What is Python?
This object-oriented, interpreted programming language was released in 1991 by Guido van Rossum. It was created to be better than the language, ABC. Python contains dynamic features and functions, referred to as a dynamically-typed language. Additionally, it has support for GUI programming, a large library, and a small learning curve.
Small startup businesses and corporations alike use Python in their systems. Some major corporations utilizing it include:
Python doubles as a scripting language in software, alongside its use in video games, AI, and machine learning products. The popularity Python has gained makes it akin to a modern parallel to Java’s legacy.
What is Julia?
Released 9 years ago in 2012, Julia is known for its high-quality performance. It also excels in completing scientific computing and numerical tasks. Julia can handle large amounts of data and, like Python, is a dynamically typed programming language. Similarly, it’s free, open-sourced, and easy to learn. It supports a compiled structure, which decreases development speed while increasing performance. On top of that, it works well with parallel and distributed computing.
The fast performance speed has been Julia’s most popular characteristic. It’s attracted mathematicians, data scientists, and companies like BlackRock, the Federal Reserve Bank of New York, and CISCO. Those businesses incorporate Julia in varying ways, adding to the praise of its flexibility.
Julia vs. Python
- Performance Speed
Surprisingly, as fast as both languages are, Julia is faster. Python falls behind because it has to implement code, optimization methods, and external libraries where Julia doesn’t.
This is where Python’s experience and history benefit it. Newer programming languages like Julia tend to have smaller communities or libraries available. Also, those libraries aren’t always run well, which may hinder the development process in some cases.
- Easy to Use
Both languages were designed to be simpler than languages such as Java. They have smaller learning curves, but their specialties determine how easy they each are to learn. For example, Julia is easier for people with math backgrounds or interests. Python is more general and may not be a good fit for every project.
- Community of Developers
The reliability of Python puts it ahead of Julia in most areas. Alternatively, Julia’s speed and quality performance capabilities can outshine Python. For developers in search of a solid, reputable programming language, Python is the way to go. But, if speed and high performance are the focus, Julia is a winner.