# My Awesome EDA Module

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> 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! 💻🧐
