The Ultimate Guide to Web Scraping: How to Extract Data Like a Pro

Web scraping is a method of obtaining information and data from the Internet (also known as data scraping). This data is usually saved in a local file to be changed and analyzed later. Web scraping is the same as copying and pasting text from a website into an Excel spreadsheet but on a much smaller scale.

However, when most people refer to “web scrapers,” they refer to computer programs. Online scraping software, also known as “bots,” is designed to crawl websites, scrape relevant pages, and extract information. These bots can quickly collect massive amounts of data by automating this process. This has obvious advantages in the digital age, where big data is so important and constantly updated and changed.

What is Web Scraping used for?

Web scraping has numerous applications, particularly in data analytics. Companies that conduct market research use scrapers to obtain information from online forums or social media for purposes such as customer sentiment analysis. Some people “scrape” data from product sites such as Amazon or eBay to assist with competitor analysis.

Google frequently uses web scraping to evaluate, rank, and index it’s content. They can also use web scraping to take information from other websites and place it on their own (for instance, they scrape e-commerce sites to populate Google Shopping).

Several businesses also use “contact scraping,” searching the internet for contact information to use in marketing. You’ve already permitted them to do this if you’ve ever agreed to use a company’s services in exchange for giving them access to your contacts.

Web Scraping using Python 

In this article we will study about web scraping using python 

How does web scraping function?

We now understand web scraping and how it can be used in various ways. But how does web scraping work? Even though the exact steps vary depending on the software or tools used, all online scraping bots adhere to three basic rules:

1.Send an HTTP request to a server as the first step.

2.Obtaining the code for the website, extracting it, and running it through a program. Step 3: 3.Local data storage.

4.Let’s take a closer look at each of these.

Send an HTTP request to a server as the first step

You send an HTTP request when you visit a website with your browser. This is similar to knocking on someone’s door and inviting them in. After your request has been approved, you can go to that site and look around. Like any other person, a web scraper requires permission to view a website. As a result, a web scraper first sends an HTTP request to the site it wishes to examine.

The second step is to obtain and read the site’s code.

Once a website allows a scraper in, the bot can read and copy the HTML or XML code on the site. This code determines how the website’s content is organized. The scraper will then “parse” the principle or break it down into its constituent parts to locate and extract any elements or objects provided to the bot’s creator before its release. They may contain specific content, rankings, categories, tags, IDs, and other data.

 Save the required data locally.

The web scraper will save the important information locally after reaching the HTML or XML, scraping it, and determining what it means. As previously stated, you have control over what data is extracted (having told the bot what you want it to collect). Structured data is typically stored in an Excel file, typically in.csv or.xls format.

After you have completed these steps, you are ready to begin using the data to achieve your objectives. Isn’t that simple? These three steps give the impression that data scraping is simple. In reality, the process is repeated over and over. This has its own set of issues that must be addressed. Scrapers with bad code, for example, may send too many HTTP requests, causing a website to go down. There are also different rules for what bots can and cannot do on each website.

Python, as we all know, has a wide range of applications and several libraries.Lets discuss about different libraries of Python –

Beautiful Soup

Beautiful Soup is a Python package that can read XML and HTML files and extract data from them. It was designed specifically for “screen scraping” tasks. This library provides simple methods and Pythonic idioms for traversing, searching, and changing a parse tree. When this program is used, documents are automatically converted from UTF-8 to Unicode.

Beautiful Soup can be installed using the system package manager if you are running a current version of Debian or Ubuntu Linux:


The Python program lxml makes use of the C libraries libxml2 and libxslt. It is regarded as one of the best Python libraries for processing XML and HTML due to its extensive feature set and ease of use. It is unique in that it functions similarly to the well-known Element Tree API. Still, it is superior in that it combines these libraries’ speed and XML functionality with the ease of use of a native Python API.

Mechanical Soup

You can automate how people interact with web pages using Python’s Mechanical Soup module. This library automatically sends and stores cookies, follows redirects, clicks on links, and fills out forms. Mechanical Soup has a comparable API. It is built on the robust Python libraries Requests (for HTTP sessions) and Beautiful Soup (for document navigation). Because Python 3 was not supported, this tool was put on hold for a while.

You Must Like: Object-Oriented Programming Best Practices: Tips and Tricks for Success

Python requests 

Python Requests is the only Python HTTP library that does not use GMOs. It enables users to send HTTP/1.1 requests without manually adding query strings to URLs or form-encoding POST data. Numerous features are available, including HTTP(S) proxy support, automatic decompression, automatic content decoding, SSL verification similar to a browser, and much more. Requests are written in PyPy and are officially compatible with Python 2.7, 3.4, and 3.7.


Scrapy is an open-source platform that allows people to collaborate to extract information from websites. Scrapy is a lightweight Python framework for web crawling and scraping. Python was used as the programming language. It can be used for various tasks, including data mining, monitoring, and automated testing. It provides a platform for application developers to create web crawlers that search for website data. Scrapy obtains information from a website using classes called “Spiders” that the user defines (or a group of websites).


Selenium Python is a free web-based automation tool that uses a simple API to create functional or acceptance tests with Selenium WebDriver. Selenium is a collection of software tools that work together to help automate tests in various ways. The complete tool kit provides a comprehensive set of testing features designed to meet the needs of testing all types of web applications. The Selenium Python API simplifies the use of all Selenium WebDriver features. Python 2.7, 3.5, and higher are now all supported.


URLs can be opened using the urllib Python module. It combines URL-related modules, such as urllib. Request for opening and reading URLs, most HTTP and urllib. Error for defining exception classes for errors thrown by urllib. Request and urllib. Error. The parse module provides a common way for components and urllib to cut up Uniform Resource Locator (URL) strings. RobotFileParser is the only class offered by robotparser. It responds to queries about whether a specific user agent can access a URL on the website that hosted the robots.txt file.

Press ESC to close