Metadata-Version: 2.1
Name: eda_toolkit
Version: 0.0.7
Summary: A Python library for EDA, including visualizations, directory management, data preprocessing, reporting, and more.
Author: Leonid Shpaner, Oscar Gil
Author-email: lshpaner@ucla.edu
Project-URL: Leonid Shpaner's Website, https://www.leonshpaner.com
Project-URL: Oscar Gil's Website, https://www.oscargildata.com
Project-URL: Documentation, https://lshpaner.github.io/eda_toolkit/
Project-URL: Zenodo Archive, https://zenodo.org/records/13162633
Project-URL: Source Code, https://github.com/lshpaner/eda_toolkit/
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7.4
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: jinja2>=3.1.4
Requires-Dist: numpy>=1.21.6
Requires-Dist: pandas>=1.3.5
Requires-Dist: matplotlib>=3.5.3
Requires-Dist: seaborn>=0.12.2
Requires-Dist: xlsxwriter>=3.2.0

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[![Downloads](https://pepy.tech/badge/eda_toolkit)](https://pepy.tech/project/eda_toolkit)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/lshpaner/eda_toolkit/blob/main/LICENSE.md)
[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.13162633.svg)](https://doi.org/10.5281/zenodo.13162633)

<br>

<img src="https://raw.githubusercontent.com/lshpaner/eda_toolkit/main/assets/eda_toolkit_logo.svg" width="300" style="border: none; outline: none; box-shadow: none;" oncontextmenu="return false;">

<br> 

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`:

- `numpy`: version `1.21.6` or higher
- `pandas`: version `1.3.5` or higher
- `matplotlib`: version `3.5.3` or higher
- `seaborn`: version `0.12.2` or higher
- `jinja2`: version `3.1.4` or higher
- `xlsxwriter`: version `3.2.0` or higher


## Installation

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


```bash
pip install eda_toolkit
```

## 📄 Official Documentation

https://lshpaner.github.io/eda_toolkit 


## 🌐 Authors' Websites

1. [Leonid Shpaner](https://www.leonshpaner.com)
2. [Oscar Gil](https://www.oscargildata.com)


## 🙏 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](https://github.com/lshpaner/eda_toolkit/blob/main/LICENSE.md) 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.

```bibtex

@software{shpaner_2024_13162633,
  author       = {Shpaner, Leonid and
                  Gil, Oscar},
  title        = {EDA Toolkit},
  month        = aug,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {0.0.7},
  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](https://doi.org/10.1109/MCSE.2007.55)

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

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





