Comparison of Web Scraping Tools and Libraries

Jan 16, 2023

Web scraping is the process of extracting data from websites. As a Python developer, you have access to a variety of libraries and tools to help automate this process. In this article, we'll compare some of the most popular Python libraries and frameworks for web scraping, including HTTP libraries like Requests and HTTPX, parsing libraries like Beautiful Soup, browser automation tools like Selenium and Playwright, and the comprehensive Scrapy framework.

HTTP Libraries: Requests and HTTPX

The foundation of any web scraping project is making HTTP requests to retrieve the content of web pages. Two popular Python libraries for this are Requests and HTTPX.

Requests is simple to use and great for basic scraping tasks. HTTPX offers more advanced features like async support and HTTP/2. Their core functionality and syntax are similar, so HTTPX is recommended even for smaller projects to allow for easy scaling.

Some key features:

  • Both support proxies, timeouts, and TLS verification

  • HTTPX supports async and HTTP/2, Requests does not

  • HTTPX has custom exception classes

Parsing HTML with Beautiful Soup

Once you have retrieved the HTML content of a page, you need to parse it to extract the desired data. Beautiful Soup is the most popular Python library for parsing HTML.

Beautiful Soup provides an easy way to navigate and search the parsed HTML tree structure. Its simple setup and straightforward syntax make it a great choice for small to medium web scraping projects and beginners.

However, Beautiful Soup struggles with JavaScript-heavy websites and has limited performance for very large scale projects. Here's an example of using Beautiful Soup with HTTPX to scrape article data from Hacker News:

from bs4 import BeautifulSoup

import httpx

response = httpx.get("")

soup = BeautifulSoup(response.content)

articles = soup.find_all(class_="athing")

for article in articles:

data = {

"URL": article.find(class_="titleline").find("a").get('href'),

"title": article.find(class_="titleline").getText(),

"rank": article.find(class_="rank").getText().replace(".", "")



Browser Automation: Selenium and Playwright

Some websites heavily rely on JavaScript to dynamically load content. In those cases, just parsing the initial HTML is not enough. You need to use browser automation tools like Selenium or Playwright to fully load the page, including any JS-rendered content.

Selenium and Playwright allow you to programmatically control a web browser to interact with pages, click buttons, fill out forms, etc. They are used for testing and automation, but also for scraping dynamic pages.

The core functionality is similar between Selenium and Playwright, but Playwright is more modern and fully featured. It can automatically wait for elements before interacting and provides an async API.

Here's an example of using Playwright to scrape data about a book on Amazon:

import asyncio

from playwright.async_api import async_playwright

async def main():

async with async_playwright() as p:

browser = await p.firefox.launch(headless=False)

page = await browser.new_page()

await page.goto("")

selectors = ['#productTitle', ' a', '#productSubtitle', '.a-size-base.a-color-price.a-color-price']

book_data = await asyncio.gather(*(page.query_selector(sel) for sel in selectors))

book = {}

book["book_title"], book["author"], book["edition"], book["price"] = [await elem.inner_text() for elem in book_data if elem]


await page.screenshot(path="book.png")

await browser.close()

The main downside of browser automation tools is that they are much more resource intensive compared to simply parsing HTML. For large scale scraping, the overhead of launching browser instances becomes a major bottleneck. Therefore, tools like Playwright are used sparingly and often in combination with faster libraries like Beautiful Soup or Scrapy.

Scrapy Framework

Scrapy is the most popular and comprehensive web scraping framework available for Python. It provides a complete set of tools for scraping large amounts of data.

Compared to Beautiful Soup, Scrapy offers better performance and integrates well with other Python data processing libraries. It includes many useful built-in features tailored for scraping:

  • Powerful spidering and crawling

  • Support for exporting data in multiple formats

  • Extensible architecture with middleware, pipelines, etc.

  • Distributed scraping support

  • Robust error handling

  • Support for authentication, cookies, and more

Here's an example of a basic Scrapy spider to extract data from Hacker News:

import scrapy

class HackernewsSpiderSpider(scrapy.Spider):

name = 'hackernews_spider'

allowed_domains = ['']

start_urls = ['']

def parse(self, response):

articles = response.css('tr.athing')

for article in articles:

yield {

"URL": article.css(".titleline a::attr(href)").get(),

"title": article.css(".titleline a::text").get(),

"rank": article.css(".rank::text").get().replace(".", "")


Run this spider and output the results to a JSON file:

scrapy crawl hackernews -o hackernews.json

Choosing the Right Tool

The best Python web scraping library depends on the needs of your specific project:

  • For simple tasks, Requests/HTTPX + Beautiful Soup is easy to use

  • For scraping dynamic JavaScript sites, Playwright or Selenium may be necessary

  • For large scale, high performance scraping, Scrapy is the most powerful and comprehensive option

As an expert scraper, it's valuable to be familiar with the strengths and use cases of each library. This allows you to always select the optimal tool for the job at hand.


In this article, we compared several of the most popular Python libraries and tools for web scraping, including:

  • HTTP libraries Requests and HTTPX for fetching page content

  • Beautiful Soup for parsing and extracting data from HTML

  • Selenium and Playwright for automated browsing and scraping dynamic pages

  • The Scrapy framework for large scale, high performance scraping

We looked at code examples of how to use each library for common scraping tasks. While each tool has its strengths, an expert scraper should be familiar with all of them in order to choose the best one for a given project. Factors to consider include the size of the project, need for JavaScript rendering, ease of use vs performance, and more.

With these powerful libraries, you can scrape data from almost any website using Python. The key is understanding how the different tools work and when to use each one.

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