📅 2025-06-05 — Session: Implemented and Optimized Web Scraping and Data Export
🕒 06:30–07:20
🏷️ Labels: Selenium, Python, JSONL, Data Processing, Web Scraping
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session focused on setting up a Selenium-based web scraping pipeline, resolving clipboard issues on Linux, exporting data to JSONL format, and improving data processing scripts.
Key Activities
- Selenium Web Scraping Setup: Implemented a Selenium-based web scraping pipeline to capture dynamic content from web pages using clipboard actions.
- Clipboard Management: Addressed clipboard interaction issues on Linux with Pyperclip by installing xclip or xsel.
- Data Export: Exported DataFrame to JSONL format, evaluated JSONL for job data storage, and implemented batch processing and hashing for JSONL exports.
- Pandas File Operations: Managed JSONL and CSV file operations using pandas, ensuring consistent naming conventions and organizing output files.
- CSV Review and Script Updates: Reviewed CSV structure, suggested improvements, and updated scripts for SERP data processing, including HTML decoding and CSV output consistency.
- Code Refactoring: Refactored batch processing logic to eliminate code duplication and improve clarity.
Achievements
- Successfully set up a Selenium-based web scraping pipeline.
- Resolved Linux clipboard issues with Pyperclip.
- Exported data to JSONL format and implemented batch processing.
- Improved data processing scripts and file management with pandas.
Pending Tasks
- Further evaluate the effectiveness of JSONL format for other data types.
- Continue refining data processing scripts for efficiency and clarity.