How to scrape product images from an online store?

Marzieh Daniela · 2024-12-18T07:42:33+00:00

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 BeautifulSoupurl "https://example.com/products"headers {"User-Agent": "Mozilla/5.0"}response requests.get(url, headersheaders)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 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?

General Web Scraping

How to scrape product images from an online store?

Posted by Marzieh Daniela on 12/18/2024 at 7:42 am
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?
Marzieh Daniela replied 4 days, 10 hours ago 1 Member · 0 Replies
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