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==============
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 Python
- Indexing and fast iteration over elements (ufunc)
- Interoperability protocols with other data container implementations (like
:ref:`__array_ufunc__ and __array_function__ <basics.dispatch>`.
- **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).
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