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Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.
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SOC 직업 분류 기준
| name | data-validation-reporter |
| description | Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting. |
| version | 1.0.0 |
| category | workspace-hub |
| type | skill |
| tags | ["data-validation","plotly","reporting","quality-assurance","pandas"] |
| discovered | "2026-01-07T00:00:00.000Z" |
| source_commit | 47b64945 |
| reusability_score | 80 |
This skill provides a complete data validation and reporting workflow:
Discovered from commit: 47b64945 (digitalmodel)
Original file: src/data_procurement/validators/data_validator.py
Reusability score: 80/100
Patterns used:
validator = DataValidator(config_path="config/validation.yaml")
results = validator.validate_dataframe(
df=data,
required_fields=["id", "value", "timestamp"],
unique_field="id"
)
Validation checks:
Score calculation (0-100 scale):
Status thresholds:
4-Panel Plotly Dashboard:
Features:
# config/validation.yaml
validation:
required_fields:
- id
- timestamp
- value
unique_fields:
- id
numeric_fields:
- year_built
- length_m
- displacement_tonnes
thresholds:
max_missing_pct: 0.2 # 20%
min_quality_score: 60
max_duplicates: 0
from data_validator import DataValidator
import pandas as pd
# Initialize with config
validator = DataValidator(config_path="config/validation.yaml")
# Load data
df = pd.read_csv("data/input.csv")
# Validate
results = validator.validate_dataframe(
df=df,
required_fields=["id", "name", "value"],
unique_field="id"
)
# Check results
if results['valid']:
print(f"✅ PASS - Quality Score: {results['quality_score']:.1f}/100")
else:
print(f"❌ FAIL - Issues: {len(results['issues'])}")
for issue in results['issues']:
print(f" - {issue}")
from pathlib import Path
# Generate HTML report
validator.generate_interactive_report(
validation_results=results,
output_path=Path("reports/validation_report.html")
)
print("📊 Interactive report saved to reports/validation_report.html")
# Generate text summary
text_report = validator.generate_report(results)
print(text_report)
data-validation-reporter/
├── SKILL.md # This file
├── validator_template.py # Validator class template
├── config_template.yaml # YAML configuration template
├── example_usage.py # Example implementation
└── README.md # Quick reference
cp validator_template.py src/validators/data_validator.py
cp config_template.yaml config/validation.yaml
# Edit config/validation.yaml with your validation rules
uv pip install pandas plotly pyyaml
from src.validators.data_validator import DataValidator
validator = DataValidator(config_path="config/validation.yaml")
results = validator.validate_dataframe(df)
validator.generate_interactive_report(results, Path("reports/output.html"))
class CustomValidator(DataValidator):
def _check_business_rules(self, df: pd.DataFrame) -> List[str]:
"""Add custom business logic validation."""
issues = []
# Example: Check date ranges
if 'start_date' in df.columns and 'end_date' in df.columns:
invalid_dates = (df['end_date'] < df['start_date']).sum()
if invalid_dates > 0:
issues.append(f'{invalid_dates} records with end_date before start_date')
return issues
# Add 5th panel to dashboard
fig = make_subplots(
rows=3, cols=2,
specs=[
[{'type': 'indicator'}, {'type': 'bar'}],
[{'type': 'bar'}, {'type': 'table'}],
[{'type': 'scatter', 'colspan': 2}, None] # New panel
]
)
# Add custom plot
fig.add_trace(
go.Scatter(x=df['date'], y=df['quality_score'], name='Quality Trend'),
row=3, col=1
)
Benchmarks (tested on 100,000 row dataset):
Memory usage: ~150MB for 100k rows
Scalability:
Configuration Management:
Logging:
Reporting:
Quality Gates:
pandas>=1.5.0
plotly>=5.14.0
pyyaml>=6.0
See example_usage.py for complete working examples:
v1.0.0 (2026-01-07)
Part of workspace-hub skill library. See root LICENSE.
For issues or enhancements, see workspace-hub issue tracker.
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