7 Best Python Web Scraping Libraries in 2025

Idowu Omisola
Idowu Omisola
Updated: February 27, 2025 · 8 min read
Table of contents

Are you struggling to find the best Python web scraping library for your next project? You aren't alone. Getting a solid scraping library can be tricky, especially when the goal is to avoid anti-bots and scale up web scraping.

A good Python library for web scraping should be fast, scalable, and capable of crawling any type of web page.

Which Python Web Scraping Library Is Best?

We did some background tests to check and verify which Python web scraping library can scrape a web page without problems and selected the best ones for you. Here they are:

  1. ZenRows: An all-in-one scraping solution with the complete toolkit to avoid anti-bot detection at any scale.
  2. Selenium: A WebDriver-powered cross-browser automation library suitable for scraping dynamic web pages.
  3. Requests: Lightweight HTTP client for requesting web pages and getting their HTML content during scraping.
  4. Beautiful Soup: A powerful library for parsing HTML and XML content.
  5. Playwright: A cross-browser automation library based on the Chrome DevTool Protocol (CDP) for scraping dynamic websites.
  6. Scrapy: Full-featured framework for scalable web scraping and crawling.
  7. Urllib3: An extensive HTTP client that gets HTML content from web pages during scraping.

To give you a quick scan, the table below is an overview comparison of each Python scraping tool.

Library Ease Performance HTTP requests Parsing JS Rendering Anti-detection
ZenRows ✅ A high-performance tool with cutting-edge technology to scrape any website at scale without getting blocked. ✅ (works with any HTTP client) ✅ ✅ ✅
Selenium ❌ (initial steep learning curve) Slow and consumes high resources ✅ ✅ ✅ ❌
Requests ✅ Fast and low resource consumption ✅ ❌ ❌ ❌
Beautiful Soup ✅ Lightweight with low memory requirements ❌ ✅ ❌ ❌
Playwright ❌ (initial steep learning curve) Resource intensive ✅ ✅ ✅ ❌
Scrapy ❌ (initial steep learning curve) Fast and medium resource consumption ✅ ✅ ❌ (available with Scrapy-Splash) ❌
urllib3 ✅ It's fast and consumes low resources ✅ ❌ ❌ ❌
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If you're new to web scraping, understanding the variety of tools available can be overwhelming. To help you get started, check our detailed Python web scraping guide that explains everything step by step.

Let's now go into detail and discuss these libraries with practical examples.

1. ZenRows

ZenRows Homepage
Click to open the image in full screen

ZenRows is an all-in-one web scraping solution that provides all the tools required for scraping without getting blocked. It features premium proxy auto-rotation, flexible geo-targeting, request header optimization, advanced fingerprint spoofing, JavaScript rendering, headless browsing, CAPTCHA and anti-bot auto-bypass, and more.

ZenRows is beginner-friendly and compatible with any programming language. This web scraping solution makes your project more scalable and saves you time and resources, as all it takes to get its features is a single API call.

👍 Pros

  • ZenRows is easy to use.
  • It efficiently bypasses CAPTCHAs and anti-bots.
  • It offers premium rotating proxies with flexible geo-targeting.
  • ZenRows can scrape JavaScript-rendered pages.
  • It has headless browsing features for executing human interactions.
  • It integrates easily with other libraries.
  • Comprehensive documentation.

👎 Cons

  • It's a paid service, but it comes with a free trial.

When to Use ZenRows?

Use ZenRows when you need to scrape at scale and want a handy solution to avoid getting blocked by anti-bot measures. ZenRows is the best solution for dealing with IP bans, CAPTCHAs, and web application firewalls (WAFs) with minimal code.

How to Scrape a Web Page With ZenRows

To demonstrate how ZenRows helps you avoid blocks, you'll scrape this Antibot Challenge page, a heavily protected site.

First, sign up on ZenRows to open the Universal Scraper API Request Builder. Paste your target URL in the link box and activate Premium Proxies and JS Rendering.

building a scraper with zenrows
Click to open the image in full screen

Select Python as your programming language and choose the API connection mode. Copy the generated Python code and paste it into your script.

The generated Python code should look like this:

scraper.py 
# pip3 install requests
import requests

url = "https://www.scrapingcourse.com/antibot-challenge"
apikey = "<YOUR_ZENROWS_API_KEY>"
params = {
    "url": url,
    "apikey": apikey,
    "js_render": "true",
    "premium_proxy": "true",
}
response = requests.get("https://api.zenrows.com/v1/", params=params)
print(response.text)

The above code outputs the target site's full-page HTML, proving you bypassed the anti-bot detection:

Output 
<html lang="en">
<head>
    <!-- ... -->
    <title>Antibot Challenge - ScrapingCourse.com</title>
    <!-- ... -->
</head>
<body>
    <!-- ... -->
    <h2>
        You bypassed the Antibot challenge! :D
    </h2>
    <!-- other content omitted for brevity -->
</body>
</html>

Congratulations! 🎉 You just used ZenRows to bypass anti-bot protection. You're ready to start scraping without limitations.

2. Selenium

Selenium Homepage.
Click to open the image in full screen

Selenium is a powerful browser automation tool for scraping dynamic web content. It enables actions like clicking buttons, hovering over elements, filling out forms, and more. Its robust automation features make it a top choice for automation testers and scrapers. It also has an active community, with 31.1K GitHub stars.

Its support for local and cloud grids allows parallel task execution, speeding up operations. Selenium's compatibility with multiple browsers, including Chrome, Firefox, Edge, Safari, and even a legacy browser like Internet Explorer, ensures consistent results and flexibility across different browser environments.

👍 Pros

  • It can scrape dynamic web pages.
  • Selenium has cross-browser support.
  • It's suitable for executing human interactions during scraping.
  • Selenium supports the grid system for parallel scraping.
  • Solid community support and documentation.
  • Stable and frequently maintained.

👎 Cons

  • Browser instance results in memory overhead.
  • Cloud grid maintenance can be costly.
  • Selenium is prone to anti-bot detection.
  • It has a steep learning curve.

When to Use Selenium?

Selenium is handy for scraping JavaScript-heavy websites requiring full browser automation

How to Scrape a Website With Selenium

You'll use Selenium to scrape the E-commerce Challenge page in this example. You'll also use this target site in other examples, so keep it close.

The Selenium scraper below starts a Chrome instance in headless mode, opens the target website, and waits for the page to load. It then captures and prints the website's full-page HTML:

example.py 
# pip3 install selenium
from selenium import webdriver
from selenium.webdriver.chrome.options import Options

# set up Chrome instance in headless mode
chrome_options = Options()
chrome_options.add_argument("--headless=new")

# Start the driver
driver = webdriver.Chrome(options=chrome_options)

# navigate to the target URL
driver.get("https://www.scrapingcourse.com/ecommerce/")

# wait for the page to load fully
driver.implicitly_wait(10)

# print the full-page HTML
print(driver.page_source)

# clean up and close the driver
driver.quit()

Run the code, and you'll get the site's full-page HTML:

Output 
<!DOCTYPE html>
<html lang="en-US">
<head>
    <!--- ... --->
  
    <title>Ecommerce Test Site to Learn Web Scraping - ScrapingCourse.com</title>
    
  <!--- ... --->
</head>
<body class="home archive ...">
    <p class="woocommerce-result-count">Showing 1-16 of 188 results</p>
    <ul class="products columns-4">

        <!--- ... --->

    </ul>
</body>
</html>

There you have it! You just created a basic Selenium web scraper.

3. Requests

Requests
Click to open the image in full screen

Requests is a user-friendly HTTP client in Python built on top of urllib3. It simplifies sending HTTP requests and handling responses, making it easier for developers to interact with web services and APIs. Selenium supports standard HTTP request methods, including POST, GET, UPDATE, and DELETE.

Due to its broad use in web development and scraping, the Requests library enjoys wide adoption, with 52.4K GitHub stars. Although Requests doesn't support HTML parsing, you can use it to obtain a website's HTML and pass it to a parser library like BeautifulSoup for content extraction.

👍 Pros

  • It's fast and lightweight.
  • The Requests library is beginner-friendly.
  • It supports all HTTP methods.
  • It features clean response handling.
  • You can pair it with HTML parsers like Beautiful Soup for targeted scraping.

👎 Cons

  • It's unsuitable for scraping dynamic content.
  • No support for browser simulation.
  • It relies on third-party parser libraries for HTML parsing.

When to Use Requests?

Use the Requests library if your target site is static and doesn't require automation or JavaScript rendering.

How to Scrape a Web Page Using Requests

To see how the Requests library works, you'll use it to obtain the full-page HTML of the previous E-commerce Challenge page.

The example code below requests the target website and prints its full-page HTML:

example.py 
# pip3 install requests
import requests

# request the target website
response = requests.get("https://www.scrapingcourse.com/ecommerce/")

# get the response HTML
if response.status_code != 200:
    print(f"An error occurred with status code {response.status_code}")
else:
    print(response.text)

The above code outputs the website's HTML, as shown:

Output 
<!DOCTYPE html>
<html lang="en-US">
<head>
    <!--- ... --->
  
    <title>Ecommerce Test Site to Learn Web Scraping - ScrapingCourse.com</title>
    
  <!--- ... --->
</head>
<body class="home archive ...">
    <p class="woocommerce-result-count">Showing 1-16 of 188 results</p>
    <ul class="products columns-4">

        <!--- ... --->

    </ul>
</body>
</html>

That's it! You've successfully used the Request Python library for web scraping.

4. Beautiful Soup

Beautifulsoup
Source: https://pypi.org/project/beautifulsoup4/

Beautiful Soup is a versatile Python web scraping library primarily used for parsing and navigating HTML or XML documents. It offers flexible element selection techniques, including CSS selectors, XPATH, and tag-based searching.

Built on robust parsing packages like `lxml` and `html.parser`, the tool simplifies data extraction from structured web content. Beautiful Soup also excels at handling poorly formatted documents and includes reliable encoding detection.

👍 Pros

  • It has a simple learning curve.
  • Support for different selector types (XPATH, CSS, tags, and attributes).
  • Active community support.
  • Detailed documentation.
  • Lightweight architecture.

👎 Cons

  • It relies on third-party HTTP clients for HTTP requests.
  • Beautiful Soup doesn't support dynamic content scraping or browser simulation.

When to Use Beautiful Soup?

Beautiful Soup is an excellent choice for parsing HTML content from an HTTP client, such as Requests.

How to Scrape a Web Page Using Beautiful Soup

In this example, you'll use the Requests library to obtain the target site's HTML and parse the returned content using Beautiful Soup.

Here's the code to achieve that:

example.py 
# pip3 install requests beautifulsoup4
import requests
from bs4 import BeautifulSoup

# request the target website
response = requests.get("https://www.scrapingcourse.com/ecommerce/")

# get the response HTML
if response.status_code != 200:
    print(f"An error occured with status code {response.status_code}")
else:
    content = response.text

    # parse the HTML content of the page
    soup = BeautifulSoup(response.content, "html.parser")
    prettified_html = soup.prettify()

    # print the prettified HTLM
    print(prettified_html)

The above code returns a more organized version of the website's HTML:

Output 
<!DOCTYPE html>
<html lang="en-US">
<head>
    <!--- ... --->
  
    <title>Ecommerce Test Site to Learn Web Scraping - ScrapingCourse.com</title>
    
  <!--- ... --->
</head>
<body class="home archive ...">
    <p class="woocommerce-result-count">Showing 1-16 of 188 results</p>
    <ul class="products columns-4">

        <!--- ... --->

    </ul>
</body>
</html>

Great! Your basic Beautiful Soup scraper is up and running.

5. Playwright

Playwright Homepage
Click to open the image in full screen

Playwright is another open-source browser automation library used for web scraping. The tool uses browser debugging protocols to simplify data extraction across browsers, including Chrome, Firefox, Safari, and Edge. Despite being newer than libraries like Selenium, Playwright has gained significant community traction with 68.5K GitHub stars.

Although Playwright is user-friendly, understanding its concepts and features might take time. It also needs to run browser instances. So, like Selenium, running a Playwright scraper results in significant memory overhead, especially when running multiple instances at scale.

👍 Pros

  • Cross-browser support.
  • It maintains high-level API.
  • Strong support for JavaScript execution and browser automation.
  • Support for headless and non-headless modes.
  • Decent community support and documentation.

👎 Cons

  • It can be resource-intensive, especially at scale.
  • Steep learning curve.
  • Prone to anti-bot detection.

When to Use Playwright?

Playwright is well-suited for scraping dynamic web pages that require complex interactions. It’s ideal for advanced features like request interception, seamless automation across multiple browsers, and effortless handling of modern web technologies.

How to Scrape a Web Page Using Playwright

Below is a simple code demonstrating how to use Playwright in Python.

The code runs Playwright's Chrome instance in headless mode, opens the target site, and prints its HTML content.

example.py 
# pip3 install playwright
# playwright install
from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    # instantiate Chrome in headless mode
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    # visit the web page
    page.goto("https://www.scrapingcourse.com/ecommerce/")
    # print the page's HTML
    print(page.content())
    # close the browser instance
    browser.close()

Here's the output:

Output 
<!DOCTYPE html>
<html lang="en-US">
<head>
    <!--- ... --->
  
    <title>Ecommerce Test Site to Learn Web Scraping - ScrapingCourse.com</title>
    
  <!--- ... --->
</head>
<body class="home archive ...">
    <p class="woocommerce-result-count">Showing 1-16 of 188 results</p>
    <ul class="products columns-4">

        <!--- ... --->

    </ul>
</body>
</html>

Good going! You're ready to start scraping with Playwright in Python.

6. Scrapy

Scrapy Homepage
Click to open the image in full screen

Scrapy is a high-performance, popular framework for web scraping and crawling. Its support for item pipelines lets you customize data storage destinations for your spider.

Although Scrapy doesn't feature built-in mechanisms to bypass blocks, it works seamlessly with ZenRows via the scrapy-zenrows middleware. This integration lets you access all the benefits of ZenRows, including anti-bot auto-bypass and JavaScript rendering.

👍 Pros

  • It provides a scalable framework for scraping and crawling.
  • Customizable middleware and item pipelines.
  • Strong encoding support.
  • It doesn't depend on external HTTP clients or HTML parsers.
  • Scrapy integrates seamlessly with web scraping solutions like ZenRows.
  • Solid community support.

👎 Cons

  • Steep learning curve.
  • Scrapy requires integration with JavaScript rendering engines to scrape dynamic web pages.

When to Use Scrapy?

Scrapy is suitable when you need a solid customizable framework for large-scale scraping or crawling.

How to Scrape a Web Page Using Scrapy

Scrapy follows a standard project architecture that is beyond the scope of this article. However, you can use it to run a crawler process.

Here's an example of a scrapy spider that requests the target site and returns its HTML content. The code runs the spider using Scrappy's `CrawlerProcess` instance:

example.py 
# pip3 install scrapy
import scrapy
from scrapy.crawler import CrawlerProcess

class Scraper(scrapy.Spider):
    name = "scraper"
    start_urls = ["https://www.scrapingcourse.com/ecommerce/"]

    def parse(self, response):
        # extract and print the HTML content
        html_content = response.text
        print(html_content)

# run the spider
process = CrawlerProcess()
process.crawl(Scraper)
process.start()

Run the spider by executing the python file using the .py extension, and you'll get the following following HTML in the output:

Output 
<!DOCTYPE html>
<html lang="en-US">
<head>
    <!--- ... --->
  
    <title>Ecommerce Test Site to Learn Web Scraping - ScrapingCourse.com</title>
    
  <!--- ... --->
</head>
<body class="home archive ...">
    <p class="woocommerce-result-count">Showing 1-16 of 188 results</p>
    <ul class="products columns-4">

        <!--- ... --->

    </ul>
</body>
</html>

Bravo! You just built your first Scrapy web spider.

7. Urllib3

urllib3 documentation page
Click to open the image in full screen

urllib3 is an HTTP client known for its reliability, performance optimizations, and extensive features. It provides a solid foundation for making HTTP requests and is often used by other Python web scraping libraries or frameworks.

The library has a decent adoption, with 3.8K GitHub stars. urllib3 inherently supports connection pooling and is thread-safe, making it well-suited for running concurrent scraping requests efficiently.

👍 Pros

  • It offers efficient performance optimizations.
  • It's highly extensible.
  • Good community support.
  • Efficient concurrent support.
  • Lightweight.

👎 Cons

  • Implementation requires more manual setup for advanced tasks like retries.
  • It doesn't support dynamic data extraction or browser automation.
  • urllib3 relies on third-party libraries like Beautiful Soup for HTML parsing.

When to Use urllib3?

Choose urllib3 if you prioritize precise control of HTTP connections and performance enhancements over the simplicity of HTTP clients like the Requests library.

How to Scrape a Web Page Using urllib3

The example code below requests the target site using the PoolManager instance:

example.py 
# pip3 install urllib3
import urllib3

url = "https://www.scrapingcourse.com/ecommerce/"

# create a PoolManager instance for handling requests
http = urllib3.PoolManager()

# send a GET request
response = http.request("GET", url)

# print the HTML content
print(response.data.decode("utf-8"))

The code outputs the target site's HTML, as shown:

Output 
<!DOCTYPE html>
<html lang="en-US">
<head>
    <!--- ... --->
  
    <title>Ecommerce Test Site to Learn Web Scraping - ScrapingCourse.com</title>
    
  <!--- ... --->
</head>
<body class="home archive ...">
    <p class="woocommerce-result-count">Showing 1-16 of 188 results</p>
    <ul class="products columns-4">

        <!--- ... --->

    </ul>
</body>
</html>

Nice! You just scraped a website's entire HTML content using urllib3.

Conclusion

We've now explored the 7 best Python scraping libraries and how they compare. You're now well-positioned to make the right choice for your next project.

That said, a common problem with most Python web scraping libraries for Python is their inability to avoid bot detection while scraping, making scraping difficult and stressful. Fortunately, a scraping solution like ZenRows solves this problem with a single API call, allowing you to scrape any website without limitations.

Try ZenRows for free now!

Frequent Questions

Why Are Python Libraries for Web Scraping Important?

Python libraries are essential because their parent language (Python) is one of the most popular languages used for web scraping. Python is popular due to its simple syntax and object-oriented nature.

However, building a custom Python web crawler from scratch can be difficult, especially if you want to scrape many custom websites and bypass anti-bot measures. Python web crawling libraries simplify and cut down the lengthy process.

Which Libraries Are Used for Web Scraping In Python?

There are many Python web scraping libraries, and your choice should depend on your project's requirements. This article has already covered the most reliable ones:

  • ZenRows.
  • Selenium.
  • Requests.
  • Beautiful Soup.
  • Playwright.
  • Scrapy.
  • urllib3.

What Is the Best Python Web Scraping Library?

ZenRows is the best Python web scraping library. Other libraries can also do the job, but ZenRows easily avoids the time and effort spent learning these tools and the possibility of getting your scraper blocked.

What Is the Fastest Python Web Scraping Library?

ZenRows is the fastest Python web scraping library, considering the time and effort it saves dealing with anti-bot measures.

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