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<section id="the-numpy-ma-module">
<span id="module-numpy.ma"></span><span id="maskedarray-generic"></span><h1>The <a class="reference internal" href="#module-numpy.ma" title="numpy.ma"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.ma</span></code></a> module<a class="headerlink" href="#the-numpy-ma-module" title="Link to this heading">#</a></h1>
<section id="rationale">
<h2>Rationale<a class="headerlink" href="#rationale" title="Link to this heading">#</a></h2>
<p>Masked arrays are arrays that may have missing or invalid entries.
The <a class="reference internal" href="#module-numpy.ma" title="numpy.ma"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.ma</span></code></a> module provides a nearly work-alike replacement for numpy
that supports data arrays with masks.</p>
</section>
<section id="what-is-a-masked-array">
<h2>What is a masked array?<a class="headerlink" href="#what-is-a-masked-array" title="Link to this heading">#</a></h2>
<p>In many circumstances, datasets can be incomplete or tainted by the presence
of invalid data. For example, a sensor may have failed to record a data point,
or recorded an invalid value. The <a class="reference internal" href="#module-numpy.ma" title="numpy.ma"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.ma</span></code></a> module provides a convenient
way to address this issue, by introducing masked arrays.</p>
<p>A masked array is the combination of a standard <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> and a
mask. A mask is either <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.nomask" title="numpy.ma.nomask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">nomask</span></code></a>, indicating that no value of the
associated array is invalid, or an array of booleans that determines for each
element of the associated array whether the value is valid or not. When an
element of the mask is <code class="docutils literal notranslate"><span class="pre">False</span></code>, the corresponding element of the associated
array is valid and is said to be unmasked. When an element of the mask is
<code class="docutils literal notranslate"><span class="pre">True</span></code>, the corresponding element of the associated array is said to be
masked (invalid).</p>
<p>The package ensures that masked entries are not used in computations.</p>
<div class="try_examples_outer_container docutils container" id="f3d4fe56-088e-41f5-b990-0aecbd2b9bc0">
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<p>As an illustration, let’s consider the following dataset:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</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="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
</pre></div>
</div>
<p>We wish to mark the fourth entry as invalid. The easiest is to create a masked
array:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked_array</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<p>We can now compute the mean of the dataset, without taking the invalid data
into account:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="go">2.75</span>
</pre></div>
</div>
</div>
</div>
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<section id="id1">
<h2>The <a class="reference internal" href="#module-numpy.ma" title="numpy.ma"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.ma</span></code></a> module<a class="headerlink" href="#id1" title="Link to this heading">#</a></h2>
<p>The main feature of the <a class="reference internal" href="#module-numpy.ma" title="numpy.ma"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.ma</span></code></a> module is the <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaskedArray</span></code></a>
class, which is a subclass of <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>. The class, its
attributes and methods are described in more details in the
<a class="reference internal" href="maskedarray.baseclass.html#maskedarray-baseclass"><span class="std std-ref">MaskedArray class</span></a> section.</p>
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<p>The <a class="reference internal" href="#module-numpy.ma" title="numpy.ma"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.ma</span></code></a> module can be used as an addition to <a class="reference internal" href="index.html#module-numpy" title="numpy"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code></a>:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
</pre></div>
</div>
<p>To create an array with the second element invalid, we would do:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">y</span> <span class="o">=</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="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<p>To create a masked array where all values close to 1.e20 are invalid, we would
do:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">z</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked_values</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.e20</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="mf">1.e20</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div id="9946e4bf-5bb3-4f87-bbcc-9629423ba572" class="try_examples_outer_iframe hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('4908f089-9221-45ae-9785-8a9360fc5d59','9946e4bf-5bb3-4f87-bbcc-9629423ba572')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('4908f089-9221-45ae-9785-8a9360fc5d59','9946e4bf-5bb3-4f87-bbcc-9629423ba572')">Open In Tab</button></div><div id="8b043ed9-9575-403e-8137-781e14d28ecc" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script><p>For a complete discussion of creation methods for masked arrays please see
section <a class="reference internal" href="#maskedarray-generic-constructing"><span class="std std-ref">Constructing masked arrays</span></a>.</p>
</section>
</section>
<section id="using-numpy-ma">
<h1>Using numpy.ma<a class="headerlink" href="#using-numpy-ma" title="Link to this heading">#</a></h1>
<section id="constructing-masked-arrays">
<span id="maskedarray-generic-constructing"></span><h2>Constructing masked arrays<a class="headerlink" href="#constructing-masked-arrays" title="Link to this heading">#</a></h2>
<p>There are several ways to construct a masked array.</p>
<ul>
<li><p>A first possibility is to directly invoke the <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaskedArray</span></code></a> class.</p></li>
<li><p>A second possibility is to use the two masked array constructors,
<a class="reference internal" href="generated/numpy.ma.array.html#numpy.ma.array" title="numpy.ma.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">array</span></code></a> and <a class="reference internal" href="generated/numpy.ma.masked_array.html#numpy.ma.masked_array" title="numpy.ma.masked_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">masked_array</span></code></a>.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.array.html#numpy.ma.array" title="numpy.ma.array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a>(data[, dtype, copy, order, mask, ...])</p></td>
<td><p>An array class with possibly masked values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.masked_array.html#numpy.ma.masked_array" title="numpy.ma.masked_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_array</span></code></a></p></td>
<td><p>alias of <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaskedArray</span></code></a></p></td>
</tr>
</tbody>
</table>
</div>
</li>
<li><p>A third option is to take the view of an existing array. In that case, the
mask of the view is set to <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.nomask" title="numpy.ma.nomask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">nomask</span></code></a> if the array has no named fields,
or an array of boolean with the same structure as the array otherwise.</p></li>
</ul>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</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="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">ma</span><span class="o">.</span><span class="n">MaskedArray</span><span class="p">)</span>
<span class="go">masked_array(data=[1, 2, 3],</span>
<span class="go"> mask=False,</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</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="mf">1.</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">2.</span><span class="p">)],</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[(</span><span class="s1">'a'</span><span class="p">,</span><span class="nb">int</span><span class="p">),</span> <span class="p">(</span><span class="s1">'b'</span><span class="p">,</span> <span class="nb">float</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">ma</span><span class="o">.</span><span class="n">MaskedArray</span><span class="p">)</span>
<span class="go">masked_array(data=[(1, 1.0), (2, 2.0)],</span>
<span class="go"> mask=[(False, False), (False, False)],</span>
<span class="go"> fill_value=(999999, 1e+20),</span>
<span class="go"> dtype=[('a', '<i8'), ('b', '<f8')])</span>
</pre></div>
</div>
</div>
</div>
<div id="40795b02-ccd1-4178-bf0d-7c2e16a69c74" class="try_examples_outer_iframe hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('31366515-11d6-46b3-9a63-0cff7b1bbcda','40795b02-ccd1-4178-bf0d-7c2e16a69c74')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('31366515-11d6-46b3-9a63-0cff7b1bbcda','40795b02-ccd1-4178-bf0d-7c2e16a69c74')">Open In Tab</button></div><div id="3891b7be-93e6-4c74-918c-0a57f665daf3" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script><ul>
<li><p>Yet another possibility is to use any of the following functions:</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.asarray.html#numpy.ma.asarray" title="numpy.ma.asarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asarray</span></code></a>(a[, dtype, order])</p></td>
<td><p>Convert the input to a masked array of the given data-type.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.asanyarray.html#numpy.ma.asanyarray" title="numpy.ma.asanyarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asanyarray</span></code></a>(a[, dtype, order])</p></td>
<td><p>Convert the input to a masked array, conserving subclasses.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.fix_invalid.html#numpy.ma.fix_invalid" title="numpy.ma.fix_invalid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fix_invalid</span></code></a>(a[, mask, copy, fill_value])</p></td>
<td><p>Return input with invalid data masked and replaced by a fill value.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.masked_equal.html#numpy.ma.masked_equal" title="numpy.ma.masked_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_equal</span></code></a>(x, value[, copy])</p></td>
<td><p>Mask an array where equal to a given value.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.masked_greater.html#numpy.ma.masked_greater" title="numpy.ma.masked_greater"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_greater</span></code></a>(x, value[, copy])</p></td>
<td><p>Mask an array where greater than a given value.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.masked_greater_equal.html#numpy.ma.masked_greater_equal" title="numpy.ma.masked_greater_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_greater_equal</span></code></a>(x, value[, copy])</p></td>
<td><p>Mask an array where greater than or equal to a given value.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.masked_inside.html#numpy.ma.masked_inside" title="numpy.ma.masked_inside"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_inside</span></code></a>(x, v1, v2[, copy])</p></td>
<td><p>Mask an array inside a given interval.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.masked_invalid.html#numpy.ma.masked_invalid" title="numpy.ma.masked_invalid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_invalid</span></code></a>(a[, copy])</p></td>
<td><p>Mask an array where invalid values occur (NaNs or infs).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.masked_less.html#numpy.ma.masked_less" title="numpy.ma.masked_less"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_less</span></code></a>(x, value[, copy])</p></td>
<td><p>Mask an array where less than a given value.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.masked_less_equal.html#numpy.ma.masked_less_equal" title="numpy.ma.masked_less_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_less_equal</span></code></a>(x, value[, copy])</p></td>
<td><p>Mask an array where less than or equal to a given value.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.masked_not_equal.html#numpy.ma.masked_not_equal" title="numpy.ma.masked_not_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_not_equal</span></code></a>(x, value[, copy])</p></td>
<td><p>Mask an array where <em>not</em> equal to a given value.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.masked_object.html#numpy.ma.masked_object" title="numpy.ma.masked_object"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_object</span></code></a>(x, value[, copy, shrink])</p></td>
<td><p>Mask the array <em class="xref py py-obj">x</em> where the data are exactly equal to value.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.masked_outside.html#numpy.ma.masked_outside" title="numpy.ma.masked_outside"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_outside</span></code></a>(x, v1, v2[, copy])</p></td>
<td><p>Mask an array outside a given interval.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/numpy.ma.masked_values.html#numpy.ma.masked_values" title="numpy.ma.masked_values"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_values</span></code></a>(x, value[, rtol, atol, copy, ...])</p></td>
<td><p>Mask using floating point equality.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/numpy.ma.masked_where.html#numpy.ma.masked_where" title="numpy.ma.masked_where"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_where</span></code></a>(condition, a[, copy])</p></td>
<td><p>Mask an array where a condition is met.</p></td>
</tr>
</tbody>
</table>
</div>
</li>
</ul>
</section>
<section id="accessing-the-data">
<h2>Accessing the data<a class="headerlink" href="#accessing-the-data" title="Link to this heading">#</a></h2>
<p>The underlying data of a masked array can be accessed in several ways:</p>
<ul class="simple">
<li><p>through the <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray.data" title="numpy.ma.MaskedArray.data"><code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code></a> attribute. The output is a view of the
array as a <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> or one of its subclasses, depending on the
type of the underlying data at the masked array creation.</p></li>
<li><p>through the <a class="reference internal" href="generated/numpy.ma.MaskedArray.__array__.html#numpy.ma.MaskedArray.__array__" title="numpy.ma.MaskedArray.__array__"><code class="xref py py-meth docutils literal notranslate"><span class="pre">__array__</span></code></a> method. The output is then a
<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>.</p></li>
<li><p>by directly taking a view of the masked array as a <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>
or one of its subclass (which is actually what using the
<a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray.data" title="numpy.ma.MaskedArray.data"><code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code></a> attribute does).</p></li>
<li><p>by using the <a class="reference internal" href="generated/numpy.ma.getdata.html#numpy.ma.getdata" title="numpy.ma.getdata"><code class="xref py py-func docutils literal notranslate"><span class="pre">getdata</span></code></a> function.</p></li>
</ul>
<p>None of these methods is completely satisfactory if some entries have been
marked as invalid. As a general rule, where a representation of the array is
required without any masked entries, it is recommended to fill the array with
the <a class="reference internal" href="generated/numpy.ma.filled.html#numpy.ma.filled" title="numpy.ma.filled"><code class="xref py py-meth docutils literal notranslate"><span class="pre">filled</span></code></a> method.</p>
</section>
<section id="accessing-the-mask">
<h2>Accessing the mask<a class="headerlink" href="#accessing-the-mask" title="Link to this heading">#</a></h2>
<p>The mask of a masked array is accessible through its <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray.mask" title="numpy.ma.MaskedArray.mask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code></a>
attribute. We must keep in mind that a <code class="docutils literal notranslate"><span class="pre">True</span></code> entry in the mask indicates an
<em>invalid</em> data.</p>
<p>Another possibility is to use the <a class="reference internal" href="generated/numpy.ma.getmask.html#numpy.ma.getmask" title="numpy.ma.getmask"><code class="xref py py-func docutils literal notranslate"><span class="pre">getmask</span></code></a> and <a class="reference internal" href="generated/numpy.ma.getmaskarray.html#numpy.ma.getmaskarray" title="numpy.ma.getmaskarray"><code class="xref py py-func docutils literal notranslate"><span class="pre">getmaskarray</span></code></a>
functions. <code class="docutils literal notranslate"><span class="pre">getmask(x)</span></code> outputs the mask of <code class="docutils literal notranslate"><span class="pre">x</span></code> if <code class="docutils literal notranslate"><span class="pre">x</span></code> is a masked
array, and the special value <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.nomask" title="numpy.ma.nomask"><code class="xref py py-data docutils literal notranslate"><span class="pre">nomask</span></code></a> otherwise. <code class="docutils literal notranslate"><span class="pre">getmaskarray(x)</span></code>
outputs the mask of <code class="docutils literal notranslate"><span class="pre">x</span></code> if <code class="docutils literal notranslate"><span class="pre">x</span></code> is a masked array. If <code class="docutils literal notranslate"><span class="pre">x</span></code> has no invalid
entry or is not a masked array, the function outputs a boolean array of
<code class="docutils literal notranslate"><span class="pre">False</span></code> with as many elements as <code class="docutils literal notranslate"><span class="pre">x</span></code>.</p>
</section>
<section id="accessing-only-the-valid-entries">
<h2>Accessing only the valid entries<a class="headerlink" href="#accessing-only-the-valid-entries" title="Link to this heading">#</a></h2>
<p>To retrieve only the valid entries, we can use the inverse of the mask as an
index. The inverse of the mask can be calculated with the
<a class="reference internal" href="generated/numpy.logical_not.html#numpy.logical_not" title="numpy.logical_not"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.logical_not</span></code></a> function or simply with the <code class="docutils literal notranslate"><span class="pre">~</span></code> operator:</p>
<div class="try_examples_outer_container docutils container" id="5bfd8729-2f06-46e5-8ab6-6a654b13b846">
<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIframe('5bfd8729-2f06-46e5-8ab6-6a654b13b846','3fd51715-8d84-440d-8fde-8467e3fbe3cf','b1da0cb7-3d5d-4d3c-8793-fb3ab1e5c063','../lite/tree/../notebooks/index.html?path=3e58e93b_0b07_496b_8223_95190ac098bf.ipynb','None')">Try it in your browser!</button></div><div class="try_examples_content docutils container">
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">mask</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="o">~</span><span class="n">x</span><span class="o">.</span><span class="n">mask</span><span class="p">]</span>
<span class="go">masked_array(data=[1, 4],</span>
<span class="go"> mask=[False, False],</span>
<span class="go"> fill_value=999999)</span>
</pre></div>
</div>
<p>Another way to retrieve the valid data is to use the <a class="reference internal" href="generated/numpy.ma.compressed.html#numpy.ma.compressed" title="numpy.ma.compressed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compressed</span></code></a>
method, which returns a one-dimensional <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a> (or one of its
subclasses, depending on the value of the <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray.baseclass" title="numpy.ma.MaskedArray.baseclass"><code class="xref py py-attr docutils literal notranslate"><span class="pre">baseclass</span></code></a>
attribute):</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">compressed</span><span class="p">()</span>
<span class="go">array([1, 4])</span>
</pre></div>
</div>
<p>Note that the output of <a class="reference internal" href="generated/numpy.ma.compressed.html#numpy.ma.compressed" title="numpy.ma.compressed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compressed</span></code></a> is always 1D.</p>
</div>
</div>
<div id="b1da0cb7-3d5d-4d3c-8793-fb3ab1e5c063" class="try_examples_outer_iframe hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('5bfd8729-2f06-46e5-8ab6-6a654b13b846','b1da0cb7-3d5d-4d3c-8793-fb3ab1e5c063')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('5bfd8729-2f06-46e5-8ab6-6a654b13b846','b1da0cb7-3d5d-4d3c-8793-fb3ab1e5c063')">Open In Tab</button></div><div id="3fd51715-8d84-440d-8fde-8467e3fbe3cf" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script></section>
<section id="modifying-the-mask">
<h2>Modifying the mask<a class="headerlink" href="#modifying-the-mask" title="Link to this heading">#</a></h2>
<section id="masking-an-entry">
<h3>Masking an entry<a class="headerlink" href="#masking-an-entry" title="Link to this heading">#</a></h3>
<p>The recommended way to mark one or several specific entries of a masked array
as invalid is to assign the special value <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.masked" title="numpy.ma.masked"><code class="xref py py-attr docutils literal notranslate"><span class="pre">masked</span></code></a> to them:</p>
<div class="try_examples_outer_container docutils container" id="d651a66c-6b6a-43c9-bf1b-ce0dc8fe3f4f">
<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIframe('d651a66c-6b6a-43c9-bf1b-ce0dc8fe3f4f','8159742d-034b-4869-b417-e09123dae007','42532719-9bc3-4f63-9d83-66c8119e1e93','../lite/tree/../notebooks/index.html?path=a26dbfc0_e42d_4ada_9a96_6cb72d5677e4.ipynb','None')">Try it in your browser!</button></div><div class="try_examples_content docutils container">
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[--, 2, 3],</span>
<span class="go"> mask=[ True, False, False],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</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="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">y</span><span class="p">[(</span><span class="mi">0</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="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">masked_array(</span>
<span class="go"> data=[[1, --, 3],</span>
<span class="go"> [4, 5, --],</span>
<span class="go"> [--, 8, 9]],</span>
<span class="go"> mask=[[False, True, False],</span>
<span class="go"> [False, False, True],</span>
<span class="go"> [ True, False, False]],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">z</span> <span class="o">=</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="mi">4</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">z</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked</span>
<span class="gp">>>> </span><span class="n">z</span>
<span class="go">masked_array(data=[--, --, 3, 4],</span>
<span class="go"> mask=[ True, True, False, False],</span>
<span class="go"> fill_value=999999)</span>
</pre></div>
</div>
</div>
</div>
<div id="42532719-9bc3-4f63-9d83-66c8119e1e93" class="try_examples_outer_iframe hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('d651a66c-6b6a-43c9-bf1b-ce0dc8fe3f4f','42532719-9bc3-4f63-9d83-66c8119e1e93')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('d651a66c-6b6a-43c9-bf1b-ce0dc8fe3f4f','42532719-9bc3-4f63-9d83-66c8119e1e93')">Open In Tab</button></div><div id="8159742d-034b-4869-b417-e09123dae007" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script><p>A second possibility is to modify the <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray.mask" title="numpy.ma.MaskedArray.mask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code></a> directly,
but this usage is discouraged.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When creating a new masked array with a simple, non-structured datatype,
the mask is initially set to the special value <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.nomask" title="numpy.ma.nomask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">nomask</span></code></a>, that
corresponds roughly to the boolean <code class="docutils literal notranslate"><span class="pre">False</span></code>. Trying to set an element of
<a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.nomask" title="numpy.ma.nomask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">nomask</span></code></a> will fail with a <a class="reference external" href="https://docs.python.org/3/library/exceptions.html#TypeError" title="(in Python v3.14)"><code class="xref py py-exc docutils literal notranslate"><span class="pre">TypeError</span></code></a> exception, as a boolean
does not support item assignment.</p>
</div>
<p>All the entries of an array can be masked at once by assigning <code class="docutils literal notranslate"><span class="pre">True</span></code> to the
mask:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="kc">True</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[--, --, --],</span>
<span class="go"> mask=[ True, True, True],</span>
<span class="go"> fill_value=999999,</span>
<span class="go"> dtype=int64)</span>
</pre></div>
</div>
<p>Finally, specific entries can be masked and/or unmasked by assigning to the
mask a sequence of booleans:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, --, 3],</span>
<span class="go"> mask=[False, True, False],</span>
<span class="go"> fill_value=999999)</span>
</pre></div>
</div>
</div>
</div>
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<section id="unmasking-an-entry">
<h3>Unmasking an entry<a class="headerlink" href="#unmasking-an-entry" title="Link to this heading">#</a></h3>
<p>To unmask one or several specific entries, we can just assign one or several
new valid values to them:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, 2, --],</span>
<span class="go"> mask=[False, False, True],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, 2, 5],</span>
<span class="go"> mask=[False, False, False],</span>
<span class="go"> fill_value=999999)</span>
</pre></div>
</div>
</div>
</div>
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<p class="admonition-title">Note</p>
<p>Unmasking an entry by direct assignment will silently fail if the masked
array has a <em>hard</em> mask, as shown by the <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray.hardmask" title="numpy.ma.MaskedArray.hardmask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">hardmask</span></code></a>
attribute. This feature was introduced to prevent overwriting the mask.
To force the unmasking of an entry where the array has a hard mask,
the mask must first to be softened using the <a class="reference internal" href="generated/numpy.ma.soften_mask.html#numpy.ma.soften_mask" title="numpy.ma.soften_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">soften_mask</span></code></a> method
before the allocation. It can be re-hardened with <a class="reference internal" href="generated/numpy.ma.harden_mask.html#numpy.ma.harden_mask" title="numpy.ma.harden_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">harden_mask</span></code></a> as
follows:</p>
</div>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">hard_mask</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, 2, --],</span>
<span class="go"> mask=[False, False, True],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, 2, --],</span>
<span class="go"> mask=[False, False, True],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">soften_mask</span><span class="p">()</span>
<span class="go">masked_array(data=[1, 2, --],</span>
<span class="go"> mask=[False, False, True],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, 2, 5],</span>
<span class="go"> mask=[False, False, False],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">harden_mask</span><span class="p">()</span>
<span class="go">masked_array(data=[1, 2, 5],</span>
<span class="go"> mask=[False, False, False],</span>
<span class="go"> fill_value=999999)</span>
</pre></div>
</div>
</div>
</div>
<div id="df0f5b31-f3e4-43d0-a76f-27874e9d660a" class="try_examples_outer_iframe hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('c5f7377c-c860-4b75-9f7b-98f86637e6f5','df0f5b31-f3e4-43d0-a76f-27874e9d660a')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('c5f7377c-c860-4b75-9f7b-98f86637e6f5','df0f5b31-f3e4-43d0-a76f-27874e9d660a')">Open In Tab</button></div><div id="de9c539c-d5a0-4731-9a66-03919ec740b2" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script><p>To unmask all masked entries of a masked array (provided the mask isn’t a hard
mask), the simplest solution is to assign the constant <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.nomask" title="numpy.ma.nomask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">nomask</span></code></a> to the
mask:</p>
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<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIframe('c1298a52-a13a-4cda-9fbb-ceb549216751','0e787c71-f05f-47c2-9af5-e59ecd697ed3','6cc69b69-ba20-4c64-b5ec-90c7ad779b74','../lite/tree/../notebooks/index.html?path=9ed54871_0ad5_458f_bb12_f7b297f24dbe.ipynb','None')">Try it in your browser!</button></div><div class="try_examples_content docutils container">
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, 2, --],</span>
<span class="go"> mask=[False, False, True],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">nomask</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">masked_array(data=[1, 2, 3],</span>
<span class="go"> mask=[False, False, False],</span>
<span class="go"> fill_value=999999)</span>
</pre></div>
</div>
</div>
</div>
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</section>
<section id="indexing-and-slicing">
<h2>Indexing and slicing<a class="headerlink" href="#indexing-and-slicing" title="Link to this heading">#</a></h2>
<p>As a <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaskedArray</span></code></a> is a subclass of <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, it inherits
its mechanisms for indexing and slicing.</p>
<p>When accessing a single entry of a masked array with no named fields, the
output is either a scalar (if the corresponding entry of the mask is
<code class="docutils literal notranslate"><span class="pre">False</span></code>) or the special value <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.masked" title="numpy.ma.masked"><code class="xref py py-attr docutils literal notranslate"><span class="pre">masked</span></code></a> (if the corresponding entry of
the mask is <code class="docutils literal notranslate"><span class="pre">True</span></code>):</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="go">1</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="go">masked</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked</span>
<span class="go">True</span>
</pre></div>
</div>
</div>
</div>
<div id="1664c9c4-0931-4b59-a9f5-a0d4da67f04e" class="try_examples_outer_iframe hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('cef9d99d-4e78-4141-9b97-5d6a004a50f0','1664c9c4-0931-4b59-a9f5-a0d4da67f04e')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('cef9d99d-4e78-4141-9b97-5d6a004a50f0','1664c9c4-0931-4b59-a9f5-a0d4da67f04e')">Open In Tab</button></div><div id="0d09d32e-71de-45c7-8fc6-a3e4b7a84c27" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script><p>If the masked array has named fields, accessing a single entry returns a
<a class="reference internal" href="arrays.scalars.html#numpy.void" title="numpy.void"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.void</span></code></a> object if none of the fields are masked, or a 0d masked
array with the same dtype as the initial array if at least one of the fields
is masked.</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">masked_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="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)],</span>
<span class="gp">... </span> <span class="n">mask</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)],</span>
<span class="gp">... </span> <span class="n">dtype</span><span class="o">=</span><span class="p">[(</span><span class="s1">'a'</span><span class="p">,</span> <span class="nb">int</span><span class="p">),</span> <span class="p">(</span><span class="s1">'b'</span><span class="p">,</span> <span class="nb">int</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="go">(1, 2)</span>
<span class="gp">>>> </span><span class="n">y</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="go">(3, --)</span>
</pre></div>
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<div id="471bc5a2-3c5e-4a76-96c7-3b0920047e79" class="try_examples_outer_iframe hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('b7ebd2de-2f23-4b76-b561-ed34489d58e1','471bc5a2-3c5e-4a76-96c7-3b0920047e79')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('b7ebd2de-2f23-4b76-b561-ed34489d58e1','471bc5a2-3c5e-4a76-96c7-3b0920047e79')">Open In Tab</button></div><div id="f94941b3-fd45-4599-b78d-a27e09e7c334" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script><p>When accessing a slice, the output is a masked array whose
<a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.MaskedArray.data" title="numpy.ma.MaskedArray.data"><code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code></a> attribute is a view of the original data, and whose
mask is either <a class="reference internal" href="maskedarray.baseclass.html#numpy.ma.nomask" title="numpy.ma.nomask"><code class="xref py py-attr docutils literal notranslate"><span class="pre">nomask</span></code></a> (if there was no invalid entries in the original
array) or a view of the corresponding slice of the original mask. The view is
required to ensure propagation of any modification of the mask to the original.</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy.ma</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ma</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</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="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">mx</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">mx</span>
<span class="go">masked_array(data=[1, --, 3],</span>
<span class="go"> mask=[False, True, False],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">mx</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="gp">>>> </span><span class="n">mx</span>
<span class="go">masked_array(data=[1, -1, 3],</span>
<span class="go"> mask=[False, False, False],</span>
<span class="go"> fill_value=999999)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">mask</span>
<span class="go">array([False, False, False, False, True])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">data</span>
<span class="go">array([ 1, -1, 3, 4, 5])</span>
</pre></div>