Java vs. NumPy vs. Scala vs. Scilab Comparison Chart

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

Numerical analysis or Scientific computing is the study of approximation techniques for numerically solving mathematical problems. Scilab provides graphics functions to visualize, annotate and export data and offers many ways to create and customize various types of plots and charts. Scilab is a high level programming language for scientific programming. It enables a rapid prototyping of algorithms, without having to deal with the complexity of other more low level programming language such as C and Fortran (memory management, variable definition). This is natively handled by Scilab, which results in a few lines of code for complex mathematical operations, where other languages would require much longer codes. It also comes with advanced data structure such as polynomials, matrices and graphic handles and provides an easily operable development environment.