Julia vs Python: Which one should you learn?

Julia vs Python: Which one should you learn?

Python is one of the most widely used general-purpose programming languages. Python is said to be the market leader in Data Science & Julia is another emerging general-purpose programming language that gives python competition in Data science use case of Julia is expanding in various other fields. In this article, I will tell you the differences between Julia vs Python, their use cases, frameworks & libraries used, advantages and disadvantages, etc.

Let’s dive in.

Introduction to Julia

Julia is a high-level dynamically typed (or loosely typed) general-purpose programming language. It is a relatively newer programming language compared to other established programming languages like Python, Java, C, etc. Its development started at MIT in 2009. It launched as an open-source programming language in 2012.

The inventors of Julia intended it to have computing power at faster speeds like C and C++ while maintaining a cleaner syntax and matching the general programming abilities of Python.

How to get started

You can start learning it from Julia’s official youtube channel, The Julia Programming Language & a free tutorial is available on freecodecamp.org.

Advantages and Disadvantages of Julia


  • Easy to learn
  • Faster speed than Python
  • Clear syntax
  • Versatile (used in Machine Learning, Parallel Super Computing, Robotics, etc.)
  • Automatic memory management (garbage collection)


  • Less startup speed as compared to Python
  • Julia is still a young language
  • Limitations in market adaptation due to a small ecosystem
  • Several compilations issues

Different Use Cases of Julia

Although Julia is known for its importance in Scientific Machine Learning, it is emerging in various other fields. Some of them are listed below:-

  1. Machine learning:- It is specially used for scientific machine learning because of its good computing power and easy-to-use
  2. Desktop Applications:- We can also build Electron GUI apps using Julia packages such as Blink.Jl, Weave.Jl, etc
  3. Game development:- People started using Julia in game development after the release of GameZero.Jl package in April 2020. It’s simple to use for beginners.
  4. Web applications:- Julia is used for building web applications after the introduction of Full stack frameworks, Genie. Jl & Dash. Jl. Developers use it to work with data-related tasks on the web.
  5. Pharma sector:- Julia used to make pharmaceutical models. It is mainly used in the testing of new drugs and to optimize existing treatments. Bio Julia and Pumas are two of the mainly used Julia packages in the pharma industry.
  6. Finance sector:- Used for developing financial models. One of the most commonly used Julia packages is Julia Fin, specifically used in asset management and trading.
  7. Robotics:- Julia is used to test the robots and train them to walk, jump, etc. The commonly used package is Julia Robotics.

Introduction to Python

Python is a Dynamic typed (loosely typed) general-purpose programming language. Python was launched on Feb 20, 1991, by a Dutch programmer named Guido Van Rossum.

It is a well-established programming language launched more than 30 years ago. It is one of the most popular programming languages for Data Science nowadays, and it is also a major player in AI & ML, Back end development, etc.

How to get started

If you are a beginner and want to learn it in a structured manner, you should follow Codedamn’s Python 3.10 Masterclass – From Noob To Pro. It is a beginner-friendly course.

After completing this course, you can easily handle any programming project. You’ll be able to use core python features and its hidden secrets to developing consistent and efficient python programs. You’ll also learn about the often-forgotten basics and special features of programming and Python in general.

Advantages and Disadvantages of Python


  • Easy to learn
  • Used in different domains (AI & ML, Data Science, web development, etc.)
  •  Loosely typed programming language (developers don’t have to worry about data type and declaring a variable)
  • Python has a vast ecosystem (plenty of libraries and frameworks for your use case)
  • Huge community (Helpful for a beginner, as there might be answers available online for all their doubts and queries)


  • Slower speeds as compared to other languages like C and C++; because it is an interpreted language (It does line-by-line execution of code)
  • Not suggested if you want to do optimized memory tasks (due to its higher memory consumption)
  • Vulnerable to a lot of runtime errors (because Python is a loosely typed language)
  • It can be a problem for you if you want to switch languages later (because of the simple-to-use syntax of Python)
  • Not suggested to use it in front-end or mobile app development (due to its higher memory consumption and slower speed)

Different Use Cases of Python

We can use Python in a variety of domains. Some of them are listed below:-

  1. AI & ML:- Python is extensively used to make robust AI models and train machines to perform various tasks. Some commonly used python libraries and frameworks are Pytorch, TensorFlow, Pandas, etc.
  2. Back-end development:- We can also use Python to program our servers. There are plenty of popular websites using Python as their back end. For example:- Pinterest, Instagram, Spotify, etc. Commonly used back-end frameworks for Python are Flask and Django. You can learn back-end development using Codedamn’s Backend Development with Python course.
  3. Game development:- Although it is not a widely used language to develop games. But, We can use Python to build small games and beginner-friendly projects. Battlefield 2 is one of the popular games made using Python.
  4. Desktop GUI development:- We can use Python to build the GUI of our desktop applications. Tk GUI library is one of the popular choices for this use case.

Julia vs Python Differences

Faster processing speed than Python.Slower in terms of speed as compared to Julia.
The most common uses are Scientific Machine Learning, Robotics, Pharma, etc.The most common uses are AI & ML, Data Science, Backend development, etc.
Slower start-up speed.Higher start-up speed than Julia.
Julia’s ecosystem is Miniature as compared to Python’s.Python already has a vast ecosystem of libraries and frameworks.
Only experienced Data Scientists prefer Julia.A large number of freshers use python.
Fewer jobs are available for a Julia developer.Higher jobs are available for a Python developer.
Julia vs Python Differences


During this article, we talked about Julia vs Python, its introduction, advantages and disadvantages, use cases, etc. You can choose from Julia and Python according to your use case.

I hope this article will help you to understand the differences between Julia and Python and which one to learn.

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