- No. Parsing runs in your browser only and clears on refresh.
- Do you support quoted fields with commas?
- Yes. PapaParse honors quoted fields; unquoted commas will increase the column count and be flagged.
- Does this handle TSV?
- Use the TSV validator for tab-separated files; this tool assumes commas.
- Can I validate huge CSVs?
- Keep it to a few thousand rows for the browser. Use chunked or server/CI validation for large datasets.
- Do you validate data types?
- No. This checks structure and column counts. Apply type/business rules separately.
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Structure-only check; does not enforce data types or business rules.