Python requests vs Selenium: Web Scraping Guide

This guide examines two widely used approaches for web scraping with Python: using the requests library and employing browser automation tools such as Selenium or Playwright. It outlines the technical differences, use cases, and performance considerations to help you choose the most effective tool for your scraping tasks.

Introduction

Web scraping is an essential skill for data analysts, developers, and researchers who need to extract information from websites. The choice of tooling can have significant implications for speed, resource usage, and complexity. While browser automation provides flexibility for dynamic sites, simpler solutions like requests are often overlooked. This tutorial explains when and why you should prefer requests and when you must use full browser automation.

Why Use Python requests?

The requests library allows you to send HTTP requests to web servers and receive responses directly. For sites that serve static HTML or expose data through API endpoints, requests offers several advantages:

• Performance: Fetching HTML over HTTP is faster than rendering pages in a browser engine.
• Lightweight Resource Usage: No graphical browser means reduced memory and CPU requirements.
• Deployment Simplicity: Easier to run in server environments where graphical interfaces are unavailable.

Example: Retrieving Static HTML

import requests

url = "https://quotes.toscrape.com"
response = requests.get(url)
if response.status_code == 200:
    print(response.text[:500])  # Preview first 500 characters of HTML
else:
    print("Failed to retrieve page")

When Browser Automation Is Required

Many modern websites are built as single-page applications (SPAs) that rely heavily on JavaScript for rendering content dynamically. In such cases, fetching the raw HTML with requests may not provide access to the required data.

Browser automation frameworks like Selenium and Playwright control a real browser instance to load pages, execute JavaScript, and simulate user actions. These tools are appropriate when:

• The site uses infinite scroll or delayed content loading.
• User authentication and session handling are required.
• Interacting with forms, dropdowns, or buttons is necessary.

Example: Using Selenium for Dynamic Content

from selenium import webdriver
from selenium.webdriver.common.by import By

# Start browser instance
driver = webdriver.Chrome()
driver.get("https://quotes.toscrape.com")

# Extract rendered page content
print(driver.page_source[:500])

driver.quit()

Identifying API Endpoints With Developer Tools

Many websites fetch their data from backend APIs via asynchronous requests. Detecting and using these APIs directly can allow you to avoid browser automation altogether.

Step-by-Step Process

1. Open browser developer tools (typically F12 or Ctrl+Shift+I).
2. Navigate to the Network tab and reload the page.
3. Filter for XHR or Fetch requests.
4. Inspect requests returning JSON or structured data.
5. Recreate these API calls using requests in Python.

Example: Accessing an API Directly

import requests

api_url = "https://example.com/api/data"
headers = {"Authorization": "Bearer YOUR_API_TOKEN"}
response = requests.get(api_url, headers=headers)
if response.ok:
    print(response.json())
else:
    print("API request failed")

Comparison Table

Conclusion

For most static websites and API-driven applications, Python’s requests library offers a fast, efficient, and easy-to-deploy solution. For sites with dynamic content, browser automation tools such as Selenium or Playwright are essential. By understanding the trade-offs, developers can build more efficient and maintainable web scraping systems.