7 Best Python Web Scraping Libraries

A good Python web scraping library should be fast, scalable, and capable of handling any type of web page. Looking for the best option for your project?

In this article, we’ve handpicked the top Python scraping libraries, including tools that can bypass anti-bot protections. By the end, you’ll know exactly which library fits your use case.

Key Takeaways

• The Requests + Beautiful Soup stack remains the industry standard for scraping static pages. It's lightweight, fast, and beginner-friendly, but it cannot render JavaScript or bypass modern anti-bot protections.
• Playwright and Selenium are the leaders for scraping JavaScript-heavy sites. Playwright is generally preferred for its modern API and faster execution, while Selenium offers the broadest legacy browser support.
• Scrapy is the most powerful web scraping framework for large-scale projects. Its item pipelines and asynchronous architecture make it the best choice for processing millions of pages, though it has a steeper learning curve.
• SeleniumBase and Scrapling are emerging as top free choices for anti-bot evasion. Scrapling is particularly notable for its Adaptive Parsing, which can help maintain scrapers even when a site's HTML structure changes.
• While open-source libraries are excellent for small-scale scraping projects, they often struggle with memory bloat and anti-bot blocks at scale.
• For production-grade uptime (>99.9%), a managed scraping API is recommended to handle proxy rotation and anti-bot updates automatically.

TLDR: Quick Selection Guide

• Static Content: Use Requests + Beautiful Soup.
• Dynamic Interaction: Use Playwright.
• Large-Scale Crawling: Use Scrapy. or Crawlee
• Prioritized Small-Scale Anti-Bot Evasion: Use Scrapling or SeleniumBase.
• Enterprise Reliability: Use a web scraping API.

Which Python Web Scraping Library Is Best?

We did background research to identify which Python web scraping libraries can scrape a web page without issues and selected the best ones for you. Here they are:

1. Requests and Beautiful Soup: Lightweight stack for scraping static web pages. Requests is an HTTP client for requesting raw HTML, while Beautiful Soup is for parsing HTML to extract target data.
2. Selenium: A WebDriver-powered cross-browser automation library suitable for scraping dynamic web pages.
3. Playwright: A cross-browser automation library based on the Chrome DevTool Protocol (CDP) for scraping dynamic websites.
4. Scrapy: Full-featured framework for scalable web scraping and crawling.
5. Crawlee: Versatile scraping and crawling tool for extracting data from static and dynamic web pages.
6. SeleniumBase: A browser automation library built on top of Selenium with web scraping and stealth capabilities for basic anti-bot evasion.
7. Scrapling: A web scraping library with anti-bot evasion and adaptive HTML parsing capabilities.

For a quick scan, the table below is an overview of each Python scraping tool.

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.

For those specifically interested in collecting data for machine learning projects, our guide on building AI datasets with web scraping covers best practices and strategies.

Let's now go into detail and discuss these libraries, including popularity metrics (GitHub stars and PyPi stats), update frequency, and practical examples on how to use each tool.

1. Requests and Beautiful Soup

Requests and Beautiful Soup are often paired as a single scraping stack. The Requests library fetches the page content, while Beautiful Soup parses the HTML to extract specific data. This combination is ideal for scraping static web pages; where JavaScript rendering or browser automation isn’t necessary.

Requests is a popular, beginner-friendly Python HTTP client built on urllib3. Requests supports all standard HTTP request methods, including GET, POST, UPDATE, and DELETE, making it the go-to Python library for accessing APIs and web services.

Beautiful Soup is a widely used Python library dedicated to parsing and navigating HTML or XML. It relies on parsing packages such as lxml and html.parser, offering flexible data extraction via CSS selectors, XPath, and tag-based queries. Its robust ability to handle poorly formatted documents and detect encodings makes it an essential tool for extracting structured information from raw HTML.

GitHub Stars: Requests has ~53.8K GitHub stars.

Monthly Downloads: Requests recieves ~1.1B monthly downloads. Beautiful Soup gets ~224M.

Update Frequency: Maintenance release every few months.

👍 Pros

• The stack is lightweight with low memory overhead.
• No browser instance overhead.
• Both tools are beginner-friendly.
• Requests supports all HTTP methods.
• Easy debugging.
• You get parsing flexibility.
• Requests and BeautifulSoup enjoy huge community support.

👎 Cons

• The stack can't render JavaScript, making it unsuitable for scraping dynamic content.
• No support for browser simulation.
• Easily blocked by anti-bot measures.
• The Requests library has no built-in support for proxy rotation or request header management.

When to Use Requests

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

How to Scrape a Web Page Using Requests and Beautiful Soup.

To see how the Requests and Beautiful Soup libraries work, you'll use the stack to obtain product data from the Ecommerce Challenge page.

The example code below requests the target website HTML with Requests and parses it with Beautiful Soup to extract data using CSS selectors.

# 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 occurred with status code {response.status_code}")
else:
    content = response.text

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

    # extract product data using CSS selectors
    product_data = {
        "product_names": soup.select(".product-name"),
        "prices": soup.select(".price"),
    }

    data = []
    # print extracted product names and prices
    for name, price in zip(product_data["product_names"], product_data["prices"]):
        product = {
            "name": name.get_text(strip=True),
            "price": price.get_text(strip=True),
        }
        data.append(product)
    print(data)

The above code returns the following product data:

[
    {"name": "Abominable Hoodie", "price": "$69.00"},
    # ...,
    {"name": "Artemis Running Short", "price": "$45.00"},
]

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

2. Selenium

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.

Selenium's support for local and cloud grids allows parallel task execution, which can speed up scraping 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.

GitHub Stars: ~34.1K.

Monthly Downloads: ~41.9M.

Update Frequency: Typically on a monthly basis.

👍 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.
• It supports taking web page screenshots.

👎 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 headless Chrome instance, opens the target website, and waits for the page to load. It then captures and prints the website's full-page HTML:

# 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:

<!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. Playwright

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.

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.

GitHub Stars: ~83.3K.

Monthly Downloads: ~36.2M

Update Frequency: Typically on a monthly basis.

👍 Pros

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

👎 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.

# 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:

<!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.

4. Scrapy

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 at scale, including anti-bot auto-bypass and JavaScript rendering.

GitHub Stars: ~60.5K.

Monthly Downloads: ~2.9M

Update Frequency: Several times per year.

👍 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 Scrapy's CrawlerProcess instance:

# 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 HTML in the 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.

5. Crawlee for Python

Crawlee is a versatile web scraping and crawling tool. It supports scraping both static and dynamic websites via standard HTTP request and a headless browser instance. As a relatively new web scraping library, Crawlee is moderately popular, with 8.2K GitHub stars.

Crawlee shares some features with Scrapy, including data storage management, queuing, session and proxy management, and concurrency. However, Crawlee's inherent support for asynchronous scraping and JavaScript rendering with Playwright makes it more modern and easier to use. In addition to advanced queueing support, Crawlee's built-in automatic page navigation eliminates the need to write complex crawling logic and simplifies the overall scraping process.

GitHub Stars: ~8.2K.

Monthly Downloads: ~389K

Update Frequency: Multiple pre-release and stable versions are published each month.

👍 Pros

• It has native support for JavaScript rendering.
• Support for both HTTP and browser scraping modes.
• Crawlee has versatile crawling features, including request queuing, automatic retries, and session management.
• Support for parallel crawling.
• It supports easy switching between storage destinations, ranging from CSV files to remote and local databases.
• The static page crawler natively fuses with Beautiful Soup for easy HTML parsing and data extraction.
• It supports taking screenshots through Playwright's API.
• Built-in page navigation support simplifies web crawling.

👎 Cons

• The initial learning curve can be steep.
• Smaller community compared to established libraries like Scrapy.
• Limited support for customizable middlewares.

When to Use Crawlee

Use Crawlee when you want to keep your web scraping stack for static and dynamic pages clean and unified. Its design makes it suitable for scalable scraping projects.

How to Scrape a Web Page With Crawlee

The example below is a basic script showing how to scrape a web page with Crawlee.

This code opens the target site, crawls all its pages, returns their titles, and stores the scraped data in the default dataset folder:

# pip3 install crawlee
import asyncio
from crawlee.crawlers import BeautifulSoupCrawler, BeautifulSoupCrawlingContext


async def main() -> None:
    crawler = BeautifulSoupCrawler(
        # limit the crawl to max requests.
        max_requests_per_crawl=10,
    )

    # define the default request handler
    @crawler.router.default_handler
    async def request_handler(context: BeautifulSoupCrawlingContext) -> None:
        context.log.info(f"Processing {context.request.url} ...")

        # extract data from the page.
        data = {
            "url": context.request.url,
            "title": context.soup.title.string if context.soup.title else None,
        }

        # store the extracted data in the default dataset.
        await context.push_data(data)

        # enqueue all links found on the page.
        await context.enqueue_links()

    # run the crawler with the initial list of URLs.
    await crawler.run(["https://www.scrapingcourse.com/ecommerce/"])

if __name__ == "__main__":
    asyncio.run(main())

The above Crawlee script returns the page title and URL as separate JSON objects. Here's a sample response:

{
  "url": "https://www.scrapingcourse.com/ecommerce/",
  "title": "Ecommerce Test Site to Learn Web Scraping - ScrapingCourse.com"
}

Nice! You just scraped a website with Crawlee.

6. SeleniumBase

SeleniumBase is a browser automation library built on top of Selenium. While the library is specifically designed for end-to-end automation testing, its stealth capabilities make it suitable for scraping dynamic content, especially on a small-scale. SeleniumBase is pretty newer compared to Selenium.

An important feature for scraping in SeleniumBase is the Undetected ChromeDriver mode, which patches the browser instance to use realistic browser fingerprints. This increases your chances of evading anti-bot detection. However, like Selenium, SeleniumBase is memory-intensive because it uses a heavy browser instance.

GitHub Stars: ~12.4K.

Monthly Downloads: ~3.1M

Update Frequency: Multiple stable versions are released each month.

Pros

• It provides anti-bot-evasion patches.
• Ideal for bypassing basic anti-bot protections, especially Cloudflare Turnstile.
• Suitable for scraping JavaScript-rendered websites with cross-browser support.
• Support for screenshots.

Cons

• Since it depends on the browser instance, it can become memory-intensive.
• Not suitable for large-scale scraping.
• SeleniumBase has a steep learning curve.
• It's stealth mode only work in the GUI (non-headless) mode, making it even more resource-intensive.

When to Use SeleniumBase

SeleniumBase is mostly suitable when you need to scrape an anti-bot-protected site on a small scale.

How to Scrape a Web Page With SeleniumBase

The following SeleniumBase example sets up a headless browser in the Undetected ChromeDriver (uc) mode. It then opens the target page and returns its HTML:

# pip3 install seleniumbase
from seleniumbase import Driver

# initialize the driver in headless mode
driver = Driver(headless=True, uc=True)

# open the target website
driver.open("https://www.scrapingcourse.com/ecommerce/")

# extract the full-page HTML
page_html = driver.get_page_source()

# print HTML
print(page_html)

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

<!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 a basic SeleniumBase setup.

7. Scrapling

Scrapling is one of the upcoming and promising Python web scraping libraries for 2026. Despite being new, it has gained over 20K GitHub stars. Scrapling features page fetchers tailored to various scraping scenarios. These include standard requests for scraping static pages and a dynamic page fetcher for extracting data from JavaScript-rendered websites. Scrapling also includes a stealth fetcher, which increases the chances of bypassing anti-bot measures, especially Cloudflare.

Scrapling also supports session management, allowing you to spin up many sessions within a single spider. Its asynchronous fetcher enhances speed and efficiency, enabling you to scrape multiple web pages concurrently.

One of the features that makes Scrapling stand out from the other ones on our list is its adaptive parsing capability, which enables it to automatically relocate elements, even when a page has been updated. While the adaptive parser seems to work for a few websites, our tests show that it fails on more complex sites, such as Zillow.

GitHub Stars: ~20K.

Monthly Downloads: ~79K

Update Frequency: New versions are released almost every month.

Pros

• Scrapling supports both static and dynamic page scraping.
• Its stealth capabilities are decent for bypassing basic anti-bot measures.
• It supports asynchronous scraping.
• Support for session management.
• Scrapling provides adaptive parsing for easy element targeting and data extraction.
• Support for interactive CLI.
• It supports selector chaining, allowing to scrape similar elements by pointing to a single selector.

Cons

• Scrapling is still suitable for small experiments and may not be scalable for large-scale projects.
• The stealth capability is limited to Cloudflare.
• It doesn't guarantee full-scale stealth against advanced Cloudflare protection.
• Scrapling is still new with a small community.

When to Use Scrapling

Use Scrapling when you need a web scraping framework that can handle one-off requests to large concurrent crawls while automatically adapting to site HTML layout changes. Scrapling is also suitable if scraping sites with basic Cloudflare protection.

How to Scrape a Page With Scrapling

The following Scrapling script visits the target page and extracts its HTML using the standard static page Fetcher:

# pip3 install "scrapling[all]"
from scrapling.fetchers import Fetcher

# fetch the webpage
page = Fetcher.get("https://www.scrapingcourse.com/ecommerce/")

# extract raw HTML
print(page.html_content)

The above Scrapling code outputs the page's HTML as shown:

<!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 great! You just set up a basic Scrapling script.

Best Scraping Solution for Scalability

The best Python web scraping tool should handle edge cases, such as large-scale, enterprise-level data extraction, and automatically bypass even the most advanced anti-bot measures.

Such a tool must adapt as the website's anti-bot security changes. It should manage infrastructure for you at scale. That's where dedicated web scraping solutions such as the ZenRows Universal Scraper API come in handy.

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

With ZenRows' Adaptive Stealth Mode, you get the optimal configuration for successful scraping with minimal setup and zero infrastructure overhead at the lowest possible cost. ZenRows Adaptive Stealth Mode auto-adjusts under the hood as your target website's anti-bot updates its security. This reduces latency and makes your scraping more efficient.

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. All it takes to get its features is a single API call.

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 Adaptive Stealth Mode.

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:

# pip3 install requests
import requests

url = "https://www.scrapingcourse.com/antibot-challenge"
apikey = "<YOUR_ZENROWS_API_KEY>"
params = {
    "url": url,
    "apikey": apikey,
    "mode": "auto",
}
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:

<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.

Conclusion

We’ve now reviewed the top 7 Python scraping libraries and explored how they stack up against one another. You're now well-positioned to make the right choice for your next scraping project.

That said, a common problem with most Python web scraping libraries for Python is their inability to avoid bot detection, especially when scaling up. This makes 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 or speak with a sales representative!