Optimized Python Schema and PDF Data Extraction

• Day: 2024-09-17
• Time: 00:00 to 01:17
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Schema Extraction, Pdf Processing, Nosql, Data Quality

Description

Session Goal

The session aimed to enhance Python functions for schema extraction and PDF data processing, focusing on optimizing code logic and ensuring accurate data handling.

Key Activities

• Traversal Logic Fix: Addressed issues in the extract_parameters function to preserve nested key structures, ensuring relevant keys maintain their full hierarchy.
• Schema Extraction Optimization: Implemented a simplified approach to extract top-level properties while retaining nested structures, avoiding recursion.
• Debugging: Resolved a TypeError in dictionary handling and corrected dictionary assignment issues in schema extraction.
• Data Formatting: Fixed JSON output formatting in Pandas to ensure valid JSON arrays.
• PDF Data Extraction: Explored methods for extracting non-selectable text from PDFs using PyMuPDF and Tesseract OCR, including installation guidance and troubleshooting.
• NoSQL Data Processing: Planned strategies for leveraging NoSQL data, focusing on quality assessment and strategic data utilization.

Achievements

• Successfully optimized schema extraction logic and fixed JSON formatting issues.
• Enhanced PDF data extraction capabilities using OCR technologies.
• Developed strategic plans for NoSQL data utilization and quality assessment.

Pending Tasks

• Further testing and validation of the optimized schema extraction functions.
• Implementation of NoSQL data strategies in live environments.
• Continued refinement of PDF data extraction techniques.

Evidence

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• event_ids: []