📅 2024-09-11 — Session: Developed and Optimized Data Crawling Workflows
🕒 17:00–19:00
🏷️ Labels: Data Crawling, Web Scraping, Geospatial Analysis, Python, Scrapy
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and optimize workflows for data gathering, processing, and analysis, focusing on value-based investment strategies and geospatial data visualization.
Key Activities
- Data Gathering and Processing Workflow: Outlined a comprehensive workflow for creating subsets of stores, crawling data, and processing it for value-based investment baskets.
- Precios Claros Scraping: Developed a workflow for scraping store information and prices using the Precios Claros crawler, followed by data organization and analysis.
- Store Selection with Python: Implemented a Python approach to select the closest stores by group using DataFrame operations.
- GeoJSON Fetching: Utilized the Georef API to obtain GeoJSON files for Buenos Aires and CABA.
- Geospatial [[Data Visualization]]: Optimized visualization techniques using GeoPandas and Matplotlib for mapping geospatial data.
- Scrapy Crawler Optimization: Improved Scrapy crawler efficiency for specific store IDs.
- Scrapy Log Analysis: Analyzed Scrapy spider logs to identify errors and optimize scraping efficiency.
- Savings Opportunity Calculation: Calculated savings opportunities by comparing current prices to median prices.
- Bash Command History Timestamps: Enabled timestamps in Bash command history for better tracking.
Achievements
- Established detailed workflows for data crawling and processing.
- Enhanced geospatial data visualization techniques.
- Optimized Scrapy crawler and analyzed execution logs for improvements.
- Calculated and prepared savings opportunities for investment baskets.
Pending Tasks
- Further refine the data analysis process for more accurate investment decision-making.
- Continue optimizing the Scrapy crawler based on log analysis insights.