Best 7 Python Web Scraping Libraries in 2025

A good Python library for web scraping should be fast, scalable, and capable of crawling any type of web page. In this article, I’ll show you my top seven picks in 2025, their pros and cons, and some quick examples to help you understand how they work.

Although a user may perform web scraping manually, the term typically refers to automated tasks completed with the help of web scraping software.

The following are the prerequisites you will need to follow along with this tutorial:

• Download and install the latest version of Python
• Install pip — python -m ensurepip –upgrade
• A code editor of your choice

Once you’ve met all the prerequisites above, create a project directory and navigate into the directory. Open your terminal and run the commands below.

mkdir python_scraper

cd python_scraper

7 Best Python Web Scraping Libraries 

These are the top seven Python libraries for web scraping that you should be familiar with in 2025.

1. ScraperAPI

ScraperAPI is a powerful web scraping API that handles proxies, browsers, and CAPTCHAs, allowing you to scrape any web page with a simple API call. It simplifies your web scraping operations by managing the complexities of IP rotation (through its smart proxy rotation algorithm), geolocation targeting, and JavaScript rendering.

Underneath, ScraperAPI uses headless browsers to mimic real user interactions. It also supports no-code web scraping and simplifies e-commerce data extraction with its Structured Data Endpoints.

Ease of use 

ScraperAPI is designed for ease of use. With minimal setup, you can integrate it into your Python applications. Just send the URL you would like to scrape to the API along with your API key, and the API will return the HTML response to the URL you want to scrape.

Note: If you haven’t signed up for an account yet, sign up for a free trial today to get 5,000 free API credits!

You can also use the API to scrape web pages, API endpoints, images, documents, PDFs, or other files just as you would any other URL.

Pros:

• ScraperAPI is easy to use.
• It can efficiently bypass CAPTCHAs and anti-bots.
• Suitable for both small and large-scale scraping tasks
• It offers smart rotating proxies.
• It can scrape JavaScript-rendered pages.
• It also works with other libraries.

Cons:

• Requires API key management
• It’s a paid service, but it comes with a free trial.

How to use ScraperAPI for web scraping

To get started with ScraperAPI, sign up for an API key. ScraperAPI exposes a single API endpoint for you to send GET requests. Simply send a GET request to http://api.scraperapi.com with two query string parameters as the payload, and the API will return the HTML response for that URL:

• api_key, which contains your API key.
• url, which contains the URL you would like to scrape.

You should format your requests as follows:

import requests
payload = {'api_key': 'APIKEY', 'url': 'https://httpbin.org/ip'}
r = requests.get('https://api.scraperapi.com', params=payload)
print(r.text)

Here’s a basic example of how to use ScraperAPI with BeautifulSoup to scrape data from an actual website like Quotes to Scrape and display the results:

import requests
from bs4 import BeautifulSoup

api_key = "YOUR_SCRAPERAPI_KEY"
url = 'http://quotes.toscrape.com/'

payload = {
    'api_key': api_key,
    'url': url
}

response = requests.get('http://api.scraperapi.com', params=payload)

if response.status_code == 200:
    soup = BeautifulSoup(response.content, 'lxml')
    quotes = soup.find_all('div', class_='quote')
    for quote in quotes:
        text = quote.find('span', class_='text').get_text()
        author = quote.find('small', class_='author').get_text()
        print(f'"{text}" - {author}')
else:
    print(f"Request failed with status code: {response.status_code}")

I’ll use BeautifulSoup to parse the returned HTML. The library has different methods, like find and find_all, to help extract elements with specific IDs or classes from the HTML tree.

How to use ScraperAPI’s structured endpoints

ScraperAPI provides specialized endpoints that help in getting parsed, ready-to-use data frequently and easily, meaning you don’t have to pair it up with a parser like we saw in the previous example.

ScraperAPIs Structured Data Endpoints cover popular websites, including Google Search, Amazon, Walmart, and eBay. With ScraperAPI’s Structured Data Endpoints, you can:

• Gather structured data in a JSON format
• Customize your datasets with rich parameters
• Work with clean data for faster workflow
• Collect Localized Data anywhere

Let’s quickly demo using ScraperAPI’s Google SDE:

import requests
import json
 
APIKEY = "YOUR_API_KEY"
QUERY = "christmas toys"
 
payload = {'api_key': APIKEY, 'query': QUERY, 'country_code': 'us'}
 
r = requests.get('https://api.scraperapi.com/structured/google/search', params=payload)
data = r.json()
 
with open('results.json', 'w') as json_file:
    json.dump(data, json_file, indent=4)
 
print("Data stored to results.json")

By using ScraperAPI’s Structured Data Endpoints, you can bypass the need for additional parsing libraries and directly obtain clean, structured data, accelerating your workflow.

2. Requests

Requests is the most downloaded Python library, with over 53 million weekly downloads, and it’s one of the most popular Python scraping frameworks. Built on top of urllib3, Requests simplifies sending HTTP requests and handling responses, making it easier for developers to interact with web pages. Once you make a GET request, you can access the web page’s contents using the content property on the response object.

Naturally, it won’t help you with anti-scraping measures, but it’s excellent for basic HTTP requests.

Features of Requests

• Simple and intuitive API.
• Can handle GET, POST, PUT, DELETE, HEAD, and OPTIONS requests.
• Automatic Content Decoding
• Easily add headers, parameters, and cookies to requests.
• Supports proxy configuration.

Ease of use

Requests is known for its simplicity and user-friendliness, making it perfect for beginners and simple scraping tasks. To install the Requests library, use pip, the Python package manager:

pip install requests

Code example:

Let’s go over a simple example of making a GET request and handling the response

import requests

response = requests.get('https://httpbin.org/')
if response.status_code == 200:
    data = response.text
    print(data)
else:
    print(f"Request failed with status code: {response.status_code}")

Pros

• It’s fast.
• Very beginner-friendly. 
• It supports SOCKS and HTTP(S) proxy protocols
• Comprehensive documentation
• Lightweight and easy to integrate

Cons

• It can’t scrape interactive or dynamic sites with JavaScript rendering. 
• No data parsing capabilities
• Lacks built-in asynchronous capabilities

Requests is perfect for basic web scraping, but combining it with a parsing library like BeautifulSoup is key for more complex tasks.  Want to see how they work together? Check out our guide on using Requests with BeautifulSoup.

3. BeautifulSoup

BeautifulSoup is one of the most helpful Python web scraping libraries for parsing HTML and XML documents into a tree structure to identify and extract data. With over 10 million weekly downloads, it has become a go-to library for web scraping.

BeautifulSoup supports parsing using accessible Pythonic methods like find and find_all. This approach is more accessible and readable than CSS or XPath selectors for beginners and is often easier to maintain and develop when working with highly complex HTML documents.

BeautilfulSoup4 also comes with many utility functions like HTML formatting and editing. 

Installation

To install BeautifulSoup, use pip to install the beautifulsoup4 and lxml packages for faster parsing. Open your terminal and run the following command:

pip install beautifulsoup4 lxml

Code example

from bs4 import BeautifulSoup

html = """
<html>
  <title>My Title</title>
  <body>
    <div class="title">Product Title</div>
  </body>
</html>
"""

soup = BeautifulSoup(html, 'lxml')  # note: bs4 can use lxml under the hood which makes it really fast!

# find single element by node name
print(soup.find("title").text)
# find multiple using find_all and attribute matching
for element in soup.find_all("div", class_="title"):
    print(element.text)

Features of BeautifulSoup

• Simple and easy-to-use API.
• Parses HTML and XML documents.
• Supports different parsers (e.g., lxml, html.parser).
• Automatically converts incoming documents to Unicode and outgoing documents to UTF-8.
• Selective parsing, HTML formatting, and cleanup.

Pros

• Easy to use and navigate.
• Can deal with broken HTML code.
• Very Active community support.
• Detailed documentation.

Cons

• Inability to scrape JavaScript-heavy websites.
• Slow when paired with the default html.parser (works better with lxml).
• You need to install multiple dependencies.
• Limited to static content.

4. Scrapy

Scrapy is an open-source framework offering comprehensive features designed explicitly for scraping large amounts of data from websites.

The term “Spider” or “Scrapy Spider” is often used in this context. Spiders refers to Python classes that define how a particular site or group of sites will be scraped, including how to perform the crawl and how to extract structured data from the pages.

Features of Scrapy

• Support for XPath and CSS selectors.
• Extensive support for middleware, pipelines, and extensions.
• Scrapy provides a Telnet console which you can use to monitor and debug your crawler.
• Supports data exports in various file types (JSON, CSV, and XML).
• Support for authentication and cookies.

Installation

You can install Scrapy using pip:

pip install Scrapy

Here’s an example of how to use Scrapy to scrape data from the Hacker News website (news.ycombinator.com):

import scrapy

class HackernewsSpiderSpider(scrapy.Spider):
    name = 'hackernews_spider'
    allowed_domains = ['news.ycombinator.com']
    start_urls = ['<http://news.ycombinator.com/>']

    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(".", "")
        }

In Scrapy, Requests are scheduled and processed asynchronously, meaning that Scrapy doesn’t need to wait for a request to be processed before it sends another, allowing the user to perform more requests and save time.

We can use the following command to run this script and save the resulting data to a JSON file:

scrapy crawl hackernews -o hackernews.json

Pros

• Highly customizable and extensible.
• Full proxy support.
• Excellent performance for large-scale scraping.
• Distributed scraping.
• Easy Integration with other Python web scraping tools.

Cons

• Steeper learning curve.
• More complex setup.
• Can only be used with Python v3.7 and above.

5. Selenium

Selenium is the most popular browser automation Python library, with over 26.8k Github stars. It is a free and open-source web driver that enables you to automate most web scraping tasks. Selenium often mimics real human interaction, like filling out site forms, clicking links, and scrolling through pages. It works efficiently on JavaScript-rendered web pages, which is unusual for other Python libraries.

Selenium supports headless browsing, which means you can browse websites without the user interface. It also works well with any browser, such as Firefox, Chrome, Edge, etc., for testing.

Features of Selenium

• Browser automation capabilities.
• Capture Screenshots.
• Provide JavaScript execution.
• Supports various programming languages such as Python, Ruby, node.js. and Java.

Installation 

To install Selenium, run this command in your terminal or command prompt:

pip install selenium

Code Example:

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.by import By



# Initialize Chrome driver
driver = webdriver.Chrome(
    service=Service(ChromeDriverManager().install()),
)

# Go to the target webpage
driver.get("https://httpbin.io/ip")

print(driver.find_element(By.TAG_NAME, "body").text)

# release the resources and close the browser
driver.quit()

Pros

• Can scrape JavaScript-heavy web pages.
• Support for multiple browsers (Chrome, Firefox, Safari, Opera, and Microsoft Edge).
• Capable of simulating complex user interactions.

Cons

• Slower compared to headless scraping solutions like ScraperAPI.
• It can’t get status codes.
• Requires additional setup for different browsers (e.g., installing WebDriver).
• More resource-intensive, especially for large-scale scraping tasks.

6. Playwright

Playwright is an open-source web scraping library designed for web testing and automation. It is maintained by Microsoft and offers powerful capabilities for interacting with web pages, providing excellent cross-browser automation with support for Chromium, Firefox, and WebKit.

 As an alternative to Selenium, it offers a few key features. It’s generally considered faster than Selenium and offers overall better performance, making it ideal for larger web scraping projects. 

Features of Playwright

• Cross-Browser Support.
• Multi-language support.
• Emulates real devices like mobile phones and tablets.
• Supports for headless and headed execution.
• Parallel execution (Useful for large-scale scraping).

Installation 

To install Playwright, run this command in your terminal or command prompt:

pip install playwright

Then, you need to install the necessary browser binaries:

playwright install

Example of Playwright launching headless Chrome to interact with a page and extract data:

from playwright.sync_api import sync_playwright

# start playwright in synchronous mode
with sync_playwright() as playwright:
    # launch the browser in headless mode
    browser = playwright.chromium.launch(headless=True)
    page = browser.new_page()

    # open the target website
    page.goto("https://www.scrapingcourse.com/ecommerce/")
    print(page.content())

    # close the page and exit the browser instance
    page.close()
    browser.close()

Pros

• Cross-Browser Support.
• Comes with Its Own Built-In Test Runner.
• Superior Performance in Handling JavaScript-Heavy Sites (Compared to Selenium).
• Provides More Advanced Features (Compared to Selenium).
• Headless Mode.

Cons

• Steep Learning Curve.
• Can Be Easily Blocked by Anti-Bot Prevention Measures.
• Lower Community Support Compared to Selenium.
• Doesn’t Work with Legacy Browsers and Devices.

7. MechanicalSoup

MechanicalSoup is a Python library that simulates a web browser, allowing you to automate website interactions. Built on top of requests and BeautifulSoup, MechanicalSoup offers a similar API to these powerful web scraping libraries. This web scraper can follow redirects, send cookies automatically, follow links, and submit forms.

It’s lightweight and straightforward, making it ideal for basic automation tasks and simple scraping jobs.

Features

• Simulates browser behavior with a simple API.
• Automatically stores and sends cookies.
• Minimalistic and easy to use.
• It offers support for proxy configuration.
• Supports session management.

Installation

To install MechanicalSoup, run this command in your terminal or command prompt:

pip install MechanicalSoup

Here’s a basic example of how to use MechanicalSoup:

import mechanicalsoup

def main():

    browser = mechanicalsoup.StatefulBrowser()  
    browser.open("https://en.wikipedia.org/wiki/Web_scraping")

    title = browser.page.select_one('span.mw-page-title-main')

    print("title: " + title.string)
    
main()

Pros

• Ideal for smaller or quick scraping tasks.
• It supports CSS & XPath selectors.
• Supports form submission and session handling.
• Offers proxy support for anonymity and bypassing restrictions.
• Combines strengths of Requests and BeautifulSoup.

Cons

• Limited advanced features compared to ScraperAPI, Scrapy, or Playwright.
• It is not very suitable for large-scale scraping projects.
• Performance might degrade with complex websites.

What is a Python Web Scraping Library? 

A Python web scraping library is a tool, built or written in Python, capable of automating web scraping activities. In its simplest form, a web scraping library is a collection of pre-written code (functions or methods) you can use in your project without building the entire logic from scratch, saving you time and effort.

There are numerous Python web scraping libraries available today, each with its own strengths, as highlighted in the previous section. While some are widely appreciated for their speed when scraping static websites (e.g., Requests and BeautifulSoup), others can simulate browsers and human interactions to scrape dynamic content (e.g., Selenium and Playwright).

Which Python scraping library is right for you?

So, which library should you use for your web scraping project? This table summarizes the features, purposes, and ease of use of all the libraries covered in this guide:

While libraries like Requests and BeautifulSoup are perfect for beginners and simple static scraping, others like Scrapy and Selenium cater to more complex needs, offering advanced features for large-scale or dynamic scraping. Playwright and Selenium excel at JavaScript-rendered content, but their browser overhead can slow things down. For lightweight automation, MechanicalSoup gets the job done for smaller tasks.

However, a common problem with web scraping libraries is their inability to avoid bot detection while scraping a web page, making scraping difficult and stressful. 
ScraperAPI solves this problem with a single API call. Sign up today and get 5,000 free requests with no credit card required!

Why should you use a Python library for web scraping?

Python libraries make web scraping faster and more efficient in every scenario. Without libraries, you would have to build even the simplest functions (e.g., sending a GET request) from scratch. Basically reinventing the wheel over and over.

Libraries like Beautiful Soup allow you to parse, navigate, and extract HTML content more efficiently, while others like Scrapy let you build and manage hundreds of spiders to scale your data collection infrastructure. Selenium lets you use Python to control a headless browser, giving you access to dynamic content you wouldn’t be able to scrape otherwise.

FAQs on Python Web Scraping Libraries