Top 5 Python HTML Parsers

In this article, we’ll explore the top 5 Python HTML parsers: Beautiful Soup, html.parser, html5lib, requests-html, and PyQuery. We’ll delve into their features and guide you on selecting the most suitable parser for your Python projects. 
by Josephine Loo · March 2024


    HTML parsers are essential for extracting and manipulating data from HTML documents. They help developers parse HTML code into structured data, making it easier to work with web content. In this article, we'll explore the top 5 Python HTML parsers, discussing their features and how to choose the right one for your project.

    What is an HTML Parser

    An HTML parser takes HTML as input and breaks it down into individual components. These components are organized into a Document Object Model (DOM) tree, that represents the hierarchical structure of the HTML document. HTML parsers are used in various scenarios, including:

    • Web scraping : HTML parsers are commonly used in web scraping to extract specific data from web pages, such as product prices, news articles, or job listings.
    • HTML validation: HTML parsers can be used to validate HTML documents against the HTML specification, to check for syntax errors, missing tags, and other issues.
    • Dynamic content manipulation : HTML parsers allow developers to modify or manipulate the content of a web page dynamically, such as changing the text of a button or updating an image source.

    Top 5 Python HTML Parser

    1. Beautiful Soup

    Beautiful Soup is a Python library for scraping data from HTML and XML files. It transforms complex HTML/XML documents into a Python object tree and provides simple methods for navigating, searching, and modifying the tree:

    • Navigate - Down (.head, .title, .body, etc.), up (.parent, .parents), sideways (.next_sibling, .previous_sibling, .etc.), back and forth (.next_element, .previous_element, etc.)
    • Search - find_all(), find(), find_next(), etc.
    • Modify - append(), extend(), insert(), clear(), etc.

    Beautiful Soup is beginner-friendly and intuitive. Here’s an example of using BeautifulSoup to parse a piece of HTML code:

    from bs4 import BeautifulSoup
    html = """
        <p>This is an example of an HTML file.</p>
    soup = BeautifulSoup(html, 'html.parser')

    You can also use requests to retrieve the HTML code from a URL:

    import requests
    from bs4 import BeautifulSoup
    url = ''
    response = requests.get(url)
    if response.status_code == 200:
        html = response.content
    soup = BeautifulSoup(html, 'html.parser')

    After turning the HTML code into a BeautifulSoup object, you can use it to navigate, search, or modify the DOM tree.

    # navigate
    # search
    # modify

    2. html.parser (Built-in)

    Python provides a built-in HTML parser accessible via the html.parser module. While it offers fewer features than BeautifulSoup, it can be useful for simple tasks. This module defines a class named HTMLParser that serves as the basis for parsing HTML and XML files, and can be subclassed to implement custom parsing behavior.

    When you pass HTML data to an instance of HTMLParser, it automatically invokes handler methods such as handle_starttag, handle_endtag, and handle_data. These methods are triggered when the parser encounters start tags, end tags, text, comments, and other markup elements. By overriding these methods in a subclass, you can tailor the parsing behavior to your specific needs.

    Here’s a simple example from the Python documentation:

    from html.parser import HTMLParser
    class MyHTMLParser(HTMLParser):
        def handle_starttag(self, tag, attrs):
            print("Encountered a start tag:", tag)
        def handle_endtag(self, tag):
            print("Encountered an end tag :", tag)
        def handle_data(self, data):
            print("Encountered some data :", data)
    parser = MyHTMLParser()
                '<body><h1>Parse me!</h1></body></html>')
    # Encountered a start tag: html
    # Encountered a start tag: head
    # Encountered a start tag: title
    # Encountered some data : Test
    # Encountered an end tag : title
    # Encountered an end tag : head
    # Encountered a start tag: body
    # Encountered a start tag: h1
    # Encountered some data : Parse me!
    # Encountered an end tag : h1
    # Encountered an end tag : body
    # Encountered an end tag : html

    3. html5lib

    html5lib is a pure Python library designed for parsing HTML. It adheres to the WHATWG HTML specification which is implemented by major web browsers. This ensures its compatibility with the web browsers’ behavior.

    It serves as an HTML parser, allowing you to extract structured information from HTML documents. You can parse an HTML document from a file using the following pattern:

    import html5lib
    with open("mydocument.html", "rb") as f:
        document = html5lib.parse(f)

    …or, parse a string directly:

    document = html5lib.parse("<p>Hello World!")

    By default, the parsed document is represented as an xml.etree element instance. That said, you can also choose other tree formats, like Accelerated ElementTree (usually xml.etree.cElementTree on Python 2.x), xml.dom.minidom, or lxml.etree.

    🐻 Bear Tips: Besides its built-in functionalities, you can also use third-party libraries like lxml, Genshi, and Chardet for additional functionalities.

    4. requests-html

    requests-html is a Python library that intends to make parsing HTML as simple and intuitive as possible. It is built on top of requests, extending the HTTP-making library with HTML parsing abilities. Therefore, you can easily make an HTTP request to a URL and navigate its HTML using the requests-html library.

    requests-html has full JavaScript support—this allows you to interact with web pages that use JavaScript to render dynamic content. Besides that, it also uses a mocked user agent to mimic a real web browser, which can be useful for avoiding bot detection.

    Here’s an example of using requests-html to find an HTML element from a web page using its ID, and extracting the text:

    from requests_html import HTMLSession
    session = HTMLSession()
    r = session.get('')
    about = r.html.find('#about', first=True)
    # About
    # Applications
    # Quotes
    # Getting Started
    # Help
    # Python Brochure

    You can also make requests to several URLs at the same time, using async sessions:

    from requests_html import AsyncHTMLSession
    asession = AsyncHTMLSession()
    async def get_pythonorg():
    	r = await asession.get('')
    async def get_reddit():
    	r = await asession.get('')
    async def get_google():
    	r = await asession.get('')
    result =, get_reddit, get_google)

    Reference: requests-html

    5. PyQuery

    PyQuery is a Python library that allows you to make jQuery queries on XML and HTML documents in Python, with an API that resembles its syntax. Therefore, it would be a big advantage for developers familiar with web development.

    The API enables you to extract data from web pages, navigate the document tree, and modify content. You can use the PyQuery class to load the HTML/XML document from a string, a file, or a URL, and use the PyQuery object (d below) like the $ in jQuery:

    from pyquery import PyQuery as pq
    from lxml import etree
    import urllib
    d = pq("<html></html>")
    d = pq(etree.fromstring("<html></html>"))
    d = pq(url=your_url)
    d = pq(url=your_url,
    	opener=lambda url, **kw: urlopen(url).read())
    d = pq(filename=path_to_html_file)
    # [<p#hello.hello>]
    p = d("#hello")
    # Hello world !

    Reference: PyQuery

    🐻 Bear Tips : Some pseudo-classes that are available in jQuery such as :first, :last, :even, :odd, :eq, :lt, :gt, :checked, :selected, and :file can be used in PyQuery too, e.g. d('p:first').

    How to Choose the Right Parser

    When selecting a parser for your project, it’s essential to understand the strengths and weaknesses of each option. Here are some factors to consider:

    • Performance and resource usage : Some parsers are faster while some may use more memory or CPU resources. If you're working with large HTML files or need to parse many files quickly, it’s important to evaluate the speed and resource usage.
    • Ease of Use : Choosing a parser that is easy to use and integrates well with your existing codebase can minimize your learning curve. Parsers with clear documentation and examples also have a strong advantage over others.
    • Features : Consider the features offered by the parser. For example, some parsers may be better suited for handling poorly formatted HTML, while others may offer advanced capabilities for web scraping. You should also compare the specific features, such as support for CSS selectors, XPath, DOM manipulation, and error handling.
    • Compatibility : Ensure that the parser is compatible with your Python version and any other libraries or frameworks you're using in your project.
    • Community Support : Parsers with a strong community of users can be helpful if you run into any issues that are not covered in the documentation.

    Comparison of HTML Parsers

    To help you decide better, here's a comparison table summarizing the pros and cons of each HTML parser and their suitability for different projects:

    | Parser | Pros | Cons | Suitability | | — | — | — | — | | BeautifulSoup | - Easy to use and clean
    - Clear documentation
    - Supports various parsers (e.g., lxml, html5lib)
    - Provides helpful features for web scraping | Slower than some other parsers | Most web scraping and HTML parsing tasks, especially when readability and ease of use are important. | | html.parser | - Built-in with Python
    - Simple and easy to use | - Limited functionality compared to other parsers
    - Not as robust for handling poorly formatted HTML | Basic HTML parsing tasks. | | html5lib | - Implements the HTML5 specification
    - Good for handling badly formatted HTML
    - Use of third-party libraries for additional functionality | Slower than other parsers | Projects that require strict adherence to the HTML5 specification or need to handle poorly formatted HTML. | | requests-html | - Simple and easy to use
    - Integrates well with the requests library
    - Full JavaScript support | Dependency on external libraries | Projects that require web scraping and parsing of dynamic web pages, especially when using the requests library. | | PyQuery | - jQuery-like syntax for selecting elements
    - Efficient memory usage | - Less documentation and community support
    - Require knowledge of jQuery | Projects where familiarity with jQuery is an advantage or when complex HTML selection is required. |


    Choosing the right HTML parser for your Python project is essential for efficient data extraction and manipulation. Consider the factors mentioned above carefully to choose the right HTML parser for your Python project. Good luck!

    About the authorJosephine Loo
    Josephine is an automation enthusiast. She loves automating stuff and helping people to increase productivity with automation.

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    Top 5 Python HTML Parsers
    Top 5 Python HTML Parsers