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Compare Top 7 Python Web Scraping Libraries & Tools in 2024

Updated on May 17
7 min read
Written by
Gulbahar Karatas
Gulbahar Karatas
Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers.

She previously worked as a marketer in U.S. Commercial Service.

Gülbahar has a Bachelor's degree in Business Administration and Management.
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Compare Top 7 Python Web Scraping Libraries & Tools in 2024Compare Top 7 Python Web Scraping Libraries & Tools in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

When it comes to web scraping, there are four common approaches for gathering data: 

  1. Off-the-shelf web scrapers
  2. In-house web scrapers
  3. Web scraping APIs
  4. Ready-made datasets

Developers use web scraping libraries to create in-house web crawlers. In-house web crawlers can be highly customized, requiring  significant development and maintenance time. Building a web scraper in a language you are familiar with will allow you to reduce the development time and resources needed  to build the scraper.

Python is the most commonly used programming language of 2022. 1 It provides third-party libraries, including Beautiful Soup, Scrapy, and Playwright, for automating web scraping tasks.

In this article, we summarized the main features, pros and cons of the most common open-source Python web scraping libraries.

LibraryGitHub StarsKey FeaturesLicense
Beautiful Soup84HTML/XML parsing
Easy navigation
Modifying parse tree
MIT
Requests50.9KCustom headers
Session objects
SSL/TLS verification
Apache 2.0
Scrapy49.9KWeb crawling
Selectors
Built-in support for exporting data
3-Clause BSD
Selenium28.6K

Web browser automation
Supports major browsers,
Record and replay actions

Apache 2.0
Playwright58.3KAutomation for modern web apps
Headless mode
Network manipulation
Apache 2.0
Lxml2.5K

High-performance XML processing
XPath and XSLT support
Full Unicode support

BSD
Urllib33.6KHTTP client library
Reusable components
SSL/TLS verification
MIT
MechanicalSoup4.5KBeautiful Soup integration
Browser-like web scraping
Form submission
MIT

Here is more explanation for the best web scraping libraries & tools:

1. Beautiful Soup

Beautiful Soup is a Python web scraping library that extracts data from HTML and XML files. 2 It parses HTML and XML documents and generates a parse tree for web pages, making data extraction easy.

Beautiful Soup Installation: You can install Beautiful Soup 4 with “the pip install beautifulsoup4″ script.

Prerequisites:

  • Python
  • Pip: It is a Python-based package management system.

Supported features of Beautiful Soup:

  • Beautiful Soup works with the built-in HTML parser in Python and other third-party Python parsers, such as HTML5lib and lxml.
  • Beautiful Soup uses a sub-library like Unicode and Dammit to detect the encoding of a document automatically.
  • BeautifulSoup provides a Pythonic interface and idioms for searching, navigating and modifying a parse tree.
  • Beautiful Soup converts incoming HTML and XML entities to Unicode characters automatically.

Benefits of Beautiful Soup:

  • Provides Python parsers like”lxml” package for processing xml data and specific data parsers for HTML.
  • Parses documents as HTML. You need to install lxml in order to parse a document as XML.
  • Reduces time spent on data extraction and parsing the web scraping output.
  • Lxml parser is built on the C libraries libxml2 and libxslt, allowing  fast and efficient XML and HTML parsing and processing.
  • The Lxml parser is capable of handling large and complex HTML documents. It is a good option if you intend to scrape large amounts of web data.
  • Can deal with broken HTML code.

Challenges of Beautiful Soup:

  • BeautifulSoup html.parser and html5lib are not suitable for time-critical tasks. If response time is crucial, lxml can accelerate the parsing process.

Most websites employ detection techniques like browser fingerprinting and bot protection technology, such as Amazon’s, to prevent users from grabbing a web page’s HTML. For instance, when you send a get request to the target server, the target website may detect that you are using a Python script and block your IP address in order to control malicious bot traffic.

Bright Data provides a residential proxy network with 72+ million IPs from 195 countries, allowing developers to circumvent restrictions and IP blocks.

Check out our research for an in-depth analysis of providers of residential proxies. You can utilize web unblocker technology, an advanced proxy option ideal for sites with stringent anti-bot measures. Explore the top web unblocker solutions.

Source: Bright Data

2. Requests

Requests is an HTTP library that allows users to make  HTTP calls to collect data from web sources. 3

Requests Installation: Requests’s source code is available on GitHub for integration into your Python package. Requests officially supports Python 3.7+.

Prerequisites:

  • Python
  • Pip: You can import Requests library with the “pip install requests” command in your Python package.

Features of Requests:

  • Requests automatically decode web content from the target server. There’s also a built-in JSON decoder if you’re working with JSON data.
  • It uses a request-response protocol to communicate between clients and servers in a network.
  • Requests provides in-built Python request modules, including GET, DELETE, PUT, PATCH and HEAD, for making HTTP requests to the target web server.
    • GET: Is used to extract data from the target web server.
    • POST: Sends data to a server to create a resource.
    • PUT: Deletes the specified resource.
    • PATCH: Enables partial modifications to a specified resource.
    • HEAD: Used to request data from a particular resource, similar to GET, but does not return a list of users.

Benefits of Requests:

  • Requests supports SOCKS and HTTP(S) proxy protocols.

Figure 2: Showing how to import proxies into the user’s coding environment

Requests support SOCKS and HTTP(S) proxy protocols.
Source: Requests4
  • It supports Transport Layer Security (TLS) and Secure Sockets Layer (SSL) verification. TLS and SSL are cryptographic protocols that establish an encrypted connection between two computers on a network.

Challenges of Requests:

3. Scrapy

Scrapy is an open-source web scraping and web crawling framework written in Python.5

Scrapy installation: You can install Scrapy from PyPI by using the “pip install Scrapy” command. They have a step-by-step guideline for installation for  more information.

Features of Scrapy:

  • Extract data from HTML and XML sources using XPath and CSS selectors.
  • Offer a built-in telnet console for monitoring and debugging your crawler. It is important to note that using the telnet console over public networks is not secure because it does not provide transport-layer security.
  • Include built-in extensions and middlewares for handling:
    • Robots.txt
    • User-agent spoofing
    • Cookies and sessions
  • Support for HTTP proxies.
  • Save extracted data in CSV, JSON, or XML file formats.

Benefits of Scrapy:

  • Scrapy shell is an in-built debugging tool. It allows users to debug scraping code without running  the spider to figure out what needs to be fixed.
  • Support robust encoding and auto-detection to handle foreign, non-standard, and broken encoding declarations.

Challenges of Scrapy:

  • Python 3.7+ is necessary for Scrapy.

4. Selenium

Selenium offers different open-source extensions and libraries to support web browser automation. 6 Its toolkit contains the following:

  1. WebDriver APIs: Utilizes browser automation APIs made available by browser vendors for browser automation and web testing.
  2. IDE (Integrated Development Environment): Is a Chrome and Firefox extension for creating test cases.
  3. Grid: Make it simple to run tests on multiple machines in parallel.

Figure 3: Selenium’s toolkit for browser automation

Selenium offers three products for web browser automation: WebDriver API, IDE and Grid.
Source: Selenium7

Prerequisites:

  • Eclipse
  • Selenium Web Driver for Python

To learn how to setup Selenium, check Selenium for beginners.

Features of Selenium:

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

Benefits of Selenium:

  • Offers headless browser testing. A headless web browser lacks user interface elements such as icons, buttons, and drop-down menus. Headless browsers extract data from web pages without rendering the entire page. This speeds up data collection because you don’t have to wait for entire web pages to load visual elements like videos, gifs, and images.
  • Can scrape JavaScript-rich web pages.
  • Operates in multiple browsers (Chrome, Firefox, Safari, Opera and Microsoft Edge).

Challenges of Selenium:

  • Taking screenshots of PDFs is not possible.

5. Playwright

Playwright is an open-source framework designed for web testing and automation. It is maintained by Microsoft team.8

Features of Playwright:

  • Supports search engines such as Chromium, WebKit, and Firefox.
  • Can be used popular programming languages including Python, JavaScript, C#, Java and TypeScript.
  • Downloads web browsers automatically.
  • Provides APIs for monitoring and modifying HTTP and HTTPS network traffic.
  • Emulates real devices like mobile phones and tablets.
  • Supports for headless and headed execution.

Playwright installation:

Three things are required to install Playwright:

  1. Python
  2. Pytest plugin
  3. Required browsers

Benefits of Playwright:

  • Are capable of scraping JavaScript-rendered websites.
  • Takes a screenshot of either a single element or the entire scrollable page.

Challenges of Playwright:

  • It does not support data parsing.

6. Lxml

Lxml is another Python-based library for processing and parsing XML and HTML content. Lxml is a wrapper over the C libraries libxml2 and libxslt. Lxml combines the speed of the C libraries with the simplicity of the Python API.

Lxml installation: You can download and install the lxml library from Python Package Index (PyPI).

Requirements

  • Python 2.7 or 3.4+
  • Pip package management tool (or virtualenv)

Features of LXML:

  • Lxml provides two different API for handling XML documents:
    • lxml.etree: It is a generic API for handling XML and HTML. lxml.etree is a highly efficient library for XML processing.
    • lxml.objectify: It is a specialized API for handling XML data in Python object syntax.
  • Lxml currently supports DTD (Document Type Definition), Relax NG, and XML Schema schema languages.

Benefits of LXML:

  • The key benefit of lxml is that it parses larger and more complex documents faster than other Python libraries. It performs at C-level libraries, including libxml2 and libxslt, making lxml fast.

Challenges of LXML:

  • lxml does not parse Python unicode strings. You must provide data that can be parsed in a valid encoding.
  • The libxml2 HTML parser may fail to parse meta tags in broken HTML.
  • Lxml Python binding for libxml2 and libxslt is independent of existing Python bindings. This results in some issues, including manual memory management and inadequate documentation.

7. Urllib3

Python Urllib is a popular Python web scraping library used to fetch URLs and extract information from HTML documents or URLs. 9 Urllib is a package containing several modules for working with URLs, including:

  • urllib.request: for opening and reading URLs (mostly HTTP).
  • urllib.parse: for parsing URLs.
  • urllib.error: for the exceptions raised by urllib.request.
  • urllib.robotparser: for parsing robot.txt files. The robots.txt file instructs a web crawler on which URLs it may access on a website.

Urllib has two built-in Python modules including urllib2 and urllib3.

  1. urllib2: Sends HTTP requests and returns the page’s meta information, such as headers. It is included in Python version 2’s standard library.

Figure 4: urllib2 sends request to retrive the target page’s meta information

Source: Urllib210
  1. urllib3: urllib3 is one of the most downloaded PyPI (Python Package Index) packages.

Urllib3 installation: Urllib3 can be installed using pip (package installer for Python). You can execute the “pip install urllib3” command to install urllib in your Python environment. You can also get the most recent source code from GitHub.

Figure 5: Installing Urllib3 using pip command

Source: GitHub11

Features of Urllib3:

  • Proxy support for HTTP and SOCKS.
  • Provide client-side TLS/SSL verification.

Benefits of Urllib3:

  • Urllib3’s pool manager verifies certificates when making requests and keeps track of required connection pools.
  • Urllib allows users to access and parse data from HTTP and FTP protocols.

Challenges of Urllib3:

  • It might be challenging than other libraries such as Requests.

8. MechanicalSoup

MechanicalSoup is a Python library that automates website interaction.12

MechanicalSoup installation: Install Python Package Index (Pypi), then write “pip install MechanicalSoup” script to locate MechanicalSoup on PyPI.

Features of MechanicalSoup:

  • Mechanicalsoup uses BeautifulSoup (BS4) library. You can navigate through the tags of a page using BeautifulSoup.
  • Automatically stores and sends cookies.
  • Utilizes Beautiful Soup’s find() and find all() methods to extract data from an HTML document.
  • Allows users to fill out forms using a script.

Benefits of MechanicalSoup:

  • Supports CSS and XPath selectors. XPaths and CSS Selectors enable users to locate elements on a web page.

Challenges of MechanicalSoup:

  • MechanicalSoup is only compatible with HTML pages. It does not support JavaScript. You cannot access and retrieve elements on JavaScript-based web pages.
  • Does not support JavaScript rendering and proxy rotation

Further reading

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Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security. She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers. She previously worked as a marketer in U.S. Commercial Service. Gülbahar has a Bachelor's degree in Business Administration and Management.

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