Editing Array programming (section)

{{Short description|Applying operations to whole sets of values simultaneously}}
In [[computer science]], '''array programming''' refers to solutions that allow the application of operations to an entire set of values at once. Such solutions are commonly used in [[computational science|scientific]] and engineering settings. 

Modern programming languages that support array programming (also known as [[vector (data structure)|vector]] or [[multidimensional analysis|multidimensional]] languages) have been engineered specifically to generalize operations on [[scalar (computing)|scalar]]s to apply transparently to [[vector (geometric)|vector]]s, [[matrix (mathematics)|matrices]], and higher-dimensional arrays. These include [[APL (programming language)|APL]], [[J (programming language)|J]], [[Fortran]], [[MATLAB]], [[Analytica (software)|Analytica]], [[GNU Octave|Octave]], [[R (programming language)|R]], [[Cilk Plus]], [[Julia (programming language)|Julia]], [[Perl Data Language|Perl Data Language (PDL)]], [[Raku (programming language)]]. In these languages, an operation that operates on entire arrays can be called a ''vectorized'' operation,<ref>{{cite journal |title=The NumPy array: a structure for efficient numerical computation |author=Stéfan van der Walt |author2=S. Chris Colbert |author3=Gaël Varoquaux |name-list-style=amp |journal=Computing in Science and Engineering |volume=13 |issue=2 |pages=22–30 |publisher=IEEE |year=2011 |doi=10.1109/mcse.2011.37|bibcode=2011CSE....13b..22V |arxiv=1102.1523 |s2cid=16907816 }}</ref> regardless of whether it is executed on a [[vector processor]], which implements vector instructions. Array programming primitives concisely express broad ideas about data manipulation. The level of concision can be dramatic in certain cases: it is not uncommon{{Example needed|date=September 2021}} to find array programming language [[one-liner program|one-liners]] that require several pages of object-oriented code.