data-analysis-framework 1.0.0

AI-powered analysis framework for structured data files and databases

Data Analysis Framework

📈 Purpose

Specialized framework for analyzing structured data files with AI-powered pattern detection and insights.

📦 Supported Formats

Spreadsheets & Tables

• Excel: XLSX, XLS with multiple sheets
• CSV/TSV: Delimiter detection and parsing
• Apache Parquet: Columnar data analysis
• JSON: Nested and flat structure analysis
• JSONL: Line-delimited JSON streams

Configuration Data

• YAML: Configuration files and data serialization
• TOML: Configuration file analysis
• INI: Legacy configuration parsing
• Environment Files: .env variable analysis

Database Exports

• SQL Dumps: Schema and data analysis
• SQLite: Database file inspection
• Database Connection: Live data analysis

🤖 AI Integration Features

• Schema Detection: Automatic column type inference
• Pattern Analysis: Anomaly and trend detection
• Data Quality Assessment: Missing values, duplicates, outliers
• Relationship Discovery: Cross-table dependencies
• Business Logic Extraction: Rules and constraints
• Predictive Insights: Forecasting and recommendations

🚀 Quick Start

from data_analysis_framework import DataAnalyzer

analyzer = DataAnalyzer()
result = analyzer.analyze("sales_data.xlsx")

print(f"Data Type: {result.document_type.type_name}")
print(f"Schema: {result.analysis.schema_info}")
print(f"Quality Score: {result.analysis.quality_metrics['overall_score']}")
print(f"AI Insights: {result.analysis.ai_insights}")

🏗️ Status

🚧 Planned - Architecture designed, implementation pending