“Python is the future of programming.”
“Python has become the lingua franca of coding.”
Top Programming Languages (Source: GitHut 2.0)
So which of the two programming languages is going to rule in the coming years?
When it comes to software development, there are only a handful of languages that can be used almost anywhere, be it web development, mobile application development, IoT software development, or AI & ML solution development.
We can say that the battle is neck-to-neck.
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To understand which language will rule in the coming years, we need to go through several parameters of both the languages while comparing them.
So let’s dive into the comparisons –
However, it is not wise to replace languages which are frontend by nature – even if you utilize Python interpreter, the interpretation of code makes the execution process slower.
It has evolved to become an advanced backend technology which comes with fewer dependencies and is very simple to learn.
If we compare Python with Node.js, the latter one is best suited for smaller projects, runs faster, and offers scalability while the former is suited for a wide range of projects (from numerical computations, web development solutions, to network programming and AI & ML), is easy to learn, and is awesome at error handling (also takes less time to fix bugs).
Data science has quickly moved from experimental to applied technology, and the level of its adoption is growing rapidly.
While implementing a data science project, a few substantial factors come into play.
4) ETL processes: ETL stands for extract, transform, load which are the three fundamental functions of database management. Node.js, because of its asynchronous nature, is commonly used in ETL applications. Databases with hundreds of rows can be quickly processed with non-blocking calls over Node.js. This decreases waiting time and increases processing efficiency.
1) Large set of libraries: A decent library ecosystem is one of the main reasons why Python is the most preferred language for data science projects. Python libraries offer developers with all the basic items so that they don’t have to code everything from the scratch. For e.g., Pandas, NumPy, SciPy, Keras, and PyBrain.
2) Data visualization: As mentioned above, Python supports a variety of pre-built libraries. Some of them act as visualization tools. With this, developers have the opportunity to represent data through histograms and charts. This is easily comprehensible to the non-technical people.
3) Code simplicity: Coding with Python is super easy to learn and implement. Building data science solutions involve complex algorithms and versatile workflows, where Python’s simplicity allows developers to write reliable codes.
4) Multi-threading: It is often helpful to process large data sets or run simulations in parallel. Python supports multi-threading which makes the data science job pretty easy.
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Performance is the most critical element of any programming language. This is because it directly impacts the speed of the application which is dependent on how fast the code is getting executed.
Moreover, with its high performance and speed, Node.js is a perfect solution for applications featuring real-time messaging or chatting, as well as for heavy-weight applications, content management platforms, multi-vendor marketplaces, e-commerce solutions, and more largely depending on the speed of processing.
Python is a programming language that is mostly the beginner’s choice, especially for those who don’t have a programming background.
Several factors that make Python a user-friendly language are:
- High readability of Python code
- Fewer lines of code in comparison to languages like C and C++
- Fewer structural rules & restrictions
- Availability of numerous frameworks that contain pre-written code to speed up the project implementation
- Difficult to debug
- More structural rules & additional characters (such as curly brackets & semicolons)
So the score is 2 – 2!
We have gone through the 5 most crucial factors which will determine the future of any programming language – frontend, backend, data science, performance, and user-friendliness.
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