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How to scrape restaurant data from DoorDash.com using Python?
Scraping restaurant data from DoorDash.com using Python can provide insights into menu items, prices, and restaurant names. Python’s requests library can fetch page content, while BeautifulSoup parses the HTML to extract relevant information. This script demonstrates how to gather restaurant names and menu item prices from DoorDash.
import requests from bs4 import BeautifulSoup # Target URL for DoorDash url = "https://www.doordash.com/food-delivery/" headers = { "User-Agent": "Mozilla/5.0" } response = requests.get(url, headers=headers) if response.status_code == 200: soup = BeautifulSoup(response.content, "html.parser") restaurants = soup.find_all("div", class_="restaurant-card") for restaurant in restaurants: name = restaurant.find("h3").text.strip() if restaurant.find("h3") else "Name not available" price = restaurant.find("span", class_="price").text.strip() if restaurant.find("span", class_="price") else "Price not available" print(f"Restaurant: {name}, Price: {price}") else: print("Failed to fetch DoorDash page.")This script extracts restaurant names and menu prices from DoorDash. It is designed for a sample page and can be extended to handle pagination for additional listings. Adding delays between requests reduces the risk of being detected by anti-scraping systems.
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2 Replies
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Error handling improves the reliability of the DoorDash scraper by addressing missing elements or page structure changes. For example, if some restaurants lack menu prices, the scraper should skip those entries without crashing. Logging skipped entries helps refine the scraper and identify patterns. Regular updates to the script ensure that it remains functional despite website changes. These practices make the scraper robust and dependable over time.
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Adding pagination handling is crucial for collecting data from all restaurant listings on DoorDash. Restaurants are often distributed across multiple pages, and automating navigation ensures a complete dataset. Random delays between requests mimic human browsing behavior and reduce detection risks. Pagination functionality enhances the scraper’s ability to gather comprehensive data for analysis. This makes the scraper more effective in collecting insights from a large number of listings.
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