Validate CSV before import — without uploading your file.

Blank column headers (the #1 reason imports fail silently), duplicate headers, rows with wrong column counts, fully empty rows, encoding problems (replacement chars, null bytes), columns that are mostly empty, mixed numeric/text columns, and leading/trailing whitespace in headers or values.

A blank header is a column where the header row has no value. Salesforce, HubSpot, Mailchimp, and most databases won't know what to call that column on import — some skip it, some use "Column1", some fail the whole import. Almost always a mistake.

If two columns are both named "email", systems either ignore the second one or overwrite the first. Either way, data is lost silently. The validator flags duplicates so you can rename them before import.

A row with more or fewer cells than the header indicates a parsing problem — usually an unescaped comma inside a free-text field. Example: an address cell like 123 Main, Apt 4 without quotes creates an extra column. The validator catches this before your import does.

Your file has replacement characters (�) or null bytes (unprintable). Almost always the file was saved in one encoding and read as another — usually Windows-1252 opened as UTF-8. Re-save as "CSV UTF-8" from Excel and re-validate.

For some — yes. Each fixable issue has a "Fix & download" button. The validator applies the fix (trim headers, rename duplicates, remove empty rows) and gives you a cleaned CSV in one click.

Yes. After your file loads, scroll to Check column contents, pick your email column, select Valid email address, and click Run column check. You'll get a count of valid vs. invalid rows, a scrollable list of every bad row with its row number, plus one-click downloads of either the valid-only or invalid-only rows — useful before pushing to Salesforce, HubSpot, or Mailchimp.

Yes. The phone check accepts common US and international formats — +1 (555) 123-4567, 555-123-4567, 5551234567, +44 20 7123 4567, and similar. It strips spaces, parentheses, dots, and dashes, then checks the digit count (7–15) with an optional leading +. Rows that are clearly wrong — short IDs, garbage strings, empty-looking values — are flagged so you can clean them before a CRM or SMS-platform import.

Yes. The URL check uses the browser's built-in URL parser — more reliable than a regex. It accepts https://example.com, http://example.com, and bare domains like www.example.com. Malformed entries like htp:/broken or plain text are flagged as invalid. Good for cleaning social-media exports, supplier lists, or link columns before pushing to any system that requires real URLs.

Yes. Pick Custom regex as the check type and enter your pattern. For example, ^[A-Z]{2}-\d{4}$ for internal SKUs, ^\d{5}(-\d{4})?$ for US ZIP codes, or ^[A-Z]{3}$ for ISO country codes. Everything that doesn't match is listed with row number and cell value, and you can download the invalid rows for correction.

No. The validator only reads. Fixes and column-check downloads produce new CSVs — your original file on disk is untouched.

Error: the file will likely fail to import. Warning: the file will probably import but the data is suspicious (e.g. 80% of a column is empty). Info: low-priority observations.

Yes. The validator reads Excel files natively. Structural checks apply identically. Some issues (like encoding) are specific to CSV and won't show up for XLSX.