Let’s Talk about Julia, Python’s Arch Enemy!

Now, don’t get me wrong, Python will always be the OG (well at least for the next while) but we can’t ignore the whispers in the Data Science community that Julia is pretty good.

Python’s popularity continues to be bolstered by a strong community of data engineers, data scientists, and artificial intelligence experts. However, if you’ve been having conversation with these people you may have picked up that Python does have some cons such as its’ speed since Python is an interpreted language.

So, in this post we will take you through what Julia is, and why is it becoming the favorite programming language for Data Scientists. We’re also going to cover what the differences and similarities are between Julia and Python.

What is Python?

Python is an interpreted and object-oriented programming language. It’s a versatile coding language that allows programmers to write dynamic code in just a few lines.

It’s a high-level, interpreted, general-purpose, multi-paradigm language that was first released in 1991. It has many libraries and tools dedicated to web and software development, AI, and Machine Learning (ML). Python is likely to be used if you wish to program something.

It’s a fast and efficient programming language with features like dynamic typing, high-level data structures, and dynamic binding. Python is a popular scripting language for rapid application development because of these qualities.

Python also has the advantage of supporting standard data formats such as CSV and JSON files, as well as the ability to work with SQL tables.

What is Julia?

Julia is a programming language created by MIT students. It’s a programming language with a flexible coding syntax that’s comparable to Python and a high execution speed that’s close to C.

Julia is a free and open-source programming language for data analysis and statistical computation. It’s also suitable for Big Data and Cloud Computing.

Julia is a dynamic, high-level, high-performance programming language with a syntax similar to Python, designed primarily for technical computing. Because linear algebra is a basic component of this language, it is commonly utilized in Machine Learning, Data Science, data mining, numerical analysis, and any mathematical purpose.

Julia’s simplicity, high performance, and speed are its selling qualities, and it was designed to handle complicated data models. The idea of translating Science’s formulaic language into code, however, is a selling point for scientists: Julia supports Greek letters, allowing for the direct usage of mathematical formulas in the code without than having to translate them into coding language.

Python vs Julia – Who wins?

Look, we’re not here to determine if Julia is better or worse than Python. Let’s face it, for the past three decades, Python’s value has been amply demonstrated. We’re utilizing it as a point of comparison to fully grasp the capabilities of this new programming language.


Julia is built with speed in mind. It is so quick that only C can keep up with it. Python is a diverse, powerful, but slow language, owing to the fact that it is an interpreted language.

In Julia, code is compiled using the LLVM framework. Julia solves the performance problems that provide speed without the use of any optimization approaches or manual profiling. Julia is an excellent solution for projects in Big Data, Cloud Computing, Data Analysis, and Statistical Computing.


The value of community support for any programming language cannot be overstated. The presence of a large community indicates that there are numerous resources available to tackle difficulties. Julia is a relatively new language with a small but growing and enthusiastic community.

Python is better since it has a strong community behind it, whereas Julia is still in its infancy. Python’s extensive community support is beneficial in obtaining more resources capable of resolving issues and resolving coding-related questions.


Python offers a large number of libraries that make coding in Python easier by allowing you to import and use their functions. Julia lacks a substantial library collection, which is a disadvantage when compared to Python.

Furthermore, Python is supported by many third-party libraries. Julia also has a library disadvantage here because packages are not adequately maintained. That being said, Julia can communicate with libraries written in the C programming language.

Julia is a young language, hence it requires more mature libraries to develop.

Conversion of Code

In the case of Julia, code conversion is simple and well supported. Julia code can be easily converted from Python or C, but not the other way around. It is difficult to convert code from Python to C or from C to Python. Julia, in fact, can readily connect with libraries written in C or Fortran

Data Science Ease of Use

Julia has a larger scientific following since it assists in the solution of mathematical programming challenges. Julia’s community differs from that of Python, which is primarily focused on application programming.

Julia is superior in terms of data science ease of use. Julia’s syntax is more akin to mathematical equations, and programmers find Julia to be simple to use for coding and solving mathematical problems. Despite the fact that Python is more user-friendly than Julia, a lot of the scientific community prefers Julia.

Compiled and Interpreted

Julia is a compiled language, not an interpreted one. It compiles with the LLVM framework, which improves execution speed but may cause issues when recompiling the code. Python, on the other hand, is an interpreted language that does not require compilation.

Conclusion – do we expect to see more of Julia?

Julia is a specialized language that is largely used by a small community of people. That being said, Julia will undoubtedly become a popular and in-demand language as developers and the community increase its capabilities and breadth.

Python is a mature language and is one of the foundational languages in any developer’s education. It is unlikely to be replaced anytime soon because new languages always find a way to work with it.

Is Julia a worthwhile to have a look at? Yes! Especially when working with data models. The amount of data created nowadays necessitates a sophisticated language like Julia that can work with complex models quickly. Julia’s superpowers are already being employed in financial analysis and climate data, and developers are constantly coming up with new methods to use them in new applications.

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Written by

Devasha Naidoo

Senior Technology Architect

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