Scraping a Large Set of Products: Data Collection
This page continues the discussion on our project involving scraping the Mayesh online shop. Here, we delve deeper into the data collection process and outline the initial steps of data processing.
4. Detailed Data Collection Process
We developed a Python script using Scrapy and Selenium to navigate and extract data from product listings. Here’s a step-by-step breakdown:
- Initialize Scrapy: Start a Scrapy Spider to crawl through the product pages.
- Dynamic Content Handling: Use Selenium to ensure all dynamic content is loaded, especially for JavaScript-rendered pages.
- Extract Data: Identify and extract the product name, price, image URL, and description using Beautiful Soup.
- Handle Pagination: Automatically detect and navigate to the next page of products until all products are scraped.
5. Data Processing
After collecting the data, the next step is to process and clean it for analysis. This includes:
- Removing duplicates and irrelevant entries.
- Normalizing data formats (e.g., standardizing prices to a common format).
- Extracting and refining additional attributes like color or type from the product descriptions.
6. Challenges and Solutions
During data collection, we encountered several challenges:
- Rate Limiting: To avoid being blocked by the website, we implemented polite scraping practices with delays and retries.
- Dynamic Content: Some product details were loaded asynchronously. We used Selenium to wait until the necessary elements were fully loaded.
- Data Quality: We wrote additional functions to clean and verify the integrity of the scraped data.