SheetCompareHow to Validate a CSV File
Catch structural and data problems, malformed rows, wrong types, missing fields, before they break an import.
A CSV that looks fine can still fail an import for reasons you won't see by scrolling: a row with too many fields from an unescaped comma, a date in the wrong format, a required column that's blank, or the wrong character encoding. Validating before you load saves hours of debugging downstream.
Validation happens at two levels: structural (every row has the right number of fields, quoting is correct, encoding is consistent) and semantic (values match expected types and rules). Check structure first, since a structural break can make every semantic check meaningless.
- 1
Check the structure
Confirm every row has the same number of fields as the header. A row with extra or missing fields usually means an unescaped delimiter or a stray line break inside a value.
- 2
Confirm the encoding and delimiter
Make sure the file is UTF-8 and uses the delimiter you expect. A wrong encoding garbles characters; a wrong delimiter collapses or splits columns.
- 3
Validate the values
Check that types and required fields meet your rules: dates parse, numbers are numeric, IDs are present and unique, and key columns have no unexpected blanks.
- 4
Fix at the source
Where you find problems, fix the export or clean the file (trim whitespace, fix quoting, set the right encoding) rather than patching downstream.
Frequently asked questions
What's the most common CSV structural error?▾
An unescaped comma or line break inside a value, which shifts every following column for that row. Proper quoting prevents it.
How do I check a CSV's encoding?▾
A text editor or command-line tool can report it. If accented characters look wrong, the encoding is mismatched, standardize on UTF-8.
What counts as a required-field check?▾
Confirming that columns which must always have a value (like an ID or key) are never blank, since nulls in a key column break joins and comparisons.