Python Parse HTML

Discover web scraping for large data collection. This summary presents top Python HTML parsers for easier web scraping tasks.

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Zawwad Ul Sami
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The web holds tons of useful data. This data helps businesses make smarter choices. But, most of this data isn’t organized and ready for use. That’s where web scraping helps.

Web scraping lets you collect data from websites and save it in a useful format for analysis. But websites use HTML, you need to turn this HTML into a structured format to use the data.

Many tools can parse HTML, and picking one can be tricky. Here, we’ll look at some top Python tools for this job. Python is a go-to language for data scraping, so it has many options. We picked these tools based on their:

  • Open-source availability
  • Regular updates
  • Being easy to use
  • Strong community support
  • Fast performance

Beautiful Soup

Beautiful Soup is not just an HTML parser; it’s a flexible tool for pulling out data from HTML and XML. It’s great for web scraping and editing HTML files. Though simple to learn, it packs a punch for complex scraping needs, often being the only library required.

Before you start, you must install Beautiful Soup since it’s not included with Python. It’s easy to set up: just run pip install beautifulsoup4 in your terminal. After installation, load your HTML and use Beautiful Soup’s various functions to extract the needed data quickly.

Consider this example from the Beautiful Soup docs. To scrape from an actual website, you’d use something like Requests to fetch the webpage first. If you want to learn more on web scraping techniques and tools, exploring Google crawl API is a good idea

Here is the HTML:

html_doc = """<html><head><title>The Dormouse's story</title></head>
<body>
<p class="title"><b>The Dormouse's story</b></p>
 
<p class="story">Once upon a time there were three little sisters; and their names were
<a href="http://example.com/elsie" class="sister" id="link1">Elsie</a>,
<a href="http://example.com/lacie" class="sister" id="link2">Lacie</a> and
<a href="http://example.com/tillie" class="sister" id="link3">Tillie</a>;
and they lived at the bottom of a well.</p>
 
<p class="story">...</p>
"""

Before you start parsing, you need to bring in the Beautiful Soup library with this code.

from bs4 import BeautifulSoup

Now you can finally turn the HTML into a structured format with this simple line of code:

soup = BeautifulSoup(html_doc, 'html.parser')

With this line, you just give the document you want to analyze (here, “html_doc”) to the BeautifulSoup constructor, and it does the parsing. You might have seen that the BeautifulSoup constructor needs a second argument besides the document. This is where you specify the parser. If you want to use ‘lxml’ instead of ‘html.parser’, just use the following command:

soup = BeautifulSoup(html_doc, 'lxml')

Remember, different parsers can give different results.

After parsing the HTML, you can start searching for and extracting the elements you need. For instance, to find the title, you just use soup.title. To get the text of the title, use soup.title.string. Beautiful Soup has handy functions like find() and find_all() that make it super easy to locate elements. For example, to find all paragraphs or links, just type soup.find_all(‘p’) or soup.find_all(‘a’).

In short, Beautiful Soup is a strong, adaptable, and user-friendly tool for web scraping. It’s ideal for everyone, but with its comprehensive guides and simplicity, it’s particularly good for beginners. To discover more about what Beautiful Soup offers, look through its documentation.

lxml

Another top-notch HTML parser is lxml. You’ve already seen it mentioned: Beautiful Soup allows the use of the lxml parser when you pass ‘lxml’ as the second argument. In the past, lxml was praised for its speed, and Beautiful Soup was favored for handling messy HTML. Now, combining them means you get fast processing and robust HTML handling all at once.

Lxml lets you pull data from both XML and imperfect lxml before using it. You can easily install it with ‘pip install lxml’. After installation, you can use functions like parse() and fromstring() to parse HTML. For instance, you can parse the ‘html_doc’ used in the Beautiful Soup example with the following code:

from lxml import etree

from io import StringIO



parser = etree.HTMLParser()



tree = etree.parse(StringIO(html_doc), parser)

Now, the HTML file will be stored in a variable called ‘tree’. From this point, you can pull out the necessary data using different methods, like XPath or CSSSelect. To documentation of lxml.

Lxml is not only lightweight and regularly updated, but it also has comprehensive guides and resources. It shares several features with Beautiful Soup, so if you’re familiar with one, learning the other should be relatively easy.

pyquery

If you like the jQuery API and wish it was available in Python, then pyquery is exactly what you need. It’s a library that allows for parsing of both XML and HTML, offering fast processing and an API very similar to jQuery.

Like the other HTML parsers we’ve discussed, pyquery lets you navigate and extract information from XML or HTML files. It also enables you to change the HTML by adding, inserting, or deleting elements. You can start with simple HTML to create document trees, then tweak and pull data from them. Plus, with pyquery’s various helper functions, you can streamline your coding process significantly.

To start using pyquery, install it using pip install pyquery, then bring it into your project with from pyquery import PyQuery as pq. After that, you can start working with documents from strings, URLs, or even lxml environments. Below is an example of how to use this code:

d = pq(html_doc) # loads the html_doc we introduced previously

d = pq(url="https://www.serply.io/") # loads from an inputted url

Pyquery works mucb like lxml and Beautiful Soup, but its syntax mirrors jQuery’s, making it unique. For example, in the above code, ‘d’ functions similarly to jQuery’s ‘$’. This makes pyquery not as widely used as BeautifulSoup or lxml, which means it has less community support. Despite this, it remains lightweight, is regularly updated, and comes with clear documentation.

Explore the advantages of using the Google Images API for your projects.

jusText

justText is not as comprehensive as the other parsers mentioned, but it’s very handy in certain situations, like when you only want to keep complete sentences from a webpage. Its main purpose is to clear away all non-essential content from a webpage, leaving just the core text. This means if you run a webpage through jusText, it’ll strip away items from the header, menu, footer, sidebar, and other non-critical sections, leaving just the primary content.

You can test it out with the jusText demo. Just enter a webpage URL, and it will remove all the unnecessary parts, keeping only the key content. If this sounds beneficial, you can explore more about jusText here.

To start using jusText, install it with pip install justext. You’ll also need the Requests module to fetch the webpage you want to clean up. Below is a code example to extract the main text from any webpage:

import requests

import justext



url = "https://www.Serply.io/"



response = requests.get(url)

paragraphs = justext.justext(response.content, justext.get_stoplist("English"))



for paragraph in paragraphs:

if not paragraph.is_boilerplate:

print(paragraph.text)

This code fetches the main content from the Serply.io homepage. To use it for a different page, simply replace the URL in the code. Remember, this works best with English content.

Like pyquery, jusText isn’t as well-known as Beautiful Soup. Its use is specific: it’s designed to strip away filler content from web pages. While it doesn’t offer as much documentation or frequent updates as lxml or Beautiful Soup, jusText is still an efficient,lightweight tool for its specialized purpose of eliminating unnecessary content.

Scrapy

Beautiful Soup is excellent for parsing HTML and extracting data, but for crawling and scraping an entire website, there are stronger tools. For complex web scraping tasks, you might want to use a framework like Scrapy. It’s more complex and has a steeper learning curve than Beautiful Soup, but it’s also significantly more robust.

Calling Scrapy just an HTML parser doesn’t do it justice: HTML parsing is just a tiny aspect of its capabilities. Scrapy is a complete framework for Python web scraping. It offers features like crawling a whole site from one URL, saving data in various formats and databases, controlling the crawl rate, and more. It’s powerful, efficient, and customizable.

Scrapy also provides methods for HTML parsing. You start with a request for the needed URL using the start_requests method. Then, you can parse the response web page with the parse method, which extracts data and returns it in formats like item objects, Request objects, or dictionaries. The yield command then passes this data to an Item Pipeline.

The main challenge with Scrapy is its complexity; it’s not beginner-friendly due to its vast range of features.

But mastering Scrapy allows you to extract virtually any data from a website, making it an invaluable tool for advanced web scraping projects. While it’s not as lightweight as other parsers mentioned, it’s well-supported, with extensive documentation and a strong community, making it one of the most popular Python tools available.

Conclusion

The internet’s data is constantly growing, and so is the need to process this data into useful information. To collect data effectively, you first need to parse the HTML of a web page and extract needed information. This requires an HTML parser.

We’ve looked at several Python HTML parsers in this article, evaluating them on their openness, lightness, maintenance, performance and community support.

For your upcoming web scraping tasks, consider one of these parsers. Beautiful Soup is usually the best starting point for most projects. With lxml as another good choice. If you like using jQuery, pyquery could be your pick. When you just need the main content from a webpage, jusText is an excellent choice. And for large-scale web scraping, like a whole website with thousands of pages, Scrapy is likely the most suitable option.