📅 2025-09-24 — Session: Developed robust header parsing and repair scripts
🕒 22:40–23:25
🏷️ Labels: Python, Data Processing, Header Parsing, Data Cleaning, Automation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The objective of this session was to develop and refine Python scripts for processing and auditing column headers in text files, focusing on intersection and difference calculations, and handling corrupted or non-standard headers.
Key Activities
- Implemented a Jupyter notebook cell to process text files, extract headers, and generate JSON and Markdown outputs summarizing column intersections and differences.
- Developed Python scripts to audit column headers, compute intersections, and identify outliers.
- Addressed non-ASCII control characters in headers, ensuring proper alignment across files.
- Created a byte-level tolerant parser to clean corrupted control characters from headers.
- Normalized tokens in parsers by removing quotes and handling control bytes.
- Implemented a solution for handling Unicode ‘ghosts’ in header parsing.
- Established a consensus-based approach for header repair, applying trimming rules without reprocessing raw data.
- Computed canonical order and intersections from corrected headers, generating output files for further analysis.
Achievements
- Successfully developed a comprehensive set of scripts for header processing, cleaning, and auditing.
- Enhanced data integrity by implementing robust error handling and normalization techniques.
- Established a consensus-based method for header repair, improving data quality and consistency.
Pending Tasks
- Further validation and testing of the scripts with diverse datasets to ensure reliability and robustness.
- Integration of these scripts into the broader data processing pipeline for automated execution.