How to Use Python for Web Scraping?

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Knowing how Python can offer you the best results can be hugely beneficial when it comes to web scraping! Fortunately,  learning how to do with Python is surprisingly easy and can provide excellent results for your scraping efforts. In turn, this can help boost your business.

You can read general details about how web scraping is done by clicking on the hyperlink, but if you are already aware of the that, then let’s dive into the process of scraping using Python.

How to Use Python for Your Web Scraping Goals

There are many ways in which using web scraping in Python can benefit your business, and understanding these can be useful for your business ventures!  But how can you benefit from web scraping methods? 

All legally accessible information uncovered by automated data extraction is up to date and relevant at that time; by contrast, by doing it manually,  the information could change by the time you finish digging up data, which could impact the accuracy of the information.

If you are wondering who needs web scraping, its all about data and automation so if you decide that automated web extraction is the right solution for your business – and we’d be inclined to agree with this, you need to look at how to use Python for the best results. 

Luckily, learning basic coding is a relatively straightforward process. It shouldn’t take too long, and with our support, you’ll soon be able to use web scraping for your market research too!

Step One: Check Your Knowledge of HTML

For starters, you’ll need to have a certain degree of HTML knowledge.  You don’t need to know how to create a whole website in HTML, though, don’t panic!  Just make sure you know some of the basics of HTML. These include the following:

  • Everything is included within tags in HTML. Tags open with <> and close with </>, such as the following tags that are some of the most commonly encountered tags with Python web scraping:
    • <head> </head> (used to set the head of the document)
    • <body> </body> (used to contain the content of the page)
    • <li> </li> (used for listing items, such as in bullet points)
    • <h2></h2> Heading 2 text. There are numerous headings in HTML, with H2 being the most frequently used
    • <a> </a> Anchor tags, usually used to embed links

These are just a few of the different HTML codes that you might see while scraping.  If you encounter a piece of code that you’re not familiar with, though, don’t worry about it – you can easily search for the code snippet on Google to find out more about that tag!

Step Two: Setting Up BeautifulSoup

Once you’ve checked your HTML knowledge, you can begin by looking at the basics of Python web scraping.

This is the best way to start as it will allow you to develop your skills over time by starting small and improving your skills, tackling more complex scraping once you’ve mastered the basics.

You’ll need to use BeautifulSoup with Python to extract data from HTML – if this has not been installed already, you’ll need to use the following command to start the process of installation:

pip install beautifulsoup4

Once BeautifulSoup has been installed, you can begin inputting data into the program. To do this, you need to create an HTML string, which is done by obtaining the HTML data from your target website;

Python web crawler can automate this process for you. To begin with, though, download the HTML of a website.

Once you have the code that you want to analyze, you can then begin the BeautifulSoup program by writing the following code:

#Feed the HTML to BeautifulSoup

soup = bs(some_html_str)

Once you have set up this code, the BeautifulSoup program will be ready to analyze the HTML provided.  It can do this in numerous ways, but the functions find and find_all are the two most commonly used analysis commands that we’ll be using.

Find will retrieve the first instance of what you’ve searched for, while find_all will provide every example of the search term in the HTML code.

Step Three: Using Commands to Find Data

We’ve highlighted the two most common commands so far: find and find_all. 

These two commands can be used along with some others, but let’s first focus on the find function to demonstrate how to use BeautifulSoup in your Python code to find data from an HTML string. 

To use a command, first, write the following line of code:


In the above example, we instruct the Soup program (as set up in Step Two) to find the first instance of the H3 tag in the text.

After running this script, we will get a coded result in the form as follows:

<h3>This is a Generic H3 Heading</h3>

If you’re looking for a single item, this is fine.  However, if you’re searching for something typically grouped – such as a list of data – you’ll need to use the find_all ­­function to retrieve all of the data from the list.

Step Four: Getting More Specific

The above example is fine if there is only one instance of what you’re searching for in your text – but how many articles or web pages have just one heading, one list, or the like? To make the search more specific, you can also consider the class and the ID of the search term.

This can be formatted as such when using the find or find_all functions:

ourList = soup.find(attrs={“class”:”coolclassList”, “id”:”list”)


This will provide the information we want more precisely than just telling BeautifulSoup to provide us with all the information contained within lists. As such, it can be used to search for any specific section in the HTML string that’s needed!

But how can you find out the specific class for your data? Well, this is easy – load up your chosen webpage and right-click on the text that you want to scrape.

After doing so, choose the option “inspect,” This should bring up the HTML information for the section! From this, you can find the class and ID for the data and input it into BeautifulSoup. Easy, right?

Step Five: How to Scrape Multiple Webpages

If you want to scrape multiple web pages, there are two options. Either you can create a web crawler that will harvest the webpage URLs and HTML for you, or you can choose to do so manually.

If doing so manually, repeat the steps above for each webpage and wait for the BeautifulSoup program to provide you with the results!

You’ll typically need to wait a minute or so for results, but once you’ve got them, it’s easy to analyze than the information that the software has provided!

Learn More About Python Web Scraping

If you are still trying to understand the basic meaning of web scraping or how it might benefit you, keep in mind that data scrapers save time by automating repetitive tasks so that developers can focus on other work.

The more complex your project is, the less likely it will be automated with web scraping services alone. Sometimes this means hiring a developer or dev team to handle some parts of your project while using data scraping to aid other areas.

In any case, don’t hesitate to contact us anytime if you have questions about how we can help you get started! We’ll answer all of them promptly and provide information from our blog posts about who needs web scraping too.

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