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Julia VS Python: Can This New Programming Language Unseat The King?

python vs julia

Interested in which language wins in the Julia VS Python. 

The question of whether this new programming language can unseat the king is one we will answer here.

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Python: A brief introduction
Julia: An introduction to the potential competitor to Python
The key features of Python
What Julia offers: Its notable features
Julia vs Python: A comparison
Planning to undertake a strategic project involving AI, ML, or data science?

Python: A brief introduction

Guido Van Rossum had started to create Python in 1989, and he first did it as a project of personal interest. Rossum had earlier created a programming language called ABC, which had exception handling capabilities. It also had an interface with the Amoeba Operating System.

Rossum liked many of its features and worked to create a different language that would be free from the challenges that ABC had. He took the syntax of ABC and the good features, and he created a new language.

Rossum was a fan of the BBC’s TV show called “Monty Python’s Flying Circus”, and he named the new language as “Python”. Read more about this in “History of Python”.

Rossum had developed Python at Centrum Wiskunde & Informatica (CWI), which was based in Netherland. The 1st release of Python was in 1991, and it used fewer lines of code than Java, C++, and C to express the key concepts. It’s an open-source language.

Python has had many versions, with Python 2.x and 3.x attaining great popularity. Read “Has Python completely edged out R in data science field” to understand the degree of its popularity in data science. At the time of writing, Python 3.7.3 is the latest version.

Many technology giants like IBM, Dropbox, Google, HP, and Cisco use it for various purposes. Programmers use it for developing, scripting, etc., and creators of languages like Ruby and Swift took inspiration from Python.

Julia: An introduction to a potential competitor to Python

Alan Edelman, Jeff Bezanson, Stefan Karpinski, and Viral Shah started to create Julia in 2009, and they took inspiration from Python. Their objective was to create a programming language for better and faster numerical computing.

They launched the 1st version of Julia in February 2019, and it’s an open-source language. Julia caters specifically to scientific computing, machine learning, data mining, and large-scale linear algebra. The language also caters to distributed and parallel computing.

The creators of Julia wanted a language as fast as C, moreover, it should be as dynamic as Ruby. They intended that their creation should be as useful for general purpose as Python, however, it should be as useful as R for statistics.

The team of 4 lead developers also wanted Julia to have the good features of Perl and MATLAB. Read more about the history of Julia in “Julia | Definition, Programming, History”.

Julia has seen plenty of development already. At the time of writing, its stable release is v1.2.0, which was released in August 2019. Despite it being a new language, the usage of Julia is picking up, as you can read in “How a new programming language created by four scientists now used by the world’s biggest companies”. NASA, Oracle, IBM, Apple, and Amazon are just a few organizations that use it.

The key features of Python

Python has gained a wide following over the years due to its powerful features. Its features are as follows:

  • Programmers find it easy to code using Python since they can quickly learn its syntax.
  • Since Python is a high-level language, it’s also quite easy to read.
  • Python provides developers with many constructs, therefore, programmers can focus on the problem they need to solve over the syntax. This makes Python a very expressive language.
  • You can easily access its source code since Python is an open-source language.
  • Programmers don’t need to manage the memory, which makes Python a developer-friendly language.
  • Python is a portable language. If you write a Python program for Windows, you can also run it on Mac without making any changes.
  • Since Python is an interpreted language, you don’t need to compile it. Its source-code is internally converted into “bytecode”, and the source code is executed line by line. Developers find it easier to debug Python, as you can read in “Python features”.
  • Python is an object-oriented language, therefore, it focuses on objects. However, Python can support both procedure-oriented and object-oriented programming.
  • You can write some of your Python code in another language like C++, making Python an extensible language.
  • On the other hand, you can embed your Python code within the source code written in another language like C++.
  • Python comes with a large standard library, and this has code already written for many functions. It has libraries for regular expressions, image manipulation, unit testing, threading, etc., therefore, you save time.
  • Since Python is a dynamically-typed language, the type for a variable is decided at runtime.
  • You can create GUI for Python with standard packages like Tkinter.

Read “13 unique features of Python programming language” for more insights.

What Julia offers: Its notable features

Julia is emerging as a potential competitor of Python on the back of its features, which are as follows:

  • It’s an open-source language with an MIT license, and it’s free.
  • Julia is a dynamically-typed language, however, it also offers you the ability to specify types. Developers can create hierarchies of types, which enables them to handle variables of specific types.
  • Julia is a compiled language, which makes it fast. It uses the LLVM compiler framework, which is created by the “LLVM Compiler Infrastructure project”. This makes the compilation of Julia “Just-in-Time” (JIT) and contributes to its speed.
  • A language with metaprogramming capabilities, Julia programs can create other Julia programs. Programs written in Julia can even modify their source code.
  • Julia has similar syntax like Python, however, its syntax for mathematical operations resembles how mathematical formulae are written outside of the world of programming. This helps people from the scientific community without a programming background to learn Julia quickly.
  • This language can Interface directly with external libraries based on C and Fortran, moreover, it can interface with code written in Python. Read more about this in “Julia programming language and its features”.
  • Julia facilitates parallel processing using full resources available on a computer, which helps with scientific programming.
  • The growing number of packages represents a key feature of Julia, and it has a built-in package manager. You can also use C, Fortran, and Python packages, therefore, it’s easy to reuse existing code.
  • Julia supports both objected-oriented and functional programming, moreover, the language is good for interactive use.

Read more about the features of Julia in “Julia tutorial”.

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. On 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, “TIOBE Index for October 2019” ranks Python significantly higher than Julia. This report shows that Python is the 3rd most popular language, whereas Julia is ranked 42nd.

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 Julia 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?”.

3. Performance

Julia is a compiled language, whereas Python is an interpreted language. Programs written in Julia are executed directly on a computer processor as executable 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 forms a key segment of the target audience of Julia. This community is quite different from the general application programming community, and its focus is specifically on mathematics-based programming.

Here, Julia has an advantage over Python. Julia’s syntax 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, however, if you belong to the scientific programming community then 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”.

5. Packages

Python has been very popular over a considerable time, and developers have built a large number of packages for it. These make the work of a data scientist easier.

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 should you learn to enter the data science industry today?”.

6. Versatility

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 programming 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 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 is a vetted community of expert dev teams supported by an AI-powered agile process.

Companies like Samsung, Airbus, NEC, and startups rely on us to build great online products. We can help you too, by helping you to hire and effortlessly manage expert developers.

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