JavaScript owns the web, earning a spot next to CSS and HTML as one of the building blocks of the internet. Python, on the other hand, is incredibly popular outside of the web and is considered data science royalty. What happens when we compare the most used online language to one of the most used offline languages? Let’s start out with an overview of both and then put both side by side to see their differences.
Python
It’s open-sourced for commercial projects and otherwise under the GPL license. Python also has perks; like a rich standard library and garbage collection. And Python can also integrate with many other languages and platforms using third-party modules from the Python Package Index (PyPI).
Node.js
Node.js isn’t a language—it’s a runtime that lets another language out of it’s browser based cage. The interpreter for JavaScript is typically built into browsers, and before Node.js it was difficult to run JavaScript outside of a browser for practical use. Now with Node.js the titan of the web has been released into the rest of the world of programming.
While it can run on OSX, Windows, and Linux, Node.js is designed for server-side applications. It’s easier to integrate with a backend that’s in the same language as the front end, so having the whole stack be written in JavaScript is really useful. Node.js allows JavaScript to run on the server and complete the stack: it’s the N in stacks like MEAN or NERD.
Node.js was created by Ryan Dahl and first released in 2009. It’s built using Google Chrome’s JavaScript Engine, also called the V8 Engine. It’s open-source, and built around server-side applications. Node.js also acts as a library: it includes a rich library of various JavaScript modules which simplifies the development of web applications using Node.js.
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.
JavaScript, while very useful, is a little bit harder to pick up. JavaScript has advanced in many ways over the years, and it’s not easy to become familiar with good coding practices in JavaScript. This is even more true when considering Node.js, as development can be significantly slowed down by inexperienced developers. Mistakes made in Python are a little more forgivable by comparison, and the project can move forward without coming to a screeching halt when someone makes a small mistake.
Python is also built around readability. It has readability built into its DNA. For example, instead of curly braces to delineate blocks 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.
JavaScript is also more difficult to debug than Python. JavaScript (and Node.js) tends to fail silently, which means that while your code doesn’t return any tracebacks for an error, the code doesn’t run for some reason anyways. It also has all of the C language accoutrements, like curly braces and semicolons. The only benefit to this is that programmers coming from C and C-like languages might be able pick up JavaScript faster than Python because JavaScript’s syntax is closer to C.
Python vs Node.js: Performance
When it comes to speed, both JavaScript in Node.js and Python are pretty close in form. They are both interpreted, dynamically typed, and high-level languages. However, when benchmarked, Node.js comes out on top by a mile. When it comes to crunching large numbers Node.js is blazingly fast, leaving Python in the dust.
Part of this performance comes from the Node.js interpreter: the chrome V8 engine. This engine is built around speed and it definitely shows, bringing JavaScript closer to compiled and typed languages in terms of raw speed.
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
JavaScript is fast and powerful when used with Node.js. However, this doesn’t mean it’s capable of doing anything. JavaScript is happiest on the web, and Node.js doesn’t change that too much. Node.js is built around making JavaScript work as the backend for web apps and websites, and not much else. An example of some applications where Node.js lags behind is in data science and machine learning.
Data Science
Data scientists use several languages, but Python is high up on their list. This is because Python is well suited to data science, and the machine learning and data science communities have set up camp around it. While it’s possible to build data science models in Node.js, it isn’t preferred because there’s less community built around it. To start working with machine learning in JavaScript you would have to build everything from absolute scratch, as opposed to Python where other developers are actively creating modules and frameworks for machine learning.
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.
While you might argue that Node.js is better for data science because it’s more efficient when computing large numbers (and you wouldn’t be wrong), the reason it’s not used is that, if speed is a requirement, most developers just choose to skip over JavaScript straight to the C languages; which are even faster and still have communities built around them for data science and analysis like Python does.
However, there are some tools available for machine learning with Node.js and JavaScript. The Tensorflow.js library provides the opportunity for people to try out machine learning right in their browser. However, there isn’t a lot more available yet. While it may gain speed in the future, JavaScript just isn’t the best choice for developing machine learning applications at the moment.
On the Web
When it comes to the web, especially the front end, JavaScript is in the position of power. It’s a language designed around making front end applications and dynamic websites. However, we aren’t comparing JavaScript, we’re comparing Node.js, which is designed to take JavaScript off of the front end and apply it in the backend. Here is where Node.js and Python compete directly, as Python is also used for backend systems.
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.
The first point goes to Node.js by default, it uses the same language as most front end systems. If your entire stack is JavaScript everything goes a little smoother and less translation issues pop up. If you use Python for backend systems, chances are your front end will still be written in JavaScript. This means you’ll have to pass data between the two. And while Python may have prewritten assets to help this transfer, if you’re making something custom you may run into some issues.
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.
In the end, both languages are in high use and demand. If you’re planning on working in a web field, picking up JavaScript would probably be the wisest choice, even if you plan to develop in Python. If you are planning to do anything that isn’t on the web—especially any kind of data science or AI applications—or if you’re looking for a good first language to start with, Python is probably the better choice.
Have you made a choice? Decided to learn both? Either way, download the Career Karma app today!
