dvcrn 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
| Metric | Description | Threshold |
|---|---|---|
| Completeness | % non-null values | ≥ 95% |
| Accuracy | Values within valid range | ≥ 98% |
| Consistency | Unique IDs, valid relationships | ≥ 99% |
| Validity | Match expected patterns | ≥ 95% |
| Timeliness | Records updated recently | ≥ 80% |
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-pipelinefor BIM-specific validation - See
etl-pipelinefor data processing pipelines - See
data-visualizationfor quality dashboards