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<li class="toctree-l2"><a class="reference internal" href="nep-0049.html">NEP 49 — Data allocation strategies</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0050-scalar-promotion.html">NEP 50 — Promotion rules for Python scalars</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0052-python-api-cleanup.html">NEP 52 — Python API cleanup for NumPy 2.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0055-string_dtype.html">NEP 55 — Add a UTF-8 variable-width string DType to NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0056-array-api-main-namespace.html">NEP 56 — Array API standard support in NumPy's main namespace</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="numpy-roadmap">
<h1>NumPy roadmap<a class="headerlink" href="#numpy-roadmap" title="Link to this heading">#</a></h1>
<p>This is a live snapshot of tasks and features we will be investing resources
in. It may be used to encourage and inspire developers and to search for
funding.</p>
<section id="interoperability">
<h2>Interoperability<a class="headerlink" href="#interoperability" title="Link to this heading">#</a></h2>
<p>We aim to make it easier to interoperate with NumPy. There are many NumPy-like
packages that add interesting new capabilities to the Python ecosystem, as well
as many libraries that extend NumPy’s model in various ways. Work in NumPy to
facilitate interoperability with all such packages, and the code that uses them,
may include (among other things) interoperability protocols, better duck typing
support and ndarray subclass handling.</p>
<p>The key goal is: <em>make it easy for code written for NumPy to also work with
other NumPy-like projects.</em> This will enable GPU support via, e.g, CuPy, JAX or PyTorch,
distributed array support via Dask, and writing special-purpose arrays (either
from scratch, or as a <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> subclass) that work well with SciPy,
scikit-learn and other such packages. A large step forward in this area was
made in NumPy 2.0, with adoption of and compliance with the array API standard
(v2022.12, see <a class="reference internal" href="nep-0047-array-api-standard.html#nep47"><span class="std std-ref">NEP 47 — Adopting the array API standard</span></a>). Future work in this direction will include
support for newer versions of the array API standard, and adding features as
needed based on real-world experience and needs.</p>
<p>In addition, the <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> protocols
fulfill a role here - they are stable and used by several downstream projects.</p>
</section>
<section id="performance">
<h2>Performance<a class="headerlink" href="#performance" title="Link to this heading">#</a></h2>
<p>Improvements to NumPy’s performance are important to many users. We have
focused this effort on Universal SIMD (see <a class="reference internal" href="nep-0038-SIMD-optimizations.html#nep38"><span class="std std-ref">NEP 38 — Using SIMD optimization instructions for performance</span></a>) intrinsics which
provide nice improvements across various hardware platforms via an abstraction
layer. The infrastructure is in place, and we welcome follow-on PRs to add
SIMD support across relevant NumPy functionality.</p>
<p>Transitioning from C to C++, both in the SIMD infrastructure and in NumPy
internals more widely, is in progress. We have also started to make use of
Google Highway (see <a class="reference internal" href="nep-0054-simd-cpp-highway.html#nep54"><span class="std std-ref">NEP 54 — SIMD infrastructure evolution: adopting Google Highway when moving to C++?</span></a>), and that usage is likely to expand. Work
towards support for newer SIMD instruction sets, like SVE on arm64, is ongoing.</p>
<p>Other performance improvement ideas include:</p>
<ul class="simple">
<li><p>A better story around parallel execution (related is support for free-threaded
CPython, see further down).</p></li>
<li><p>Enable the ability to allow NumPy to use faster, but less precise,
implementations for ufuncs.
Until now, the only state modifying ufunc behavior has been <code class="docutils literal notranslate"><span class="pre">np.errstate</span></code>.
But, with NumPy 2.0 improvements in the <code class="docutils literal notranslate"><span class="pre">np.errstate</span></code> and the ufunc C
implementation make this type of addition easier.</p></li>
<li><p>Optimizations in individual functions.</p></li>
</ul>
<p>Furthermore we would like to improve the benchmarking system, in terms of coverage,
easy of use, and publication of the results. Benchmarking PRs/branches compared
to the <cite>main</cite> branch is a primary purpose, and required for PRs that are
performance-focused (e.g., adding SIMD acceleration to a function). In
addition, we’d like a performance overview like the one we had <a class="reference external" href="https://pv.github.io/numpy-bench">here</a>, set up in a way that is more
maintainable long-term.</p>
</section>
<section id="documentation-and-website">
<h2>Documentation and website<a class="headerlink" href="#documentation-and-website" title="Link to this heading">#</a></h2>
<p>The NumPy <a class="reference external" href="https://www.numpy.org/devdocs">documentation</a> is of varying
quality. The API documentation is in good shape; tutorials and high-level
documentation on many topics are missing or outdated. See <a class="reference internal" href="nep-0044-restructuring-numpy-docs.html#nep44"><span class="std std-ref">NEP 44 — Restructuring the NumPy documentation</span></a> for
planned improvements. Adding more tutorials is underway in the
<a class="reference external" href="https://github.com/numpy/numpy-tutorials">numpy-tutorials repo</a>.</p>
<p>We also intend to make all the example code in our documentation interactive -
work is underway to do so via <code class="docutils literal notranslate"><span class="pre">jupyterlite-sphinx</span></code> and Pyodide.</p>
<p>Our website (<a class="reference external" href="https://numpy.org">https://numpy.org</a>) is in good shape. Further work on expanding the
number of languages that the website is translated in is desirable. As are
improvements to the interactive notebook widget, through JupyterLite.</p>
</section>
<section id="extensibility">
<h2>Extensibility<a class="headerlink" href="#extensibility" title="Link to this heading">#</a></h2>
<p>We aim to continue making it easier to extend NumPy. The primary topic here is to
improve the dtype system - see for example <a class="reference internal" href="nep-0041-improved-dtype-support.html#nep41"><span class="std std-ref">NEP 41 — First step towards a new datatype system</span></a> and related NEPs linked
from it. In NumPy 2.0, a <a class="reference external" href="https://numpy.org/devdocs/reference/c-api/array.html#custom-data-types">new C API for user-defined dtypes</a>
was made public. We aim to encourage its usage and improve this API further,
including support for writing a dtype in Python.</p>
<p>Ideas for new dtypes that may be developed outside of the main NumPy repository
first, and that could potentially be upstreamed into NumPy later, include:</p>
<ul class="simple">
<li><p>A quad-precision (128-bit) dtype</p></li>
<li><p>A <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> dtype</p></li>
<li><p>A fixed-width string dtype which supports encodings (e.g., <code class="docutils literal notranslate"><span class="pre">utf8</span></code> or
<code class="docutils literal notranslate"><span class="pre">latin1</span></code>)</p></li>
<li><p>A unit dtype</p></li>
</ul>
<p>We further plan to extend the ufunc C API as needs arise.
One possibility here is creating a new, more powerful, API to allow hooking
into existing NumPy ufunc implementations.</p>
</section>
<section id="user-experience">
<h2>User experience<a class="headerlink" href="#user-experience" title="Link to this heading">#</a></h2>
<section id="type-annotations">
<h3>Type annotations<a class="headerlink" href="#type-annotations" title="Link to this heading">#</a></h3>
<p>Type annotations for most NumPy functionality is complete (although some
submodules like <code class="docutils literal notranslate"><span class="pre">numpy.ma</span></code> are missing return types), so users can use tools
like <a class="reference external" href="https://mypy.readthedocs.io">mypy</a> to type check their code and IDEs can improve their support
for NumPy. Improving those type annotations, for example to support annotating
array shapes (see <a class="reference external" href="https://github.com/numpy/numpy/issues/16544">gh-16544</a>),
is ongoing.</p>
</section>
<section id="platform-support">
<h3>Platform support<a class="headerlink" href="#platform-support" title="Link to this heading">#</a></h3>
<p>We aim to increase our support for different hardware architectures. This
includes adding CI coverage when CI services are available, providing wheels on
PyPI for platforms that are in high enough demand (e.g., we added <code class="docutils literal notranslate"><span class="pre">musllinux</span></code>
ones for NumPy 2.0), and resolving build issues on platforms that we don’t test
in CI (e.g., AIX).</p>
<p>We intend to write a NEP covering the support levels we provide and what is
required for a platform to move to a higher tier of support, similar to
<a class="reference external" href="https://peps.python.org/pep-0011/">PEP 11</a>.</p>
</section>
<section id="further-consistency-fixes-to-promotion-and-scalar-logic">
<h3>Further consistency fixes to promotion and scalar logic<a class="headerlink" href="#further-consistency-fixes-to-promotion-and-scalar-logic" title="Link to this heading">#</a></h3>
<p>NumPy 2.0 fixed many issues around promotion especially with respect to scalars.
We plan to continue fixing remaining inconsistencies.
For example, NumPy converts 0-D objects to scalars, and some promotions
still allowed by NumPy are problematic.</p>
</section>
<section id="support-for-free-threaded-cpython">
<h3>Support for free-threaded CPython<a class="headerlink" href="#support-for-free-threaded-cpython" title="Link to this heading">#</a></h3>
<p>CPython 3.13 will be the first release to offer a free-threaded build (i.e.,
a CPython build with the GIL disabled). Work is in progress to support this
well in NumPy. After that is stable and complete, there may be opportunities to
actually make use of the potential for performance improvements from
free-threaded CPython, or make it easier to do so for NumPy’s users.</p>
</section>
<section id="binary-size-reduction">
<h3>Binary size reduction<a class="headerlink" href="#binary-size-reduction" title="Link to this heading">#</a></h3>
<p>The number of downloads of NumPy from PyPI and other platforms continues to
increase - as of May 2024 we’re at >200 million downloads/month from PyPI
alone. Reducing the size of an installed NumPy package has many benefits:
faster installs, lower disk space usage, smaller load on PyPI, less
environmental impact, easier to fit more packages on top of NumPy in
resource-constrained environments and platforms like AWS Lambda, lower latency
for Pyodide users, and so on. We aim for significant reductions, as well as
making it easier for end users and packagers to produce smaller custom builds
(e.g., we added support for stripping tests before 2.1.0). See
<a class="reference external" href="https://github.com/numpy/numpy/issues/25737">gh-25737</a> for details.</p>
</section>
<section id="support-use-of-cpython-s-limited-c-api">
<h3>Support use of CPython’s limited C API<a class="headerlink" href="#support-use-of-cpython-s-limited-c-api" title="Link to this heading">#</a></h3>
<p>Use of the CPython limited C API, allowing producing <code class="docutils literal notranslate"><span class="pre">abi3</span></code> wheels that use
the stable ABI and are hence independent of CPython feature releases, has
benefits for both downstream packages that use NumPy’s C API and for NumPy
itself. In NumPy 2.0, work was done to enable using the limited C API with
the Cython support in NumPy (see <a href="#id1"><span class="problematic" id="id2">`gh-25531 <https://github.com/numpy/numpy/pull/25531`__</span></a>).
More work and testing is needed to ensure full support for downstream packages.</p>
<p>We also want to explore what is needed for NumPy itself to use the limited
C API - this would make testing new CPython dev and pre-release versions across
the ecosystem easier, and significantly reduce the maintenance effort for CI
jobs in NumPy itself.</p>
</section>
<section id="create-a-header-only-package-for-numpy">
<h3>Create a header-only package for NumPy<a class="headerlink" href="#create-a-header-only-package-for-numpy" title="Link to this heading">#</a></h3>
<p>We have reduced the platform-dependent content in the public NumPy headers to
almost nothing. It is now feasible to create a separate package with only
NumPy headers and a discovery mechanism for them, in order to enable downstream
packages to build against the NumPy C API without having NumPy installed.
This will make it easier/cheaper to use NumPy’s C API, especially on more
niche platforms for which we don’t provide wheels.</p>
</section>
</section>
<section id="numpy-2-0-stabilization-downstream-usage">
<h2>NumPy 2.0 stabilization & downstream usage<a class="headerlink" href="#numpy-2-0-stabilization-downstream-usage" title="Link to this heading">#</a></h2>
<p>We made a very large amount of changes (and improvements!) in NumPy 2.0. The
release process has taken a very long time, and part of the ecosystem is still
catching up. We may need to slow down for a while, and possibly help the rest
of the ecosystem with adapting to the ABI and API changes.</p>
<p>We will need to assess the costs and benefits to NumPy itself,
downstream package authors, and end users. Based on that assessment, we need to
come to a conclusion on whether it’s realistic to do another ABI-breaking
release again in the future or not. This will also inform the future evolution
of our C API.</p>
</section>
<section id="security">
<h2>Security<a class="headerlink" href="#security" title="Link to this heading">#</a></h2>
<p>NumPy is quite secure - we get only a limited number of reports about potential
vulnerabilities, and most of those are incorrect. We have made strides with a
documented security policy, a private disclosure method, and maintaining an
OpenSSF scorecard (with a high score). However, we have not changed much in how
we approach supply chain security in quite a while. We aim to make improvements
here, for example achieving fully reproducible builds for all the build
artifacts we publish - and providing full provenance information for them.</p>
</section>
<section id="maintenance">
<h2>Maintenance<a class="headerlink" href="#maintenance" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">numpy.ma</span></code> is still in poor shape and under-maintained. It needs to be
improved, ideas include:</p>
<ul>
<li><p>Rewrite masked arrays to not be a ndarray subclass – maybe in a separate project?</p></li>
<li><p>MaskedArray as a duck-array type, and/or</p></li>
<li><p>dtypes that support missing values</p></li>
</ul>
</li>
<li><p>Write a strategy on how to deal with overlap between NumPy and SciPy for <code class="docutils literal notranslate"><span class="pre">linalg</span></code>.</p></li>
<li><p>Deprecate <code class="docutils literal notranslate"><span class="pre">np.matrix</span></code> (very slowly) - this is feasible once the switch-over
from sparse matrices to sparse arrays in SciPy is complete.</p></li>
<li><p>Add new indexing modes for “vectorized indexing” and “outer indexing” (see <a class="reference internal" href="nep-0021-advanced-indexing.html#nep21"><span class="std std-ref">NEP 21 — Simplified and explicit advanced indexing</span></a>).</p></li>
<li><p>Make the polynomial API easier to use.</p></li>
</ul>
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