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How to build a Wayfair price tracker using Python and BeautifulSoup?
Building a Wayfair price tracker can help monitor product price changes over time for personal or analytical purposes. Python’s BeautifulSoup library is well-suited for scraping static content, such as product titles, prices, and URLs. If Wayfair uses dynamic JavaScript to load content, Selenium can be used to render the page fully before extracting data. The process involves identifying the product’s price and title in the HTML, scraping this data at regular intervals, and storing it for comparison.Here’s an example of using BeautifulSoup to scrape product prices:
import requests from bs4 import BeautifulSoup import time def fetch_price_tracker(url): headers = { "User-Agent": "Mozilla/5.0" } response = requests.get(url, headers=headers) if response.status_code == 200: soup = BeautifulSoup(response.content, "html.parser") product_title = soup.find("h1", class_="product-title").text.strip() price = soup.find("span", class_="price").text.strip() print(f"Product: {product_title}, Price: {price}") else: print("Failed to fetch product details.") # Example product URL product_url = "https://www.wayfair.com/product-example" fetch_price_tracker(product_url) # Adding a delay to monitor changes at intervals time.sleep(3600) # Run every hourFor larger-scale tracking, you can monitor multiple URLs and store the data in a database like SQLite or PostgreSQL. How do you handle dynamic content or changes in the website’s structure?
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3 Replies
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Retrying failed requests with exponential backoff helps avoid breaking the scraper due to temporary server issues or connectivity problems.
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To handle structural changes, I use flexible selectors and regularly test the scraper to adapt to updates in Wayfair’s HTML layout.
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Using rotating proxies and randomized headers reduces the risk of being blocked by the server, ensuring smoother operation over long scraping sessions.
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