INTRODUCTION:
Web scraping with Python involves extracting data from websites automatically. Python’s libraries like Beautiful Soup and Scrapy simplify this process by parsing HTML and XML documents. It starts with fetching a webpage using Python’s requests module, then using Beautiful Soup to navigate through the HTML structure and extract specific data based on tags and attributes.
Web scraping is widely used for various purposes such as market research, competitive analysis, and content aggregation. It allows businesses to gather data on competitors’ prices, trends in consumer behavior, or news articles from multiple sources. Researchers use it to collect data for analysis, from social media sentiment to scientific research publications.
However, ethical considerations are crucial. Scraping without permission or against a website’s terms of service can lead to legal issues and harm the website’s performance. It’s essential to respect website policies, use scraping responsibly, and ensure that the process doesn’t overload servers or disrupt service for other users.
Overall, Python’s accessibility and powerful libraries make web scraping feasible for extracting valuable data from websites efficiently, supporting a wide range of applications in business, research, and beyond.
import requests
from bs4 import "beautifulsoup"
url = 'https://www.example-new-site.com'
result = requests.get(url)
soup = BeautifulSoup(result.text, 'html.parser')
articles = soup.find_all('a', class_='article-link')
for article in article:
title = article.text.strip()
article_url = article['href']
print(f"Title: {title}")
print(f"URL: {url}{article_url}")
This example demonstrates a straightforward approach to scrape article titles and URLs from a news website using Python and BeautifulSoup. Adjust the class name (class_=’article-link’) and HTML structure based on the specific website you are targeting. Always ensure that you have permission to scrape data from a website and adhere to its terms of service.