---

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

• code
• guidance
• do-dont
• examples description: '"Implements data validation, cleaning, outlier detection, and quality assurance techniques to ensure reliable datasets for model training"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-anomaly-detection, ds-data-collection, ds-data-profiling, ds-missing-data role: implementation scope: implementation triggers: data validation, data cleaning, outlier detection, data quality, how do i clean data, missing values 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 Quality

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

When to Use This Skill

• Solving real-world data collection & ingestion problems
• Building machine learning pipelines with data quality
• Implementing best practices for data quality
• Optimizing model performance using data quality techniques
• Learning industry-standard approaches to data quality

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require data quality 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 Quality 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 Quality

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

def basic_data_quality_check(df: pd.DataFrame) -> Dict[str, Any]:
    """
    Perform basic data quality checks on a DataFrame.
    Checks for missing values, duplicates, data types, and basic statistics.
    """
    if not isinstance(df, pd.DataFrame):
        raise TypeError("Input must be a pandas DataFrame")

    quality_report = {
        "total_rows": len(df)
        "total_columns": len(df.columns)
        "missing_values": df.isnull().sum().to_dict()
        "duplicate_rows": int(df.duplicated().sum())
        "data_types": df.dtypes.astype(str).to_dict()
        "numeric_summary": {}
    }

    for col in df.select_dtypes(include=[np.number]).columns:
        quality_report["numeric_summary"][col] = {
            "mean": float(df[col].mean())
            "std": float(df[col].std())
            "min": float(df[col].min())
            "max": float(df[col].max())
            "null_count": int(df[col].isnull().sum())
        }

    return quality_report

Pattern 2: Production-Ready Data Quality

import logging
import pandas as pd
import numpy as np
from typing import Any, Dict, List, Optional
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler

logger = logging.getLogger(__name__)

class ProductionDataQuality:
    """
    Production-grade data quality handler following DRY principles.
    Handles validation, imputation, scaling, and outlier detection.
    """
    def __init__(self, missing_threshold: float = 0.5, outlier_std: float = 3.0):
        self.missing_threshold = missing_threshold
        self.outlier_std = outlier_std
        self.imputer = SimpleImputer(strategy="median")
        self.scaler = StandardScaler()
        self.quality_issues: List[str] = []

    def execute(self, data: pd.DataFrame) -> Dict[str, Any]:
        """Execute comprehensive data quality pipeline."""
        if data.empty:
            raise ValueError("Input DataFrame cannot be empty")

        issues = []
        clean_data = data.copy()

        missing_cols = clean_data.columns[clean_data.isnull().mean() > self.missing_threshold]
        if len(missing_cols) > 0:
            issues.append(f"Dropping columns with >{self.missing_threshold*100}% missing: {list(missing_cols)}")
            clean_data.drop(columns=missing_cols, inplace=True)

        numeric_cols = clean_data.select_dtypes(include=[np.number]).columns
        if len(numeric_cols) > 0:
            clean_data[numeric_cols] = self.imputer.fit_transform(clean_data[numeric_cols])

        for col in numeric_cols:
            z_scores = np.abs((clean_data[col] - clean_data[col].mean()) / clean_data[col].std())
            outlier_mask = z_scores > self.outlier_std
            if outlier_mask.any():
                issues.append(f"Detected {outlier_mask.sum()} outliers in column '{col}'")
                clean_data.loc[outlier_mask, col] = clean_data[col].median()

        if len(numeric_cols) > 0:
            clean_data[numeric_cols] = self.scaler.fit_transform(clean_data[numeric_cols])

        self.quality_issues = issues
        return {
            "cleaned_data": clean_data
            "issues_found": issues
            "rows_processed": len(data)
            "columns_retained": len(clean_data.columns)
        }

BAD vs GOOD Example

# BAD: Bypasses error handling, uses magic numbers, and mutates input silently
def bad_quality_check(df):
    df.dropna()
    df = df[(df['val'] > -999) & (df['val'] < 999)]
    return df

# GOOD: Explicit validation, configurable thresholds, and proper return structure
def good_quality_check(df: pd.DataFrame, threshold: float = 0.5) -> pd.DataFrame:
    if not isinstance(df, pd.DataFrame):
        raise TypeError("Input must be a DataFrame")
    if df.empty:
        raise ValueError("DataFrame cannot be empty")
    clean_df = df.dropna()
    numeric_cols = clean_df.select_dtypes(include=[np.number]).columns
    for col in numeric_cols:
        q1, q3 = clean_df[col].quantile(0.25), clean_df[col].quantile(0.75)
        iqr = q3 - q1
        clean_df = clean_df[(clean_df[col] >= q1 - 1.5 * iqr) & (clean_df[col] <= q3 + 1.5 * iqr)]
    return clean_df

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 Quality — Wikipedia
• Great Expectations Documentation
• NIST Data Quality Guide
• Data Quality Framework (TDWI)
• Kaggle Data Quality Tutorial