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!
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⚙️ 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.