Julia vs. Python: The Superior Option for Data Science

julia language

Among many uses of Python Language, data analytics has become the most significant one. The Python consists of libraries, applications, and tools that make the process of data analysis and scientific computing very fast and smooth.

However, the Julia Language was aimed specifically for doing machine learning, large-scale linear algebra, scientific computing, data mining, distributed and parallel computing.

As compared to the Julia Language, the Python Language is not fast enough. Python can only be used for some parts of the data analytics but is not capable to do other tasks.

To get more thorough and accurate learning regarding these two programming languages, let’s dive into the segment mentioned below.

Overview: Python and Julia With its Advantages

Both Python and Julia are outstanding in their places. They are both advantageous and preeminent as well. To know more about it, Let’s get a brief overview of the languages and the advantages of them respectively.

Python Programming Language

Guido van Rossum created Python language in 1989 and released it in 1991. It was a language that was capable of developing anything from desktop software to frameworks and web applications. 

Advantages of Python Language

Although Julia was mainly built for the purpose of data science, Python has evolved quite a lot. Data scientists can get some amazing advantages with the help of Python. The reasons why Python may be a better choice for data science work are:-

  • Python is Mature 

Julia is relatively new, it was introduced in 2012. It went through many changes since then. However, Python has been with us for about 30 years. 

  • Third-Party Packages in Python

One of the main attractions of Python is its useful third-party packages.  However, Julia’s culture of software is still small due to its relative newness. Some of its ability is compensated by the ability to use C and Python libraries. 

  • Millions of Users

Due to the long existence of Python, it has millions of users. A language is useless if it does not generate devoted users around it. The users of Pythons are in greater number than that of Julia. Python’s huge number of users are its greatest advantages.

  • Faster

Python has become faster over the years along with the improvement in the Python interpreter. The mypyc project helped to translate type-annotated Python into native C, which is not as clunky as done by Cython. It managed to get a four-fold performance boost, and much more if done for pure mathematical operations. 

Julia Programming Language

Julia Language was introduced in 2009. A team of four people developed it and made it public in 2012. Julia made us aware of the shortcomings in other languages, like Pythons. Including that, this language is flexible in nature and is useful for both scientific as well as numerical computing. It has a performance similar to that of statically-typed languages. 

Advantages of Julia Language

Julia was designed for scientific and numerical computation. So it will come as no surprise that it has many features for such computation cases. Here I have listed some of the advantages of Julia language below:

  • Faster

Julia was made by Just-in-time (JIT) by using the LLVM compiler framework which gave it very fast runtime performance. At its peak performance, it can almost match the speed of C.

  • Interactiveness of Julia

Just like in Python, Julia uses REPL (Read-Eval-Print-Loop) and interactive command line. Command and quick one-off scripts can be easily added to it.

  • Contains a Simple Syntax

Python and Julia have similar syntax- terse, but that on the Julia is more powerful and effective.

  • Incorporates the Benefits of Both Dynamic and Static Typing

In Julia, you can specify the types for variables, like “unsigned 32-bit integer”. To allow general cases to handle variables like- write a function that accepts integers, or signing integers, you can form hierarchies of different types. You can even manage this without typing if it is not required in some context. 

  • Connect Libraries of Python, C, and Fortran

It has the capability to connect libraries of languages like Python, C, and Fortran. Julia can also connect with Python code by using the PyCall library. It can also share data between Julia and Python.

  • Supports Metaprogramming

It has the ability to create other Julia programs and can make improvements in them. This kind of process will remind you of a language like Lisp. 

  • Full-Featured Debugger inside Julia

Julia 1.1 came with a new debugging suit. It could execute codes written in local REPL. This will allow you to go through results, examine variables, and add breakpoints in code easily.

You can also perform compact tasks like going through a function generated code without any hassle.

Summing Up

However, now the question is which one is better for data science? Julia or Python? 

The answer to this question is not that straightforward. Although Julia is fast and is an improvement when compared to python, there is still the requirement of Python in this field.

For someone who is new in the data science and programming world, learning Python and R is the best way to go. 

The Python is not going anywhere as there are many codebases and frameworks that run on Python. 

Scientists who are skilled in Python are high in demand. This is because they have the potential to improve in the future.

Furthermore, Julia is not yet adopted by all the industries. It will take around 10 years before it is adopted by a considerable amount of industries. 

Till then, Python will be the main player in data science.   

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