Metadata-Version: 2.4
Name: myawesomeeda
Version: 1.0.1
Summary: 📊Simple yet cool EDA module
Home-page: https://github.com/iliapopov17/MyAwesomeEDA
Author: Ilia Popov
Author-email: iljapopov17@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: ipython==9.3.0
Requires-Dist: matplotlib==3.10.3
Requires-Dist: matplotlib-inline==0.1.7
Requires-Dist: numpy==2.3.0
Requires-Dist: pandas==2.3.0
Requires-Dist: seaborn==0.13.2
Requires-Dist: ipykernel==6.29.5
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# My Awesome EDA Module

![Python3](https://img.shields.io/badge/Language-Python3-steelblue)
![Pandas](https://img.shields.io/badge/Dependecy-Pandas-steelblue)
![Seaborn](https://img.shields.io/badge/Dependecy-Seaborn-steelblue)
![Matplotlib](https://img.shields.io/badge/Dependecy-Matplotlib-steelblue)
![OS](https://img.shields.io/badge/OS-_Windows_|_Mac_|_Linux-steelblue)
![License](https://img.shields.io/badge/License-MIT-steelblue)
[![Downloads](https://static.pepy.tech/badge/myawesomeeda)](https://pepy.tech/project/myawesomeeda)
[![Tests](https://github.com/iliapopov17/myawesomeeda/workflows/Tests/badge.svg)](https://github.com/iliapopov17/myawesomeeda/actions?workflow=Tests)

> Welcome to the My Awesome EDA (Exploratory Data Analysis) Module! This Python module provides a set of tools for exploring and analyzing your dataset. Whether you're a data scientist, analyst, or enthusiast, this module will help you gain insights into your data quickly and efficiently.

## Features
- **Welcome Gif**: A fun welcome gif to kick off your exploration.
- **Basic Dataset Information**: Quickly get an overview of the number of observations (rows) and parameters (columns) in your dataset.
- **Data Type Summary**: Understand the data types of each column in your dataset.
- **Categorization of Features**: Categorize features into numerical, string, and categorical based on unique threshold.
- **Summary Statistics**: Get descriptive statistics for numerical features, including mean, standard deviation, minimum, 25th percentile, median, 75th percentile, and maximum values.
- **Outliers Detection**: Identify outliers in numerical features using the interquartile range (IQR) method.
- **Missing Values Analysis**: Investigate missing values in your dataset, including total missing values, rows with missing values, and columns with missing values.
- **Duplicate Rows Detection**: Identify duplicate rows in your dataset.
- **Visualizations**: Generate informative visualizations including bar plots of missing values by variable, correlation heatmap for numerical features, and histograms with boxplots for numerical features.

## Installation
```bash
pip install myawesomeeda
```

```python
from my_awesome_eda import run_eda
```

## Usage Guide

- Demonstrational python notebook is available in [GitHub repo](https://github.com/iliapopov17/MyAwesomeEDA) in `demo.ipynb` file<br>

🔗 Visit [MyAwesomeEDA wiki](https://github.com/iliapopov17/MyAwesomeEDA/wiki) page

## Contributing
Contributions are welcome! If you have any ideas, bug fixes, or enhancements, feel free to open an issue or submit a pull request.

## Contact
For any inquiries or support, feel free to contact me via [email](mailto:iljapopov17@gmail.com)

Happy data exploring! 💻🧐
