Metadata-Version: 2.4
Name: quickinsights
Version: 0.1.1
Summary: A creative and innovative Python library for data analysis with single command
Home-page: https://github.com/erena6466/quickinsights
Author: Eren Ata
Author-email: erena6466@gmail.com
Project-URL: Bug Reports, https://github.com/erena6466/quickinsights/issues
Project-URL: Source, https://github.com/erena6466/quickinsights
Project-URL: Documentation, https://github.com/erena6466/quickinsights/docs
Keywords: data analysis,data science,visualization,pandas,numpy
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: matplotlib>=3.3.0
Requires-Dist: seaborn>=0.11.0
Requires-Dist: plotly>=5.0.0
Requires-Dist: scipy>=1.7.0
Provides-Extra: fast
Requires-Dist: numba>=0.56.0; extra == "fast"
Requires-Dist: dask[complete]>=2022.1.0; extra == "fast"
Provides-Extra: gpu
Requires-Dist: cupy-cuda11x>=10.0.0; extra == "gpu"
Provides-Extra: cloud
Requires-Dist: boto3>=1.26.0; extra == "cloud"
Requires-Dist: azure-storage-blob>=12.16.0; extra == "cloud"
Requires-Dist: google-cloud-storage>=2.8.0; extra == "cloud"
Provides-Extra: profiling
Requires-Dist: psutil>=5.9.0; extra == "profiling"
Provides-Extra: dev
Requires-Dist: pytest>=6.0; extra == "dev"
Requires-Dist: pytest-cov>=2.0; extra == "dev"
Requires-Dist: black>=21.0; extra == "dev"
Requires-Dist: flake8>=3.8; extra == "dev"
Requires-Dist: mypy>=0.800; extra == "dev"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: project-url
Dynamic: provides-extra
Dynamic: requires-dist
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Dynamic: summary

# 🚀 QuickInsights

A creative and innovative Python library for data analysis that goes beyond basic libraries like NumPy and Pandas. Provides advanced features for big data analysis with a single command.

## ✨ Features

- 🔍 **Comprehensive Data Analysis**: Single-command data set analysis
- 📊 **Advanced Visualization**: Matplotlib, Seaborn and Plotly integration
- 🚀 **Performance Optimization**: Lazy evaluation, caching, parallel processing
- ☁️ **Cloud Integration**: AWS S3, Azure Blob, Google Cloud Storage
- 🤖 **AI-Powered Insights**: Automatic pattern detection and trend analysis
- 📈 **Real-time Pipeline**: Streaming data processing
- 🔧 **Modular Architecture**: Easily extensible and customizable

## 🚀 Installation

### **Install from Main PyPI (Recommended):**

```bash
pip install quickinsights
```

### **Install from Test PyPI (Developer Version):**

```bash
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ quickinsights
```

### **Developer Installation:**

```bash
git clone https://github.com/erena6466/quickinsights.git
cd quickinsights
pip install -e .
```

## 📖 Quick Start

```python
import quickinsights as qi
import pandas as pd

# Sample dataset
df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [4, 5, 6, 7, 8],
    'C': ['a', 'b', 'a', 'b', 'a']
})

# Comprehensive analysis with single command
result = qi.analyze(df, show_plots=True, save_plots=True)

# Dataset information
info = qi.get_data_info(df)

# Outlier detection
outliers = qi.detect_outliers(df)

# Performance optimization
optimized_df = qi.memory_optimize(df)
```

## 🔧 Advanced Usage

### **AI-Powered Analysis:**
```python
from quickinsights.ai_insights import AIInsightEngine

ai_engine = AIInsightEngine(df)
insights = ai_engine.get_insights()
trends = ai_engine.predict_trends()
```

### **Cloud Integration:**
```python
# Upload to AWS S3
qi.upload_to_cloud('data.csv', 'aws', 'my-bucket/data.csv', bucket_name='my-bucket')

# Process data from cloud
result = qi.process_cloud_data('aws', 'my-bucket/data.csv', processor_func, bucket_name='my-bucket')
```

### **Real-time Pipeline:**
```python
from quickinsights.realtime_pipeline import RealTimePipeline

pipeline = RealTimePipeline()
pipeline.add_transformation(lambda x: x * 2)
pipeline.add_filter(lambda x: x > 10)
results = pipeline.process_stream(data_stream)
```

## 📚 Documentation

For detailed API documentation, see [docs/api.md](docs/api.md).

For command list, see [COMMANDS.md](COMMANDS.md).

## 🤝 Contributing

To contribute, please read [CONTRIBUTING.md](CONTRIBUTING.md).

## 📄 License

This project is licensed under the MIT License.

## 🆘 Support

- **GitHub Issues**: [https://github.com/erena6466/quickinsights/issues](https://github.com/erena6466/quickinsights/issues)
- **Documentation**: [docs/](docs/) folder
- **Examples**: [examples/](examples/) folder

## 🎯 Project Status

- ✅ **Core Library**: Completed
- ✅ **Modular Architecture**: Completed
- ✅ **Test Suite**: 100% success rate
- ✅ **Test PyPI**: Successfully uploaded
- ✅ **Main PyPI**: Main PyPI upload successful
- 🔄 **CI/CD**: Automated testing with GitHub Actions
- 📚 **Documentation**: Comprehensive documentation

## 🚀 Future Plans

- [ ] Main PyPI upload
- [ ] ReadTheDocs integration
- [ ] Community building
- [ ] Performance benchmarks
- [ ] Additional ML algorithms
- [ ] Web dashboard

---

**QuickInsights** - Simplifying data analysis and enhancing performance with Python! 🚀📊
