parquetframe 2.0.0

A universal data processing framework with multi-engine support (pandas, Polars, Dask) and multi-format I/O (CSV, JSON, Parquet, ORC, Avro) with intelligent backend selection

ParquetFrame

High-performance data analytics with AI/ML capabilities

ParquetFrame is a unified data platform combining SQL, time series, geospatial, financial analysis, and AI/ML capabilities - all with familiar DataFrame interfaces.

✨ Features

• SQL Engine: Query DataFrames with SQL (DataFusion/DuckDB)
• Time Series: .ts accessor for resampling, rolling windows
• GeoSpatial: .geo accessor for spatial operations
• Financial: .fin accessor for technical indicators
• AI/ML: Tetnus ML framework + RAG with Knowlogy knowledge graph
• Cloud: S3, GCS, Azure Blob Storage support
• Interactive CLI: Rich REPL with syntax highlighting

🚀 Quick Start

pip install parquetframe

import pandas as pd
import parquetframe as pf
import parquetframe.sql
import parquetframe.time
import parquetframe.finance

# SQL queries
result = pf.sql("SELECT * FROM df WHERE value > 100", df=df)

# Time series
daily = df.ts.resample('1D', agg='mean')

# Financial indicators
rsi = df.fin.rsi('close', 14)
macd = df.fin.macd('close')

📚 Documentation

• Getting Started
• API Reference
• SQL Guide
• Time Series
• Financial Analysis
• GeoSpatial

🎯 Use Cases

Financial Analysis

import parquetframe.finance

prices = pd.read_csv("stock.csv", index_col='date', parse_dates=True)
prices['SMA_20'] = prices.fin.sma('close', 20)
prices['RSI'] = prices.fin.rsi('close', 14)

Time Series Forecasting

import parquetframe.time

sensor_data = df.ts.resample('1H', agg='mean')
smoothed = sensor_data.ts.rolling('24H', agg='mean')

GeoSpatial Analysis

import geopandas as gpd
import parquetframe.geo

cities = gpd.read_file("cities.geojson")
buffered = cities.geo.buffer(1000)

AI-Powered RAG

from parquetframe.ai import SimpleRagPipeline
from parquetframe import knowlogy

# Query knowledge graph
formula = knowlogy.get_formula("variance")

# RAG with formula grounding
result = pipeline.run_query("Explain variance", user_context="analyst")

🏗️ Architecture

ParquetFrame combines:

• Rust Core: High-performance kernels (pf-time-core, pf-geo-core, pf-fin-core)
• Python API: Familiar pandas-style accessors
• AI/ML: Tetnus framework + Knowlogy knowledge graph
• Cloud: Multi-cloud storage integration

🔗 Project Links

• Documentation
• Examples
• Tutorials

📄 License

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License

🙏 Acknowledgments

Built on top of:

• Apache Arrow / Polars / pandas
• DataFusion / DuckDB
• GeoPandas / Shapely
• PyTorch (Tetnus)