Array programming

In computer science, array programming languages (also known as vector or multidimensional languages) generalize operations on scalars to apply transparently to vectors, matrices, and higher-dimensional arrays.

Array programming primitives concisely express broad ideas about data manipulation. The level of concision can be dramatic in certain cases: it is not uncommon to find array programming language one-liners that require more than a couple of pages of Java code.

Modern programming languages that support array programming are commonly used in scientific and engineering settings; these include Fortran 90, Mata, MATLAB, Analytica, TK Solver (as lists), Octave, R, Cilk Plus, Julia, and the NumPy extension to Python. In these languages, an operation that operates on entire arrays can be called a vectorized operation, regardless of whether it is executed on a vector processor or not.

The fundamental idea behind array programming is that operations apply at once to an entire set of values. This makes it a high-level programming model as it allows the programmer to think and operate on whole aggregates of data, without having to resort to explicit loops of individual scalar operations.

Iverson described the rationale behind array programming (actually referring to APL) as follows:

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