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How to scrape product images from an online store?
Scraping product images from an online store involves identifying the image URLs embedded in the HTML. These are typically found in img tags with attributes pointing to the image location. For static sites, BeautifulSoup is perfect for extracting these URLs, while JavaScript-heavy sites may require Puppeteer or Selenium. Once the URLs are extracted, you can download the images locally using Python’s requests library. Additionally, ensuring the scraper handles high-resolution images or multiple image formats is essential for quality data collection.
import requests from bs4 import BeautifulSoup url = "https://example.com/products" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) if response.status_code == 200: soup = BeautifulSoup(response.content, "html.parser") images = soup.find_all("img", class_="product-image") for idx, img in enumerate(images, 1): img_url = img["src"] img_data = requests.get(img_url).content with open(f"product_{idx}.jpg", "wb") as file: file.write(img_data) print(f"Downloaded: product_{idx}.jpg") else: print("Failed to fetch product images.")Dynamic image galleries often use JavaScript for lazy loading, requiring browser automation to ensure all images are loaded before scraping. How do you handle large-scale image scraping efficiently?
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3 Replies
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For large-scale projects, I use multithreading to download multiple images simultaneously. This speeds up the process significantly compared to sequential downloads.
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Lazy-loaded images can be tricky. I use Selenium to scroll through the page and trigger the loading of all images before starting the scraping process.
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To avoid storing duplicate images, I check for existing files before saving new ones. This reduces redundancy and saves storage space.
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