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nep-0017-split-out-maskedarray.html
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<section id="nep-17-split-out-masked-arrays">
<span id="nep17"></span><h1>NEP 17 — Split out masked arrays<a class="headerlink" href="#nep-17-split-out-masked-arrays" 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>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-even">Status<span class="colon">:</span></dt>
<dd class="field-even"><p>Rejected</p>
</dd>
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Standards Track</p>
</dd>
<dt class="field-even">Created<span class="colon">:</span></dt>
<dd class="field-even"><p>2018-03-22</p>
</dd>
<dt class="field-odd">Resolution<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference external" href="https://mail.python.org/pipermail/numpy-discussion/2018-May/078026.html">https://mail.python.org/pipermail/numpy-discussion/2018-May/078026.html</a></p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>This NEP proposes removing MaskedArray functionality from NumPy, and
publishing it as a stand-alone package.</p>
</section>
<section id="detailed-description">
<h2>Detailed description<a class="headerlink" href="#detailed-description" title="Link to this heading">#</a></h2>
<p>MaskedArrays are a sub-class of the NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> that adds
masking capabilities, i.e. the ability to ignore or hide certain array
values during computation.</p>
<p>While historically convenient to distribute this class inside of NumPy,
improved packaging has made it possible to distribute it separately
without difficulty.</p>
<p>Motivations for this move include:</p>
<blockquote>
<div><ul class="simple">
<li><p>Focus: the NumPy package should strive to only include the
<cite>ndarray</cite> object, and the essential utilities needed to manipulate
such arrays.</p></li>
<li><p>Complexity: the MaskedArray implementation is non-trivial, and imposes
a significant maintenance burden.</p></li>
<li><p>Compatibility: MaskedArray objects, being subclasses <a class="footnote-reference brackets" href="#id3" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> of <cite>ndarrays</cite>,
often cause complications when being used with other packages.
Fixing these issues is outside the scope of NumPy development.</p></li>
</ul>
</div></blockquote>
<p>This NEP proposes a deprecation pathway through which MaskedArrays
would still be accessible to users, but no longer as part of the core
package.</p>
</section>
<section id="implementation">
<h2>Implementation<a class="headerlink" href="#implementation" title="Link to this heading">#</a></h2>
<p>Currently, a MaskedArray is created as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">ma</span>
<span class="n">ma</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
</pre></div>
</div>
<p>This will return an array where the values 1 and 3 are masked (no
longer visible to operations such as <cite>np.sum</cite>).</p>
<p>We propose refactoring the <cite>np.ma</cite> subpackage into a new
pip-installable library called <cite>maskedarray</cite> <a class="footnote-reference brackets" href="#id4" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>, which would be used
in a similar fashion:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">maskedarray</span> <span class="k">as</span> <span class="nn">ma</span>
<span class="n">ma</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
</pre></div>
</div>
<p>For two releases of NumPy, <cite>maskedarray</cite> would become a NumPy
dependency, and would expose MaskedArrays under the existing name,
<cite>np.ma</cite>. If imported as <cite>np.ma</cite>, a <cite>NumpyDeprecationWarning</cite> will
be raised, describing the impending deprecation with instructions on
how to modify code to use <cite>maskedarray</cite>.</p>
<p>After two releases, <cite>np.ma</cite> will be removed entirely. In order to obtain
<cite>np.ma</cite>, a user will install it via <cite>pip install</cite> or via their package
manager. Subsequently, <cite>importing maskedarray</cite> on a version of NumPy that
includes it integrally will raise an <cite>ImportError</cite>.</p>
<section id="documentation">
<h3>Documentation<a class="headerlink" href="#documentation" title="Link to this heading">#</a></h3>
<p>NumPy’s internal documentation refers explicitly to MaskedArrays in
certain places, e.g. <cite>ndarray.concatenate</cite>:</p>
<p>> When one or more of the arrays to be concatenated is a MaskedArray,
> this function will return a MaskedArray object instead of an ndarray,
> but the input masks are <em>not</em> preserved. In cases where a MaskedArray
> is expected as input, use the ma.concatenate function from the masked
> array module instead.</p>
<p>Such documentation will be removed, since the expectation is that
users of <cite>maskedarray</cite> will use methods from that package to operate
on MaskedArrays.</p>
<section id="other-appearances">
<h4>Other appearances<a class="headerlink" href="#other-appearances" title="Link to this heading">#</a></h4>
<p>Explicit MaskedArray support will be removed from:</p>
<ul class="simple">
<li><p><cite>numpygenfromtext</cite></p></li>
<li><p><cite>numpy.libmerge_arrays</cite>, <cite>numpy.lib.stack_arrays</cite></p></li>
</ul>
</section>
</section>
</section>
<section id="backward-compatibility">
<h2>Backward compatibility<a class="headerlink" href="#backward-compatibility" title="Link to this heading">#</a></h2>
<p>For two releases of NumPy, apart from a deprecation notice, there will
be no user visible changes. Thereafter, <cite>np.ma</cite> will no longer be
available (instead, MaskedArrays will live in the <cite>maskedarray</cite>
package).</p>
<p>Note also that new PEPs on array-like objects may eventually provide
better support for MaskedArrays than is currently available.</p>
</section>
<section id="alternatives">
<h2>Alternatives<a class="headerlink" href="#alternatives" title="Link to this heading">#</a></h2>
<p>After a lively discussion on the mailing list:</p>
<ul class="simple">
<li><p>There is support (and active interest in) making a better <em>new</em> masked array
class.</p></li>
<li><p>The new class should be a consumer of the external NumPy API with no special
status (unlike today where there are hacks across the codebase to support it)</p></li>
<li><p><cite>MaskedArray</cite> will stay where it is, at least until the new masked array
class materializes and has been tried in the wild.</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>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id3" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p>Subclassing ndarray,
<a class="reference external" href="https://docs.scipy.org/doc/numpy/user/basics.subclassing.html">https://docs.scipy.org/doc/numpy/user/basics.subclassing.html</a></p>
</aside>
<aside class="footnote brackets" id="id4" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
<p>PyPI: maskedarray, <a class="reference external" href="https://pypi.org/project/maskedarray/">https://pypi.org/project/maskedarray/</a></p>
</aside>
</aside>
</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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