Metadata-Version: 2.2
Name: quickinsights
Version: 0.2.0
Summary: A creative and innovative Python library for data analysis with single command
Home-page: https://github.com/ErenAta16/quickinsight_library
Author: Eren Ata
Author-email: erena6466@gmail.com
Project-URL: Bug Reports, https://github.com/ErenAta16/quickinsight_library/issues
Project-URL: Source, https://github.com/ErenAta16/quickinsight_library
Project-URL: Documentation, https://github.com/ErenAta16/quickinsight_library/docs
Project-URL: Changelog, https://github.com/ErenAta16/quickinsight_library/releases
Keywords: data analysis,data science,visualization,pandas,numpy,machine learning,quantum computing,neural networks,big data,dask
Classifier: Development Status :: 4 - Beta
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<2.0.0,>=1.3.0
Requires-Dist: numpy<2.0.0,>=1.20.0
Requires-Dist: matplotlib<3.8.0,>=3.3.0
Requires-Dist: seaborn>=0.11.0
Requires-Dist: plotly>=5.0.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: dask[complete]>=2022.1.0
Requires-Dist: torch>=1.9.0
Requires-Dist: transformers>=4.0.0
Requires-Dist: qiskit>=0.40.0
Requires-Dist: cupy-cuda11x>=10.0.0; sys_platform != "win32"
Provides-Extra: fast
Requires-Dist: numba>=0.56.0; extra == "fast"
Requires-Dist: dask[complete]>=2022.1.0; extra == "fast"
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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"
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Dynamic: author
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# 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.

## What is it?

QuickInsights is a Python package that provides comprehensive data analysis capabilities through an intuitive interface. It aims to be a powerful tool for data scientists, analysts, and researchers who need to perform complex data analysis tasks efficiently.

## Main Features

- **Comprehensive Data Analysis**: Single-command data set analysis with detailed insights
- **Advanced Visualization**: Integration with Matplotlib, Seaborn and Plotly for professional charts
- **Performance Optimization**: Lazy evaluation, caching, parallel processing for large datasets
- **Big Data (Dask)**: Intelligent distributed analysis and pipelines
- **Unique Modules**: Neural pattern mining, quantum-inspired sampling and correlation, holographic 3D projections
- **Cloud Integration**: Support for AWS S3, Azure Blob, Google Cloud Storage
- **AI-Powered Insights**: Automatic pattern detection and trend analysis using machine learning
- **Real-time Pipeline**: Streaming data processing capabilities
- **Modular Architecture**: Easily extensible and customizable framework

## Installation

### From PyPI (Recommended)

```bash
pip install quickinsights
```

### From Test PyPI (Developer Version)

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

### From Source

```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)
```

## New Unique Modules (Highlights)

### Neural Patterns
```python
from quickinsights import neural_pattern_mining, autoencoder_anomaly_scores, sequence_signature_extract
patterns = neural_pattern_mining(df, n_patterns=5)
anoms = autoencoder_anomaly_scores(df)
sigs = sequence_signature_extract(df.select_dtypes(float).iloc[:, 0], window=128, step=32, n_components=3)
```

### Quantum-Inspired
```python
from quickinsights import quantum_superposition_sample, amplitude_pca, quantum_correlation_map
sample = quantum_superposition_sample(df, n_samples=5000)
pca = amplitude_pca(df, n_components=8)
qc = quantum_correlation_map(df, n_blocks=3)
```

### Holographic (3D, non‑VR)
```python
from quickinsights import embed_3d_projection, plotly_embed_3d
emb = embed_3d_projection(df)
fig_res = plotly_embed_3d(emb["embedding"])  # {"success": True, "figure": fig}
```

### Acceleration (GPU/Memory)
```python
from quickinsights import gpu_available, gpu_corrcoef, memmap_array, chunked_apply
print("GPU usable:", gpu_available())
corr = gpu_corrcoef(df.to_numpy())
mmap = memmap_array('./quickinsights_output/tmp.mmap', 'float32', (1_000_000, 8))
parts = chunked_apply(lambda x: x.sum(), df.to_numpy(), chunk_rows=50_000)
```

## Dependencies

- **Core**: pandas>=1.3.0, numpy>=1.20.0, matplotlib>=3.3.0
- **Visualization**: seaborn>=0.11.0, plotly>=5.0.0
- **Scientific**: scipy>=1.7.0
- **Optional**: numba, dask, cupy, boto3, azure-storage-blob, google-cloud-storage

## Documentation

For detailed API documentation, see [docs/API_REFERENCE.md](docs/API_REFERENCE.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
- **PyPI Release**: Version 0.1.1 available
- **Documentation**: Comprehensive documentation

## Future Plans

- [ ] Enhanced ML algorithms
- [ ] Web dashboard interface
- [ ] Performance benchmarks
- [ ] Community building
- [ ] Additional data sources

---

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