Haskell vs. NumPy vs. Python vs. Swift 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.

Writing Swift code is interactive and fun, the syntax is concise yet expressive, and Swift includes modern features developers love. Swift code is safe by design and produces software that runs lightning-fast. Swift is the result of the latest research on programming languages, combined with decades of experience building Apple platforms. Named parameters are expressed in a clean syntax that makes APIs in Swift even easier to read and maintain. Even better, you don’t even need to type semi-colons. Inferred types make code cleaner and less prone to mistakes, while modules eliminate headers and provide namespaces. To best support international languages and emoji, Strings are Unicode-correct and use a UTF-8 based encoding to optimize performance for a wide-variety of use cases. You can even write concurrent code with simple, built-in keywords that define asynchronous behavior, making your code more readable and less error-prone.