Automate Web Scraping Using The Python Auto Scraper Library

  • 01/10/2022

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Web Scraping is a technique or practice for obtaining information from websites and keeping it securely on your network. It is essentially a method of acquiring a large quantity of information from the internet. The sheer volume of such data is wrongly wired in an HTML format that is transformed into structured data so that it may be accessed in various applications. There are several ways to execute web scraping to obtain information from the world wide web. These involve using online businesses, unique APIs, and composing code for web scraping without training.

In a situation of such a high demand for information, Python has been offering exceptional varieties of libraries for anyone to scrape the web. This article will explore these libraries and answer a simple question: How to automate web scraping using Python?

Here are a few of the Python auto scraping libraries that are widely used in the tech community.


Many techies have a constant dilemma about what to opt for when it is Python-based automated web scraping. One of the ways to go is Scrapy. When it comes to Python web scraping libraries, Scrapy makes one of the most effective options. 

This tool helps you to crawl and scrape the web methodically as it is a comprehensive tool and not a library. Scrapy can be employed to inspect and extract data over the internet and also cater to automated and meticulous testing. It is important to note that Scrapy was initially designed to create automatically crawling web spiders. 

The process of an auto web scraper is made significantly more straightforward with the use of this tool. Scrapy does show a disadvantage of a tedious experience in the sequence of installing and operating it on your device. Nevertheless, the tool is impressively efficient in terms of memory compared to the other scraping automation.


  • Asynchronous
  • Support for multiple plugins
  • Consumes low CPU and memory
  • Detailed Documentation
  • Plentiful online resources are available


  • Can be difficult for beginners
  • Has a steep learning curve


Automated data scraping provides numerous options for a user. And if you are looking for a straight and uncomplicated HTTP library, Requests is the web scraping automation tool you may be looking for. An exciting feature depicted by Requests is that it enables you to send requests and receive the same response in JSON or HTTP formats. 

Requests demonstrates the true potential of a well-designed abstract high-level API. The most ideal moment to use Requests is when you are still pondering the question of how to automate web scraping using Python. Basically, when you're at the beginning of your web scraping procedure. 

The system requires no prior experience to get the hang of it. It is a straightforward tool, and more effective as an API to contact is available. However, one must refrain from using Requests for web scraping automation if you are attempting it through JavaScript. In such cases, the data may not be reliable as the responses may not be accurate.


  • It is simple to use
  • Provides support for HTTP(S) Proxy
  • Supports chunked requests
  • Has support for International domains and URLs


  • Can scrape only the static content from a webpage
  • Doesn’t provide support for parsing HTML

Beautiful Soup

Beautiful Soup is said to be a parser library. This Python library is employed to draw data from files in XML and HTML formats. It is possible to extract more precise information from an HTML text via Beautiful Soup, which is made possible due to its feature of pinpointing page encoding. 

Parsers have a relatively pivotal role in this situation. Parsers enable a programmer to achieve HTML file data. In the absence of a parser, the user may have to use Regex to keep up and achieve patterns from the text. Such an approach is not very practical. Holistically, Beautiful Soup is another simple tool to answer all your questions on how to automate web scraping.


  • Can be set up using a few lines of code
  • Easy to learn and use
  • Provides automatic encoding detection


  • Can be a little slower compared to others


What if you could simply visit a web page and access any button to extract the information stored there? Web scraping automation through Selenium allows the same as it is a web driver. This tool is written in Java for most of its quantity to automate tests. 

One of the most significant reasons why Selenium makes essential scraping automation is it doesn't demand a deep understanding of the process and also enables the code to identify human behavior. A beginner can easily use this tool as it is much more user-friendly and compassionate for a newbie in automated scraping.


  • User-friendly
  • Support for dynamic data scraping
  • Automates web browsers


  • Can consume significant CPU and memory
  • Slow


Urllib makes a complex web scraping automation compared to Requests. However, one does not need to install Urllib as it already is among the built-in Python libraries. 

The usability of Urllib includes parse websites but does not offer a lot of compatibilities. Urllib is a Python library that permits coders to access and interpret content from HTTP and FTP protocols. Urllib provides the ability to manage and open URLs.


  • In-built
  • Multi-functional


  • It can be a bit slow sometimes


These libraries are the perfect options for a streamlined process of automated data scraping using Python. Many of these are very viable for beginners who do not have precise knowledge of web scraping and also for the ones who have been spending years in the same field. Ensure you choose the correct tool to meet your requirements for scraping automation.

If you are looking for a web scraping service in USA that uses Python for automated data extraction, feel free to contact us

We provide effective, efficient, and high-quality web scraping, crawling, and data extraction services. We use cutting-edge technologies, such as Python auto scraper libraries, to go deep into the internet and extract every bit of data so that you can make informed decisions about your company's expansion.


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