I have developed a Python web scraper using requests, BeautifulSoup, and pandas to extract vital information about Lego sets from the official website. The scraper gathers details such as set names, prices, piece counts, age recommendations, ratings, and themes.
- Data Extraction: The scraper efficiently collects diverse data about Lego sets, facilitating comprehensive analysis.
- Technological Foundation: Leveraging Python libraries enables seamless web scraping, HTML parsing, and data structuring.
- Objective: This scraper forms the foundation for in-depth analysis, aiming to uncover pricing trends, correlations, and patterns across Lego themes.
The repository houses the scraper code, showcasing expertise in web scraping and data extraction. Insights and analysis derived from this data will be presented separately, potentially through a blog on platforms like Medium.
The collected dataset holds potential for market analysis, user behavior understanding, and thematic trend identification within the Lego ecosystem.
This Lego website scraper demonstrates proficiency in data collection and sets the stage for subsequent analysis. While the code resides here, insights and findings will be shared separately to enrich the narrative of data-driven perspectives within the Lego domain.