JavaScript and Python are two of the most popular programming languages today. They’re used for various tasks and functions, including web and mobile development, data science, and web scraping.
If you’re looking to get started with web scraping, you might want to know what the pros and cons of using JavaScript and Python are. In this article, we’ll go through the key reasons why these programming languages are widely used for web scraping. We’ll also take a look at some perks and limitations you’ll need to watch out for before choosing a programming language for your web scraping needs.
Why is Python used for web scraping?
Python is mainly used for web scraping as it’s pretty straightforward to get started with. Not only is the syntax quite simple to understand, but there are also thriving Python communities that can help beginners get proficient with this programming language. Besides, Python carries an extensive collection of libraries that aid with the extraction and manipulation of data.
A few examples of Python libraries used for web scraping purposes are Beautiful Soup, Scrapy, and Selenium, which are easy to install and use. There are other Python libraries as well, such as Pandas and Numpy, that can be used to handle data retrieved from the internet.
Because of the popularity of Python, there are different coding environments and IDEs [Integrated Development Environment] such as Visual Studio Code and PyCharm that support this language. The said programs make it easier for beginners to get started with Python programming.
To scrape data from a web page with Python, you’ll first need to select a public URL to scrape from. Once you’ve chosen a target, you can navigate to the page and inspect it. After finding the publicly available data you want to extract, you can write the code in Python and run it.
There are different ways to extract data from a web page using Python. One method is to use the string methods available in this language, such as find[] to search through the HTML text for specific tags. Alternatively, Python supports regular expressions through its ‘re’ module, or you can take advantage of the findall[] method to find any text that matches a regular expression.
Many programmers use dedicated HTML parsers such as Beautiful Soup to parse out HTML pages to make the task easier when it comes to data parsing. Another common solution is the Ixml library, which is more flexible than Beautiful Soup and is often used in conjunction with the Python Requests library, a powerful tool for sending HTTP requests.
Considering interaction with HTML forms, different packages compatible with Python can be utilized. One such example is Selenium, a framework designed for web browser automation. It allows you to enter a browser and perform human-being tasks such as clicking buttons or filling out forms. Besides, Selenium gives you access to a headless browser, which is a web browser without a graphical user interface, making data scraping even more efficient.
Perks and limitations of using Python for web scraping
When using Python for public web scraping, you should be aware of a couple of perks and limitations associated with this programming language. First of all, it’s suitable for both beginners and advanced programmers. Python has a simple syntax, and dynamic typing helps pick up while providing enough features for all but the most demanding projects.
Being one of the most used programming languages for web scraping, Python stands out with its huge community and a wide range of tools and libraries. Thanks to that, finding help when in need or making improvements related to web scraping might be a breeze if you use Python.
Also, Python is capable of all task management techniques: multithreading, multiprocessing, and asynchronous programming. Specifically, multithreading enables several threads to run at a time, and multiprocessing is the ability of an operating system to run several programs simultaneously. In terms of asynchronous programming, operations can work independently from other processes. All this combined enhances the efficiency of Python.
When it comes to shortcomings, Python has limited performance when compared to statically typed languages like C++. As an improvement to that, you can integrate critical sections written in faster programming languages to mitigate most of the performance considerations.
Python also requires slightly more work to scale properly due to Global Interpreter Lock [GIL], which works as a lock that allows only one thread to run at a time. As a result, some tasks might be executed slower.
Lastly, the nature of dynamic typing usually leaves more room for mistakes that would otherwise be caught during compilation, a process of turning a programming language into a language understandable for computers. Yet, type-hints and static type-checkers like MyPy can help prevent such errors.
Why is JavaScript used for web scraping?
JavaScript is a famous programming language that almost every web developer is familiar with. Thus, the learning curve for getting started with web scraping using JavaScript is usually low for most web developers.
Since JavaScript is very popular, there are many resources on the internet that anyone can use to learn the language. What’s more, this programming language is relatively fast, versatile, and can be used for a wide range of tasks.
Similar to Python, the JavaScript code can be written in any code editor, including Visual Studio Code, Atom, and Sublime Text. To use JavaScript for your public web scraping projects, you’ll have to install Node.js from the official download page. Node.js, a powerful JavaScript runtime, will provide developers with a set of tools to scrape publicly available data from websites with minimal hassle.
Node.js Package Manager [NPM] also features many useful libraries, such as Axios, Cheerio, JSDOM, Puppeteer, and Nightmare, that make web scraping using JavaScript a breeze. Axios is a popular promise-based HTTP client package used to send HTTP requests, while Cheerio and JSDOM are tools that make parsing the retrieved HTML page and manipulating the DOM easier.
Puppeteer and Nightmare are high-level libraries that allow you to programmatically control headless browsers to scrape both static and dynamic content from web pages. Getting started with these tools is quite easy, and you can get help from their documentation sites.
Summing up, the general process of web scraping with JavaScript is similar to web scraping with Python. First, you pick a target URL that you want to extract publicly available data from. Then, using the available tools, you fetch the web page, extract the data, process it, and then save it in a useful format.
Perks and limitations of using JavaScript for web scraping
First and foremost, JavaScript excels at its speed, as Node.js is based on a powerful Chrome V8 engine. Its event-based model and non-blocking Input/Output [I/O] optimizes memory usage; thus, Node.js can efficiently handle many concurrent web page requests at a time.
Also, libraries written to be run natively on Node.js might be quite fast and help you improve the overall development workflow. For example, Gulp can assist in task automation, while Cheerio aids in working with asynchronous JavaScript. Other instances of such libraries include Async, Express, and Nodemailer.
Yet, standard libraries often leave users wanting additional tools to make working with JavaScript quicker and easier. Since JavaScript carries a vast community, there are a lot of community-driven packages available for Node.js.
Considering the limitations of JavaScript, one flaw of using Javascript for web scraping is that Node.js doesn’t perform very well when handling sizeable CPU-based computing tasks due to its single-threaded and event-driven nature. However, the “worker threads” module, introduced in 2018, makes it possible to execute multiple threads simultaneously.
Node.js uses callbacks extensively as a result of its asynchronous approach. Unfortunately, this often results in a situation known as callback hell, where callback nesting goes several layers deep, making the code quite challenging to understand and maintain. Nevertheless, you can avoid this issue by using proper coding standards or the recently introduced async/await syntax that handles the asynchronicity without relying on callbacks.
Just like Python, JavaScript is a dynamically typed language. Hence, it’s also essential to watch out for bugs that may occur at runtime. As a way out, programmers who have experience with a statically typed language can choose to work with Typescript, a superset of JavaScript that supports type checking. Typescript is compiled to JavaScript and makes it easier to spot and handle type errors before runtime.
Web scraping with Python vs. JavaScript compared
Python is more widely used for web scraping purposes due to the popularity and ease of using the Beautiful Soup library, making it simple to navigate and search through parse trees. Yet, JavaScript might be a better option for programmers who already have experience with this programming language.
Whether you’re working with Python or JavaScript, the process of scraping data from a web page remains the same. That is, you send a request to the publicly available page you want to scrape, parse the response, and save the data in a useful format.
Here’s a quick table showing how Python compares to JavaScript for web scraping.
As we have seen, both Python and JavaScript are excellent options for public web scraping. They are pretty easy to learn and work with and have many useful libraries that make it simple to scrape publicly available data from websites.
We hope this article has helped you to see how Python and JavaScript compare for web scraping. If you want to learn more about web scraping with Python and JavaScript, check out these detailed articles on Python Web Scraping and JavaScript Web Scraping. You can also learn how to get started with Puppeteer from this article.