Introduction

For many decades, Fortran has been the language of choice for scientific computing because of its speed. In the 1980s, when a programmer’s time was becoming more valuable than computation time, there was a need for languages that were easier to learn and use. For the purpose of research, code-compile-execute workflow gave way to the interact-explore-visualize workflow. In this context, MATLAB, IDL, Mathematica, and Maple were born.

Modern scientific computing is not just about numerical computing. It needs to be versatile: deal with large datasets, offer richer data structures than just numerical arrays, make network calls, interface with databases, interwork with web apps, handle data in various formats, enable team collaboration, enable easy documentation, etc.

MATLAB vs. Python

MATLAB is proprietary, expensive, and hard to extend. Python is open, community-driven, portable, powerful, and extensible. Python is also better with strings, namespaces, classes, and GUIs. While MATLAB(along with Simulink) has vast libraries, Python is catching up, since many scientific projects are adopting it. MATLAB is said to be poor at scalability, complex data structures, memory handling, system tasks, and database programming.