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all with familiar DataFrame interfaces.\n\n## ✨ Features\n\n- **SQL Engine**: Query DataFrames with SQL (DataFusion/DuckDB)\n- **Time Series**: `.ts` accessor for resampling, rolling windows\n- **GeoSpatial**: `.geo` accessor for spatial operations\n- **Financial**: `.fin` accessor for technical indicators\n- **AI/ML**: Tetnus ML framework + RAG with Knowlogy knowledge graph\n- **Cloud**: S3, GCS, Azure Blob Storage support\n- **Interactive CLI**: Rich REPL with syntax highlighting\n\n## 🚀 Quick Start\n\n```bash\npip install parquetframe\n```\n\n```python\nimport pandas as pd\nimport parquetframe as pf\nimport parquetframe.sql\nimport parquetframe.time\nimport parquetframe.finance\n\n# SQL queries\nresult = pf.sql(\"SELECT * FROM df WHERE value \u003e 100\", df=df)\n\n# Time series\ndaily = df.ts.resample('1D', agg='mean')\n\n# Financial indicators\nrsi = df.fin.rsi('close', 14)\nmacd = df.fin.macd('close')\n```\n\n## 📚 Documentation\n\n- [Getting Started](docs/tutorials/getting_started.md)\n- [API Reference](docs/api_reference.md)\n- [SQL Guide](docs/sql/index.md)\n- [Time Series](docs/time/index.md)\n- [Financial Analysis](docs/finance/index.md)\n- [GeoSpatial](docs/geo/index.md)\n\n## 🎯 Use Cases\n\n### Financial Analysis\n\n```python\nimport parquetframe.finance\n\nprices = pd.read_csv(\"stock.csv\", index_col='date', parse_dates=True)\nprices['SMA_20'] = prices.fin.sma('close', 20)\nprices['RSI'] = prices.fin.rsi('close', 14)\n```\n\n### Time Series Forecasting\n\n```python\nimport parquetframe.time\n\nsensor_data = df.ts.resample('1H', agg='mean')\nsmoothed = sensor_data.ts.rolling('24H', agg='mean')\n```\n\n### GeoSpatial Analysis\n\n```python\nimport geopandas as gpd\nimport parquetframe.geo\n\ncities = gpd.read_file(\"cities.geojson\")\nbuffered = cities.geo.buffer(1000)\n```\n\n### AI-Powered RAG\n\n```python\nfrom parquetframe.ai import SimpleRagPipeline\nfrom parquetframe import knowlogy\n\n# Query knowledge graph\nformula = knowlogy.get_formula(\"variance\")\n\n# RAG with formula grounding\nresult = pipeline.run_query(\"Explain variance\", user_context=\"analyst\")\n```\n\n## 🏗️ Architecture\n\nParquetFrame combines:\n- **Rust Core**: High-performance kernels (pf-time-core, pf-geo-core, pf-fin-core)\n- **Python API**: Familiar pandas-style accessors\n- **AI/ML**: Tetnus framework + Knowlogy knowledge graph\n- **Cloud**: Multi-cloud storage integration\n\n## 🔗 Project Links\n\n- [Documentation](docs/)\n- [Examples](examples/)\n- [Tutorials](docs/tutorials/)\n\n## 📄 License\n\nCreative Commons Attribution-NonCommercial-NoDerivatives 4.0\nInternational Public License\n\n## 🙏 Acknowledgments\n\nBuilt on top of:\n- Apache Arrow / Polars / pandas\n- DataFusion / DuckDB\n- GeoPandas / Shapely\n- PyTorch (Tetnus)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleechristophermurray%2Fparquetframe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fleechristophermurray%2Fparquetframe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleechristophermurray%2Fparquetframe/lists"}