Web Scraping Node JS vs Python: The Differences

JavaScript and Python are currently popular programming languages. They are used for web or mobile app development, data science, etc. They are also the top choices for web scraping.

In this article, we will go through the key reasons for using NODe JS and python for web scraping. And which language is better for web scraping Javascript or Python?

What is Web Scraping?

Web scraping or data extraction refers to the process of gathering large amounts of data from websites. It can be done manually, but it’s very time and energy consuming. 

Automated web scraping tools or scraping bots can extract data for you much faster. The data is then converted into Excel or CSV file for analysis. Web scrapers can be coded using two programming languages i.e. Python or Javascript. 

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Web Scraping Python

Python is well known as a scraping language. This language syntax is easy to understand and learn for beginners. One of the most famous frameworks of Python-based scraping is Beautiful Soup. It makes tasks like searching and navigation easier. If coded correctly, they do accurate data targeting and scraping. 

Other web scrapers based on Python libraries are Scrapy and Selenium. They are easy to install and can be used right away. Since Python is a popular language. It has many coding environments and Integrated Development Environments (IDEs). These include Visual Studio and PyCharm. They support Python and make its coding easier for beginners. 

Using Python, for scraping data from a webpage, first select the URL you want to extract data from. Once chosen, you can go to that page for inspection. After finding the public data you want to scrape, simply write the code in Python and run it. 

Related: Web Scraping vs APIs

Using Node.JS for Web Scraping 

JavaScript is another popular web language and Node.JS is one of the reasons. It is a simple language that enables dynamic functionalities in websites. When a website is accessed, the browser examines the Javascript. It is then transformed into a code computer can understand. 

NodeJS creates network applications and runs them efficiently. Node.JS gives Javascript the ability to make a server-side script. This will help the scrapers extract data from the dynamic structured websites quickly. 

Even though Javascript is a famous programming language. Thus the learning curve for doing Javascript web scraping is low for web developers. Javascript is relatively versatile. To use JavaScript for web scraping. You have to install Node.Js and you can scrape publicly available data without any hassle. 

Like Python, the Javascript code can also be written into a code editor. These include Sublime Text, Visual Studio, etc. 

Summing it up, the general web scraping process of both JavaScript and Python is similar. You choose a target URL for extracting data. Using tools, then you can fetch the page, scrape the data, and convert it into a readable format. 

Web Scraping Node vs Python Difference

Here we will let discuss some pros and cons of Python or Node.js . So it’s easier for you to make a decision on which programming language is better for web scraping. 

Python

Pros:

• Python has a very simple syntax providing a great learning curve. It is suitable for both beginners and experienced programmers. Dynamic typing provides all the right features and functionalities. 
• It is one of the most used web scraping programming languages. Python has a huge community with many tools and libraries. So if you ever need any help, you will get answers to all your questions in the community. 
• Python is capable of supporting task management techniques. It includes multithreading, asynchronous programming, and multiprocessing. All these approaches combined together make Python really efficient.

Cons:

• In comparison with statistically typed languages like C++, Python has very limited performance. To improve it, you can integrate fast programming language into critical sections. 
• Python is pretty challenging for scaling projects properly due to the (GIL) Global Interpreter Lock. This lock lets only one threat run at a time. This will slow down the task execution. 
• Dynamic typing sometimes also leads to mistakes. These mistakes are caught during the compilation process. 

JavaScript

The Perks and limitations of using Node JavaScript for web scraping are.

Pros:

• JavaScript has a very excellent speed since Node.Js is based on the Chrome V8 engine. It optimizes memory usage and can handle concurrent web requests. 
• The libraries that are written to run on Node.JS are pretty fast. They will improve the development workflows. 
• JavaScript has a very rich community. This is why there are a lot of valuable tools and packages for Node.JS. This will make Javascript work easier and faster. 

Cons:

• Node.JS doesn’t work well with sizeable CPU computing tasks. These tasks usually have event-driven and single-threaded nature. So it lowers the performance. However, “worker threads” can be used for executing multiple threads simultaneously. 
• Node.Js has an asynchronous approach. It uses a lot of callbacks. This will pile up the callbacks that go into layers. It makes the code difficult to understand and maintain. These issues can be avoided by using structured coding standards. 
• Like Python, JavaScript is also a dynamically typed language. It means many potential bugs might occur during runtime.

Python vs. JavaScript, Which is better?

Python is most commonly used for web scraping. It has an easy-to-use Beautiful Soup library. It will make navigation and searching through parse trees easier. Still, Python is avoided when large projects are scaled. 

 Whereas, Javascript is a good option for programmers who have a hold on this language. 

Both of them are excellent options for scraping publicly available data. Python and Javascript are easy to learn and work on. 

Whether you choose javascript or Python, the web scraping process will remain the same. You will send a request to the webpage you want to scrape. Parse the request-response. Then the data is stored in a readable format i.e. Excel spreadsheet or CSV file.