data-quality-auditor

name	data-quality-auditor
description	Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.

Data Quality Auditor

Comprehensive data quality assessment for CSV/Excel datasets.

Features

• Completeness: Missing values analysis
• Uniqueness: Duplicate detection
• Validity: Type validation and constraints
• Consistency: Pattern and format checks
• Quality Score: Overall data quality metric
• Reports: Detailed HTML/JSON reports

Quick Start

from data_quality_auditor import DataQualityAuditor

auditor = DataQualityAuditor()
auditor.load_csv("customers.csv")

# Run full audit
report = auditor.audit()
print(f"Quality Score: {report['quality_score']}/100")

# Check specific issues
missing = auditor.check_missing()
duplicates = auditor.check_duplicates()

CLI Usage

# Full audit
python data_quality_auditor.py --input data.csv

# Generate HTML report
python data_quality_auditor.py --input data.csv --report report.html

# Check specific aspects
python data_quality_auditor.py --input data.csv --missing
python data_quality_auditor.py --input data.csv --duplicates
python data_quality_auditor.py --input data.csv --types

# JSON output
python data_quality_auditor.py --input data.csv --json

# Validate against rules
python data_quality_auditor.py --input data.csv --rules rules.json

API Reference

DataQualityAuditor Class

class DataQualityAuditor:
    def __init__(self)

    # Data loading
    def load_csv(self, filepath: str, **kwargs) -> 'DataQualityAuditor'
    def load_dataframe(self, df: pd.DataFrame) -> 'DataQualityAuditor'

    # Full audit
    def audit(self) -> dict
    def quality_score(self) -> float

    # Individual checks
    def check_missing(self) -> dict
    def check_duplicates(self, subset: list = None) -> dict
    def check_types(self) -> dict
    def check_uniqueness(self) -> dict
    def check_patterns(self, column: str, pattern: str) -> dict

    # Validation
    def validate_column() -> 
     () -> 

    
     () -> 
     () -> 

Quality Checks

Missing Values

missing = auditor.check_missing()
# Returns:
{
    "total_cells": 10000,
    "missing_cells": 150,
    "missing_percent": 1.5,
    "by_column": {
        "email": {"count": 50, "percent": 5.0},
        "phone": {"count": 100, "percent": 10.0}
    },
    "rows_with_missing": 120
}

Duplicates

dups = auditor.check_duplicates()
# Returns:
{
    "total_rows": 1000,
    "duplicate_rows": 25,
    "duplicate_percent": 2.5,
    "duplicate_groups": [...],
    "by_columns": {
        "email": {"duplicates": 15},
        "phone": {"duplicates": 20}
    }
}

Type Validation

types = auditor.check_types()
# Returns:
{
    "columns": {
        "age": {
            "detected_type": "int64",
            "unique_values": 75,
            "sample_values": [25, 30, 45],
            "issues": []
        },
        "date": {
            "detected_type": "object",
            "unique_values": 365,
            "sample_values": ["2023-01-01", "invalid"],
            "issues": ["Mixed date formats detected"]
        }
    }
}

Validation Rules

Define custom validation rules:

{
    "columns": {
        "email": {
            "required": true,
            "unique": true,
            "pattern": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
        },
        "age": {
            "type": "integer",
            "min": 0,
            "max": 120
        },
        "status": {
            "allowed_values": ["active", "inactive", "pending"]
        },
        "created_at": {
            "type": 
             
        
    

results = auditor.validate_dataset(rules)

Quality Score

The quality score (0-100) is calculated from:

• Completeness (30%): Missing value ratio
• Uniqueness (25%): Duplicate row ratio
• Validity (25%): Type and constraint compliance
• Consistency (20%): Format and pattern adherence

score = auditor.quality_score()
# 85.5

Output Formats

Audit Report

{
    "file": "data.csv",
    "rows": 1000,
    "columns": 15,
    "quality_score": 85.5,
    "completeness": {
        "score": 92.0,
        "missing_cells": 800,
        "details": {...}
    },
    "uniqueness": {
        "score": 97.5,
        "duplicate_rows": 25,
        "details": {...}
    },
    "validity": {
        "score": 78.0,
        "type_issues": [...],
        "details": {...}
    },
    "consistency": {
        "score": 80.0,
        "pattern_issues": [...],
        "details": {...}
    },
    "recommendations": [
        "Column 'phone' has 10% missing values",
        "25 duplicate rows detected",
        "Column 'date' has inconsistent formats"
    ]
}

Example Workflows

Pre-Import Validation

auditor = DataQualityAuditor()
auditor.load_csv("import_data.csv")

report = auditor.audit()
if report['quality_score'] < 80:
    print("Data quality below threshold!")
    for rec in report['recommendations']:
        print(f"  - {rec}")
    exit(1)

ETL Pipeline Check

auditor = DataQualityAuditor()
auditor.load_dataframe(transformed_df)

# Check critical columns
email_check = auditor.validate_column("email", {
    "required": True,
    "unique": True,
    "pattern": r"^[\w.+-]+@[\w-]+\.[\w.-]+$"
})

if email_check['issues']:
    raise ValueError(f"Email validation failed: {email_check['issues']}")

Generate Documentation

auditor = DataQualityAuditor()
auditor.load_csv("dataset.csv")

# Generate comprehensive report
auditor.generate_report("quality_report.html", format="html")

# Or get summary text
print(auditor.summary())

Dependencies

• pandas>=2.0.0
• numpy>=1.24.0