---

name: ds-eda compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Provides Performs exploratory data analysis using summary statistics distributions, correlations, and descriptive methods to understand dataset characteristic"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-correlation-analysis, ds-data-profiling, ds-data-visualization ds-dimensionality-reduction ds-missing-data role: implementation scope: implementation triggers: exploratory data analysis, EDA, summary statistics, distributions, data exploration, how do i explore data 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"

---

Exploratory Data Analysis

Comprehensive guide to exploratory data analysis in machine learning and data science workflows.

When to Use This Skill

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

When NOT to Use This Skill

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

Exploratory Data Analysis 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 Exploratory Data Analysis

import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

def perform_basic_eda(df: pd.DataFrame) -> dict:
    """Perform basic exploratory data analysis on a DataFrame."""
    if df.empty:
        raise ValueError("DataFrame cannot be empty")
    
    # Summary statistics
    summary = df.describe().to_dict()
    
    # Correlation matrix for numeric columns
    numeric_cols = df.select_dtypes(include=[np.number])
    corr_matrix = numeric_cols.corr().to_dict() if not numeric_cols.empty else {}
    
    # Missing values count
    missing = df.isnull().sum().to_dict()
    
    # Distribution skewness for numeric columns
    skewness = {col: float(stats.skew(df[col].dropna())) for col in numeric_cols.columns}
    
    return {
        'summary': summary
        'correlations': corr_matrix
        'missing_values': missing
        'skewness': skewness
    }

# BAD vs GOOD Example
# BAD: Ignoring data types and missing values
# bad_results = df.describe().to_dict()
# GOOD: Explicit type filtering, missing value tracking, and statistical validation
# good_results = perform_basic_eda(df)

Pattern 2: Production-Ready Exploratory Data Analysis

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

logger = logging.getLogger(__name__)

class ExploratoryDataAnalysis:
    """Production implementation of Exploratory Data Analysis"""
    
    def __init__(self, log_level: int = logging.INFO):
        self.logger = logging.getLogger(__name__)
        self.logger.setLevel(log_level)
        self.scaler = StandardScaler()
        
    def execute(self, data: pd.DataFrame) -> Dict[str, Any]:
        """Execute Exploratory Data Analysis on data"""
        if not isinstance(data, pd.DataFrame):
            raise TypeError("Input must be a pandas DataFrame")
        if data.empty:
            raise ValueError("Input DataFrame is empty")
            
        results = {
            'shape': data.shape
            'dtypes': data.dtypes.astype(str).to_dict()
            'missing_counts': data.isnull().sum().to_dict()
            'missing_pct': (data.isnull().sum() / len(data) * 100).to_dict()
            'numeric_summary': data.describe().to_dict()
            'categorical_counts': {}
        }
        
        for col in data.select_dtypes(include=['object', 'category']).columns:
            results['categorical_counts'][col] = data[col].value_counts().to_dict()
            
        numeric_data = data.select_dtypes(include=[np.number])
        if not numeric_data.empty:
            results['correlation_matrix'] = numeric_data.corr().to_dict()
            
        self.logger.info(f"EDA completed on {data.shape[0]} rows and {data.shape[1]} columns")
        return results

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.

• Exploratory Data Analysis — Wikipedia
• EDA in Python (Towards Data Science)
• Pandas Profiling / ydata-profiling
• Seaborn EDA Examples
• Kaggle EDA Tutorial