Julia vs Python: Which one is the best programming language? Which one should we use for data science? Which one between the two is more versatile?
For years, Python has been winning the hearts of programmers. In fact, it is currently rated among the most popular programming languages. One of the reasons is its simplicity and most beginners use it as a perfect landing platform. Its versatility makes it an ideal language for experienced developers.
Python is undoubtedly the most popular language among data scientist and machine learning professionals. But Julia, founded by Viral B Shah, Deepak Vinchhi, Alan Edelman, Jeff Bezanson, Stefan Karpinski and Keno Fischer, is now gaining popularity in the field. This year in August, Julia developers announced the 1.0 release of their project, which means that the language is no longer at a ‘developer’s stage’ and is now an ‘expert’.
Julia-an MIT-created programming language with the ambition of combining the speed of C with the usability of Python, the dynamism of Ruby, the mathematical prowess of MatLab, and the statistical chops of R — with the creators going as far as to describe it as a language for developers “who want it all”.
Before moving on to their comparison, it is important to indicate and establish that it is not fair to expect that Julia can beat Python hands down. Python has been in the market since ages and its success stories are everywhere. Julia, on the other hand, is quite new and does not compete with Python in many areas.
Julia is designed for speed and to be used for high performance computing requirements. The programming language leverages the positive aspects of similar programming languages like Python as well as eliminate their shortcomings. Some of the features of Julia programming language include:
#1. Built for speed as it is compiled at Just-In-Time or runtime using the LLVM framework. The fact that it is not interpreted makes it a fast programming language and ideal for machine learning and AI-related activities.
#2. Julia is a dynamically typed programming language where you don’t have to specify variables.
#3. While using Julia you can access the libraries of other programming languages such as C and Python.
#4. The metaprogramming ability of Julia programming language allows developers to create a Julia programs from another Julia program with unique codes.
Key features of Python:
#1. Python is a high-level and object-oriented programming language.
#2. Python is also a dynamically typed language like Julia.
#3. There is no need to compile Python as it is an interpreted language.
#4. Python is an open source language and you can download and use it freely.
#5. Python programming language is highly portable and can run on any machine.
Is Julia “the language of the future”?
The company recently revealed figures to show its rapid growth over the past year ahead of an award Julia co-creators Jeff Bezanson, Stefan Karpinski, and Viral Shah will receive for creating the language.
Downloads of Julia have grown 78 percent since January 2018, from 1.8 million to 3.2 million downloads. The number of Julia packages from the Julia developer community has also expanded significantly, now numbering 2,462, up from 1,688 packages at the beginning of last year.
As Julia 1.0 was released last August, MIT said there were 1,900 registered packages and two million downloads, so it would appear it has picked up steam since then.
The number of GitHub Stars for Julia, excluding Julia packages, has also doubled over the past year to 19,472. The language has also been cited in over 1,000 academic publications.
The jury is still out on whether Julia programming language will be able to outperform Python in the long run and become the go-to programming language for programmers to build high performance computational projects using AI and machine learning.
As stated earlier in this article, it is not really fair to compare Julia and Python, as they are not at the same level currently. It can be said that Julia beats Python over its weaknesses but it cannot yet beat Python in its strengths. Currently, it cannot replace Python as a general scripting language. But Julia is fast pacing with its developments and may sometime in the future be able to give a tough fight to Python.
Which one to choose among the two depends on your requirement. If your project is much into mathematics, Julia definitely shines there. It has great support for functional programming.
The one that works for you should be the best language for you. All you need is to learn and master either of the two programming languages. You will use it to accomplish whichever task that is at hand. Thus, feel free to share your experience of working with Julia in comments. Or if you consider that there is no one better than python, argument it!
Thank you for this article! I am sure that Julia will be an important tool in the near future besides Python and R as programming language for data analysis. And because I am so sure, I have written a book about data analysis with Julia (in German, ISBN-13: 978-3749485086).