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Interested in knowing which language wins in the Julia vs Python comparison.
The question of whether this new programming language can unseat the king is one we will answer here.
Julia vs Python: A comparison
Both Julia and Python are equally appealing in some aspects, e.g., both are very good for data science, both are open-source, both have automatic memory management, etc. However, there are other aspects where Python and Julia vary.
I will now compare them based on such aspects. Here we go:
1. The popularity of Python vs Julia
Many senior leaders in organizations prefer to use well-established programming languages for their projects. There are many reasons for this, e.g., it is easier to find developers for popular languages. In this aspect, Python has a lead over Julia.
Python has been around for 3 decades, whereas Julia was released only in 2012. As you would expect, the “TIOBE Index for May 2022” ranks Python as the top most popular programming language whereas Julia is in 25th position.
2. The maturity of Julia vs Python
Closely related to the above-mentioned aspect, there is a significant difference in maturity between these two languages. Over 3 decades, Python has seen much development, therefore, it’s a mature language.
Given that the development work on the Julia programming language has started only in 2009, the language is still seeing a lot of churn concerning its features. If you want to work with a mature language in your project, then Python is certainly a better bet.
Read more about this in “Julia vs. Python: Which is best for data science?”.
Julia is a compiled language, whereas Python is an interpreted language. Programs written in Julia are executed directly on a computer processor as executable Julia code.
Compiler outputs can be optimized, however, that’s not an option with interpreted languages. You can certainly optimize Python that is implemented in C as the Cython package.
External libraries and 3rd party JIT compilers like PyPy can help you to optimize Python. However, Julia is designed to be fast, and that’s an advantage over Python.
Read more about this in “Will Julia replace Python and R as a data science tool?”.
4. Ease of use for data science
The scientific programming community including data scientists and developers forms a key segment of Julia’s target audience. This community is quite different from the general application programming community, and its focus is specifically on mathematics-based programming.
Here, in data science, Julia has an advantage over Python. Julia’s syntax for writing code for mathematical operations is just like how you write mathematical formulae, and this makes it an ideal language for scientific programming.
Python is certainly a user-friendly language when it comes to tasks like data analysis, data mining, web scripting, natural language processing, etc. however if you belong to the scientific programming community, then a math-oriented language like Julia will likely be your choice.
Read more about this in “Julia vs Python: This is why the fledgling programming language is winning new fans”.
Python has been very popular for a considerable time, and developers have built a large number of packages for it. These make the work of a data scientist easier.
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Consider the case of Machine Learning (ML). Python has over 145,000 custom-built software packages, and many of them support and use ML for data crunching. This makes Python a very popular choice for ML programming.
Being a relatively new language, Julia doesn’t have that many high-utility packages. Python is certainly ahead here, as you can read in “Julia vs Python – Which programming language is best to learn first?”.
We discussed how Julia could be a better bet for the scientific programming community, however, Python has more versatility. You can use Python for scripting, automation, web development, etc. If you are looking for a general-purpose language, then Python is a better choice.
7. Community support
Python has great community support. If you need help with anything, you will likely find the resolution quite easily, thanks to the vibrant Python community. Being a recent entrant, Julia is yet to have such a large and vibrant community.
8. Tooling support
You will likely opt for a coding language with great tooling support when you undertake a software development project. That’s only natural.
In this aspect, Python is ahead of Julia. A vibrant programming community has built many excellent tools for Python, however, this is still a work-in-progress in the case of Julia.
E.g., Julia doesn’t yet have as many great tools for debugging or resolving performance issues as Python has. Read more about this in “Should data scientists using R and Python switch over to Julia?”.
9. Running operations in parallel
Both Python and Julia support running operations in parallel, however, Julia performs better in this aspect. You need to serialize and deserialize data between threads or nodes when running parallel operations with Python, however, it’s easier with Julia.
10. Working with Shell
When you work with Julia, you will find it easy to work with shells. You can export the variables in Julia as environment variables in Shell, open them there for editing, etc. Julia is ahead of Python in this regard, as you can read in “Can Julia be the new Python? Here’s what you need to know”.
Planning to undertake a strategic project involving AI, ML, or data science?
Python is an established language for AI, ML, and data science with many advantages. Julia is relatively new and has less tooling and library support, however, it offers many advantages. Your project requirements will influence your choice of language, however, keep in mind that AI/ML/data-science projects can be complex.
Consider using a reputed and professional software development company for such projects. Our guide “How to find the best software development company?” can help you to find one.
DevTeam.Space can help you outsource high-quality software developers via its software development community expert in the latest technologies and programming languages.
You can partner with these software developers easily by sending us your initial project specifications via this form. One of our account managers will get back to you to discuss how we can help with project planning, developers onboarding, team management, etc.
Frequently Asked Questions on Julia versus Python
Julia is a dynamically typed language that is compiled during run-time and not at execution. It includes a read eval print loop (REPL) or an interactive command line.
Python was created in 1991 as an interpreted, high-level, and general-purpose programming language. It is among the popular programming languages for web and software development in general with various artificial intelligence and web development frameworks.
It is generally accepted that Julia is faster than Python and high-performance programming language. For more information on the differences and which is better, read this article.