cleanengine 0.1.1

The Ultimate Data Cleaning & Analysis Toolkit

🧹 CleanEngine

🚀 The Ultimate Data Cleaning & Analysis CLI Tool
Transform messy datasets into clean, insights-rich data with intelligent cleaning and advanced ML analysis.

CleanEngine is a powerful command-line toolkit that handles missing values, removes duplicates, detects outliers, and provides comprehensive statistical analysis using machine learning techniques.

📊 Comparison with Other Tools

Feature	CleanEngine 🧹	pandas-profiling	Sweetviz	Great Expectations
Data Cleaning	✅ Complete Pipeline	❌ No	❌ No	⚠️ Limited
Profiling & Stats	✅ Advanced Analytics	✅ Yes	✅ Yes	⚠️ Minimal
Correlation Analysis	✅ Multi-Method	✅ Yes	✅ Yes	❌ No
Feature Importance	✅ ML-Powered	❌ No	❌ No	❌ No
Clustering & Patterns	✅ 3 Algorithms	❌ No	❌ No	❌ No
Anomaly Detection	✅ 2 Methods	❌ No	❌ No	❌ No
Rule Engine	✅ YAML-Driven	❌ No	❌ No	✅ Yes
Interfaces	✅ CLI + GUI + Watcher	CLI/Notebook	Notebook	CLI/Notebook
Automation	✅ Folder Watcher	❌ No	❌ No	✅ Yes

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🚀 Installation

Using pip (Recommended)

pip install cleanengine

From source

git clone https://github.com/I-invincib1e/CleanEngine.git
cd CleanEngine
pip install -e .

Verify Installation

cleanengine --help

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🎯 Quick Start

Clean a CSV file

cleanengine clean data.csv

Analyze data without cleaning

cleanengine analyze data.xlsx

Generate sample data to test

cleanengine samples

Launch web interface

cleanengine gui

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📋 CLI Commands

Core Commands

Command	Flags	Description	Example
clean	--output, -o, --verbose, -v, --force	Clean a dataset with full pipeline	cleanengine clean data.csv --output ./cleaned/ --verbose
analyze	--output, -o, --verbose, -v	Analyze data without cleaning	cleanengine analyze data.csv --output ./analysis/ --verbose
validate-data	--verbose, -v	Validate data with rules	cleanengine validate-data data.csv --verbose
profile	--output, -o, --verbose, -v	Generate data profile report	cleanengine profile data.csv --output ./profile/ --verbose
clean-only	--output, -o, --verbose, -v	Clean without analysis	cleanengine clean-only data.csv --output ./cleaned/ --verbose
samples	--output, -o, --count, -n, --verbose, -v	Create sample datasets	cleanengine samples --output ./samples/ --count 5 --verbose
test	--verbose, -v, --coverage	Run test suite	cleanengine test --verbose --coverage
gui	--port, -p, --host, -h	Launch Streamlit web interface	cleanengine gui --port 8501 --host localhost
info	None	Show CleanEngine information	cleanengine info

Advanced Analysis Commands

Command	Flags	Description	Example
correlations	--method, -m, --threshold, -t, --output, -o, --verbose, -v	Analyze variable correlations	cleanengine correlations data.csv --method pearson --threshold 0.7 --verbose
features	--output, -o, --verbose, -v	Analyze feature importance	cleanengine features data.csv --output ./features/ --verbose
clusters	--method, -m, --output, -o, --verbose, -v	Discover data clusters	cleanengine clusters data.csv --method kmeans --output ./clusters/ --verbose
anomalies	--method, -m, --contamination, -c, --output, -o, --verbose, -v	Detect anomalies/outliers	cleanengine anomalies data.csv --method isolation_forest --contamination 0.1 --verbose
quality	--output, -o, --verbose, -v	Assess data quality	cleanengine quality data.csv --output ./quality/ --verbose
statistics	--output, -o, --verbose, -v	Perform statistical analysis	cleanengine statistics data.csv --output ./stats/ --verbose

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📁 Supported File Formats

• CSV: Comma-separated values
• Excel: .xlsx and .xls files
• JSON: JavaScript Object Notation
• Parquet: Columnar storage format

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📊 Output Structure

After processing, CleanEngine creates a Cleans-<dataset_name>/ folder with:

Cleans-data/
├── cleaned_data.csv          # Your cleaned dataset
├── cleaning_report.json      # Detailed cleaning summary
├── analysis_report.json      # Comprehensive analysis results
├── visualizations/           # Generated charts and plots
└── logs/                     # Processing logs

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⚙️ Configuration

Custom Configuration File

Create a config.yaml file in your working directory:

cleaning:
  missing_values:
    strategy: "auto"  # auto, mean, median, mode, drop
  outliers:
    method: "iqr"     # iqr, zscore, custom
  encoding:
    categorical: true
    normalize: true

analysis:
  correlation:
    method: "pearson"  # pearson, spearman, kendall
  clustering:
    method: "kmeans"   # kmeans, dbscan, hierarchical

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🎨 CLI Features

• Rich Terminal Output: Beautiful tables, progress bars, and colors
• Interactive Help: cleanengine --help and cleanengine <command> --help
• Auto-completion: Tab completion for commands and file paths
• Progress Tracking: Real-time progress bars for long operations
• Error Handling: Clear error messages with suggestions

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📈 Performance

• Small Datasets (< 1MB): < 1 second
• Medium Datasets (1-100MB): 1-30 seconds
• Large Datasets (100MB-1GB): 30 seconds - 5 minutes
• Very Large Datasets (> 1GB): Configurable chunking

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🔧 Advanced Usage

Batch Processing Multiple Files

# Process all CSV files in current directory
for file in *.csv; do cleanengine clean "$file"; done

Custom Output Directory

cleanengine clean data.csv --output-dir ./my-clean-data/

Configuration File

cleanengine clean data.csv --config ./my-config.yaml

Verbose Output

cleanengine clean data.csv --verbose

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🐍 Python API

For programmatic use:

from cleanengine import DatasetCleaner

# Initialize cleaner
cleaner = DatasetCleaner()

# Clean dataset
cleaned_df = cleaner.clean_dataset('data.csv')

# Get analysis results
analysis_results = cleaner.analyze_dataset('data.csv')

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details on:

• Setting up a development environment
• Code style and standards
• Testing and quality assurance
• Pull request process

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• pandas for data manipulation
• scikit-learn for machine learning algorithms
• Typer & Rich for beautiful CLI interfaces
• Streamlit for web interface

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Made with ❤️ for data scientists and analysts

GitHub • PyPI • Documentation