📅 2025-06-05 — Session: Setup and Refactor Web Scraping and Data Export Pipeline
🕒 06:30–07:20
🏷️ Labels: Selenium, Web Scraping, Python, JSONL, CSV, Data Processing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to set up a Selenium-based web scraping pipeline, address clipboard issues on Linux, and enhance data export processes using JSONL and CSV formats.
Key Activities
- Selenium Web Scraping Setup: Implemented a Selenium-based pipeline to scrape dynamic web content by simulating keyboard actions to copy text from the clipboard.
- Fixing Clipboard Issues: Resolved clipboard interaction issues on Linux using Pyperclip by installing xclip or xsel.
- Data Export to JSONL: Exported DataFrames to JSONL files, including batch processing every 50 rows and hashing filenames for identification.
- Evaluation of JSONL Format: Assessed JSONL format’s effectiveness for job data storage, focusing on structure and best practices.
- Handling File Operations: Managed JSONL and CSV file operations in pandas, ensuring consistent naming and organization.
- CSV Structure Review: Reviewed and suggested improvements for CSV structure, focusing on data integrity and encoding.
- Script Updates: Updated scripts for processing SERP data, emphasizing HTML decoding and CSV output consistency.
- Refactoring Code: Refactored batch processing logic to improve code quality and eliminate duplication.
Achievements
- Successfully set up and refined a Selenium-based web scraping pipeline.
- Improved data export processes with batch processing and file handling.
- Enhanced code quality through refactoring.
Pending Tasks
- Further optimization of the web scraping pipeline for efficiency.
- Continued evaluation of data storage formats for scalability.