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Why The Requests Library Is Essential for Any Python Developer

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ElitesMindset Editorial Team
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Python is a popular programming language for anyone looking to make an in-house web scraping solution. Its popularity stems from the language being simple to learn and use. It is also backed by an active community and is a general-purpose language that can be used to create powerful tools. But none of these reasons and characteristics matches up to the language’s extensive libraries. With more than 137,000 libraries that contain prewritten code that can perform numerous functions, from making HTTP requests and machine learning projects to data analysis and web scraping, Python is indeed versatile. This article will focus on one of these libraries; the Python Requests library.

Python Requests Library

The Python Requests library simplifies the process of making HTTP requests to a web server using the Python programming language. The functions of the Requests library include:

  • It can be used to execute these HTTP operations such as sending DELETE, GET, PATCH, PUT, POST, and HEAD requests to a specified URL (Uniform Resource Locator)
  • It supports sending additional information to a server through headers and other parameters
  • The library can uncover errors
  • It is capable of handling redirects
  • The Requests library can encode server responses
  • It also handles HTTP authentication
  • It handles exceptions such as when a server is unreachable, the specified URL does not exist, or the webserver is not responding in a timely fashion

How to Use the Python Requests Library

To use the Requests library, you must install it onto a Python virtual environment. To achieve this, you can either use the conda or pip commands, with the pip install requests being the most common. (To use the conda command, simply type cd requests.) These commands install the latest version of the Requests library. Next, you have to import the installed library into the virtual environment using the import requests command.

With the library now installed and imported, you can start making HTTP requests. Making a request using this library is quite straightforward. If you wish to make a GET request from a website such as, you simply have to follow this syntax: r = requests.get(url, params={key: value}, args), but if you do not have predefined parameters, you can simply use r = requests.get(url). You can also specify what happens once the request has been fulfilled; for instance, you can opt to print the outcome of the request by typing print(r), where r is a variable.

Python Requests Library in Web Scraping

Web scraping refers to the process of collecting publicly available data from websites. It can involve manual forms of data collection, such as copying and pasting, or it can take the automated route. In most cases, web scraping is used when referring to the latter. Automated data collection is conducted using bots known as web scrapers. And in fact, you can create your in-house scraping bot using Python and the Python web scraping libraries.

Web data harvesting begins with the making of an HTTP request. This entails specifying the URL you wish to connect to and which you want to retrieve data. Next, the server responds to your request by sending an HTML file containing all the information you would find on a webpage. A well-designed scraper then parses the data in the HTML file, i.e., converts it from an unstructured format to a structured format that human beings and data analysis software can make sense of. Lastly, the scraper organizes the structured data in a CSV or JSON file for download.

The Python Requests library handles the sending of HTTP requests. The Requests library is used in tandem with other web scraping libraries listed below for a successful web scraping exercise.

Here’s a great blog article that delves deeper into what specific features are in a Python Requests library.

Python Web Scraping Libraries

In addition to the Python Requests library, there are four other Python web scraping libraries. These include:

  • lxml
  • Beautiful Soup
  • Selenium
  • Scrapy


lxml is a parsing library that lets you organize the HTML responses. Its function kicks in after the Requests library has made the necessary HTTP requests.

Beautiful Soup

Like lxml, Beautiful Soup is also a parsing library that organizes the unstructured data in the HTML files into a structured format. This library parses both HTML and XML files.


Selenium is designed to facilitate the extraction of data from dynamic websites that are normally written using JavaScript. Whereas Beautiful Soup and lxml are designed to parse HTML and XML files, they are not well suited to render JavaScript code. And because modern websites feature a lot of JavaScript code, undertaking automated web scraping using lxml or Beautiful Soup becomes challenging. Selenium is capable of rendering JavaScript code. It can also fill out forms, scroll pages, click on links, and more.


Scrapy is not a library. Instead, it is a framework that can crawl websites and HTTP requests and extract data. Essentially, it is a comprehensive web scraping solution.


The Requests library is invaluable in creating web scraping solutions using Python. It is used alongside other libraries such as lxml, Beautiful Soup, or Selenium.


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