Dataset Quality Audit

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

• Python 3.8+
• pandas
• numpy

pip install pandas numpy