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Julia vs Python

Julia vs Python

Julia and Python are popular options for Data Science, Statistics, Machine Learning, Scientific Computing, and Data Analytics.

Julia is a high-level, dynamic programming language that was designed for high performance. It has many great features that make it well-suited for numerical analysis and computational science.

If you are interested in working on Data Visualization & Plotting, Data Science, Machine Learning, Parallel & Heterogeneous Computing, and Scientific Computing, you should learn Julia over Python.

Julia stats

On the other hand, Python is a general-purpose programming language that focuses on simplicity and readability.

Rather than building all of its functionality into its core, Python was designed to be highly extensible through modules.

Python is commonly used for web development, GUI development, system administration, software development, automation, testing tools, machine learning, data analytics, data visualization, and data science.

If you are interested in working on web development, machine learning, automation tools, and data science, data analytics, you should learn Python.

Python stats

Comparing programming languages and choosing which one to learn can be tricky, there are many factors to consider in order to choose the right programming language for the job you want to do.

Popularity, opportunities, types of projects, salaries, resources, learning curve, etc. are some of the factors that many people consider when comparing programming languages and choosing which one to learn.

Here are some of the comparisons and considerations you should make when choosing to learn a new programming language.


Comparing the popularity of programming languages is not an easy task because each programming language is different and they all seek to solve different problems.

Plus, other programming languages have been around for a longer time than others, giving them more time to be tried and tested, so, bear that in mind.

If you want to learn a programming language solely for its popularity among developers, you should learn Python over Julia.

Generally, Python is more popular than Julia. The TIOBE index 2023 ranks Python as the most popular programming language while Julia is ranked as the 33rd most popular programming language.

According to a Stack Overflow survey of 2022, Python is the 4th most commonly used programming language, it is used by 43.51% of professional developers.

On the other hand, Julia is the 35th most commonly used programming language, it is used by 1.04% of professional developers according to the same survey.

It is worth noting that Julia is a newer language compared to Python, it is actually rapidly growing in popularity. There are more developers who love Julia than those who love Python.

As of 2022, Julia is loved by 72.51% of developers versus 27.49% of developers who dreaded it. On the other hand, Python is loved by 67.34% of developers versus 32.66% of developers who dreaded it.

Ultimately, if your choice of which programming language to learn depends on popularity, you should learn Python.

Plus, there are more opportunities for Python Developers than for Julia developers because Python has been around for a very long time and has been used on many programs than Julia.

Also Read Best Way to Learn Julia


Another popular criterion that many people use to compare programming languages and as an incentive to learn a new programming language is salary.

Salaries for developers differ from one company to the other and from one country to the other.

Experience is another factor that comes into play as far as salaries are concerned. The more experience you have with a certain technology or programming language, the more likely you are of getting a higher salary.

Generally, Julia developers get higher salaries than Python developers. According to a Stack Overflow survey of top-paying programming languages, Julia developers get an average salary of $77,966 per year.

On the other hand, Python developers get an average salary of $71,105 per year, about $6,000 less than Julia developers.

Here are 10 Programming Languages that Pay more than $90,000

So, if the salary is your major incentive for learning a language, you should learn Julia over Python, because you are more likely to get a higher salary as a Julia developer than as a Python developer.

Here are some of the popular jobs you can get as a Python Developer.


Python is generally easier than Julia, Python has an English-like, easy-to-understand syntax that makes it very easy to write code.

It is also easy to maintain and debug Python code because it is more readable. Getting started with Python as a complete beginner is much easier than with Julia.

Julia is also easy to work with, but it can be difficult sometimes, especially for complete beginners. Julia has some advanced and new programming concepts like macros, which can be a little bit difficult, especially for beginners.

Julia has a steep learning curve compared to Python, but if you are coming from a numerical and statistical background, you may find Julia easy and attractive.

The good news is that there are plenty of helpful resources for both Python and Julia to help you learn the languages.

The Python and Julia communities are very active and helpful, in case you get stuck with something.


When it comes to performance comparison, Julia is a go-to language over Python, if you plan on working on applications where top-notch performance is critical, you should learn Julia.

Julia is a very fast and high-performance language, it is generally faster than Python. Julia is one of the few high-level programming languages in which petaFLOPS computations have been achieved, others being C, C++, and Fortran.


Julia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM, this makes Julia programs very fast.

Julia is a compiled language while Python is an interpreted language. Compiled languages are converted directly into machine code that the processor can execute.

For this reason, compiled languages tend to be faster and more efficient to execute than interpreted languages.


Python has a strong advantage in machine learning due to its extensive libraries, such as PyTorch, Scikit-learn, TensorFlow, SciPy, Keras, and many others.

These libraries are widely used for developing complex machine learning models, such as neural networks, decision trees, and support vector machines

These Python libraries make it easy to develop machine learning models and provide excellent performance. Python’s syntax is also more straightforward than Julia’s, making it easier for beginners to learn.

However, Julia has a growing collection of machine learning packages, such MLJ.jl, Flux.jl, Knet.jl, that provides a unified interface to common machine learning algorithms and deep learning.

These packages make it easy to create models and evaluate their performance.


Julia has many advanced features and libraries that make it a popular choice for Data Science, Machine Learning, Scientific Computing, Parallel Computing, and Data Visualization & Plotting.

Julia provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager, and more. This makes it possible to build entire applications and Microservices in Julia.

It has a built-in package manager called Pkg that handles operations such as installing, updating, and removing packages.

Julia also uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns.

The beauty of Julia is that you call C programming language functions directly without wrappers or special APIs.

It also has foreign function interfaces that make it work well with other programming languages such as Python, R, C++, Java, and many others.

Python and R packages such as PyJulia and JuliaCall can be used to call Julia packages in a Python or R codebase.

Julia is used by many companies such as Aviva, NASA, Brazilian INPE, Moderna, BlackRock, Climate Modelling Alliance, Google, Microsoft, Federal Reserve Bank of New York, and many others.

On the other hand, Python has a large standard library that provides tools and features suited for many applications. It supports many standard protocols and formats like HTTP, MIME, and many others.

It also includes modules that can be used for relational database connection, unit testing, and manipulation of regular expressions.

Python has many amazing features, libraries, and packages that make it a popular choice for web development, scientific and numeric applications, system administration, GUI development, and more.

Python powers some of the complex applications developed by companies like Google, NASA, IBM, Microsoft, Meta, Cisco, Duolingo, Pinterest, Reddit, Pixar, Netflix, and many others.


Once you have compared the languages and evaluated all the factors, you can choose which programming language to learn depending on the factors that are on your side and what you want to build.

If you want a fast and high-performance language that you can use for Data Visualization & Plotting, Data Science, Machine Learning, Parallel & Heterogeneous Computing, and Scientific Computing, you should learn Julia over Python.

If you want a popular language that you can learn easily, develop applications quickly, and work on machine learning, data science, automation tools, and web applications, you should learn Python over Julia.