Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Data Quality Check
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thres...
skill openclawclawhub Free
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thres...
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- Tencent SkillHub
- What's included
- claw.json, instructions.md, SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
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- Indexed source record
- Version
- 2.1.0
Provenance
- Publisher
- datadrivenconstruction
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Documentation
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']}")
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)
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] }
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
MetricDescriptionThresholdCompleteness% non-null values≥ 95%AccuracyValues within valid range≥ 98%ConsistencyUnique IDs, valid relationships≥ 99%ValidityMatch expected patterns≥ 95%TimelinessRecords 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-pipeline for BIM-specific validation See etl-pipeline for data processing pipelines See data-visualization for quality dashboards
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Config
- SKILL.md
- instructions.md
- claw.json