Easy Web Scraping With Python Requests-HTML: Extract and Parse Data

In this beginner-friendly tutorial, we'll teach you web scraping with Python Requests-HTML. Follow the script step-by-step and learn about extracting and parsing web data from static pages. We hope this web scraping with Python guide will kick off your own project! 🚀 Try Decodo web scraping proxies and forget IP bans: https://decodo.com/proxies/residential-proxies?utm_source=SP&utm_medium=youtube&utm_campaign=web_scraping ⚙️ Requirements for this tutorial: - A virtual environment - Requests library - Requests-HTML library Copy the full code below: import requests from requests_html import HTMLSession url = "https://smarproxy.com/blog" try: session = HTMLSession() response = session.get(url) h3 = response.html.find('h3') results = [] for heading in h3: results.append(f'{heading.text} \n") result_file = open('result.txt', 'w') result_file.writelines(results) result_file.close() except requests.exceptions.RequestException as e: print(e) Let us know in the comments below which Python web scraping topic we should cover next. 💡 Some FAQs: ❓ What is web scraping? Data or web scraping is an automated process of gathering publicly accessible data for marketing, e-commerce, and research purposes. CATPCHAs, IP blocks, and rate limitations are some of the most frequent challenges web scrapers face. Use residential proxies to have a smooth scraping experience without getting caught for being a robot. These proxies come from a residential network or, in other words, are real device IPs. In turn, any residential proxy traffic to a website looks like a request from an ordinary person. ❓ *Why choose web scraping with Python? Python is one of the most efficient languages for web scraping due to being general-purpose and is rich with web scraping frameworks or libraries, like Beautiful Soup or Scrapy. In addition, web scraping with Python has a shallow learning curve, making it suitable even for complete beginners.