Julia is a great Python challenger in data science, its development started in 2009 and the language became popular when the compiler became open source in 2012. It is currently available under the MIT license. The goal is to create a language with the advantages of Python (simplicity and dynamism), R (statistical processing), C (speed of execution), Perl (string processing), Mathlab (linear algebra), and others. It also wants to be distributed, generic.
In the tradition of Pascal, the name of the language comes from the French mathematician Gaston Julia, discoverer of fractals.
Its simplicity and scientific capabilities make it a possible alternative to Python. But is it superior? It is generally much faster than Python, which uses libraries written in C to compensate for the slowness of the interpreter. Which one is the fastest to run an algorithm is important, but what matters most is how they behave on large datasets, such as matrix calculations. See Benchmarks.
The advantage over Python is that libraries can also be written in Julia and compiled, whereas a different language must be used to add fast functions to Python.
Functionality and syntax are also compared. Both languages are meant to be easy to understand and are primarily aimed at a public that has no interest in hardware, and therefore in C-derived languages such as Go which are made to simplify the compiler’s task and not to improve the programmer’s productivity.
What is Genie
Genie is a full-stack MVC web framework that provides a streamlined and efficient workflow for developing modern web applications. It builds on Julia’s strengths (high-level, high-performance, dynamic, JIT-compiled), exposing a rich API and a powerful toolset for productive web development.