Many experts are comparing Julia vs Python, and while Python’s popularity is undeniable, Julia’s features, including high performance and built-in parallelism, are attracting the attention of data scientists, engineers, and researchers. Julia’s syntax is also easier to read and write than Python, making it an excellent choice for technical computing. However, Python has a vast and active community, a larger library, and more extensive documentation than Julia. Ultimately, the decision between Julia vs Python depends on the specific use case and the project’s requirements.

Viral B Shah, Jeff Bezanson, Stefan Karpinski, Deepak Vinchhi, Keno Fischer, and Alan Edelman founded Julia, and it was first unveiled to programmers in 2012. Julia made its official debut in 2018 when Julia 1.0 was released, meaning the language is now beyond the ‘developer’s stage’ and now is an ‘expert.’ It is a free and open-source language. According to the creators, Julia was created because they were greedy and wanted more.

We want a language that’s open-source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with an obvious, 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 glueing programs together as the shell. Something that is dirt simple to learn yet keeps the most serious hackers happy. We want it interactive and we want it compiled.

 While Python has been around for far longer than Julia, it is soon becoming the preferred choice by developers and programmers. In fact, according to a new survey, Python was named the number one language developers would be using if they weren’t using Julia.

In this blog, we explore Julia vs. Python and what may be the best choice for you.

1. Speed

Julia is as fast as C. It is built for speed since the founders wanted something ‘fast.’ Julia is not interpreted, which makes for a fast programming language; it is also compiled at Just-In-Time or runtime using the LLVM framework. Julia gives you great speed without any optimization and handcrafted profiling techniques and is your solution to performance problems. Julia is excellent for numerical computing and takes less time for big and complex codes. Julia undoubtedly beats Python in the speed and performance category. The code in Julia runs at a brilliant speed and is unmatched. However, lately, Python has become easier to speed up.

2. Community

Any language needs to have a massive and active community. The community should be devoted to the language. Julia has a community that is ever-growing and highly enthusiastic; however, since it’s a new language, the size of the community is relatively small. Python has been around for ages and hence boasts of a vast community that works to its advantage. The programmer community for Julia is at a very nascent stage. The large community for Python is a huge advantage for developers since it allows multiple resources to resolve problems and doubts.

 3. Libraries

One of the drawbacks of Julia is that packages aren’t very well maintained. It also takes too long to plot data initially. However, Julia can directly interface with libraries in C. Since Julia is relatively new, the software culture is small, and it will need mature libraries of its own to flourish.

On the other hand, Python has plenty of libraries, making work easy for every additional task. Julia lacks the number of libraries that Python has; hence, there is ease due to its rich set of libraries. More third-party libraries also support Python. Many third-party packages can support or are essential for every developer and programmer.

4. Dynamically typed

Julia and Python are dynamically typed languages, and developers don’t have to specify variables. However, you can combine and enjoy the benefits of dynamic and static typing with Julia. According to MIT,

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.

5. Identifying issues

Julia does not excel at identifying issues, especially if compared to Python. It cannot debug tools; however, more tools are expected to be developed. Julia is behind in identifying performance issues. The possibility of an unsafe interface to native APIs is also high in Julia.

6. Compiled and Interpreted 

Julia is a compiled language, and it isn’t interpreted. LLVM compiles it and hence shows problems such as recompiling the code most times on starting up. Python is an interpreted language and doesn’t need compilation at all.

7. Versatility

Python has easy readability and a code-friendly syntax; its versatility makes it easier for programmers to perform different activities simultaneously. Its rich libraries and frameworks also facilitate coding and hence save development time.

8. Parallelism

Both languages, Python and Julia, can run operations in parallel. Python’s methods, however, require serialization and deserialization of data for parallelizing between threads, whereas Julia’s parallelization is much more refined. Julis also boasts of less top-heavy parallelization syntax than Python, reducing the threshold to its use.

Conclusion

With multiple advantages and features in its kitty, Julia’s recent popularity is well explained. However, it is still a relatively immature programming language, especially if compared to Python. The fact that Python is older than Julia works to its advantage, mainly because of the massive and active community Python has built over time. That said, code conversion and speed are much more accessible and better in Julia than in Python. However, Python is speeding up with time.

However, Julia has become superior; top-tier language has a long way to go, especially for mass consumption. Python will continue to be the top choice of language for students, universities, and, in turn, job requirements and industry adaptation. Python is undoubtedly the better choice for Machine Learning and Data Science based projects. Similarly, Julia is the way to go if your project is heavy on maths.

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