# 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)
### Data Validation (New Creative Capabilities)
```python
from quickinsights.data_validation import infer_constraints, drift_radar
# 1) Infer constraints (schema-by-example)
contract = infer_constraints(df)
print(contract["contract"]) # dtype, nullable, unique, min/max or domain
# 2) Drift radar (baseline vs current)
base = df.sample(frac=0.5, random_state=42)
current = df.drop(base.index)
drift = drift_radar(base, current)
print(drift["overall_risk"]) # low | medium | high
```
### Explainable AI (New)
```python
from quickinsights import contrastive_explanations
from sklearn.linear_model import LogisticRegression
# Train a simple model
X = df.select_dtypes(float).fillna(0).to_numpy()
y = (X[:, 0] > X[:, 0].mean()).astype(int)
model = LogisticRegression().fit(X, y)
# Contrastive explanation for instance 0
cx = contrastive_explanations(model, X, y, index=0)
print(cx["suggestions"][:3]) # Minimal directional changes toward opposite class
```
```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).
For the new creative features, see [docs/CREATIVE_FEATURES.md](docs/CREATIVE_FEATURES.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 🚀
Raw data
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"description": "# QuickInsights\r\n\r\nA 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.\r\n\r\n## What is it?\r\n\r\nQuickInsights 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.\r\n\r\n## Main Features\r\n\r\n- **Comprehensive Data Analysis**: Single-command data set analysis with detailed insights\r\n- **Advanced Visualization**: Integration with Matplotlib, Seaborn and Plotly for professional charts\r\n- **Performance Optimization**: Lazy evaluation, caching, parallel processing for large datasets\r\n- **Big Data (Dask)**: Intelligent distributed analysis and pipelines\r\n- **Unique Modules**: Neural pattern mining, quantum-inspired sampling and correlation, holographic 3D projections\r\n- **Cloud Integration**: Support for AWS S3, Azure Blob, Google Cloud Storage\r\n- **AI-Powered Insights**: Automatic pattern detection and trend analysis using machine learning\r\n- **Real-time Pipeline**: Streaming data processing capabilities\r\n- **Modular Architecture**: Easily extensible and customizable framework\r\n\r\n## Installation\r\n\r\n### From PyPI (Recommended)\r\n\r\n```bash\r\npip install quickinsights\r\n```\r\n\r\n### From Test PyPI (Developer Version)\r\n\r\n```bash\r\npip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ quickinsights\r\n```\r\n\r\n### From Source\r\n\r\n```bash\r\ngit clone https://github.com/erena6466/quickinsights.git\r\ncd quickinsights\r\npip install -e .\r\n```\r\n\r\n## Quick Start\r\n\r\n```python\r\nimport quickinsights as qi\r\nimport pandas as pd\r\n\r\n# Sample dataset\r\ndf = pd.DataFrame({\r\n 'A': [1, 2, 3, 4, 5],\r\n 'B': [4, 5, 6, 7, 8],\r\n 'C': ['a', 'b', 'a', 'b', 'a']\r\n})\r\n\r\n# Comprehensive analysis with single command\r\nresult = qi.analyze(df, show_plots=True, save_plots=True)\r\n\r\n# Dataset information\r\ninfo = qi.get_data_info(df)\r\n\r\n# Outlier detection\r\noutliers = qi.detect_outliers(df)\r\n\r\n# Performance optimization\r\noptimized_df = qi.memory_optimize(df)\r\n```\r\n\r\n## Advanced Usage\r\n\r\n### AI-Powered Analysis\r\n\r\n```python\r\nfrom quickinsights.ai_insights import AIInsightEngine\r\n\r\nai_engine = AIInsightEngine(df)\r\ninsights = ai_engine.get_insights()\r\ntrends = ai_engine.predict_trends()\r\n```\r\n\r\n### Cloud Integration\r\n\r\n```python\r\n# Upload to AWS S3\r\nqi.upload_to_cloud('data.csv', 'aws', 'my-bucket/data.csv', bucket_name='my-bucket')\r\n\r\n# Process data from cloud\r\nresult = qi.process_cloud_data('aws', 'my-bucket/data.csv', processor_func, bucket_name='my-bucket')\r\n```\r\n\r\n### Real-time Pipeline\r\n\r\n```python\r\nfrom quickinsights.realtime_pipeline import RealTimePipeline\r\n\r\npipeline = RealTimePipeline()\r\npipeline.add_transformation(lambda x: x * 2)\r\npipeline.add_filter(lambda x: x > 10)\r\nresults = pipeline.process_stream(data_stream)\r\n```\r\n\r\n## New Unique Modules (Highlights)\r\n\r\n### Neural Patterns\r\n```python\r\nfrom quickinsights import neural_pattern_mining, autoencoder_anomaly_scores, sequence_signature_extract\r\npatterns = neural_pattern_mining(df, n_patterns=5)\r\nanoms = autoencoder_anomaly_scores(df)\r\nsigs = sequence_signature_extract(df.select_dtypes(float).iloc[:, 0], window=128, step=32, n_components=3)\r\n```\r\n\r\n### Quantum-Inspired\r\n```python\r\nfrom quickinsights import quantum_superposition_sample, amplitude_pca, quantum_correlation_map\r\nsample = quantum_superposition_sample(df, n_samples=5000)\r\npca = amplitude_pca(df, n_components=8)\r\nqc = quantum_correlation_map(df, n_blocks=3)\r\n```\r\n\r\n### Holographic (3D, non\u2011VR)\r\n```python\r\nfrom quickinsights import embed_3d_projection, plotly_embed_3d\r\nemb = embed_3d_projection(df)\r\nfig_res = plotly_embed_3d(emb[\"embedding\"]) # {\"success\": True, \"figure\": fig}\r\n```\r\n\r\n### Acceleration (GPU/Memory)\r\n### Data Validation (New Creative Capabilities)\r\n```python\r\nfrom quickinsights.data_validation import infer_constraints, drift_radar\r\n\r\n# 1) Infer constraints (schema-by-example)\r\ncontract = infer_constraints(df)\r\nprint(contract[\"contract\"]) # dtype, nullable, unique, min/max or domain\r\n\r\n# 2) Drift radar (baseline vs current)\r\nbase = df.sample(frac=0.5, random_state=42)\r\ncurrent = df.drop(base.index)\r\ndrift = drift_radar(base, current)\r\nprint(drift[\"overall_risk\"]) # low | medium | high\r\n```\r\n\r\n### Explainable AI (New)\r\n```python\r\nfrom quickinsights import contrastive_explanations\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\n# Train a simple model\r\nX = df.select_dtypes(float).fillna(0).to_numpy()\r\ny = (X[:, 0] > X[:, 0].mean()).astype(int)\r\nmodel = LogisticRegression().fit(X, y)\r\n\r\n# Contrastive explanation for instance 0\r\ncx = contrastive_explanations(model, X, y, index=0)\r\nprint(cx[\"suggestions\"][:3]) # Minimal directional changes toward opposite class\r\n```\r\n```python\r\nfrom quickinsights import gpu_available, gpu_corrcoef, memmap_array, chunked_apply\r\nprint(\"GPU usable:\", gpu_available())\r\ncorr = gpu_corrcoef(df.to_numpy())\r\nmmap = memmap_array('./quickinsights_output/tmp.mmap', 'float32', (1_000_000, 8))\r\nparts = chunked_apply(lambda x: x.sum(), df.to_numpy(), chunk_rows=50_000)\r\n```\r\n\r\n## Dependencies\r\n\r\n- **Core**: pandas>=1.3.0, numpy>=1.20.0, matplotlib>=3.3.0\r\n- **Visualization**: seaborn>=0.11.0, plotly>=5.0.0\r\n- **Scientific**: scipy>=1.7.0\r\n- **Optional**: numba, dask, cupy, boto3, azure-storage-blob, google-cloud-storage\r\n\r\n## Documentation\r\n\r\nFor detailed API documentation, see [docs/API_REFERENCE.md](docs/API_REFERENCE.md).\r\n\r\nFor command list, see [COMMANDS.md](COMMANDS.md).\r\n\r\nFor the new creative features, see [docs/CREATIVE_FEATURES.md](docs/CREATIVE_FEATURES.md).\r\n\r\n## Contributing\r\n\r\nTo contribute, please read [CONTRIBUTING.md](CONTRIBUTING.md).\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License.\r\n\r\n## Support\r\n\r\n- **GitHub Issues**: [https://github.com/erena6466/quickinsights/issues](https://github.com/erena6466/quickinsights/issues)\r\n- **Documentation**: [docs/](docs/) folder\r\n- **Examples**: [examples/](examples/) folder\r\n\r\n## Project Status\r\n\r\n- **Core Library**: Completed\r\n- **Modular Architecture**: Completed\r\n- **Test Suite**: 100% success rate\r\n- **PyPI Release**: Version 0.1.1 available\r\n- **Documentation**: Comprehensive documentation\r\n\r\n## Future Plans\r\n\r\n- [ ] Enhanced ML algorithms\r\n- [ ] Web dashboard interface\r\n- [ ] Performance benchmarks\r\n- [ ] Community building\r\n- [ ] Additional data sources\r\n\r\n---\r\n\r\n**QuickInsights** - Simplifying data analysis and enhancing performance with Python \ud83d\ude80\r\n",
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