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<li class="toctree-l2"><a class="reference internal" href="nep-0000.html">NEP 0 — Purpose and process</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0023-backwards-compatibility.html">NEP 23 — Backwards compatibility and deprecation policy</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0041-improved-dtype-support.html">NEP 41 — First step towards a new datatype system</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0042-new-dtypes.html">NEP 42 — New and extensible DTypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0044-restructuring-numpy-docs.html">NEP 44 — Restructuring the NumPy documentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0051-scalar-representation.html">NEP 51 — Changing the representation of NumPy scalars</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0001-npy-format.html">NEP 1 — A simple file format for NumPy arrays</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0005-generalized-ufuncs.html">NEP 5 — Generalized universal functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0007-datetime-proposal.html">NEP 7 — A proposal for implementing some date/time types in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0010-new-iterator-ufunc.html">NEP 10 — Optimizing iterator/UFunc performance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0002-warnfix.html">NEP 2 — A proposal to build numpy without warning with a big set of warning flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0003-math_config_clean.html">NEP 3 — Cleaning the math configuration of numpy.core</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0004-datetime-proposal3.html">NEP 4 — A (third) proposal for implementing some date/time types in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0006-newbugtracker.html">NEP 6 — Replacing Trac with a different bug tracker</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0008-groupby_additions.html">NEP 8 — A proposal for adding groupby functionality to NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0009-structured_array_extensions.html">NEP 9 — Structured array extensions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0011-deferred-ufunc-evaluation.html">NEP 11 — Deferred UFunc evaluation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0012-missing-data.html">NEP 12 — Missing data functionality in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0021-advanced-indexing.html">NEP 21 — Simplified and explicit advanced indexing</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0024-missing-data-2.html">NEP 24 — Missing data functionality - alternative 1 to NEP 12</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0025-missing-data-3.html">NEP 25 — NA support via special dtypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0026-missing-data-summary.html">NEP 26 — Summary of missing data NEPs and discussion</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0030-duck-array-protocol.html">NEP 30 — Duck typing for NumPy arrays - implementation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0031-uarray.html">NEP 31 — Context-local and global overrides of the NumPy API</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0037-array-module.html">NEP 37 — A dispatch protocol for NumPy-like modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0047-array-api-standard.html">NEP 47 — Adopting the array API standard</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0016-abstract-array.html">NEP 16 — An abstract base class for identifying "duck arrays"</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0017-split-out-maskedarray.html">NEP 17 — Split out masked arrays</a></li>
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<section id="scope-of-numpy">
<h1>Scope of NumPy<a class="headerlink" href="#scope-of-numpy" title="Link to this heading">#</a></h1>
<p>Here, we describe aspects of N-d array computation that are within scope for NumPy development. This is <em>not</em> 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.</p>
<ul class="simple">
<li><p><strong>In-memory, N-dimensional, homogeneously typed (single pointer + strided) arrays on CPUs</strong></p>
<ul>
<li><p>Support for a wide range of data types</p></li>
<li><p>Not specialized hardware such as GPUs</p></li>
<li><p>But, do support wide range of CPUs (e.g. ARM, PowerX)</p></li>
</ul>
</li>
<li><p><strong>Higher level APIs for N-dimensional arrays</strong></p>
<ul>
<li><p>NumPy is a <em>de facto</em> standard for array APIs in Python</p></li>
<li><p>Indexing and fast iteration over elements (ufunc)</p></li>
<li><p>Interoperability protocols with other data container implementations (like
<a class="reference external" href="https://numpy.org/devdocs/user/basics.dispatch.html#basics-dispatch" title="(in NumPy v2.3.dev0)"><span class="xref std std-ref">__array_ufunc__ and __array_function__</span></a>.</p></li>
</ul>
</li>
<li><p><strong>Python API and a C API</strong> to the ndarray’s methods and attributes.</p></li>
<li><p>Other <strong>specialized types or uses of N-dimensional arrays</strong>:</p>
<ul>
<li><p>Masked arrays</p></li>
<li><p>Structured arrays (informally known as record arrays)</p></li>
<li><p>Memory mapped arrays</p></li>
</ul>
</li>
<li><p>Historically, NumPy has included the following <strong>basic functionality
in support of scientific computation</strong>. We intend to keep supporting
(but not to expand) what is currently included:</p>
<ul>
<li><p>Linear algebra</p></li>
<li><p>Fast Fourier transforms and windowing</p></li>
<li><p>Pseudo-random number generators</p></li>
<li><p>Polynomial fitting</p></li>
</ul>
</li>
<li><p>NumPy provides some <strong>infrastructure for other packages in the scientific Python ecosystem</strong>:</p>
<ul>
<li><p>numpy.distutils (build support for C++, Fortran, BLAS/LAPACK, and other
relevant libraries for scientific computing)</p></li>
<li><p>f2py (generating bindings for Fortran code)</p></li>
<li><p>testing utilities</p></li>
</ul>
</li>
<li><p><strong>Speed</strong>: 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).</p></li>
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