Data Quality Check

Data Quality Check for Construction

Overview

Based on DDC methodology (Chapter 2.6), this skill provides comprehensive data quality assessment for construction projects. Poor data quality leads to poor decisions - validate early, validate often.

Book Reference: "Требования к качеству данных и его обеспечение" / "Data Quality Requirements"

"Качество данных определяется пятью ключевыми метриками: полнота, точность, согласованность, своевременность и достоверность." — DDC Book, Chapter 2.6

Quick Start

import pandas as pd

# Load construction data
df = pd.read_excel("bim_export.xlsx")

# Quick quality check
quality_score = {
    'completeness': (1 - df.isnull().sum().sum() / df.size) * 100,
    'unique_ids': df['ElementId'].nunique() == len(df),
    'valid_volumes': (df['Volume_m3'] >= 0).all()
}

print(f"Completeness: {quality_score['completeness']:.1f}%")
print(f"Unique IDs: {quality_score['unique_ids']}")
print(f"Valid volumes: {quality_score['valid_volumes']}")

Data Quality Dimensions

The 5 Quality Metrics

import pandas as pd
import numpy as np
import re
from datetime import datetime, timedelta

class DataQualityChecker:
    """Comprehensive data quality assessment for construction data"""

    def __init__(self, df):
        self.df = df.copy()
        self.results = {}
        self.issues = []

    def check_completeness(self, required_columns=None):
        """Check for missing values (Полнота)"""
        if required_columns is None:
            required_columns = self.df.columns.tolist()

        completeness = {}
        for col in required_columns:
            if col in self.df.columns:
                non_null = self.df[col].notna().sum()
                total = len(self.df)
                completeness[col] = (non_null / total) * 100
            else:
                completeness[col] = 0
                self.issues.append(f"Missing required column: {col}")

        overall = np.mean(list(completeness.values()))

        self.results['completeness'] = {
            'by_column': completeness,
            'overall': overall,
            'threshold': 95,
            'passed': overall >= 95
        }

        return self.results['completeness']

    def check_accuracy(self, rules=None):
        """Check data accuracy against rules (Точность)"""
        if rules is None:
            # Default construction data rules
            rules = {
                'Volume_m3': {'min': 0, 'max': 10000},
                'Area_m2': {'min': 0, 'max': 100000},
                'Weight_kg': {'min': 0, 'max': 1000000},
                'Cost': {'min': 0, 'max': 100000000}
            }

        accuracy = {}
        for col, bounds in rules.items():
            if col in self.df.columns:
                valid = self.df[col].between(
                    bounds.get('min', -np.inf),
                    bounds.get('max', np.inf)
                ).sum()
                total = self.df[col].notna().sum()
                accuracy[col] = (valid / total * 100) if total > 0 else 100

                # Log invalid values
                invalid_count = total - valid
                if invalid_count > 0:
                    self.issues.append(
                        f"{col}: {invalid_count} values outside range [{bounds.get('min')}, {bounds.get('max')}]"
                    )

        overall = np.mean(list(accuracy.values())) if accuracy else 100

        self.results['accuracy'] = {
            'by_column': accuracy,
            'overall': overall,
            'threshold': 98,
            'passed': overall >= 98
        }

        return self.results['accuracy']

    def check_consistency(self, unique_cols=None, relationship_rules=None):
        """Check data consistency (Согласованность)"""
        consistency = {}

        # Check unique columns
        if unique_cols is None:
            unique_cols = ['ElementId']

        for col in unique_cols:
            if col in self.df.columns:
                is_unique = self.df[col].nunique() == len(self.df)
                consistency[f'{col}_unique'] = 100 if is_unique else \
                    (self.df[col].nunique() / len(self.df) * 100)

                if not is_unique:
                    duplicates = self.df[self.df[col].duplicated()][col].unique()
                    self.issues.append(f"Duplicate {col}: {len(duplicates)} duplicates found")

        # Check cross-field relationships
        if relationship_rules is None:
            relationship_rules = [
                ('End_Date', '>=', 'Start_Date'),
                ('Gross_Volume', '>=', 'Net_Volume')
            ]

        for col1, op, col2 in relationship_rules:
            if col1 in self.df.columns and col2 in self.df.columns:
                if op == '>=':
                    valid = (self.df[col1] >= self.df[col2]).sum()
                elif op == '>':
                    valid = (self.df[col1] > self.df[col2]).sum()
                elif op == '==':
                    valid = (self.df[col1] == self.df[col2]).sum()

                total = self.df[[col1, col2]].notna().all(axis=1).sum()
                consistency[f'{col1}_{op}_{col2}'] = (valid / total * 100) if total > 0 else 100

        overall = np.mean(list(consistency.values())) if consistency else 100

        self.results['consistency'] = {
            'checks': consistency,
            'overall': overall,
            'threshold': 99,
            'passed': overall >= 99
        }

        return self.results['consistency']

    def check_timeliness(self, date_col='Modified_Date', max_age_days=30):
        """Check data timeliness (Своевременность)"""
        if date_col not in self.df.columns:
            self.results['timeliness'] = {
                'overall': None,
                'message': f'Column {date_col} not found'
            }
            return self.results['timeliness']

        dates = pd.to_datetime(self.df[date_col], errors='coerce')
        cutoff = datetime.now() - timedelta(days=max_age_days)

        recent = (dates >= cutoff).sum()
        total = dates.notna().sum()
        timeliness_pct = (recent / total * 100) if total > 0 else 0

        oldest = dates.min()
        newest = dates.max()
        avg_age = (datetime.now() - dates.mean()).days if dates.notna().any() else None

        self.results['timeliness'] = {
            'recent_percentage': timeliness_pct,
            'oldest_record': oldest,
            'newest_record': newest,
            'average_age_days': avg_age,
            'threshold': 80,
            'passed': timeliness_pct >= 80
        }

        return self.results['timeliness']

    def check_validity(self, patterns=None):
        """Check data validity with regex patterns (Достоверность)"""
        if patterns is None:
            patterns = {
                'ElementId': r'^[A-Z]{1,3}\d{3,6}$',  # e.g., W001, FL12345
                'Level': r'^Level\s*\d+$|^L\d+$|^Уровень\s*\d+$',
                'Email': r'^[\w\.-]+@[\w\.-]+\.\w+$',
                'Phone': r'^\+?\d{10,15}$'
            }

        validity = {}
        for col, pattern in patterns.items():
            if col in self.df.columns:
                non_null = self.df[col].dropna()
                if len(non_null) > 0:
                    matches = non_null.astype(str).str.match(pattern).sum()
                    validity[col] = (matches / len(non_null) * 100)

                    invalid = len(non_null) - matches
                    if invalid > 0:
                        self.issues.append(f"{col}: {invalid} values don't match pattern")
                else:
                    validity[col] = 100

        overall = np.mean(list(validity.values())) if validity else 100

        self.results['validity'] = {
            'by_column': validity,
            'overall': overall,
            'threshold': 95,
            'passed': overall >= 95
        }

        return self.results['validity']

    def run_full_check(self):
        """Run all quality checks"""
        self.check_completeness()
        self.check_accuracy()
        self.check_consistency()
        self.check_timeliness()
        self.check_validity()

        # Calculate overall score
        scores = []
        for metric in ['completeness', 'accuracy', 'consistency', 'validity']:
            if metric in self.results and self.results[metric].get('overall'):
                scores.append(self.results[metric]['overall'])

        self.results['overall_score'] = np.mean(scores) if scores else 0
        self.results['grade'] = self._calculate_grade(self.results['overall_score'])
        self.results['issues'] = self.issues

        return self.results

    def _calculate_grade(self, score):
        """Calculate quality grade"""
        if score >= 98:
            return 'A+'
        elif score >= 95:
            return 'A'
        elif score >= 90:
            return 'B'
        elif score >= 80:
            return 'C'
        elif score >= 70:
            return 'D'
        else:
            return 'F'

    def generate_report(self):
        """Generate quality report"""
        if not self.results:
            self.run_full_check()

        report = []
        report.append("=" * 60)
        report.append("DATA QUALITY REPORT")
        report.append("=" * 60)
        report.append(f"Records analyzed: {len(self.df)}")
        report.append(f"Columns: {len(self.df.columns)}")
        report.append("")
        report.append(f"OVERALL SCORE: {self.results['overall_score']:.1f}% (Grade: {self.results['grade']})")
        report.append("")
        report.append("-" * 60)

        # Detail by dimension
        for metric in ['completeness', 'accuracy', 'consistency', 'validity', 'timeliness']:
            if metric in self.results:
                r = self.results[metric]
                passed = '✓' if r.get('passed', False) else '✗'
                overall = r.get('overall', r.get('recent_percentage', 'N/A'))
                if isinstance(overall, (int, float)):
                    report.append(f"{metric.upper():15s}: {overall:>6.1f}% {passed}")
                else:
                    report.append(f"{metric.upper():15s}: {overall}")

        report.append("-" * 60)

        if self.issues:
            report.append("")
            report.append("ISSUES FOUND:")
            for issue in self.issues[:10]:  # Show first 10
                report.append(f"  • {issue}")
            if len(self.issues) > 10:
                report.append(f"  ... and {len(self.issues) - 10} more issues")

        report.append("")
        report.append("=" * 60)

        return "\n".join(report)

Validation Rules Builder

Custom Validation Rules

class ValidationRulesBuilder:
    """Build custom validation rules for construction data"""

    def __init__(self):
        self.rules = []

    def add_not_null(self, column):
        """Column must not have null values"""
        self.rules.append({
            'type': 'not_null',
            'column': column,
            'check': lambda df, col=column: df[col].notna().all()
        })
        return self

    def add_unique(self, column):
        """Column must have unique values"""
        self.rules.append({
            'type': 'unique',
            'column': column,
            'check': lambda df, col=column: df[col].nunique() == len(df)
        })
        return self

    def add_range(self, column, min_val=None, max_val=None):
        """Column values must be within range"""
        self.rules.append({
            'type': 'range',
            'column': column,
            'min': min_val,
            'max': max_val,
            'check': lambda df, col=column, mn=min_val, mx=max_val:
                df[col].between(mn or -np.inf, mx or np.inf).all()
        })
        return self

    def add_regex(self, column, pattern):
        """Column values must match regex pattern"""
        self.rules.append({
            'type': 'regex',
            'column': column,
            'pattern': pattern,
            'check': lambda df, col=column, p=pattern:
                df[col].astype(str).str.match(p).all()
        })
        return self

    def add_in_list(self, column, valid_values):
        """Column values must be in list"""
        self.rules.append({
            'type': 'in_list',
            'column': column,
            'valid_values': valid_values,
            'check': lambda df, col=column, vals=valid_values:
                df[col].isin(vals).all()
        })
        return self

    def add_custom(self, name, check_func):
        """Add custom validation function"""
        self.rules.append({
            'type': 'custom',
            'name': name,
            'check': check_func
        })
        return self

    def validate(self, df):
        """Run all validation rules"""
        results = []

        for rule in self.rules:
            try:
                passed = rule['check'](df)
                results.append({
                    'rule': rule.get('name', f"{rule['type']}:{rule.get('column', 'custom')}"),
                    'passed': passed,
                    'type': rule['type']
                })
            except Exception as e:
                results.append({
                    'rule': rule.get('name', f"{rule['type']}:{rule.get('column', 'custom')}"),
                    'passed': False,
                    'error': str(e)
                })

        return results

# Usage example
rules = (ValidationRulesBuilder()
    .add_not_null('ElementId')
    .add_unique('ElementId')
    .add_range('Volume_m3', min_val=0)
    .add_range('Cost', min_val=0)
    .add_in_list('Category', ['Wall', 'Floor', 'Column', 'Beam', 'Slab'])
    .add_regex('Level', r'^Level\s*\d+$')
)

results = rules.validate(df)
for r in results:
    status = '✓' if r['passed'] else '✗'
    print(f"{status} {r['rule']}")

Automated Quality Pipeline

class DataQualityPipeline:
    """Automated data quality pipeline"""

    def __init__(self, config=None):
        self.config = config or self._default_config()
        self.history = []

    def _default_config(self):
        return {
            'required_columns': ['ElementId', 'Category', 'Volume_m3'],
            'unique_columns': ['ElementId'],
            'numeric_ranges': {
                'Volume_m3': (0, 10000),
                'Area_m2': (0, 100000),
                'Cost': (0, 100000000)
            },
            'valid_categories': ['Wall', 'Floor', 'Column', 'Beam', 'Slab',
                                 'Foundation', 'Roof', 'Stair', 'Door', 'Window'],
            'min_quality_score': 90
        }

    def run(self, df, source_name='unknown'):
        """Run quality pipeline"""
        checker = DataQualityChecker(df)

        # Configure checks based on config
        checker.check_completeness(self.config['required_columns'])
        checker.check_accuracy({
            col: {'min': r[0], 'max': r[1]}
            for col, r in self.config['numeric_ranges'].items()
        })
        checker.check_consistency(self.config['unique_columns'])
        checker.check_validity()

        results = checker.run_full_check()

        # Store in history
        self.history.append({
            'timestamp': datetime.now(),
            'source': source_name,
            'records': len(df),
            'score': results['overall_score'],
            'grade': results['grade'],
            'issues_count': len(results['issues'])
        })

        # Check threshold
        passed = results['overall_score'] >= self.config['min_quality_score']

        return {
            'passed': passed,
            'score': results['overall_score'],
            'grade': results['grade'],
            'details': results,
            'report': checker.generate_report()
        }

    def get_history_summary(self):
        """Get quality history summary"""
        if not self.history:
            return "No quality checks performed yet."

        df_history = pd.DataFrame(self.history)
        return {
            'total_checks': len(self.history),
            'avg_score': df_history['score'].mean(),
            'min_score': df_history['score'].min(),
            'max_score': df_history['score'].max(),
            'latest': self.history[-1]
        }

Quality Reporting

Export Quality Report

def export_quality_report(df, output_path, include_details=True):
    """Export comprehensive quality report to Excel"""
    checker = DataQualityChecker(df)
    results = checker.run_full_check()

    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
        # Summary sheet
        summary = pd.DataFrame({
            'Metric': ['Overall Score', 'Grade', 'Records', 'Columns', 'Issues'],
            'Value': [
                f"{results['overall_score']:.1f}%",
                results['grade'],
                len(df),
                len(df.columns),
                len(results['issues'])
            ]
        })
        summary.to_excel(writer, sheet_name='Summary', index=False)

        # Completeness details
        if 'completeness' in results:
            comp_df = pd.DataFrame.from_dict(
                results['completeness']['by_column'],
                orient='index',
                columns=['Completeness_%']
            )
            comp_df.to_excel(writer, sheet_name='Completeness')

        # Issues list
        if results['issues']:
            issues_df = pd.DataFrame({'Issue': results['issues']})
            issues_df.to_excel(writer, sheet_name='Issues', index=False)

        # Missing values analysis
        if include_details:
            missing = df.isnull().sum()
            missing_df = pd.DataFrame({
                'Column': missing.index,
                'Missing_Count': missing.values,
                'Missing_%': (missing.values / len(df) * 100).round(2)
            })
            missing_df.to_excel(writer, sheet_name='Missing_Values', index=False)

    return output_path

Quick Reference

Common Validation Patterns

# Construction-specific regex patterns
PATTERNS = {
    'element_id': r'^[A-Z]{1,3}\d{3,8}$',
    'revit_id': r'^\d{5,8}$',
    'ifc_guid': r'^[A-Za-z0-9_$]{22}$',
    'level': r'^(Level|L|Уровень)\s*[-]?\d+$',
    'grid': r'^[A-Z]{1,2}[-/]?\d{0,3}$',
    'date_iso': r'^\d{4}-\d{2}-\d{2}$',
    'cost_code': r'^\d{2,3}[.-]\d{2,4}[.-]?\d{0,4}$'
}

Resources

• Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.6
• Website: https://datadrivenconstruction.io
• Great Expectations: https://greatexpectations.io

Next Steps

• See bim-validation-pipeline for BIM-specific validation
• See etl-pipeline for data processing pipelines
• See data-visualization for quality dashboards