datawhisk 1.0.3
A lightweight, practical utility library for data scientists and ML engineers
License: MIT
Requires Python: >=3.8
Keywords: data-science, machine-learning, data-analysis, pandas, utilities, eda, correlation, memory-optimization

# Datawhisk

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**A lightweight, practical utility library for data scientists and ML engineers.**

datawhisk provides a curated collection of analytical helpers designed to streamline common data science workflows. Built with speed, simplicity, and reliability in mind.

## Why datawhisk?

- **🚀 Fast**: Optimized implementations faster than manual approaches
- **🎯 Practical**: Solves real problems data scientists face daily
- **🪶 Lightweight**: Minimal dependencies, quick to install
- **📊 Reliable**: 90%+ test coverage, production-ready
- **🧩 Intuitive**: Clean APIs that "just work"

## Installation

```bash
pip install datawhisk
```

With visualization support:
```bash
pip install datawhisk[viz]
```

## Quick Start

### Memory Optimizer

Automatically optimize DataFrame memory usage:

```python
from datawhisk.analytical import optimize_memory

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'id': range(1000000),
    'category': ['A', 'B', 'C'] * 333334,
    'value': [1.5] * 1000000
})

# Optimize memory
optimized_df, report = optimize_memory(df, return_report=True)

print(f"Memory reduced by {report['reduction_percent']:.1f}%")
# Output: Memory reduced by 65.2%
```

### Correlation Analyzer

Smart correlation analysis with multicollinearity detection:

```python
from datawhisk.analytical import analyze_correlations

# Analyze correlations
results = analyze_correlations(
    df,
    target='price',
    threshold=0.8,
    method='pearson'
)

print(results.recommendations)
# Output: ['Remove feature_X (VIF=12.3)', 'Keep feature_Y (VIF=2.1)']
```

### Quick EDA Reporter

Fast exploratory data analysis:

```python
from datawhisk.analytical import quick_eda

# Generate EDA report (Standard)
report = quick_eda(df, visualize=True)

# Generate Custom Report (Modular)
# checks only missing values and structure
report_custom = quick_eda(
    df,
    check_missing=True,
    check_structure=True,
    check_outliers=False,
    check_distribution=False
)

print(report.summary)
# Dataset Shape: (1000, 5)
# Missing Values: 23 (2.3%)
# Outliers Detected: 156 total
# High cardinality columns: ['user_id', 'transaction_id']
```

## Features

### Analytical Helpers

- **Memory Optimizer**: Automatically downcast dtypes and optimize memory usage
- **Correlation Analyzer**: Calculate correlations with VIF and multicollinearity detection
- **Quick EDA Reporter**: Fast statistical summaries with anomaly detection

## Comparison with Alternatives

| Feature | datawhisk | Pandas | pandas-profiling |
|---------|---------|--------|------------------|
| Memory Optimization | ✅ Auto | Manual | ❌ |
| VIF Calculation | ✅ Built-in | Manual | ❌ |
| Speed (EDA) | ⚡ Fast | N/A | 🐌 Slow |
| Dependencies | 🪶 3 core | - | 📦 20+ |
| Learning Curve | 📉 Low | Medium | Medium |

## Documentation

- **[Installation Guide](https://github.com/Ramku3639/datawhisk/blob/main/docs/installation.md)**
- **[Quick Start](https://github.com/Ramku3639/datawhisk/blob/main/docs/quickstart.md)**
- **[API Reference](https://github.com/Ramku3639/datawhisk/blob/main/docs/api-reference.md)**
- **[Tutorials](https://github.com/Ramku3639/datawhisk/tree/main/docs/tutorials)**
- **[Full Documentation](https://github.com/Ramku3639/datawhisk/blob/main/docs/README.md)**

## Requirements

- Python 3.8+
- numpy >= 1.20.0
- pandas >= 1.3.0
- scipy >= 1.7.0

Optional:
- matplotlib >= 3.3.0 (for visualizations)
- seaborn >= 0.11.0 (for enhanced visualizations)

## Contributing

We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

```bash
# Clone the repository
git clone https://github.com/Ramku3639/datawhisk.git
cd datawhisk

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

# Run tests
pytest

# Run code quality checks
black datawhisk tests
flake8 datawhisk tests
mypy datawhisk
```

## Roadmap

### v0.2.0 (Next Release)
- [ ] Statistical utilities
- [ ] Data validation helpers
- [ ] Extended visualization options

### v0.3.0
- [ ] Time series utilities
- [ ] Text processing helpers
- [ ] Performance benchmarking tools

See [CHANGELOG.md](CHANGELOG.md) for release history.

## License

MIT License - see [LICENSE](LICENSE) for details.

## Citation

If you use datawhisk in your research, please cite:

```bibtex
@software{datawhisk,
  author = {RamMohan Reddy K},
  title = {datawhisk: A Practical Utility Library for Data Scientists},
  year = {2025},
  url = {https://github.com/Ramku3639/datawhisk}
}
```

## Support

- 📧 Email:  ramku3639@gmail.com
- 🐛 [Issue Tracker](https://github.com/Ramku3639/datawhisk/issues)
- 💬 [Discussions](https://github.com/Ramku3639/datawhisk/discussions)

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