# Scope of NumPy#

Here, we describe aspects of N-d array computation that are within scope for NumPy development. This is *not* an aspirational definition of where NumPy should aim, but instead captures the status quo—areas which we have decided to continue supporting, at least for the time being.

**In-memory, N-dimensional, homogeneously typed (single pointer + strided) arrays on CPUs**Support for a wide range of data types

Not specialized hardware such as GPUs

But, do support wide range of CPUs (e.g. ARM, PowerX)

**Higher level APIs for N-dimensional arrays**NumPy is a

*de facto*standard for array APIs in PythonIndexing and fast iteration over elements (ufunc)

Interoperability protocols with other data container implementations (like __array_ufunc__ and __array_function__.

**Python API and a C API**to the ndarray’s methods and attributes.Other

**specialized types or uses of N-dimensional arrays**:Masked arrays

Structured arrays (informally known as record arrays)

Memory mapped arrays

Historically, NumPy has included the following

**basic functionality in support of scientific computation**. We intend to keep supporting (but not to expand) what is currently included:Linear algebra

Fast Fourier transforms and windowing

Pseudo-random number generators

Polynomial fitting

NumPy provides some

**infrastructure for other packages in the scientific Python ecosystem**:numpy.distutils (build support for C++, Fortran, BLAS/LAPACK, and other relevant libraries for scientific computing)

f2py (generating bindings for Fortran code)

testing utilities

**Speed**: we take performance concerns seriously and aim to execute operations on large arrays with similar performance as native C code. That said, where conflict arises, maintenance and portability take precedence over performance. We aim to prevent regressions where possible (e.g., through asv).