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<section id="numpy-apply-over-axes">
<h1>numpy.apply_over_axes<a class="headerlink" href="#numpy-apply-over-axes" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="numpy.apply_over_axes">
<span class="sig-prename descclassname"><span class="pre">numpy.</span></span><span class="sig-name descname"><span class="pre">apply_over_axes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axes</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/main/numpy/lib/_shape_base_impl.py#L419-L504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#numpy.apply_over_axes" title="Link to this definition">#</a></dt>
<dd><p>Apply a function repeatedly over multiple axes.</p>
<p><em class="xref py py-obj">func</em> is called as <em class="xref py py-obj">res = func(a, axis)</em>, where <em class="xref py py-obj">axis</em> is the first
element of <em class="xref py py-obj">axes</em>. The result <em class="xref py py-obj">res</em> of the function call must have
either the same dimensions as <em class="xref py py-obj">a</em> or one less dimension. If <em class="xref py py-obj">res</em>
has one less dimension than <em class="xref py py-obj">a</em>, a dimension is inserted before
<em class="xref py py-obj">axis</em>. The call to <em class="xref py py-obj">func</em> is then repeated for each axis in <em class="xref py py-obj">axes</em>,
with <em class="xref py py-obj">res</em> as the first argument.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>func</strong><span class="classifier">function</span></dt><dd><p>This function must take two arguments, <em class="xref py py-obj">func(a, axis)</em>.</p>
</dd>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Input array.</p>
</dd>
<dt><strong>axes</strong><span class="classifier">array_like</span></dt><dd><p>Axes over which <em class="xref py py-obj">func</em> is applied; the elements must be integers.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>apply_over_axis</strong><span class="classifier">ndarray</span></dt><dd><p>The output array. The number of dimensions is the same as <em class="xref py py-obj">a</em>,
but the shape can be different. This depends on whether <em class="xref py py-obj">func</em>
changes the shape of its output with respect to its input.</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.apply_along_axis.html#numpy.apply_along_axis" title="numpy.apply_along_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply_along_axis</span></code></a></dt><dd><p>Apply a function to 1-D slices of an array along the given axis.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>This function is equivalent to tuple axis arguments to reorderable ufuncs
with keepdims=True. Tuple axis arguments to ufuncs have been available since
version 1.7.0.</p>
<p class="rubric">Examples</p>
<div class="try_examples_outer_container docutils container" id="bae1738e-a718-47d8-8f9d-33e5c436830d">
<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIframe('bae1738e-a718-47d8-8f9d-33e5c436830d','2a08e2bf-ed94-4ed1-b5d8-cda841d75934','d9e49feb-12ab-414f-9117-e3fd4c00b751','../../lite/tree/../notebooks/index.html?path=0480bb37_4b5e_471b_85ce_04da09af0f6d.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">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">24</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</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">a</span>
<span class="go">array([[[ 0, 1, 2, 3],</span>
<span class="go"> [ 4, 5, 6, 7],</span>
<span class="go"> [ 8, 9, 10, 11]],</span>
<span class="go"> [[12, 13, 14, 15],</span>
<span class="go"> [16, 17, 18, 19],</span>
<span class="go"> [20, 21, 22, 23]]])</span>
</pre></div>
</div>
<p>Sum over axes 0 and 2. The result has same number of dimensions
as the original array:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">apply_over_axes</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
<span class="go">array([[[ 60],</span>
<span class="go"> [ 92],</span>
<span class="go"> [124]]])</span>
</pre></div>
</div>
<p>Tuple axis arguments to ufuncs are equivalent:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">array([[[ 60],</span>
<span class="go"> [ 92],</span>
<span class="go"> [124]]])</span>
</pre></div>
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