Julia: A Competitor to Python?
Julia combines python’s convenience with C’s performance. This means it combines the convenience of dynamic languages with the performance of statically linked languages.
Humans talk to computers by giving them specific instructions that automate our world with the help of programming languages. There are many programming languages like Java, C, C++, Scratch, Python, R, Matlab, etc. Python was invented as a language which is simple to use because of which it has become extremely popular and is widely used today.
Today, we will discuss one more such language: Julia, which is simple to use, easy to understand, and is very useful for computational science.
What is Julia?
Julia is a high-level, high-performance and dynamic programming language, developed by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman. Launched in 2012, it is a general-purpose language meant to address the shortcomings of other programming languages including Python. It was designed to utilize the strongest aspects of other programming languages, such as speed and openness. It is mainly used for data processing and scientific computing. It has very simple syntax and supports Unicode and scientific notations.
In this code snippet we can see that in Julia, we can multiply a number followed by a variable without using *.
Julia is an open-source language that mixes the interactivity and syntax of scripting languages, such as Python, Matlab, and R, with the speed of compiled languages like FORTRAN and C.
Code snippet in Julia. Here we can use ‘pi’ or π.
Here, as Julia uses Unicode and scientific notations we cannot use ‘pi’ or any other such mathematical notations like ‘theta’ as a variable.
Code snippet in Python 3. Here when we cannot use ‘pi’ directly. We have to define it prior to its use.
It is easy to use and it’s simple syntax makes it an accessible language for programmers from any background.
People use C, whenever they need execution speed or performance. But the code development in C is very time consuming, while code development in Python is much faster but the execution speed is slower than C. With Julia, there’s no need to worry about any of those. It provides both, faster code development and good performance.
Julia is a compiled language and is compiled by the LLVM framework. Julia is fast because type-specific code is compiled and run and not because it uses just-in-time (i.e. every statement is run using compiled functions which are either compiled right before they are used, or cached compilations from before.) compiler. While in other compiled languages the code is compiled before execution.
Differences from Programming Viewpoint
In Julia and Python...