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How to scrape flower prices from 1-800-Flowers.com using Python?
Scraping flower prices from 1-800-Flowers.com using Python helps collect data about flower arrangements, pricing, and availability for analysis or price comparison. Using Python’s requests library for HTTP requests and BeautifulSoup for parsing HTML, you can retrieve structured data from their product pages. Below is a sample script for extracting flower details.
import requests from bs4 import BeautifulSoup # Target URL for 1-800-Flowers url = "https://www.1800flowers.com/flowers" headers = { "User-Agent": "Mozilla/5.0" } response = requests.get(url, headers=headers) if response.status_code == 200: soup = BeautifulSoup(response.content, "html.parser") flowers = soup.find_all("div", class_="product-card") for flower in flowers: name = flower.find("span", class_="product-name").text.strip() if flower.find("span", class_="product-name") else "Name not available" price = flower.find("span", class_="product-price").text.strip() if flower.find("span", class_="product-price") else "Price not available" print(f"Name: {name}, Price: {price}") else: print("Failed to fetch 1-800-Flowers page.")This script fetches the product page of 1-800-Flowers, parses the HTML content, and extracts flower names and prices. Pagination handling ensures that you scrape additional listings from other pages. Random delays between requests can be added to reduce the risk of detection by the website’s anti-scraping measures.
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Adding pagination support ensures that the scraper captures all flower listings across multiple pages. 1-800-Flowers typically organizes products over several pages, and navigating programmatically through the “Next” button helps collect all available data. Random delays between page requests mimic human browsing behavior, reducing the risk of detection. Proper pagination handling improves the scraper’s ability to collect a complete dataset for comparison and analysis. This functionality is particularly useful for analyzing seasonal pricing trends.
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Error handling is essential to ensure that the scraper remains functional even if the website layout changes. Missing elements like product names or prices can cause the scraper to fail without proper error handling. Adding conditions for null values prevents the script from crashing and ensures it skips problematic entries. Regular updates to the scraper help it adapt to changes in the website’s structure. These practices ensure the scraper remains reliable over time.
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