Considering “lot of buzzes around”;
Does it buzz about emerging technologies around the world? Probably “Yes”
Does it buzz about expanding businesses of Reliance Jio? Again “Yes”
What if the buzz is for programming languages designed for Machine Learning and Data Science? The simple answer would be “R” or “Python”, and apparently “Yes”.
Consider other programming languages that not only yield high-performance execution but also have the top-notch dynamic features, moving towards the main course of the blog, we are talking about “Julia”.
Among most of the notorious languages “Julia programming language” is one of the most popular languages that is being used by data scientists and developers.
As they state, “Code is the Art for the developers”
In terms of introduction, Julia was founded by Viral B Shah, Jeff Bezanson, Stefan Karpinski, Deepak Vinchhi, Keno Fischer and Alan Edelman, and it was revealed in 2012 for programmers. Julia 1.0 made its official appearance in 2018, it is a free and open-source language.
What is Julia?
Julia is a high-profile and high-performance programming language, designed for numerical computation. It delivers a complicated, yet the advanced compiler that shares parallel execution. Also, it gives appropriate numerical accuracy and a large mathematical function library. Moreover,
The language, being designed for scientific computation, is beneficial for the enormous applications in Physics, Chemistry, Bioinformatics, Engineering, Data Science, and many other fields.
Julia’s base libraries are extensively written in Julia itself. It combines sophisticated open-sourced C and libraries of Fortran to operate linear algebra, random number production, signal & string processing.
It directs the two programming languages’ problems by integrating the ease of the practice of languages like R and Python with the execution of C and Fortran.
(Most related: Julia: The new contender in Data Science)
“Julia is the only high-level dynamic programming language in the "petaflop club," having been used to simulate 188 million stars, galaxies, and other astronomical objects on Cori, then the world's 10th-most powerful supercomputer.” (From)
The latest version of it has released as Julia v1.5 on August 1, 2020. The newest version embraces novel features and improved performance the alliance of developers. The related codes and functions are listed on Github that are available for more consideration.
Specification of Julia
As mentioned in the Julia v1.5 Documentation, the language fundamentally is focusing on generating a novel integration of dispatch-to-implementation, potential, and productivity within a single language. The listing below some advantages of Julia over analogous systems;
Freely available and open-source which is MIT licensed.
It has user-defined types that are agile and compact.
Vectorization of codes is not necessary for performance; although devectorization of code is swift.
It is devised for parallelism and assigned computation.
A modest, yet strong symbol systems, and it redirects C functions directly
Artistic conversions and elevations for numeric and distinct types.
Efficacious shell-like capacities in order to control various processes.
Lisp-like macros and other metaprogramming conveniences.
As explained by the creators of Julia, “why they created Julia?” in 2012.
“We are greedy: we want more”
“We require a language that is open-source, with a generous license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, a familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell.”
In addition to that, Julia can be deployed across program files, the command line, or Jupyter notebook that is available as a service on JuliaBox freely. An individual can access the complete processors and memory, scrape data available from anywhere over the web, also through any device that has a browser.
(Also check: Python Interview Questions in Data Science)
Julia vs Python
Julia is the multi-examplar and functional programming language that was designed for machine learning and statistical programming.
Python is another multi-examplar programming language which is also implemented for machine-learning, and Python is accounted as object-oriented whereas Julia is an extremely functional paradigm, though it is not popular as Python is.
Julia vs Python
Let’s discuss the huge differences between Julia over Python;
The most popular feature that Julia has is speed, Julia is much faster than Python as it is built for deploying the mathematical functions more efficiently such as linear algebra and matrix description.
- It is more efficient for numerical computations.
- Various dispatches of Julia are suitable for data types like arrays and numbers.
- Most of the long and complicated algorithms that are written either in Python or Julia, Julia execute them in lesser time.
(Must read: Data Types in Python)
Broadly speaking, Julia can be employed for entirety like from easy machine learning applications to the gigantic supercomputer simulations. However, Python can do the same job up to some extent but somehow evolved into jobs.
Moreover, Python has gentle readability and syntax that are code-friendly, therefore its versatility addresses it more accessible for programmers in order to execute various activities at the same time. Its framework also facilitates coding that leads to saving development time.
If we talk about community back, Python is the most popular and widely used by developers. For instance, when you get stuck in any problem, then it can be shared at any online platform where the Python-used community can help you to get over it.
But in the case of Julia, it has a pretty tiny community. You can use Julia’s Discourse to share any problem in order to resolve or contribute. So, Python has a huge community back to solve any problem very quickly and Julia lags here
(Also read: First Step Towards Python)
To install new packages, it is easier in Julia as compared to Python.
In order to run the official and authorized model of Julia at any device, there is a necessity to download and install the package from the official website of Github. It extracts the packages straightforwardly via Read-Eval-Print Loop(REPL) through which installing a new package becomes much easier.
In contrast to this, in Python, installing a new package isn’t a bit difficult task always, where it can be quite simple for some packages, there are also very complicated and tough procedures to install some packages.
Undoubtedly, Python has well-maintained libraries and Julia doesn’t have many libraries that are also not as maintained as of Python.
But Julia is a very young language with limited available resources, and the number of libraries, it has, is much impressive and useful.
Although the libraries of Julia are expanding, they can also articulate with various libraries of C and Fortran in order to control plots and numerical functions.
(Must see: Python to represent Output)
With various advantages and characteristics in its bag, Julia’s recent fame is well-explained. It is still a naive programming language and a long way to cover to get such popularity as Python has maintained over time. However, it comes as a superior and top-level language especially developed for fast scientific computing.
Also, we have seen some characteristics where Julia can be preferred over Python but also due to the primary strengths of Python, it can be adopted over Julia in various cases. Which one to choose between the two languages, it completely depends on the requirements.