mizuio 0.1.1

A comprehensive Python data processing tool for cleaning, visualization, and analysis

mizuio - Python Data Processing Toolkit

mizuio is a comprehensive Python toolkit for data cleaning, visualization, and analysis. It provides a modern command-line interface and Python API for efficient data workflows, leveraging Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn.

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🚀 Features

Data Cleaning (DataCleaner)

• Handle missing values: drop, fill, or interpolate
• Remove duplicates by columns
• Automatic data type conversion
• Outlier detection and removal (IQR, Z-score)
• Text normalization (case, whitespace)

Data Visualization (DataVisualizer)

• Histograms and distribution plots
• Box plots for outlier analysis
• Scatter plots for variable relationships
• Correlation heatmaps
• Bar and line charts (categorical/time series)
• Missing value visualization

Utility Tools (DataUtils)

• Multi-format support: CSV, JSON, Excel, Parquet, Pickle
• Data validation (columns, types, value ranges)
• Data sampling (random, systematic, stratified)
• Data splitting (train/validation/test)
• Categorical encoding (label, one-hot, ordinal)
• Feature scaling (standard, minmax, robust)

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📦 Installation

Requirements

• Python 3.7+
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Steps

1. Clone the repository:

   	git clone https://github.com/mertskzc/mizuio.git
   	cd mizuio
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install in development mode (optional):

   pip install -e .
   

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🖥️ Usage

Command Line Interface

mizuio provides a CLI for common data tasks:

# Clean a dataset
mizuio clean data.csv --output cleaned_data.csv --remove-duplicates --fill-missing --remove-outliers

# Visualize a column
mizuio visualize data.csv --plot histogram --column age --output age_hist.png

# Show data info
mizuio info data.csv

CLI Commands

• clean: Clean data (remove duplicates, fill missing, remove outliers)
• visualize: Visualize data (histogram, boxplot, scatter, correlation)
• info: Show data summary (shape, memory, columns, missing values, duplicates)

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🧪 Testing

Run all tests:

python -m pytest tests/

Run a specific test file:

python -m pytest tests/test_cleaner.py

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🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add feature')
4. Push your branch (git push origin feature/your-feature)
5. Open a Pull Request

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📝 License

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

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📞 Contact

• Project Link: https://github.com/mertskzc/mizuio
• E-mail: [e-mail]

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🙏 Acknowledgements

mizuio uses the following open source libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn