# edaflow

A Python package for streamlined exploratory data analysis workflows.

## Description

`edaflow` is designed to simplify and accelerate the exploratory data analysis (EDA) process by providing a collection of tools and utilities for data scientists and analysts. The package integrates popular data science libraries to create a cohesive workflow for data exploration, visualization, and preprocessing.

## Features

- **Data Analysis**: Comprehensive statistical analysis tools
- **Visualization**: Interactive and static plotting capabilities
- **Data Preprocessing**: Cleaning and transformation utilities
- **Missing Data Handling**: Advanced techniques for dealing with missing values
- **Easy Integration**: Works seamlessly with pandas, numpy, and other popular libraries

## Installation

### From PyPI
```bash
pip install edaflow
```

### From Source
```bash
git clone https://github.com/evanlow/edaflow.git
cd edaflow
pip install -e .
```

### Development Installation
```bash
git clone https://github.com/evanlow/edaflow.git
cd edaflow
pip install -e ".[dev]"
```

## Requirements

- Python 3.8+
- pandas >= 1.5.0
- numpy >= 1.21.0
- matplotlib >= 3.5.0
- seaborn >= 0.11.0
- scipy >= 1.7.0
- missingno >= 0.5.0

## Quick Start

```python
import edaflow

# Test the installation
print(edaflow.hello())

# Example usage (when modules are implemented)
# import pandas as pd
# from edaflow import analyze, visualize, preprocess

# df = pd.read_csv('your_data.csv')
# summary = analyze.describe_data(df)
# visualize.plot_distributions(df)
# clean_df = preprocess.handle_missing_data(df)
```

## Usage Examples

### Basic Usage
```python
import edaflow

# Verify installation
message = edaflow.hello()
print(message)  # Output: "Hello from edaflow! Ready for exploratory data analysis."
```

### Working with Data (Future Implementation)
```python
import pandas as pd
import edaflow

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

# Perform EDA workflow
# summary = edaflow.quick_summary(df)
# edaflow.plot_overview(df)
# clean_df = edaflow.clean_data(df)
```

## Project Structure

```
edaflow/
├── edaflow/
│   ├── __init__.py
│   ├── analysis/
│   ├── visualization/
│   └── preprocessing/
├── tests/
├── docs/
├── examples/
├── setup.py
├── requirements.txt
├── README.md
└── LICENSE
```

## Contributing

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

## Development

### Setup Development Environment
```bash
# Clone the repository
git clone https://github.com/evanlow/edaflow.git
cd edaflow

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
flake8 edaflow/
black edaflow/
isort edaflow/
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Changelog

### v0.1.0 (Initial Release)
- Basic package structure
- Sample hello() function
- Core dependencies setup
- Documentation framework

## Support

If you encounter any issues or have questions, please file an issue on the [GitHub repository](https://github.com/evanlow/edaflow/issues).

## Roadmap

- [ ] Core analysis modules
- [ ] Visualization utilities
- [ ] Data preprocessing tools
- [ ] Missing data handling
- [ ] Statistical testing suite
- [ ] Interactive dashboards
- [ ] CLI interface
- [ ] Documentation website

## Acknowledgments

edaflow was developed during the AI/ML course conducted by NTUC LearningHub. I am grateful for the privilege of working alongside my coursemates from Cohort 15. A special thanks to our awesome instructor, Ms. Isha Sehgal, who not only inspired us but also instilled the data science discipline that we now possess