Python was first released by Guido van Rossum back in 1991, and was intended to be a replacement for the ABC language. Python is general purpose, interpreted, high-level, and dynamically typed. It’s also object-oriented, and designed around code readability. To that extent it has good whitespace and built in indenting that makes code look a lot better.
Python is open-sourced for commercial projects and otherwise under the GPL license. It also has many perks; like a rich standard library and garbage collection. Python can also integrate with many other languages and platforms using third-party modules from the Python Package Index (PyPI). Python is debatably the language of choice for software developers in the AI or machine learning field.
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Python vs Node.js Compared
Now that we have some information on Python and Node.js, let’s look at how they compare to one another. We will do this category by category, which will help us determine which contender will be the best for any use case.
Node.js vs Python: Typing and Syntax
Python is easy to learn. It’s highly recommended as a first language because it’s so easy to pick up while still teaching the fundamentals of programming, and it’s a useful language whether you’re inexperienced or a Python professional.
Python is also built around readability, it’s built into Python’s DNA. For example, instead of curly braces to delineate blocks and lines of code Python uses indents. Python must be indented to function, therefore all of the code written in Python will be more neat and readable than code in a language doesn’t use indents. It’s also much more forgiving in other ways, such as the fact that it doesn’t use semicolons.
Python vs Node.js: Performance
It’s important to note, however, that Python isn’t slow. It’s only slow when compared to Node.js for crunching large numbers. For most casual applications the difference will be immeasurably small, and will continue to be indiscernible until applied on a large scale. This means unless you’re trying to handle traffic like Google or Facebook does, or you’re trying to count huge datasets, you’re probably not going to be losing much of your day running Python over Node.js.
Node.js vs Python: Application
In Python you have a springboard for starting in data science, and even if you choose not to use the work done by others, the information around Python and data science is much more rich. Python is currently one of the tools of choice for data science, so finding information on applying it in this way isn’t hard either. Comparing the two for data science is like comparing a coin and an electric drill. They can both turn a screw, but one will be a lot easier on your wrist.
On the Web
The backend is the side of the internet you don’t get to see. It handles the raw information that we put into sites, so if the front end is a sink, then the backend is the pipes.
Node.js is also preferable for it’s hasty speed and performance, making it good for real-time applications, like instant messaging or chatting. Because of this, it’s also good for high-load applications or vendor applications where speed of processing is important (like booking a ticket).
Python still has some benefits for backend, however. Python is reliable and consistent. It’s also easier to use and setup, and more friendly to beginners. It’s also preferable for its scientific background; if your backend needs to perform data science, machine learning applications, or needs to function with big data then Python will work well for you.
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