Data Quality Auditor

Data Quality Auditor

Run deterministic checks from a reviewed rule file:

python scripts/validate_dataset.py data.csv --rules quality-rules.json

Supported rule keys are required_columns, non_null, unique, types, ranges, allowed_values, and row_count. The report contains counts and row numbers but never raw failing values. The command exits nonzero on violations; use --warn-only only when a non-blocking audit is explicitly intended.

Rule example

{
  "required_columns": ["id", "created_at"],
  "non_null": ["id"],
  "unique": ["id"],
  "types": {"id": "integer", "created_at": "datetime"},
  "ranges": {"score": {"min": 0, "max": 1}},
  "allowed_values": {"status": ["active", "inactive"]},
  "row_count": {"min": 1}
}

Review the rules and dataset authorization before running. A passing report proves only the declared checks; it does not establish correctness, fairness, fitness for purpose, or permission to use the data.