There are numerous websites with a large amount of data. This data is usually invaluable. Usually, the websites can contain data regarding a large set of information, including company contacts, sports stats, stock prices, and product details. The companies can acquire this data and analyze it for crucial business decisions. The users can perform web scrapping manually or use automated bots and tools to extract valuable data from any website.

Therefore, web-scrapping refers to extracting desired data from a website. The users can use efficient tools to collect and export the data into a helpful format, including a Spreadsheet, Excel, CSV, or an API. Automated web scrapping is helpful because it proves to be time and cost-efficient. However, web scrapping is difficult as only some websites are made of a similar template. Different websites have different formats hence the different underlying HTML codes. Therefore, using powerful and efficient web scraping tools is a wise approach.

Web Scrapping Tools in Python

Forms of Web Scrappers

Generally, there are two types of Web scrappers. These types are the following:

  • Browser Extensions
  • Computer Software

Following is a brief introduction to browser extensions and computer software.

Browser Extensions and Computer Software

Browser extensions refer to the application programs. These extensions are easy to work with as the users can add them directly to their required browsers such as Chrome, Firefox, Safari, and others. Some of these extensions are popular such as messaging extensions, ad blockers, themes, and many others. Although these extensions are comprehensible and configuring these into one’s browser is a more straightforward task, there is a limitation to these extensions as well. These extensions can not entertain any implementation of an advanced feature outside the browser. One such example is the limitation of a critical feature, IP Rotations.

On the other hand, users can apply computer software for web-scraping tasks. The users can download and install any desired web-scrapping software on their personal computers. Moreover, the computer software can also provide manageable implementations of any crucial feature outside the user’s browser. They are not limited to living in the browser to interact with any application. However, acquiring this software is less inconvenient that using browser extensions.

Python for Web Scrapping

There are numerous powerful programming languages, and developers can easily choose anyone to use while working with web scrapping or data extraction. However, Python is the number one choice for many authentic reasons. Some of the advantages of Python for web scrapping are the following:

  • Python code is easy to comprehend. It is like reading a statement in the English language. Moreover, the Python syntax is easy to learn. The code is easily readable and qualifies to be reusable.
  • Using Python for web scraping is both time and cost-efficient. It provides powerful, short pieces of code meant to save time and effort by building significant code quickly.
  • Python offers many valuable libraries such as Numpy, Pandas, Matplotlib, and many others. These libraries help in manipulating and retrieving crucial data.

Python Tools For Web Scrapping

Python provides a wide range of libraries that are helpful in efficient web scrapping. Some of these libraries are the following:

  1. Scrappy
  2. Selenium
  3. BeautifulSoup
  4. Urllib3
  5. Requests
  6. LXML
  7. MechanicalSoup

Here is a brief introduction to some of these libraries.


Scrappy is one of the most valuable and popular Python libraries for web scrapping. Scrappy helps in saving time and effort as the users can efficiently crawl and extract structured data from any website. Scrappy is helpful for automation testing, data mining, and monitoring. Moreover, the developers can benefit from asynchronous data extraction handling using Scrappy’s selectors’ feature. The developers can also change the crawling speed automatically using the auto-throttling method.

Moreover, users can combine Scrappy and a lightweight browser, Splash, to enhance its features. Developing countries worldwide are using Scrappy to power their products, research their competitors, collect images, texts, and videos, update partner-related data in their systems and extract valuable information from online and offline data sources.


Selenium is an open-source web driver which is helpful for automation. Unlike other Python libraries, Selenium efficiently works with JavaScript. Selenium web driver helps write efficient functional test cases and work effectively with Python. The developers can use Selenium to work with any browser, such as Chrome, Safari, Firefox, and others. Moreover, the developers can work on functional and acceptance test cases by integrating the Selenium web driver with Python using APIs. Selenium helps in login automation, adding data, deleting data, handling alerts, form submission, and other significant tasks.

Furthermore, Selenium enables developers to execute JavaScript by providing a JavaScript code interpreter. This powerful interpreter works in the background and gives developers complete control over large documents. Moreover, Selenium also provides an option to skip rendering images on the chrome browser. This approach saves significant time and effectively extracts valuable information from the browser.


BeautifulSoup is one of the most applicable web-scrapping tools for Python. It helps in parsing XML and HTML documents to form a tree structure of the information and easily extract data from it. The developers can work with its Python interface and fully automatic encoding conversions to extract valuable data from websites efficiently. The users can benefit from the latest Python idioms and methodologies of exploring, altering, and browsing in a parse tree using the latest release of BeautifulSoup. The users can automatically form Unicode out of input documents and UTF-8 out of outgoing documents.

Moreover, the users can scan the complete page, pinpoint all the data repetitions, and automatically detect encodings using simple code commands. Furthermore, BeautifulSoup helps in data and platform transformation, like transforming bug trackers from Sourceforge to Roundup. The developers can also use BeautifulSoup for research purposes, such as scrapping websites to monitor the transmission of the Covid-19 Virus.

Similarly, Python has other libraries and tools like Requests, LXML, and MechanicalSoup. These libraries and tools can help with error handling, securing URLs, tracking connection of pooling, parsing data using HTTP and FTP, generating  HTML and XML elements, easy conversion of Data, parsing complex documents, checking boxes, form submission, logging into websites, supporting CSS and XPath selectors, and many other crucial features. Therefore, all these tools are beneficial for decision-making companies to analyze the available data and make essential business decisions accordingly.