Tips & Tricks for Scraping with Python Requests Library

The Python Requests library is among the thousands of Python libraries that have endeared this general-purpose programming language to a legion of developers and data scientists. There is a library for everything. From web scraping libraries to machine learning and data analysis libraries, just to mention a few. Of these, this article will focus on the Python Requests library, which plays a fundamental role in web scraping.

Web Scraping

Web scraping refers to the automated extraction of data from websites. It is mainly carried out using bots known as web scrapers, which can either be purchased off-the-shelf or created from scratch. The latter approach can involve using any of the myriad programming languages, one of which is Python.

The fourth most popular programming language, according to a 2022 Developer Survey, Python is a general-purpose, high-level, easy-to-use, and easy-to-learn programming language. What makes it particularly useful and indispensable when it comes to web data harvesting is its extensive pool of web scraping libraries. These Python libraries include:

• Python Requests library
• lxml
• Beautiful Soup
• Selenium
• Scrapy

Python Requests Library

The process of web scraping begins with specifying the website (in the form of a URL) from which you intend to extract data. Next, the scraper makes an HTTP request to prompt the webserver to send a response containing the data being sought. This demonstrates the importance of making HTTP requests in the web scraping pipeline. And little wonder, therefore, that the Requests library is part of the thousands of Python libraries in use today.

The Python Requests library is used to make HTTP requests. Specifically, it simplifies how developers can interact with HTTP operations such as GET, POST, HEAD, DELETE, PATCH, PUT, and POST. It does this by ensuring that the developer is using a Python virtual environment, an ecosystem that allows them to interact with the computing environment as well as code that other users have developed over the years. Simply put, the Requests library creates an application programming interface through the Python virtual environment.

Steps to Make HTTP Requests with Python Requests Library

Making HTTP requests is a step-by-step process that is described below:

Step One: Create a Python Virtual Environment

Here, you can download and install Anaconda, an open-source Python distribution package that affords you access to Python libraries and frameworks as well as a typing workspace. Alternatively, you can elect to use the venv environment.

Step Two: Install the Requests Library

Type pip install requests to install the Python Requests library into the Python virtual environment.

Step Three: Import Requests

Import the Python Requests library to bring on board functions that are key to making HTTP requests.

Step Four: Make the Requests

Among the various request methods, namely GET, POST, HEAD, DELETE, PATCH, PUT, and POST, GET is the most common. Its popularity stems from the fact that it is used to request data from specific URLs/web servers. And given that this data is the main reason behind undertaking web scraping exercises, it goes without saying that you will have to use this request method.

To make a GET request, type the following: r = request.get(url); you should populate the section in parenthesis with the URL of the website from which you want to retrieve data.

Tips & Tricks on Using Python Requests Library

Several proven tips and tricks can enable you to easily start using the Python Requests library. These include:

1. Use a Python virtual environment
   As stated, this environment combines all Python libraries, enabling you to easily install and use them. Thus, to use the Python Requests library, you must create or install a Python virtual environment.
2. Monitor the status of the responses using the response.status function and the status codes to identify web pages that returned an error
3. Understand the various request methods and select the appropriate one depending on your needs
   When scraping data from websites, using the GET request method is crucial.
4. Recognize that the responses sent by a web server include headers that contain some useful information. In order to make sense of the data contained in the headers, it is important to use the headers dictionary.
5. Use other Python libraries in conjunction with the Python Requests library
   This increases the scope of operations and functions that your solution can execute. For instance, if you wish to scrape data from websites, it is important to use the Python Requests library along with the lxml or Beautiful Soup, which are parsing libraries. For dynamic websites, you can use Selenium, which is capable of rendering JavaScript code.

Conclusion

The Python Requests library is integral to operations that require access to websites. It is a fundamental part of any Python-based, custom web scraping solution. And to ensure success, we have outlined five tips on how to effectively and easily use this requests library.

Click here for more information on Python requests library.