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<section id="numpy-argpartition">
<h1>numpy.argpartition<a class="headerlink" href="#numpy-argpartition" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="numpy.argpartition">
<span class="sig-prename descclassname"><span class="pre">numpy.</span></span><span class="sig-name descname"><span class="pre">argpartition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'introselect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/main/numpy/_core/fromnumeric.py#L875-L961"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#numpy.argpartition" title="Link to this definition">#</a></dt>
<dd><p>Perform an indirect partition along the given axis using the
algorithm specified by the <em class="xref py py-obj">kind</em> keyword. It returns an array of
indices of the same shape as <em class="xref py py-obj">a</em> that index data along the given
axis in partitioned order.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Array to sort.</p>
</dd>
<dt><strong>kth</strong><span class="classifier">int or sequence of ints</span></dt><dd><p>Element index to partition by. The k-th element will be in its
final sorted position and all smaller elements will be moved
before it and all larger elements behind it. The order of all
elements in the partitions is undefined. If provided with a
sequence of k-th it will partition all of them into their sorted
position at once.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 1.22.0: </span>Passing booleans as index is deprecated.</p>
</div>
</dd>
<dt><strong>axis</strong><span class="classifier">int or None, optional</span></dt><dd><p>Axis along which to sort. The default is -1 (the last axis). If
None, the flattened array is used.</p>
</dd>
<dt><strong>kind</strong><span class="classifier">{‘introselect’}, optional</span></dt><dd><p>Selection algorithm. Default is ‘introselect’</p>
</dd>
<dt><strong>order</strong><span class="classifier">str or list of str, optional</span></dt><dd><p>When <em class="xref py py-obj">a</em> is an array with fields defined, this argument
specifies which fields to compare first, second, etc. A single
field can be specified as a string, and not all fields need be
specified, but unspecified fields will still be used, in the
order in which they come up in the dtype, to break ties.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>index_array</strong><span class="classifier">ndarray, int</span></dt><dd><p>Array of indices that partition <em class="xref py py-obj">a</em> along the specified axis.
If <em class="xref py py-obj">a</em> is one-dimensional, <code class="docutils literal notranslate"><span class="pre">a[index_array]</span></code> yields a partitioned <em class="xref py py-obj">a</em>.
More generally, <code class="docutils literal notranslate"><span class="pre">np.take_along_axis(a,</span> <span class="pre">index_array,</span> <span class="pre">axis=axis)</span></code>
always yields the partitioned <em class="xref py py-obj">a</em>, irrespective of dimensionality.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.partition.html#numpy.partition" title="numpy.partition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">partition</span></code></a></dt><dd><p>Describes partition algorithms used.</p>
</dd>
<dt><a class="reference internal" href="numpy.ndarray.partition.html#numpy.ndarray.partition" title="numpy.ndarray.partition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.partition</span></code></a></dt><dd><p>Inplace partition.</p>
</dd>
<dt><a class="reference internal" href="numpy.argsort.html#numpy.argsort" title="numpy.argsort"><code class="xref py py-obj docutils literal notranslate"><span class="pre">argsort</span></code></a></dt><dd><p>Full indirect sort.</p>
</dd>
<dt><a class="reference internal" href="numpy.take_along_axis.html#numpy.take_along_axis" title="numpy.take_along_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take_along_axis</span></code></a></dt><dd><p>Apply <code class="docutils literal notranslate"><span class="pre">index_array</span></code> from argpartition to an array as if by calling partition.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The returned indices are not guaranteed to be sorted according to
the values. Furthermore, the default selection algorithm <code class="docutils literal notranslate"><span class="pre">introselect</span></code>
is unstable, and hence the returned indices are not guaranteed
to be the earliest/latest occurrence of the element.</p>
<p><a class="reference internal" href="#numpy.argpartition" title="numpy.argpartition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">argpartition</span></code></a> works for real/complex inputs with nan values,
see <a class="reference internal" href="numpy.partition.html#numpy.partition" title="numpy.partition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">partition</span></code></a> for notes on the enhanced sort order and
different selection algorithms.</p>
<p class="rubric">Examples</p>
<div class="try_examples_outer_container docutils container" id="59005f93-128e-4be1-908a-d0a7982b0838">
<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIframe('59005f93-128e-4be1-908a-d0a7982b0838','8dc1de22-9a81-4db3-bd2d-962ba2764f9b','758d6acc-c5e5-4d5f-9dc1-06a8a2adf3ec','../../lite/tree/../notebooks/index.html?path=6838e8be_eb5f_4049_aef4_f2c3ecdd19f7.ipynb','None')">Try it in your browser!</button></div><div class="try_examples_content docutils container">
<p>One dimensional array:</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="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">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</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="n">np</span><span class="o">.</span><span class="n">argpartition</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">)]</span>
<span class="go">array([2, 1, 3, 4]) # may vary</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argpartition</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))]</span>
<span class="go">array([1, 2, 3, 4]) # may vary</span>
</pre></div>
</div>
<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="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">argpartition</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">)]</span>
<span class="go">array([2, 1, 3, 4]) # may vary</span>
</pre></div>
</div>
<p>Multi-dimensional array:</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">np</span><span class="o">.</span><span class="n">array</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">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">index_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argpartition</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">kth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># below is the same as np.partition(x, kth=1)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">take_along_axis</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">index_array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([[2, 3, 4],</span>
<span class="go"> [1, 1, 3]])</span>
</pre></div>
</div>
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