eda-toolkit 0.0.11a1

A Python library for EDA, including visualizations, directory management, data preprocessing, reporting, and more.

Welcome to EDA Toolkit, a collection of utility functions designed to streamline your exploratory data analysis (EDA) tasks. This repository offers tools for directory management, some data preprocessing, reporting, visualizations, and more, helping you efficiently handle various aspects of data manipulation and analysis.

Prerequisites

Before you install eda_toolkit, ensure your system meets the following requirements:

• Python: Version 3.7.4 or higher is required to run eda_toolkit.

Additionally, eda_toolkit depends on the following packages, which will be automatically installed when you install eda_toolkit:

• jinja2: version 3.1.4 (Exact version required)
• matplotlib: version 3.5.3 or higher, but capped at 3.9.2
• nbformat: version 4.2.0 or higher, but capped at 5.10.4
• numpy: version 1.21.6 or higher, but capped at 2.1.0
• pandas: version 1.3.5 or higher, but capped at 2.2.2
• plotly: version 5.18.0 or higher, but capped at 5.24.0
• scikit-learn: version 1.0.2 or higher, but capped at 1.5.2
• seaborn: version 0.12.2 or higher, but capped below 0.13.0
• xlsxwriter: version 3.2.0 (Exact version required)
• scipy: version 1.7.3 or higher, but capped at 1.11.1

💾 Installation

To install eda_toolkit, simply run the following command in your terminal:

pip install eda_toolkit

📄 Official Documentation

https://lshpaner.github.io/eda_toolkit

🌐 Authors' Websites

1. Leonid Shpaner
2. Oscar Gil

🙏 Acknowledgements

We would like to express our deepest gratitude to Dr. Ebrahim Tarshizi, our mentor during our time in the University of San Diego M.S. Applied Data Science Program. His unwavering dedication and mentorship played a pivotal role in our academic journey, guiding us to successfully graduate from the program and pursue successful careers as data scientists.

We also extend our thanks to the Shiley-Marcos School of Engineering at the University of San Diego for providing an exceptional learning environment and supporting our educational endeavors.

⚖️ License

eda_toolkit is distributed under the MIT License. See LICENSE for more information.

🛟 Support

If you have any questions or issues with eda_toolkit, please open an issue on this GitHub repository.

📚 Citing eda_toolkit

If you use eda_toolkit in your research or projects, please consider citing it.

@software{shpaner_2024_13162633,
  author       = {Shpaner, Leonid and
                  Gil, Oscar},
  title        = {EDA Toolkit},
  month        = aug,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {0.0.11a1},
  doi          = {10.5281/zenodo.13162633},
  url          = {https://doi.org/10.5281/zenodo.13162633}
}

🔖 References

1. Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55

2. Kohavi, R. (1996). Census Income. UCI Machine Learning Repository. https://doi.org/10.24432/C5GP7S.

3. Pace, R. Kelley, & Barry, R. (1997). Sparse Spatial Autoregressions. Statistics & Probability Letters, 33(3), 291-297. https://doi.org/10.1016/S0167-7152(96)00140-X.

4. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830. http://jmlr.org/papers/v12/pedregosa11a.html.

5. Waskom, M. (2021). Seaborn: Statistical Data Visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021.