masterclean 1.2.0

Automated Data Cleaning, Validation and Analytics Toolkit

🚀 MasterClean

Automated Data Cleaning, Validation & Analytics Toolkit for Python.

MasterClean is a professional Python package that automates dataset cleaning, preprocessing, validation, profiling, visualization, and reporting using a single command.

It is designed for:

• Data Analysts
• Data Scientists
• ML Engineers
• Researchers
• Students
• Automation workflows

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✨ Features

Data Cleaning

• Automatic missing value handling
• Duplicate row removal
• Column standardization
• String cleanup
• Encoding-aware file loading

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Datatype Optimization

• Automatic numeric conversion
• Datetime detection
• Integer optimization
• Mixed datatype handling

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Validation Engine

• Negative value detection
• Outlier detection
• Invalid boolean detection
• Dataset quality warnings

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Analytics & Profiling

• Automated dataset profiling
• Numeric statistics
• Categorical summaries
• Memory usage analysis

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Visualization Engine

• Interactive Plotly dashboards
• Histograms
• Pie charts
• Boxplots
• Distribution analysis
• Category analytics

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Reporting

• Unified HTML analytics dashboard
• Validation summaries
• Interactive charts
• Automated report generation

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Developer Features

• Command Line Interface (CLI)
• Automated testing with pytest
• GitHub Actions CI/CD pipeline
• Modular package architecture

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📦 Installation

Install From PyPI

pip install masterclean

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Development Installation

Clone Repository

git clone https://github.com/MohamedFaisal-11/masterclean.git

cd masterclean

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Create Virtual Environment

python -m venv venv

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Activate Environment

macOS / Linux

source venv/bin/activate

Windows

venv\Scripts\activate

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Install Package

pip install -e .

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🚀 CLI Usage

Clean Dataset

masterclean clean sample.csv

MasterClean automatically:

• Reads datasets
• Cleans missing values
• Removes duplicates
• Optimizes datatypes
• Detects validation issues
• Generates dashboards
• Exports cleaned data
• Creates HTML reports

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Show Version

masterclean version

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📁 Supported File Types

Currently supported:

• CSV (.csv)

Upcoming support:

• Excel (.xlsx)
• JSON
• Parquet

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📂 Generated Outputs

MasterClean automatically generates:

cleaned_data.csv
report.html

These files contain:

• cleaned datasets
• validation summaries
• interactive analytics dashboards
• profiling insights

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🐍 Python Usage

from masterclean import (
    read_file,
    clean_data,
    optimize_dtypes,
    validate_data,
    generate_profile,
    generate_charts,
    generate_report,
    export_data
)

# Read dataset
df = read_file("sample.csv")

# Clean dataset
df = clean_data(df)

# Optimize datatypes
df = optimize_dtypes(df)

# Validate dataset
warnings = validate_data(df)

# Generate profile
profile = generate_profile(df)

# Generate charts
charts = generate_charts(df)

# Generate report
generate_report(
    df=df,
    warnings=warnings,
    profile=profile,
    charts=charts
)

# Export cleaned dataset
export_data(df)

print("MasterClean pipeline completed successfully")

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📚 Examples

Example files are available inside:

examples/

Run CLI example:

masterclean clean examples/sample.csv

Run Python example:

python examples/python_example.py

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📊 Example Validation Output

VALIDATION WARNINGS
========================================

⚠ Negative values found in 'age' (1 rows)

⚠ Possible outliers detected in 'salary' (1 rows)

⚠ Invalid boolean-like values found in 'active': {'maybe'}

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📈 Dashboard Features

MasterClean generates a unified interactive HTML dashboard containing:

• Dataset summaries
• Validation warnings
• Profiling statistics
• Pie charts
• Histograms
• Boxplots
• Category analytics
• Interactive Plotly visualizations

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🖼 Dashboard Preview

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🏗 Architecture

Read
   ↓
Clean
   ↓
Optimize
   ↓
Validate
   ↓
Profile
   ↓
Visualize
   ↓
Report
   ↓
Export

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📂 Project Structure

masterclean/
│
├── cleaner.py
├── validator.py
├── datatypes.py
├── profiler.py
├── visualizer.py
├── report.py
├── exporter.py
├── reader.py
├── cli.py
│
examples/
│
├── sample.csv
├── python_example.py
├── cli_example.md
│
tests/
│
├── test_cleaner.py
├── test_validator.py
├── test_reader.py
├── test_report.py
├── test_visualizer.py
│
.github/workflows/
│
└── tests.yml

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🧪 Testing

Run tests using:

python -m pytest

Current Status:

• ✅ Automated tests passing
• ✅ GitHub Actions CI/CD passing

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🔄 CI/CD

MasterClean uses GitHub Actions for:

• automated testing
• dependency validation
• continuous integration

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📌 Current Version

v1.0.0

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🛣 Roadmap

Future improvements planned:

• Advanced schema validation
• Large dataset optimization
• Plugin architecture
• AI-powered cleaning suggestions
• Cloud deployment support
• Streamlit dashboard integration

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🤝 Contributing

Contributions are welcome.

You can:

• report bugs
• suggest features
• improve documentation
• submit pull requests

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📄 License

MIT License

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👨‍💻 Author

Mohamed Faisal Maraicar N

GitHub:

https://github.com/MohamedFaisal-11/masterclean