dataset-quality-audit
A data quality auditing tool that runs 12-dimension quality checks on tabular data, producing per-dimension scores (0–100), an overall grade, and actionable fix suggestions.
Capabilities
| Dimension | Description |
|---|
| Missing Values | Count and percentage of null/NaN values per column |
| Duplicate Rows | Number and percentage of fully duplicated rows |
| Type Consistency | Mixed types within a single column (e.g., numbers mixed with text) |
| Value Range / Outliers | Outlier detection using the IQR method |
| Format Compliance | Consistency of date, email, phone number, and other formatted fields |
| Uniqueness Constraints | Whether ID-type columns contain duplicates |
| Whitespace Issues | Leading/trailing spaces, empty strings, whitespace-only values |
| Constant Columns | Columns with only a single unique value (zero information) |
| Distribution Skewness | Whether numeric columns have excessive skewness |
| Column Naming | Spaces, special characters, or inconsistent casing in column names |
| Cardinality Anomalies | Unusually high or low number of unique values |
| Cross-Column Consistency | Logical checks across columns (e.g., start date before end date) |
Quick Start
# Basic quality check
python3 scripts/data_quality_checker.py data.csv
# Save report as JSON
python3 scripts/data_quality_checker.py data.csv --output report.json
# Specify ID columns (for uniqueness checks)
python3 scripts/data_quality_checker.py users.csv --id-columns "user_id,email"
# Specify date columns (for format checks)
python3 scripts/data_quality_checker.py orders.csv --date-columns "created_at,updated_at"
Detailed Usage
Basic Invocation
python3 scripts/data_quality_checker.py <data-file> [options]
Parameters
| Parameter | Short | Required | Default | Description |
|---|
input | — | Yes | — | Path to input file (CSV/TSV/Excel/JSON) |
--output | -o | No | stdout | Path for the JSON report output |
--id-columns | -id | No | Auto-detect | Comma-separated column names that should be unique |
--date-columns | -dc | No | Auto-detect | Comma-separated column names containing dates |
--sample | -s | No | All rows | Number of rows to sample (useful for large files) |
--encoding | -e | No | utf-8 | File encoding |
Output Format (JSON)
{
"file": "data.csv",
"rows": 10000,
"columns": 15,
"overall_score": 78.5,
"grade": "B",
"dimensions": {
"missing_values": {
"score": 85.0,
"issues": [
{"column": "age", "missing_count": 150, "missing_pct": 1.5, "suggestion": "Fill with median or mode"}
]
},
"duplicates": {
"score": 95.0,
"issues": [...]
}
},
"top_suggestions": [
"Column 'age' has 1.5% missing values — consider filling with the median",
"Found 200 fully duplicated rows — consider deduplication"
]
}
Grading Scale
| Grade | Score Range | Meaning |
|---|
| A+ | 95–100 | Excellent quality — ready for use as-is |
| A | 90–95 | Good quality — minor issues only |
| B | 80–90 | Moderate quality — recommended to fix before use |
| C | 60–80 | Poor quality — significant cleaning required |
| D | 40–60 | Very poor quality — many issues need attention |
| F | 0–40 | Essentially unusable — requires re-collection or major cleanup |
Dependencies
