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

```python
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

```python
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...