---

name: ds-data-profiling compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: Provides Extracts data profiles, schemas, metadata, and statistical summaries to understand data structure, quality, and characteristics at scale license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-data-quality, ds-data-visualization, ds-eda role: implementation scope: implementation triggers: data profiling, metadata extraction, schema analysis, data schema, how do i profile data, data structure, performance analysis, optimization archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

---

Data Profiling

Comprehensive guide to data profiling in machine learning and data science workflows.

When to Use This Skill

• Solving real-world exploratory data analysis problems
• Building machine learning pipelines with data profiling
• Implementing best practices for data profiling
• Optimizing model performance using data profiling techniques
• Learning industry-standard approaches to data profiling

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require data profiling rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Data Profiling is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Data Profiling

import pandas as pd
import numpy as np

def basic_data_profiling(df: pd.DataFrame) -> dict:
    """Generate a basic statistical and structural profile of a DataFrame."""
    if df.empty:
        raise ValueError("DataFrame cannot be empty")
    
    profile = {
        'shape': df.shape
        'columns': list(df.columns)
        'dtypes': df.dtypes.to_dict()
        'missing_values': df.isnull().sum().to_dict()
        'missing_percentages': (df.isnull().mean() * 100).round(2).to_dict()
        'numeric_summary': df.describe().to_dict() if len(df.select_dtypes(include='number').columns) > 0 else {}
        'categorical_summary': {col: df[col].nunique() for col in df.select_dtypes(include='object').columns}
    }
    return profile

# Example usage
if __name__ == "__main__":
    sample_df = pd.DataFrame({
        'age': [25, 30, 35, 40, np.nan]
        'salary': [50000, 60000, 75000, 80000, 90000]
        'department': ['HR', 'IT', 'IT', 'HR', 'Finance']
    })
    results = basic_data_profiling(sample_df)
    print("Profile generated successfully")
    print(f"Missing values: {results['missing_values']}")

Pattern 2: Production-Ready Data Profiling

import logging
import pandas as pd
import numpy as np
from typing import Any, Dict, List

logger = logging.getLogger(__name__)

class DataProfiling:
    """Production implementation of Data Profiling"""
    
    def __init__(self, include_correlations: bool = True, sample_size: int = 10000):
        self.include_correlations = include_correlations
        self.sample_size = sample_size
        logger.info("DataProfiling initialized")
    
    def execute(self, data: pd.DataFrame) -> Dict[str, Any]:
        """Execute Data Profiling on data"""
        if data is None or data.empty:
            raise ValueError("Input data cannot be None or empty")
        
        logger.info(f"Starting profiling on {data.shape[0]} rows and {data.shape[1]} columns")
        
        profile = {
            'metadata': {
                'rows': len(data)
                'columns': len(data.columns)
                'memory_usage_mb': round(data.memory_usage(deep=True).sum() / 1024**2, 2)
            }
            'data_types': data.dtypes.to_dict()
            'null_counts': data.isnull().sum().to_dict()
            'null_percentages': (data.isnull().mean() * 100).round(2).to_dict()
            'unique_counts': data.nunique().to_dict()
        }
        
        numeric_cols = data.select_dtypes(include=[np.number]).columns
        if len(numeric_cols) > 0:
            profile['descriptive_stats'] = data[numeric_cols].describe().to_dict()
            if self.include_correlations:
                profile['correlation_matrix'] = data[numeric_cols].corr().round(3).to_dict()
        
        logger.info("Profiling completed successfully")
        return profile

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Data Profiling — Wikipedia
• dbt Data Quality Guide
• Great Expectations Documentation
• ydata-profiling Library Docs
• Data Quality Best Practices (Gartner)