oussama_datalib 0.2.2

Simplified data manipulation and analysis tools

DataLib

A simplified Python library for data manipulation, analysis, and machine learning.

Features

• Data Processing: Loading, cleaning, and preprocessing functions
• Analysis: Linear regression, polynomial regression, and multiple regression
• Statistics: Basic statistical calculations
• Visualization: Various plotting functions using matplotlib and seaborn
• Machine Learning:
  • Supervised Learning: KNN, Decision Trees, Random Forests
  • Unsupervised Learning: K-means, PCA, Gaussian Mixture
  • Reinforcement Learning: Basic Q-Learning and SARSA

Installation

You can install the package directly from PyPI:

pip install oussama-datalib

Or using Poetry:

poetry add oussama-datalib

Usage

Here's a simple example of using DataLib:

from src.datalib.data_processing import load_csv, fill_missing_values
from src.datalib.visualization import plot_histogram
import pandas as pd

# Load data
df = pd.read_csv('your_data.csv')

# Fill missing values
df = fill_missing_values(df, 'column_name', method='mean')

# Create visualization
plot_histogram(df, 'column_name')

For a complete example, check out example/execution_example.py. To run it:

poetry run python example/execution_example.py

Testing

To run the tests:

poetry run pytest

Documentation

To build the documentation:

poetry run sphinx-build -b html docs docs/build

Then open docs/build/index.html in your browser.

Project Structure

datalib/
├── src/
│ └── datalib/
│     ├── init.py
│     ├── analysis.py
│     ├── data_processing.py
│     ├── reinforcement.py
│     ├── statistics.py
│     ├── supervised.py
│     ├── unsupervised.py
│     └── visualization.py
├── tests/
│   ├── conftest.py
│   ├── test_analysis.py
│   ├── test_data_processing.py
│   ├── test_reinforcement.py
│   ├── test_statistics.py
│   ├── test_supervised.py
│   ├── test_unsupervised.py
│   └── test_visualization.py
├── docs/
│   ├── conf.py
│   ├── index.rst
│   ├── modules.rst
│   └── build/
│   ├── html/
│   ├── doctrees/
│   └── static/
├── example/
│   └── execution_example.py
├── .gitignore
├── LICENSE
├── README.md
├── pyproject.toml
├── setup.py
└── .pre-commit-config.yaml

Dependencies

• Python ≥ 3.10
• NumPy ≥ 1.21.0
• Pandas ≥ 2.2.3
• Matplotlib ≥ 3.4.0
• Seaborn ≥ 0.11.0
• Scikit-learn ≥ 1.0.0
• SciPy ≥ 1.7.0

License

MIT License

Author

Oussama ELAYEB ([e-mail])

Development Setup

1. Clone the repository
2. Install dependencies:

poetry install

3. Install pre-commit hooks:

poetry run pre-commit install

4. Run tests:

poetry run pytest

5. Run linting:

poetry run flake8
poetry run black .
poetry run isort .

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

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

Contact

For questions, feedback, or support:

• Email: [e-mail]
• GitHub Issues: Create an issue

Acknowledgments

• Thanks to the Python community for the amazing libraries that made this project possible
• Special thanks to all contributors who help improve this library