What Is Beautiful Soup And How It Helps In Web Scraping?

What Is Beautiful Soup And How It Helps In Web Scraping?

Web Scraping Services is the ultimate assignment to solve all your data extraction problems as well as entice clients towards your new projects. Nowadays we are all connected to the internet and socialization is now deeply rooted within our brain cells. Hence, such a digital and personalized platform can cater all your perspectives and requirements when used in the right way. At the end of this blog you will be able to consider Web Scraping as an essential tool to give your enterprise a new life and energy that it lacked.

Web scraping Tools provide a wide range of services involving scraping of stock prices to favor better investment decisions, Scraping yellow data to generate leads, scraping store locations for better business location indication and scraping E-commerce sites for competitor analysis like Amazon and eBay.

What is Beautiful Soup?

Beautiful soup is undoubtedly an excellent python library to extract useful data from XML files along with HTML sources. It is a comparatively simpler Web Scraping Tool which offers Pythonic idioms for navigation purposes, converting incoming files to Unicode and other running documents to UTF-8. If you want access to the latest version Debian or Ubuntu Linux, Beautiful Soup will be highly supportive with the system package manager.

Beautiful Soup is a python packaged which is specifically used to parse and scrape information from web pages, HTML and XML documents. Beautiful Soup functions in such a way to create a parse tree for the page that is to be extracted for intended information. Beautiful Soup includes malformed markup and non-closed tags which are named as tag soup. At the moment Beautiful Soup is available for Python 2.7 and Python 3.

In this blog, we will inspect how to build a web scraper that can extract all the information related to Software Developer Job Listings from a well-known website i.e. Monster job. We are going to start off by investigating data sources that in our opinion well suited for data extraction.

Inspect Your Data Source

The first best thing is to explore the website. Simply click on the webpage like a normal user and search for all software developer jobs in Australia or any other country using a local search interface for the purpose. You will see that much information is encoded in the website URL, which changes as much as your interaction with the website. We have the following URL for the Monster Website.


  1. q=Software-Developer is used to select a job over the website page.
  2. where=Australia explores your location for better results.

Inspect the Site Using Developer Tools

Developer tools are important to understand the website and its format at a deeper level. Modern browsers come with build-in developer tools for websites such as Chrome. It is an essential tool to scrape websites by getting to know the website’s internal structure more clearly. The tools provide us with the site’s DOM from where we can select HTML elements that can be edited within the browser for convenience.

The process works like this {View → Developer → Developer Tools}.

Scrape HTML Content From A Page

After the previous basic steps, it is time to start with Python. The first thing is to get the HTML code in the Python script via Python’s Request Library. For this, you will need to type the code {$ pip3 install requests} the next step goes by the installation of the few lines of the HTML code.

Using Beautiful Soup To Parse HTML Content

When you have successfully gathered or scraped some messy HTML from the website, your next step includes importing Beautiful Soup class creator. The whole parsing of the text is done by a beautiful Soup object which is assigned a specific task. Let us say we assign a beautiful soup object to “html_soup”. “html.parser” argument indicates that the following parsing is being done by Python Parser.

Beautiful Soup can be imported through bs4. By the action code:

Soup = BeautifulSoup(page.content, ‘html.parser’)

For finding the element ID (attribute), we begin with selecting elements with job postings ID. You can also explore the page for more elements by right clicking over the web page to inspect.

<div id=”ResultsContainer”>

    <!– all the job listings –>


results = soup.find(id=’ResultsContainer’)

Extract Text From HTML Elements

If you want to extract useful text from the HTML Elements such as title, company name and its location for job posting, then Beautiful Soup can easily scrape such data with little effort. By adding simple (.text) to the beautiful Soup object you can turn on the text content from the HTML elements. The result of the Monster Jobs Website is as follows:

for job_elem in job_elems:

    title_elem = job_elem.find(‘h2′, class_=’title’)

    company_elem = job_elem.find(‘div’, class_=’company’)

    location_elem = job_elem.find(‘div’, class_=’location’)





When you will run this code you will see contents displayed for your convenience. In the text, there will be white space, but don’t worry it can go away by just working with Python strings and adding .strip( ) to clear the white space in between the text result. The final extracted information is as follows:

  • Python Developer
  • LanceSoft Inc
  • Woodlands, WA
  • Senior Engagement Manager
  • Zuora
  • Sydney, NSW

Extract Attributes From HTML Elements

At this stage, your site is already yielding scraped results from HTML for all job postings. However, there is still a last step to do, to get the links for the jobs where you are going to apply! This code strips away the link when accessing the .text attribute of its parent element. Tags and attributes are not part of that. In order to get the entire URL, you will have to extract one of the attributes. The following list is the list of the entire python_ jobs that we have just created with much focus. The URL is enclosed between the href attribute, you can easily unfold it and fetch it by extracting the href attribute of the <a> tag with the help of square bracket notation.

python_jobs = results.find_all(‘h2’,

                               string=lambda text: “python” in text.lower())

for p_job in python_jobs:

    link = p_job.find(‘a’)[‘href’]


    print(f”Apply here: {link}\n”)

Such yielded results will show you all the links to efficient job opportunities that include Python within the title. Also you can employ [ ] notation to extract larger HTML attributes by fetching the URL link as mentioned above.


Beautiful Soup is a web scraping essential to effectively parse HTML data. It is a well-versed and highly compatible companion in your web scraping ventures. The documentation it allows is very brief and accurate. It will support all your web scraping needs such as documentation, navigation, and advanced searching for better results.

How ITS Can Help You With Web Scraping Service?

Information Transformation Service (ITS) includes a variety of Professional Web Scraping Services catered by experienced crew members and Technical Software. ITS, is an ISO-Certified company that addresses all of your big and reliable data concerns. For the record, ITS served millions of established and struggling businesses making them achieve their mark at the most affordable price tag. Not only this, we customize special service packages that are totally worked upon your concerns highlighting all your database requirements. At ITS, our customer is our prestigious asset that we reward with unique state of the art service packages. If you are interested in ITS Web Scraping Services, you can ask for a free quote!

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