Is Only Python In Limelight? Julia Is No Behind The Race

Imagen Phyton vs Julia

Python and its robust features have arrested minds of most data scientists and machine learning professionals. Why not? The role of Python in machine learning is completely worth it. Its semantics encompasses math to a great extent that enables data scientists to understand and implement Python in machine learning operations.

Though Python continues to move ahead of other languages, Julia, founded by Alan Edelman, Viral B Shah, Deepak Vinchhi, Jeff Bezanson, and Stefan Karpinski, is also gaining traction.

With the 1.0 release of Julia, the language has come up with lots of features that are way better compared to other languages. Julia is optimized for data analysis and machine learning. It is designed for simply expressing ML algorithms. The language represents a unique blend of designs and new ideas that further support ML operations.

Python and Julia: Comparison

Comparing Python with Julia is important to understand which language can work better in different operations; however, placing Julia ahead of Python does not make sense as Julia has come to view recently while Python is veteran.

This article highlights various points regarding Julia and Python that you learn during the python programming certification and help you understand what makes one language different from another.

1. Speed

Julia is usually preferred to other languages, especially on grounds of speed. What fascinating is the calculative speed of Julia that is way better than Python in regard to the implementation of math concepts, for example, matrix representations and linear algebra.

You can easily get hands-on numerical computing via Julia. Moreover, multiple dispatches of Julia enable you to define data types, particularly arrays and numbers. Developers can easily deal with complex codes as Julia consumes less time compared to Python.

2. Online presence

Python, being old, enjoys considerable community support with so many online forums, for example,, youth4work, etc., discussing its features, stability, and speed.

If you are new to Python, you can become a member of these online forums and get answers to your questions. Since you have support 24/7, Python is widely accepted. No forums like python can you leverage in case of Julia because it is new, and people are not fully aware of it.

3. Installing Packages

You can easily install new packages in Julia that is a bit tough in Python. You can go to Julia’s official website “Github” to subscribe to your package and run the language on your system.

Since packages are directly pulled off from REPL, installment of packages becomes faster than ever. If you are a Python expert, you would be able to install packages easily in Python too.

4. Libraries

You get access to a handful of libraries when it comes to Python which makes your work far easier no matter how much complex a task is. Some of Python’s libraries for data science are Matplotlib, Pandas, and NumPy.

Use of Numpy for the conversion of Python into modelling tool is known worldwide. Its substantial data structures allow data analysts to solve complex matrices calculations effortlessly. Pandas is available under the BSD software license structure.

It belongs to SciPy open source software capable of the extensive data analysis. Matplotlib empowers data visualization. You can use it for generating charts, histograms, etc. To put it simply, Python has made data crunching easy.

The number of Julia’s libraries is less compared to Python have that gives it an edge above Julia. However, Julia can come up with libraries that can help data scientists evaluate data.

5. Code Conversion

Developers can easily change the code from Python to Julia but the change of code from C to Python or from Python to C is not a breeze unlike you are well aware of technicalities of both languages. Here, Julia can easily interface with libraries written in Fortran and C. Its PyCall library allows you to share data with Python.

6. The Working of Shell

The integration of Julia with the shell is an added advantage. Sending Julia’s arrays to the shell develops an environment array. Once the file opened, users can begin editing. The process is complex in Python compared to Julia.

This post does not compare Julia with Python; however, it gives an overview of both languages on various aspects. The article alludes to the fact that Julia is not an alternative to Python, but it wins Python over its weak points. The future of Julia is positive as it has set the pace for a simple, easy to work with a scripting language.

Considering ML and big data analytics, languages are critical, especially if you want to set new benchmarks. Experts look up to languages that support vectorization, exotic hardware, and differentiation.

The 0.1 version of Julia does not go well with each technical expert as they don’t consider it a good fit for error handling purposes and, moreover, documentation is one of the aspects where the language does not work correctly.

How much advance your skills are, and what your interest is in are the two factors that decide your choice. If you want additional benefits of functional programming and your project is more into algebra or other calculations, Julia is the best option here.

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