Python rules the hearts, minds, and souls of data engineers and data scientists worldwide. With some recent paradigm shifts in technology, there are considerable changes in Python’s market standing.
Does this mean Python is going away anytime soon? While the answer is still uncertain, other programming languages are slowly and steadily taking over Python’s spot. Julia is a direct competitor to Python; it’s one of the newest market entrants, and it’s here to slay its competitors.
Why Is Julia Becoming the Hottest Topic of Discussion?
Machine Learning and Artificial Intelligence developers are looking for newer, fresher technologies, which offer the best of C, C++, and Python’s usability.
Here are some features which make Julia one of the most trusted languages in the market currently:
- Julia uses the LLVM compiler, making Just-in-Time (JIT) compilation possible. This structure allows the language processor to offer faster processing speeds during code execution.
- The language is well-equipped to imbibe Python’s patented interactive command-line interface. If you’re already well-versed with the nuances of Python, learning Julia will be a cinch.
- You can access direct interface capabilities with libraries, which support languages like C, Fortran, and Python, amongst others.
- Julia offers metaprogramming, which means one Julia program generates another Julia program. It can modify its own code, which is a functionality not provided by any other language currently.
- You can debug your code easily with Julia’s 1.1 full-featured code debugger
Why Do Developers Prefer Julia Over Other Languages?
There are a few reasons which appropriately encapsulate the essence of Julia’s effectiveness and why it’s a preferred choice for developers these days.
1. Julia Is an Independent Programming Language: Unlike Python and other related languages, Julia is an independent language with its own native syntaxes and codes. Python, as a language, is a fusion of C and C++, and it makes its dependency on other languages well-known.
2. All Rounder Programming Language: Julia’s first aim post-development was to serve various purposes such as data science, parallel computing, scientific computing, machine learning, and data mining.
The language is multi-faceted, uses multiple dispatches, and is ideal for general coding purposes. You can work with numerous object-oriented and functional programming patterns, which can statically compile code and efficiently dictate user interfaces.
3. Enhanced Technical Computing: Julia’s developers designed the language with all the various facets of the programming world in mind, including data science, machine learning, artificial intelligence, and many more.
The speed at which this language performs numerical calculations, descriptive analytics, and various other declarations makes it a well-recognized language in the developers’ community.
4. Dynamicity at Its Peak: Julia’s dynamic nature is evident from its enhanced use as a scripted language. It offers an automatic generation of specialized code for different argument types.
There are other run-time inferences with a robust performance approach. It efficiently combines the uses of Python and R with the speed of C/C++.
5. Parallel Execution: As a programming language, Julia offers a high-level syntax, making it easy for developers to use and execute commands effectively. Julia’s programs compile native code for various platforms using LLVM.
The programming language incorporates levels of parallel computing, making parallel processing a handy task for developers and users alike. Its parallelism feature is divided into three categories: Julia Coroutines, multithreading, and distributed processing. Julia natively implements interfaces that can spread a process across multiple cores/machines.
6. Quick Processing: Julia is undoubtedly one of the fastest high-performing Open-Source computing languages in the market currently. It’s carved a niche for itself in data, algorithmic trading, analytics, machine learning, artificial intelligence, and much more.
Comparison Between Julia and Python
Despite being immediate competitors, there are some similarities and differences between the two languages.
| Points of Difference | Julia | Python |
|---|---|---|
| Libraries | Julia is limited to a few libraries since it’s still in its nascent stage. The handful of libraries which do exist are not well-maintained and take a long time to plot and execute data sets. | Python, on the other hand, has a lot to offer in terms of well-established libraries. They’re well-maintained, enriched with different functions, and support a series of third-party libraries simultaneously. |
| New versions | Many packages in the Julia ecosystem are releasing ground breaking versions, which has developers looking out for more and more options within the domains of ML and AI. | Python’s most popular packages were released a decade ago, but its wide acceptability comes in terms of what the newest version will bring for the users. |
| Performance | Julia is a compiled language; each code block written in Julia is executed directly as executable code. This means it supports languages like Python, C, R and many more. | Python takes a lot of time to implement code, as it requires various optimization methods and has an ongoing dependency on external libraries. |
| Speed | Julia ranks high on speed, as it is a proud member of the Petaflop Club. It uses Just-In-Time (JIT) compilation and type declarations while executing code. It has the capacity to perform complex numerical and computational functions in a matter of seconds. | Python is also a high performing language, but it can’t match up in comparison with Julia. On the contrary, Python’s speed can be increased by using external libraries, third-party JIT compilers, and various optimization tools. |
| Tooling Support | Julia, born in 2009 and launched in 2012, is still grappling with its support community, debugging tools, and issue resolution techniques. | Python takes a lead in this regard, with its supportive programming community at its beck and call. In short, it brags about its excellent tool support, interfaces, and systems. |
Is Julia Going to Dislodge Python?
Frankly, the time for Python’s dislocation is far off into the future. Given the fact that Julia is still young, there’s a lot of ground yet to be covered. But this doesn’t negate the fact that in the future, you might see an excellent, symbiotic relationship between Python and Julia.
It’s hard to predict, but there’s a high possibility that both languages might come together to collaborate and create a special language of their own. While none of us have a time machine to see what the real future will look like, there is always hope that something good will come out of this collaboration, if it does become a reality eventually.
Until then, stick to progressing your programming career with Julia or Python; whatever you choose, you’ve got a whole world of code in front of you—including web dev, data analysis, and more.