Why is Python most popular for parsing

Nowadays parsing is a common task for any programmer. It doesn’t matter what programming language you know best, you can be sure it is possible to use your favorite language for sites scrapping, because 90% of time while parsing you deal with HTML structure, JSON data and regular expressions, not the syntax of language itself.

But Python has become popular for parsing – and here are some short terms and reasons why:

• Python is easy to learn and widely distributed; it holds the third place among Stackoverflow most loved programming languages in 2020;
• Python makes you write clean, elegant and readable code;
• Python modules for web scraping are multipurpose – by installing only bs4 and selenium you will be able to parse any site;
• Python packages are easy to build, distribute and use, so the data exchange between parser and other parts of your project flows without headache.
• Python has lambda functions that can be created and called right where you need them without declaration that allows you, for example, to describe the terms of element presence in selenium as a function on the fly.
• Moreover Python is very handy with solving accompanying tasks during web scrapping: files downloading, setting proxies, working with strings and regular expressions, writing data to database or even connecting a neural network to your parser.

What is Python, main features and where it is widely used (API, scraping)

Python is an interpreted high-level computer language. It means that Python code doesn’t need to be complied before running and its syntax is close to the syntax of a normal human language.

As a consequence of the definition above, Python main feature can be described in a few words as it is done on Python official site – Python lets you work quickly and integrate systems more effectively.

Python is widely used for working with big data and neural networks, for creating web sites, desktop or mobile applications and of course for web scrapping and working with any APIs.

Python modules for web scrapping

Base Python modules for sites parsing are the following:

• Requests – default module for GET and POST web requests, to modify proxies, headers and user agent. Working with any API does not do without Requests.
• Beautiful Soup – powerful tool to work with HTML or XML tree, search elements and get data from any container or tag. Main limitation for using Beautiful Soup concerns sites with dynamically changing HTML structure (AJAX technology). To parse such sites use Selenium.
• Selenium – tool that gives you control over browser instance and let you do any action you can normally do in a browser: type text, scroll, click on elements or even run some JS code.
• Scrapy – framework for creating so called spiders that crawl through the list of URLs and extract data asynchronously. 

Python comparison with other programming languages for parsing

Let’s compare Python with other languages in the context of web data scraping: Python vs PHP, Ruby vs Python, Perl vs Python and Java vs Python

Brief information about PHP

PHP (Hypertext Preprocessor) is a scripting language, mainly suited for web development. It means that the code processes on web server side by PHP interpreter.

Brief comparison Python vs PHP

PHP version 5 and later has built-in modules DOM and XPath for selecting elements within HTML – that makes PHP one of the best choices for data crawling. Python has only built-in modules for regular expressions and web requests, other data extraction tools you have to install separately. If you compare Python vs PHP, it is implied that PHP parser will run on server side, but Python scraper API can be used on local machine or even mobile device as well.

Python comparison with PHP shows the other difference that is covered in selectors. With the help of PHP library htmlSQL elements on the page can be selected by SQL-like queries.

Brief information about Ruby

Ruby is an interpreted high-level thoroughly object-oriented programming language. Ruby has tons of syntax sugar and can compete with Python in elegance. Ruby has its own markup language – YAML – that replaces XML.

Brief comparison Ruby vs Python

In general to get HTML from a site Ruby uses open-uri, its own HTTP-client wrapper, and to work with HTML structure Ruby has the special tool – nokogiri. It is quite similar to Python’s Requests and Beautiful Soup, but in Ruby’s nokogiri you will deal with CSS-selectors against python-like selection methods in Beautiful Soup.

Brief information about Perl

Perl is interpreted high-level dynamic programming language. Perl is used for working with CGI (Common Gateway Interface) framework, Unix scripting and web design. As interpreted language it is good for server-side programs and scripts.

Brief comparison Perl vs Python

Python comparison with Perl shows us that Perl has much weaker community support than Python when it comes to data parsing, although it offers us a couple of tools:

• Mojolicious framework (descendant of CGI philosophy) includes HTML5 and XML parser, supports JSON and CSS3-selectors
• Web::Scraper – parser, that gets HTML data, creates DOM object and search elements by XPath or CSS selectors.

In some case these tools are similar with Python’s Beautiful Soup, but speed comparison benchmark shows that Perl parsers performance is extremely low.

Brief information about Java

Java is object-oriented class-based programming language. Java application needs to be compiled, but after compilation it can run on any machine that supports Java (has JVM – Java Virtual Machine). Java is one of the most popular languages for developing client-server web applications.

Brief comparison Java vs Python

Built-in Java WebClient can be compared with Python Selenium module and allows interacting with a site using browser instance and XPath selectors. And Jsoup is Java’s analog of Python’s Beautiful Soup – it extracts data from HTML using DOM traversal or CSS selectors. As a result of Python comparison with Java, we notice that Java and Python web scrapping tools are very similar in spite of great differences in syntax.

Comparison table of other languages on several indicators