Developed and Optimized Data Crawling Workflows

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Developed and Optimized Data Crawling Workflows

  • Day: 2024-09-11
  • Time: 17:00 to 19:00
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Data Crawling, Web Scraping, Geospatial Analysis, Python, Scrapy

Description

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.

Evidence

  • source_file=2024-09-11.sessions.jsonl, line_number=1, event_count=0, session_id=f154a4372239ab914a966aab21877b4cfbacf7f89e531effe72b199a03e99770
  • event_ids: []

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