Editing NumPy (section)

=== The ndarray data structure ===
The core functionality of NumPy is its "ndarray", for ''n''-dimensional array, [[data structure]]. These arrays are [[Stride of an array|strided]] views on memory.<ref name="cise">{{cite journal |title=The NumPy array: a structure for efficient numerical computation |first1=Stéfan |last1=van der Walt |first2=S. Chris |last2=Colbert |first3=Gaël |last3=Varoquaux |year=2011 |journal=Computing in Science and Engineering |volume=13 |issue=2 |page=22 |publisher=IEEE |doi=10.1109/MCSE.2011.37 |arxiv=1102.1523|bibcode=2011CSE....13b..22V |s2cid=16907816 }}</ref> In contrast to Python's built-in list data structure, these arrays are homogeneously typed: all elements of a single array must be of the same type.

Such arrays can also be views into memory buffers allocated by [[C Programming Language|C]]/[[C++]], [[Python (programming language)|Python]], and [[Fortran]] extensions to the CPython interpreter without the need to copy data around, giving a degree of compatibility with existing numerical libraries. This functionality is exploited by the SciPy package, which wraps a number of such libraries (notably BLAS and LAPACK). NumPy has built-in support for [[memory-mapped file|memory-mapped]] ndarrays.<ref name="cise"/>