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<section id="nep-52-python-api-cleanup-for-numpy-2-0">
<span id="nep52"></span><h1>NEP 52 — Python API cleanup for NumPy 2.0<a class="headerlink" href="#nep-52-python-api-cleanup-for-numpy-2-0" title="Link to this heading">#</a></h1>
<dl class="field-list simple">
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Ralf Gommers <<a class="reference external" href="mailto:ralf.gommers%40gmail.com">ralf<span>.</span>gommers<span>@</span>gmail<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Author<span class="colon">:</span></dt>
<dd class="field-even"><p>Stéfan van der Walt <<a class="reference external" href="mailto:stefanv%40berkeley.edu">stefanv<span>@</span>berkeley<span>.</span>edu</a>></p>
</dd>
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Nathan Goldbaum <<a class="reference external" href="mailto:ngoldbaum%40quansight.com">ngoldbaum<span>@</span>quansight<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Author<span class="colon">:</span></dt>
<dd class="field-even"><p>Mateusz Sokół <<a class="reference external" href="mailto:msokol%40quansight.com">msokol<span>@</span>quansight<span>.</span>com</a>></p>
</dd>
<dt class="field-odd">Status<span class="colon">:</span></dt>
<dd class="field-odd"><p>Final</p>
</dd>
<dt class="field-even">Type<span class="colon">:</span></dt>
<dd class="field-even"><p>Standards Track</p>
</dd>
<dt class="field-odd">Created<span class="colon">:</span></dt>
<dd class="field-odd"><p>2023-03-28</p>
</dd>
<dt class="field-even">Resolution<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference external" href="https://mail.python.org/archives/list/numpy-discussion@python.org/thread/QLMPFTWA67DXE3JCUQT2RIRLQ44INS4F/">https://mail.python.org/archives/list/numpy-discussion@python.org/thread/QLMPFTWA67DXE3JCUQT2RIRLQ44INS4F/</a></p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>We propose to clean up NumPy’s Python API for the NumPy 2.0 release.
This includes a more clearly defined split between what is public and what is
private, and reducing the size of the main namespace by removing aliases
and functions that have better alternatives. Furthermore, each function is meant
to be accessible from only one place, so all duplicates also need to be dropped.</p>
</section>
<section id="motivation-and-scope">
<h2>Motivation and scope<a class="headerlink" href="#motivation-and-scope" title="Link to this heading">#</a></h2>
<p>NumPy has a large API surface that evolved organically over many years:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">objects_in_api</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">np</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">s</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'_'</span><span class="p">)]</span>
<span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">objects_in_api</span><span class="p">)</span>
<span class="go">562</span>
<span class="gp">>>> </span><span class="n">modules</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">objects_in_api</span> <span class="k">if</span> <span class="n">inspect</span><span class="o">.</span><span class="n">ismodule</span><span class="p">(</span><span class="nb">eval</span><span class="p">(</span><span class="sa">f</span><span class="s1">'np.</span><span class="si">{</span><span class="n">s</span><span class="si">}</span><span class="s1">'</span><span class="p">))]</span>
<span class="gp">>>> </span><span class="n">modules</span>
<span class="go">['char', 'compat', 'ctypeslib', 'emath', 'fft', 'lib', 'linalg', 'ma', 'math', 'polynomial', 'random', 'rec', 'testing', 'version']</span>
<span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">modules</span><span class="p">)</span>
<span class="go">14</span>
</pre></div>
</div>
<p>The above doesn’t even include items that are public but have
been hidden from <code class="docutils literal notranslate"><span class="pre">__dir__</span></code>.
A particularly problematic example of that is <code class="docutils literal notranslate"><span class="pre">np.core</span></code>,
which is technically private but heavily used in practice.
For a full overview of what’s considered public, private or a bit in between, see
<a class="github reference external" href="https://github.com/numpy/numpy/blob/main/numpy/tests/test_public_api.py">numpy/numpy</a>.</p>
<p>The size of the API and the lacking definition of its boundaries
incur significant costs:</p>
<ul>
<li><p><strong>Users find it hard to disambiguate between similarly named
functions.</strong></p>
<p>Looking for functions with tab completion in IPython, a notebook, or an IDE
is a challenge. E.g., type <code class="docutils literal notranslate"><span class="pre">np.<TAB></span></code> and look at the first six items
offered: two ufuncs (<code class="docutils literal notranslate"><span class="pre">abs</span></code>, <code class="docutils literal notranslate"><span class="pre">add</span></code>), one alias (<code class="docutils literal notranslate"><span class="pre">absolute</span></code>), and three
functions that are not intended for end-users (<code class="docutils literal notranslate"><span class="pre">add_docstring</span></code>,
<code class="docutils literal notranslate"><span class="pre">add_newdoc</span></code>, <code class="docutils literal notranslate"><span class="pre">add_newdoc_ufunc</span></code>). As a result, the learning curve for
NumPy is steeper than it has to be.</p>
</li>
<li><p><strong>Libraries that mimic the NumPy API face significant implementation barriers.</strong></p>
<p>For maintainers of NumPy API-compatible array libraries (Dask, CuPy, JAX,
PyTorch, TensorFlow, cuNumeric, etc.) and compilers/transpilers (Numba,
Pythran, Cython, etc.) there is an implementation cost to each object in the
namespace. In practice, no other library has full support for the entire
NumPy API, partly because it is so hard to know what to include when faced
with a slew of aliases and legacy objects.</p>
</li>
<li><p><strong>Teaching NumPy is more complicated than it needs to be.</strong></p>
<p>Similarly, a larger API is confusing to learners, who not only have to <em>find</em>
functions but have to choose <em>which</em> functions to use.</p>
</li>
<li><p><strong>Developers are hesitant to grow the API surface.</strong></p>
<p>This happens even when the changes are warranted, because they are aware of
the above concerns.</p>
</li>
</ul>
<p>The scope of this NEP includes:</p>
<ul class="simple">
<li><p>Deprecating or removing functionality that is too niche for NumPy, not
well-designed, superseded by better alternatives, an unnecessary alias,
or otherwise a candidate for removal.</p></li>
<li><p>Clearly separating public from private NumPy API by use of underscores.</p></li>
<li><p>Restructuring the NumPy namespaces to be easier to understand and navigate.</p></li>
</ul>
<p>Out of scope for this NEP are:</p>
<ul class="simple">
<li><p>Introducing new functionality or performance enhancements.</p></li>
</ul>
</section>
<section id="usage-and-impact">
<h2>Usage and impact<a class="headerlink" href="#usage-and-impact" title="Link to this heading">#</a></h2>
<p>A key principle of this API refactor is to ensure that, when code has been
adapted to the changes and is 2.0-compatible, that code then <em>also</em> works with
NumPy <code class="docutils literal notranslate"><span class="pre">1.2x.x</span></code>. This keeps the burden on users and downstream library
maintainers low by not having to carry duplicate code which switches on the
NumPy major version number.</p>
</section>
<section id="backward-compatibility">
<h2>Backward compatibility<a class="headerlink" href="#backward-compatibility" title="Link to this heading">#</a></h2>
<p>As mentioned above, while the new (or cleaned up, NumPy 2.0) API should be
backward compatible, there is no guarantee of forward compatibility from 1.25.X
to 2.0. Code will have to be updated to account for deprecated, moved, or
removed functions/classes, as well as for more strictly enforced private APIs.</p>
<p>In order to make it easier to adopt the changes in this NEP, we will:</p>
<ol class="arabic simple">
<li><p>Provide a transition guide that lists each API change and its replacement.</p></li>
<li><p>Explicitly flag all expired attributes with a meaningful <code class="docutils literal notranslate"><span class="pre">AttributeError</span></code>
that points out to the new place or recommends an alternative.</p></li>
<li><p>Provide a script to automate the migration wherever possible. This will be
similar to <code class="docutils literal notranslate"><span class="pre">tools/replace_old_macros.sed</span></code> (which adapts code for a
previous C API naming scheme change). This will be <code class="docutils literal notranslate"><span class="pre">sed</span></code> (or equivalent)
based rather than attempting AST analysis, so it won’t cover everything.</p></li>
</ol>
</section>
<section id="detailed-description">
<h2>Detailed description<a class="headerlink" href="#detailed-description" title="Link to this heading">#</a></h2>
<section id="cleaning-up-the-main-namespace">
<h3>Cleaning up the main namespace<a class="headerlink" href="#cleaning-up-the-main-namespace" title="Link to this heading">#</a></h3>
<p>We expect to reduce the main namespace by a large number of entries, on the
order of 100. Here is a representative set of examples:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">np.inf</span></code> and <code class="docutils literal notranslate"><span class="pre">np.nan</span></code> have 8 aliases between them, of which most can be removed.</p></li>
<li><p>A collection of random and undocumented functions (e.g., <code class="docutils literal notranslate"><span class="pre">byte_bounds</span></code>, <code class="docutils literal notranslate"><span class="pre">disp</span></code>,
<code class="docutils literal notranslate"><span class="pre">safe_eval</span></code>, <code class="docutils literal notranslate"><span class="pre">who</span></code>) listed in
<a class="reference external" href="https://github.com/numpy/numpy/issues/12385">gh-12385</a>
can be deprecated and removed.</p></li>
<li><p>All <code class="docutils literal notranslate"><span class="pre">*sctype</span></code> functions can be deprecated and removed, they (see
<a class="reference external" href="https://github.com/numpy/numpy/issues/17325">gh-17325</a>,
<a class="reference external" href="https://github.com/numpy/numpy/issues/12334">gh-12334</a>,
and other issues for <code class="docutils literal notranslate"><span class="pre">maximum_sctype</span></code> and related functions).</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">np.compat</span></code> namespace, used during the Python 2 to 3 transition, will be removed.</p></li>
<li><p>Functions that are narrow in scope, with very few public use-cases,
will be removed. These will have to be identified manually and by issue triage.</p></li>
</ul>
<p>New namespaces are introduced for warnings/exceptions (<code class="docutils literal notranslate"><span class="pre">np.exceptions</span></code>) and
for dtype-related functionality (<code class="docutils literal notranslate"><span class="pre">np.dtypes</span></code>). NumPy 2.0 is a good opportunity
to populate these submodules from the main namespace.</p>
<p>Functionality that is widely used but has a preferred alternative may either be
deprecated (with the deprecation message pointing out what to use instead) or
be hidden by not including it in <code class="docutils literal notranslate"><span class="pre">__dir__</span></code>. In case of hiding, a <code class="docutils literal notranslate"><span class="pre">..</span>
<span class="pre">legacy::</span></code> directory may be used to mark such functionality in the
documentation.</p>
<p>A test will be added to ensure limited future growth of all namespaces; i.e.,
every new entry will need to be explicitly added to an allow-list.</p>
</section>
<section id="cleaning-up-the-submodule-structure">
<h3>Cleaning up the submodule structure<a class="headerlink" href="#cleaning-up-the-submodule-structure" title="Link to this heading">#</a></h3>
<p>We will clean up the NumPy submodule structure, so it is easier to navigate.
When this was discussed before (see
<a class="reference external" href="https://github.com/numpy/numpy/pull/18447">MAINT: Hide internals of np.lib to only show submodules</a>)
there was already rough consensus on that - however it was hard to pull off in
a minor release.</p>
<p>A basic principle we will adhere to is “one function, one location”. Functions
that are exposed in more than one namespace (e.g., many functions are present
in <code class="docutils literal notranslate"><span class="pre">numpy</span></code> and <code class="docutils literal notranslate"><span class="pre">numpy.lib</span></code>) need to find a single home.</p>
<p>We will reorganize the API reference guide along main and submodule namespaces,
and only within the main namespace use the current subdivision along
functionality groupings. Also by “mainstream” and special-purpose namespaces:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Regular/recommended user-facing namespaces for general use. Present these</span>
<span class="c1"># as the primary set of namespaces to the users.</span>
<span class="n">numpy</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">exceptions</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">fft</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">linalg</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">polynomial</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">random</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">testing</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">typing</span>
<span class="c1"># Special-purpose namespaces. Keep these, but document them in a separate</span>
<span class="c1"># grouping in the reference guide and explain their purpose.</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">array_api</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">ctypeslib</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">emath</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">f2py</span> <span class="c1"># only a couple of public functions, like `compile` and `get_include`</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">lib</span><span class="o">.</span><span class="n">stride_tricks</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">lib</span><span class="o">.</span><span class="n">npyio</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">rec</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">dtypes</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">array_utils</span>
<span class="c1"># Legacy (prefer not to use, there are better alternatives and/or this code</span>
<span class="c1"># is deprecated or isn't reliable). This will be a third grouping in the</span>
<span class="c1"># reference guide; it's still there, but de-emphasized and the problems</span>
<span class="c1"># with it or better alternatives are explained in the docs.</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">char</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">distutils</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">ma</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">matlib</span>
<span class="c1"># To remove</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">compat</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">core</span> <span class="c1"># rename to _core</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">doc</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">math</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">version</span> <span class="c1"># rename to _version</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">matrixlib</span>
<span class="c1"># To clean out or somehow deal with: everything in `numpy.lib`</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>TBD: will we preserve <code class="docutils literal notranslate"><span class="pre">np.lib</span></code> or not? It only has a couple of unique
functions/objects, like <code class="docutils literal notranslate"><span class="pre">Arrayterator</span></code> (a candidate for removal), <code class="docutils literal notranslate"><span class="pre">NumPyVersion</span></code>,
and the <code class="docutils literal notranslate"><span class="pre">stride_tricks</span></code>, <code class="docutils literal notranslate"><span class="pre">mixins</span></code> and <code class="docutils literal notranslate"><span class="pre">format</span></code> subsubmodules.
<code class="docutils literal notranslate"><span class="pre">numpy.lib</span></code> itself is not a coherent namespace, and does not even have a
reference guide page.</p>
</div>
<p>We will make all submodules available lazily, so that users don’t have to type
<code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">numpy.xxx</span></code> but can use <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">numpy</span> <span class="pre">as</span> <span class="pre">np;</span> <span class="pre">np.xxx.*</span></code>, while at the
same time not negatively impacting the overhead of <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">numpy</span></code>. This has
been very helpful for teaching scikit-image and SciPy, and it resolves a
potential issue for Spyder users because Spyder already makes all submodules
available - so code using the above import pattern then works in Spyder but not
outside it.</p>
</section>
<section id="reducing-the-number-of-ways-to-select-dtypes">
<h3>Reducing the number of ways to select dtypes<a class="headerlink" href="#reducing-the-number-of-ways-to-select-dtypes" title="Link to this heading">#</a></h3>
<p>The many dtype classes, instances, aliases and ways to select them are one of
the larger usability problems in the NumPy API. E.g.:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># np.intp is different, but compares equal too</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">int64</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">int_</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="s1">'i8'</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">sctypeDict</span><span class="p">[</span><span class="s1">'i8'</span><span class="p">]</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">float64</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">double</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float_</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="s1">'f8'</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">sctypeDict</span><span class="p">[</span><span class="s1">'f8'</span><span class="p">]</span>
<span class="go">True</span>
<span class="go">### Really?</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">clongdouble</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">clongfloat</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">longcomplex</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">complex256</span>
<span class="go">True</span>
</pre></div>
</div>
<p>These aliases can go: <a class="reference external" href="https://numpy.org/devdocs/reference/arrays.scalars.html#other-aliases">https://numpy.org/devdocs/reference/arrays.scalars.html#other-aliases</a></p>
<p>All one-character type code strings and related routines like <code class="docutils literal notranslate"><span class="pre">mintypecode</span></code>
will be marked as legacy.</p>
<p>To discuss:</p>
<ul class="simple">
<li><p>move <em>all</em> dtype-related classes to <code class="docutils literal notranslate"><span class="pre">np.dtypes</span></code>?</p></li>
<li><p>canonical way to compare/select dtypes: <code class="docutils literal notranslate"><span class="pre">np.isdtype</span></code> (new, xref array API
NEP), leaving <code class="docutils literal notranslate"><span class="pre">np.issubdtype</span></code> for the more niche use of numpy’s dtype class
hierarchy, and hide most other stuff.</p></li>
<li><p>possibly remove <code class="docutils literal notranslate"><span class="pre">float96</span></code>/<code class="docutils literal notranslate"><span class="pre">float128</span></code>? they’re aliases that may not exist,
and are too easy to shoot yourself in the foot with.</p></li>
</ul>
</section>
<section id="cleaning-up-the-niche-methods-on-numpy-ndarray">
<h3>Cleaning up the niche methods on <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code><a class="headerlink" href="#cleaning-up-the-niche-methods-on-numpy-ndarray" title="Link to this heading">#</a></h3>
<p>The <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> object has a lot of attributes and methods, some of which are
too niche to be that prominent, all that does is distract the average user.
E.g.:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">.itemset</span></code> (already discouraged)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">.newbyteorder</span></code> (too niche)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">.ptp</span></code> (niche, use <code class="docutils literal notranslate"><span class="pre">np.ptp</span></code> function instead)</p></li>
</ul>
</section>
</section>
<section id="api-changes-considered-and-rejected">
<h2>API changes considered and rejected<a class="headerlink" href="#api-changes-considered-and-rejected" title="Link to this heading">#</a></h2>
<p>For some functions and submodules it turned out that removing them would cause
too much disruption or would require an amount of work disproportional to the
actual gain. We arrived at this conclusion for such items:</p>
<ul class="simple">
<li><p>Removing business day functions: <code class="docutils literal notranslate"><span class="pre">np.busday_count</span></code>, <code class="docutils literal notranslate"><span class="pre">np.busday_offset</span></code>, <code class="docutils literal notranslate"><span class="pre">np.busdaycalendar</span></code>.</p></li>
<li><p>Removing <code class="docutils literal notranslate"><span class="pre">np.nan*</span></code> functions and introducing new <code class="docutils literal notranslate"><span class="pre">nan_mode</span></code> argument to the related base functions.</p></li>
<li><p>Hiding histogram functions in the <code class="docutils literal notranslate"><span class="pre">np.histograms</span></code> submodule.</p></li>
<li><p>Hiding <code class="docutils literal notranslate"><span class="pre">c_</span></code>, <code class="docutils literal notranslate"><span class="pre">r_</span></code> and <code class="docutils literal notranslate"><span class="pre">s_</span></code> in the <code class="docutils literal notranslate"><span class="pre">np.lib.index_tricks</span></code> submodule.</p></li>
<li><p>Functions that looked niche but are present in the Array API (for example <code class="docutils literal notranslate"><span class="pre">np.can_cast</span></code>).</p></li>
<li><p>Removing <code class="docutils literal notranslate"><span class="pre">.repeat</span></code> and <code class="docutils literal notranslate"><span class="pre">.ctypes</span></code> from <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> object.</p></li>
</ul>
</section>
<section id="related-work">
<h2>Related work<a class="headerlink" href="#related-work" title="Link to this heading">#</a></h2>
<p>A clear split between public and private API was recently established
as part of SciPy 1.8.0 (2021), see
<a class="reference external" href="https://github.com/scipy/scipy/issues/14360">tracking issue scipy#14360</a>.
The results were beneficial, and the impact on users relatively modest.</p>
</section>
<section id="implementation">
<h2>Implementation<a class="headerlink" href="#implementation" title="Link to this heading">#</a></h2>
<p>The implementation has been split over many different PRs, each touching on
a single API or a set of related APIs. Here’s a sample of the most impactful PRs:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/numpy/numpy/pull/24634">gh-24634: Rename numpy/core to numpy/_core</a></p></li>
<li><p><a class="reference external" href="https://github.com/numpy/numpy/pull/24357">gh-24357: Cleaning numpy/__init__.py and main namespace - Part 2</a></p></li>
<li><p><a class="reference external" href="https://github.com/numpy/numpy/pull/24376">gh-24376: Cleaning numpy/__init__.py and main namespace - Part 3</a></p></li>
</ul>
<p>The complete list of cleanup work done in the 2.0 release can be found by searching a dedicated label:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/numpy/numpy/labels/Numpy%202.0%20API%20Changes">Numpy 2.0 API Changes:</a></p></li>
</ul>
<p>Some PRs has already been merged and shipped with the <cite>1.25.0</cite> release.
For example, deprecating non-preferred aliases:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/numpy/numpy/pull/23302">gh-23302: deprecate np.round_; add round/min/max to the docs</a></p></li>
<li><p><a class="reference external" href="https://github.com/numpy/numpy/pull/23314">gh-23314: deprecate product/cumproduct/sometrue/alltrue</a></p></li>
</ul>
<p>Hiding or removing objects that are accidentally made public or not even NumPy objects at all:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/numpy/numpy/pull/21403">gh-21403: remove some names from main numpy namespace</a></p></li>
</ul>
<p>Creation of new namespaces to make it easier to navigate the module structure:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/numpy/numpy/pull/22644">gh-22644: Add new np.exceptions namespace for errors and warnings</a></p></li>
</ul>
</section>
<section id="alternatives">
<h2>Alternatives<a class="headerlink" href="#alternatives" title="Link to this heading">#</a></h2>
</section>
<section id="discussion">
<h2>Discussion<a class="headerlink" href="#discussion" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/numpy/numpy/issues/23999">gh-23999: Tracking issue for the NEP 52</a></p></li>
<li><p><a class="reference external" href="https://github.com/numpy/numpy/issues/24306">gh-24306: Overhaul of the main namespace</a></p></li>
<li><p><a class="reference external" href="https://github.com/numpy/numpy/issues/24507">gh-24507: Overhaul of the np.lib namespace</a></p></li>
</ul>
</section>
<section id="references-and-footnotes">
<h2>References and footnotes<a class="headerlink" href="#references-and-footnotes" title="Link to this heading">#</a></h2>
</section>
<section id="copyright">
<h2>Copyright<a class="headerlink" href="#copyright" title="Link to this heading">#</a></h2>
<p>This document has been placed in the public domain.</p>
</section>
</section>
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