Python and R are the most popular programming languages when it comes to data processing. But a relatively new kid around the block, Julia, has been steadily gaining popularity. Is it time to switch to Julia, or should you stick with other, more established programming languages for now?
Julia is not worth your time if you’re a beginner wanting to become a data scientist; it’s better to go with Python as it’s more widely accepted. But if you’re an existing Python user wanting to expand your skills, you should definitely learn Julia and give it a try for numerical computation.
This article will discuss everything you need to know about Julia. We’ll look at what it is, how it differs from other languages, how it compares with Python, and when you can use it.
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What Is Julia?
Julia is a high-level, dynamic programming language for numerical computing. Like other languages, Julia is also free and open-source. It was developed by a group of researchers at MIT. They created Julia intending to combine the speed of a compiled language like C with the ease of a dynamic language like Python.
Since Julia is a general-purpose language, it can be used to create applications. However, many of its features make it well suited for machine learning, data science, data mining, and complex linear algebra. Developers are expecting Julia to give a tough competition to Python in the coming years. Many are even claiming that it will overthrow Python as the standard language for artificial intelligence and data processing.
Although Julia is relatively new, it is gaining popularity among developers. The number of Julia downloads has grown 87% in the last year, and it now has over 25 million downloads. It also jumped up 24 ranks to claim the 23rd spot in the TIOBE index. This increase in its user base can be attributed to more packages making it into the Julia ecosystem.
Unlike other programming languages used for data science, Julia is a compiled language using a just-in-time (JIT) compiler. As we’ll see, speed is the most significant advantage of Julia. But that’s not all; Julia has several other pros as well. Let’s discuss why you would want to use it over Python or R.
4 Reasons to Use Julia Over Python and R
Julia has several advantages and features that other languages lack. It is simple to learn, expressive, and almost as fast as C (which is extremely fast, mind you). Here are some of the most significant pros of Julia:
It’s Powerful and Fast
The core idea behind Julia is to achieve higher performance without compromising ease of use and convenience. C++ is a fast language, but it comes with trade-offs. Julia is excellent at bringing the best of both worlds together. It has the efficiency of compiled languages like C++, and it also has the versatility and flexibility of Python, which is slower in comparison.
It Solves the Two-Language Problem
Developers often have a two-language setup where they prototype applications using a slow dynamic language and then rewrite it in a faster static language for production. Scientists code in a high-level language like Python and R, but they have to rewrite the program’s performance-critical parts in C++.
Julia solves this problem as it is dynamic yet fast. You don’t have to worry about rewriting anything once you’ve coded your application in Julia.
It Can Call Other Languages’ Libraries
Want to use a Python library in your Julia project? Worry not! While you can do many things natively in Julia, it is also possible to import libraries written in other languages like Python, R, and Java. You can use packages like PyCall, RCall, and JavaCall to accomplish this. Additionally, Python and R can also interface with Julia PyJulia and JuliaCall, respectively.
It Uses Multiple Dispatch
Arguably, one of the best features of Julia is multiple dispatch. Multiple dispatch is when a function acts differently based on the combination of arguments it receives. For example, in Julia, you can pass an integer, a Boolean, and a string to the same, and it would treat each case differently. We recommend checking out this article on Towards Data Science to learn more about how Julia uses multiple dispatch.
How Does Julia Compare With Python?
We’ve seen that Julia outperforms Python in some cases. It has features that Python doesn’t. So, can Julia overthrow Python, the second-best language for everything?
Well, the rising language certainly looks promising, but it’s still young. Python has been around for 30 years, so it’s a very mature language. On the other hand, Julia’s development only began in 2009, and the language has gone under a decent amount of feature churn along the way.
Since Python has been around for so long, it has many more third-party packages. In contrast, Julia’s adolescence means it has a smaller number of software packages around it. Python also has a much larger user community. If you compare the popularity of Julia and Python on StackOverflow, you’ll find that the former’s growth is still nascent as compared to the latter.
Although both programming languages have a bright future ahead, Julia has a long way to go before it becomes a viable alternative to Python. Python is currently the go-to language for all things data, and it will continue to be so for at least the next several years.
So, Should You Learn It?
We’ve already answered this question at the beginning of the article, but here we’ll do it with some context.
Julia has its advantages over other languages, but it’s still a relatively new language. Most companies are already using Python or R for their existing projects. This means you won’t find many positions listing Julia as a requirement.
So if you’re a beginner, Python is the way to go. You can always learn Julia later in your career to experiment with it. However, Python isn’t going anywhere, and anyone wanting to become a data engineer, ML engineer, etc., should learn Python.
And what about existing Python developers? Well, the general opinion among Python users is that they find Julia attractive but stick to Python for its extensive library of packages.
But wait, didn’t we say earlier that you can call Python libraries in Julia? What’s the need for Python, then?
Although PyCall is a handy tool, you need to be fluent in both Python and Julia to maintain the code. This is because the syntax will end up looking like a mix of both languages.
So if your project revolves around a huge library with a Python frontend (OpenCV, TensorFlow, etc.), it would be better to just use Python. But if you’re working on a project that requires a lot of numerical computation, you can undoubtedly try Julia. It does everything that NumPy and SciPy can do, and you can always call Python libraries as needed.
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Julia is a rising programming language that aims to combine the convenience of dynamic languages with the speed of compiled ones. It’s steadily gaining popularity among data scientists and machine learning engineers, especially for numerical computation. It’s relatively new, so it doesn’t have as many libraries and large communities as others.
Julia is currently not suitable for beginners simply because there aren’t many job opportunities in the language. Python is the language that’ll get you a job, and it’s not going anywhere in the future. However, since Julia has several unmatched features, existing Python developers can try using Julia for their new projects.
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