Web Scraping Node JS vs Python: The Differences
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 Node JS vs 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.
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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
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 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.
- 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.
- The libraries that are written to run on Node.JS are pretty fast. They will improve the development workflows.
- 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.
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.