Python vs JavaScript: Main Differences, Performance Comparison, and Areas of Application
The complexity of modern web apps lies far beyond creating eye-catching user interfaces with countless elements. To enable lag-free experience and effortless scalability, it’s important to pay due attention to the architecture design, which can be pretty challenging. Under the hood of a full-featured online app, different frameworks and libraries can peacefully coexist with different programming languages used to build software. Since the equation may contain so many variables, it’s essential to master your knowledge of each potential system component to know when and why to use them.
At the moment, JavaScript and Python attract unprecedented attention. For example, in 2021, they accounted for the majority of pull requests on GitHub:
Tons of libraries enable development flexibility, there’s no shortage of learning resources, and the average salaries of JavaScript and Python developers make you want to dive into the peculiarities of code writing as quickly as possible. Today, we’ll outline the major differences between them and learn in which areas their applications guarantee better outcomes.
A Brief Introduction
Both Python and JavaScript were invented in the 90s, so they have come a long way in development before taking their places on the pedestal. JavaScript, for instance, first appeared in 1995. Back then, its main purpose was to add some dynamic functionality to the web pages that HTML and CSS could not provide. About a decade and a half later, Node.js appeared which marked a new era in the development of JavaScript. With its help, developers could use JavaScript both for the front-end and back-end parts of the application.
Most often, JavaScript is described as an object-oriented language. But it’s important to note that its possibilities aren’t limited to OOP. Additionally, it enables event-driven, functional, and imperative paradigms. Developers who are not satisfied with the possibilities provided by this language can also build apps using TypeScript which is sometimes described as JavaScript with superpowers.
Python, in its turn, is a bit older. This general-purpose language was developed by Guido van Rossum in 1991. One of the main advantages of Python is that it’s very easy to learn and interpret. The code written with it is as easy to read as a book for those who have some experience with programming.
However, despite such simplicity, Python is not a “noob language” intended for people who just started learning how to code and want to switch to something more “serious” in the future. It supports functional, imperative, object-oriented, and procedural programming and is considered one of the best tools for Data Science. NumPy, Pandas, PyTorch, TensorFlow, and dozens of other frameworks and libraries can turn Python into a swiss knife when it comes to working with huge datasets and Machine Learning algorithms. Additionally, such popular server-side Python frameworks as Django, for example, make it a decent choice for any custom software development company delivering top-notch web apps.
Performance Comparison
Quickly learning new technologies and writing code in no time is a good thing with no doubt. But a fast development pace doesn’t necessarily mean that the final software system will work fast. Especially when we talk about interpreted programming languages since in this case programs aren’t compiled into native code.
JavaScript relies on the V8 engine that enables outstanding performance. It, thanks to the engine features, supports Just-In-Time compilation which, in a few words, means that the code can be compiled and optimized as the app executes. The code interprets in the end-users browsers. That’s why you can face significant lags when you visit a website stuffed with tons of JavaScript, especially if you use an old computer. However, Node.js enables the use of JavaScript on the server side, and its multi-thread support makes the overall picture even better. Therefore, it is an excellent choice for those who want to build a high-performance web application.
Python, on the other hand, can’t handle heavy apps with such ease. Do you remember that we considered Python as one of the easiest programming languages to read? Well, unfortunately, this luxury comes with its price. A high level of abstractions requires a longer time for interpretation. Our language differs from those which computers speak. Therefore, the easier the code is for a human to understand, the more difficult it is for a computer to interpret and execute it. Luckily, there are such Python implementations as Jython and Cython whose main purpose is to increase the overall performance, but despite all the efforts of their developers, they can’t compete with the V8 engine.
Fields of Application
The features of JavaScript and Python that we’ve mentioned don’t give a strict answer to which one to use in a given situation. You can’t choose the language that has the simplest syntax to learn and use it for implementing whatever comes to your mind from a complex web-based system to a video game. Well, probably, you can, but it won’t be the most optimal way of doing things. Every technology is initially designed to solve a specific set of tasks. In the case of programming languages, it leads to the development of libraries and frameworks that help to solve these tasks. Because of this reason, before writing the code you must understand which languages are best suited for which areas.
For example, Python takes on one of the leading roles in the world of Machine Learning. Making a software system learn from the available data using complex algorithms is a challenging task by itself. Python’s easy-to-understand syntax allows avoiding additional complexities associated with the need to understand poorly readable code. As a result, developers can focus on experimenting and make changes as quickly as possible.
Since Python is an interpreted language, you can forget about compatibility issues and run code on different platforms with no effort. Additionally, such full-featured libraries like TensorFlow and Keras enable developers to improve their efficiency when it comes to deep learning.
Data Science is another example of the area where Python can be considered a “dominant force.” As we already mentioned Pandas and NumPy can help with data analysis and mathematical functions while Matplotlib can take care of creating 2D charts.
JavaScript also has the potential to be used in data science projects. It supports TensorFlow, and D3.js, one of the richest libraries for data visualization. But, let’s be honest, we love it for a different reason. JavaScript is irreplaceable if you want to write polished front-end code for top-quality web applications. Just start googling “front end” and you’ll find countless references to React, Vue.js, Angular, and dozens more frameworks and libraries of extreme popularity.
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Moreover, there’s no need for a web developer to learn a new language to take care of the back-end part of the application since Node.js allows using JavaScript for writing server-side code.
Conclusions
Since both Python and JavaScript are pretty popular, it’s pretty natural that people wonder which one to devote their time to. Unfortunately, comparing programming languages is sometimes equal to comparing apples and spring breeze. All of them are created to write code that makes computers follow your will. But modern technology is so versatile and software solutions perform such a variety of tasks that a specific language can’t cover all the needs an average developer faces on his or her career path. This circumstance leads to the need, if not to hone the knowledge of multiple programming languages, then at least to understand well the specifics of their application.