Python is the fourth most popular programming language, according to a recent developer survey. But when it comes to web scraping, it is the most popular language, and for a good reason – as a high-level, general-purpose language, Python can be used to create a program that handles all the steps and processes related to data collection. And given that it is also utilized in the data science field, it can create other solutions that pertain to data analysis.
Web Scraping
Web scraping refers to the extraction of publicly available data from web pages. Typically, the term is commonly used when referring to automated data extraction using bots known as web scrapers. To increase the chances of success, it is important to use the scraper together with a proxy such as a Brazil proxy.
While these bots exist as ready-made programs, you can create your custom web scraper using one of the many programming languages. But given the reason we have highlighted above, Python is the preferred language.
What Is the Process of Web Scraping?
Web data extraction follows the procedure below:
- HTTP/HTTPS requests
Web scraping begins with HTTP/HTTPS requests that are sent to the website from which you want to extract data. This is much like how you would ordinarily connect to a site.
- Server responses
Following receipt of the request and depending on the request method used, the web server responds by sending an HTML or XML file. This file contains all the data that makes up a given webpage. And to a person with no web development experience, let alone data analysis software, the data in the file may not make much sense. In that regard, the file must be converted into a structured format.
- Parsing
Parsing refers to the conversion of the unstructured HTML to a structured format that can be understood by laypeople as well as data analysis software.
- Data storage
Next, the now-structured data is stored in a JSON or CSV file for download.
Python Web Scraping
Python’s status as the most popular web scraping language is due to the fact that there are existing libraries that cover each of the steps outlined above. This means that if you want to create a scraper, all you need to do is to bring together two or more web scraping libraries. These libraries, which are a collection of useful functions and lines of code that do away with the need to write codes from scratch, greatly simplify the process of creating web scrapers.
The Python web scraping libraries include:
Python Requests library
The Requests library contains pre-written functions that are used to make HTTP/HTTPS requests. It covers the most commonly-used HTTP methods, including POST, GET, PATCH, DELETE, PUT, and HEAD.
lxml
lxml is a parsing library that contains code that, when executed, converts HTML and XML data into a structured and easy-to-read format. As it is only a parsing library, lxml is used together with the Requests library, with the former taking over where the latter’s functions stop.
Beautiful Soup
Like lxml, Beautiful Soup is a parsing library. In that regard, it extracts data from HTML and XML files, converting it from an unstructured format into a structured format. Beautiful Soup is used together with the Requests library, with the latter kickstarting the data extraction process.
Selenium
Selenium is an open-source project initially created to support browser automation and automated testing. But the fact that it can render JavaScript code has made it an essential web scraping library. This is because lxml and Beautiful Soup can only extract data from HTML and XML files. And given that nowadays, most websites feature JavaScript code as developers look to make them more dynamic, Beautiful Soup and lxml cannot parse the data.
Scrapy
Scrapy is a Python framework that can crawl websites to discover pages that contain data to be collected, send HTTP/HTTPS requests, parse the response files, and store the converted data in a file for download. Simply put, this is a self-sufficient tool that makes data collection seamless.
Python is the most popular web scraping language for other reasons, too. These include:
- Python is easy to learn and understand
It employs a syntax that bears similarities to the English language
- It is a versatile language that can be used to create a wide array of tools, including data analysis solutions
Thanks to this versatility, you can create a web scraper that seamlessly works with a proxy, allowing you to extract data from geo-blocked sites. For instance, using a Brazil proxy, you can access data that only Brazilian residents can access. If you need a Brazil proxy for your own use, check out what Oxylabs offers!
- Python can be used to undertake both large- and small-scale web scraping – it is highly scalable
- The language supports automation
Thus, you can create a script that automates the entire web scraping process, freeing up time for you to undertake other functions
Conclusion
Python is the most popular web scraping language because of several factors. These include the Python web scraping libraries such as the Requests library, Selenium, lxml, Beautiful Soup, and Scrapy. In addition, the language is easy to learn and use, highly scalable, and versatile.
