Python vs. Julia
Python is a popular high-level, interpreted programming language that is easy to learn. The language is popular for its simple syntax, making it easy to write complex programs. It’s been used in many fields, such as general programming, web development, server-side applications, machine learning, and data science.
Julia
Julia is a high-level, high-performance, and dynamically-typed programming language that is well-suited for numerical analysis and scientific computing. Julia is currently being used in computational biology, statistics, image processing, machine learning, and physics.
Similarities
The similarities between both languages are as follows:
Both are high-level languages
Both are
dynamically typed The variable types are checked during run-time. Both languages allow concurrent, parallel, and distributed computing
Both are
open-sourced It is free for everyone to use. Both are
garbage collected Memory is released when the object is no longer in use
Differences
The differences between both languages are as follows:
Python | Julia |
Interpreted language | Compiled language |
Python is fast but slow compared to statically-typed languages like C | Much faster than Python and its performance matches C |
Has large community and forums where developers can actively share and resolve issues | Its developer community is smaller because it's relatively new |
Dynamically typed | Supports both static and dynamic typing |
Pyhton code is difficult to make when converting from C | Supports the direct calling of C, Python, and Fortran libraries |
Easy to use and helpful for beginner programmers. | Not as easy to code |
Array indexing starts from 0 | Array indexing starts from 1 |
Vast variety of libraries | Less libraries |
Free Resources
