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Count blank, NULL, NA, and other common missing-value markers by CSV column.
Use this before a bulk import to see which columns contain blanks or placeholder missing values. In addition to empty cells, you can treat markers such as null, na, n/a, and undefined as missing.
Missing-data patterns are hard to see by scrolling. A column with 27% missing values can hide inside a file that otherwise looks fine. Column-level percentages help you spot extraction problems or required fields that need attention.
“Missing” is not limited to an empty string. Exporters often write literal markers such as NULL or N/A. The configured markers are matched case-insensitively. Values such as 0 and false remain real values unless you explicitly add them as markers.
A customer file might show 2% missing email, 63% missing phone, and 0% missing customer ID. That could be perfectly fine if phone is optional—but a missing customer ID may be a reason to stop the import.
Do not fill columns just because their missing percentage is high. First ask whether the field is required. A sudden gap in a required ID can signal extraction trouble; a sparse optional phone field may be perfectly normal. Missing rate is a triage signal, not a repair command.