You are probably familiar with Python, but Julia might not ring any bells, right? Well, that's because it is a relatively new language that has yet to gain a significant following.
And while Python has already established itself as THE programming language in multiple areas, Julia is just starting to get its foothold in the industry.
Both languages are somewhat similar since they are used in AI and machine learning. Even so, the two have differences in application, difficulty, performance, etc.
In this blog, we will explore these differences in detail. So let's get started.
Python vs. Julia: Overview
Python is a high-level, general-purpose programming language created in 1991.
It has become one of the most popular programming languages in just three decades thanks to its wide area of application and easy-to-understand syntax.
On the other hand, Julia was created in 2012 to improve existing languages like R, Python, Ruby, Matlab, etc.
We know that Python is one of the most easy-to-learn languages, but there are a few caveats.
For instance, despite being the most popular language for data science and machine learning, Python doesn't support math symbols for variable names.
On the contrary, Julia has inbuilt support for mathematical expressions and symbols. Here is an example:
f(x) = [ a = a+2 for a in x]
The expression above will add 2 to every value in f(x). So, if we have f([1,2]), the result will be [3,4].
We cannot write code like this in Python. So, for researchers and statisticians, Julia is easier to use.
However, beginners will find Python more intuitive as the language's code is much simpler
Python vs. Julia: Application
Python's application encompasses multiple fields like data science, automation, web development, machine learning, and more.
In contrast, Julia is the most suitable language for doing computations on complex problems. Researchers and scientists adopted Julia for scientific research in Chemistry, Biology, and Mathematics.
Programmers have been using Julia in Machine Learning and Data Science, and some consider it a "next-generation" language for Machine Learning.
Python vs. Julia: Performance
Unlike Python, Julia is a compiled language, so it is much faster than Python.
One restriction to Julia's compilation is that the language is compiled at run time, which is not similar to other compiled languages like C and Java.
But this doesn't change the fact that when written efficiently, Julia is faster than even C. Julia's JIT compiler is notorious for compiling code very fast.
And since Julia's code compiles fast, it is the best language for Big Data, Cloud Computing, Data Analysis, and statistical computing.
In addition to execution speed, memory management is also better in Julia as it allows more control.
Python vs. Julia: Job Demands
When we compare the job demands of Python and Julia, we can see that Python has significantly more job openings.
Here's a table with data showing the job openings and salaries of Python and Julia programmers:
As you can see, Julia Programmers earn $81K on average. On the other hand, Python programmers can make an average of $108K.
Similarly, the job scope of Python is also extensive as you are not constrained to a particular field. You have more job opportunities if you know Python.
Python vs. Julia: Pros and Cons
Python and Julia have their benefits and useful features. They also have some flaws. Let us discuss them in detail.
Advantages of Python Over Julia
- Python one-ups Julia regarding syntax and learning difficulty. While Julia is useful for writing mathematical expressions, most beginner programmers don't need that feature. So, in general, Python is more accessible to learn than Julia.
- Since Python is an interpreted language, debugging Python code is more manageable than Julia code.
- Python has GUI support for building fully-fledged applications for data science and machine learning - something not available in Julia.
- Python is an object-oriented language that allows beginners to learn encapsulation and other essential features.
- One of the most critical features of Python is its incredible collection of libraries and frameworks that you can use to develop applications and systems.
Advantages of Julia Over Python
- Julia supports mathematical expressions and symbols, so researchers and mathematicians find this language valuable and accessible.
- Since Julia is a compiled language, the code executes much faster than Python.
- Julia is designed for parallel and distributed computing, which are used in intranets and telecommunication networks.
- Julia has dedicated libraries like Flux for machine learning and data science with more features than similar libraries in Python. Julia libraries are also more customizable.
Python vs. Julia: Comparison Table
If you don't want to jump head-first into AI and Machine Learning, you should learn Python.
But Julia might be a better option if you want to make a career in Data Science and Machine Learning or if you are a researcher.
The main advantage of Julia is its speed. It can load data quickly and supports libraries written in other languages like Python and C.
Scientists and researchers prefer Julia because it supports mathematical expressions and variables. But despite Julia's performance and features, programmers largely favor Python as it is the industry standard in many fields.
Whether it's Machine Learning, AI, or Web Development, Python is used virtually everywhere.
It is essential to remember that no matter which language you learn, you need to master the language properly in order to succeed.
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