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<section id="numpy-add">
<h1>numpy.add<a class="headerlink" href="#numpy-add" title="Link to this heading">#</a></h1>
<dl class="py data">
<dt class="sig sig-object py" id="numpy.add">
<span class="sig-prename descclassname"><span class="pre">numpy.</span></span><span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">where=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">casting='same_kind'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order='K'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subok=True</span></span></em><span class="optional">[</span>, <em class="sig-param"><span class="n"><span class="pre">signature</span></span></em><span class="optional">]</span><span class="sig-paren">)</span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ufunc</span> <span class="pre">'add'></span></em><a class="headerlink" href="#numpy.add" title="Link to this definition">#</a></dt>
<dd><p>Add arguments element-wise.</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>x1, x2</strong><span class="classifier">array_like</span></dt><dd><p>The arrays to be added.
If <code class="docutils literal notranslate"><span class="pre">x1.shape</span> <span class="pre">!=</span> <span class="pre">x2.shape</span></code>, they must be broadcastable to a common
shape (which becomes the shape of the output).</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, None, or tuple of ndarray and None, optional</span></dt><dd><p>A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or None,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.</p>
</dd>
<dt><strong>where</strong><span class="classifier">array_like, optional</span></dt><dd><p>This condition is broadcast over the input. At locations where the
condition is True, the <em class="xref py py-obj">out</em> array will be set to the ufunc result.
Elsewhere, the <em class="xref py py-obj">out</em> array will retain its original value.
Note that if an uninitialized <em class="xref py py-obj">out</em> array is created via the default
<code class="docutils literal notranslate"><span class="pre">out=None</span></code>, locations within it where the condition is False will
remain uninitialized.</p>
</dd>
<dt><strong>**kwargs</strong></dt><dd><p>For other keyword-only arguments, see the
<a class="reference internal" href="../ufuncs.html#ufuncs-kwargs"><span class="std std-ref">ufunc docs</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>add</strong><span class="classifier">ndarray or scalar</span></dt><dd><p>The sum of <em class="xref py py-obj">x1</em> and <em class="xref py py-obj">x2</em>, element-wise.
This is a scalar if both <em class="xref py py-obj">x1</em> and <em class="xref py py-obj">x2</em> are scalars.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>Equivalent to <em class="xref py py-obj">x1</em> + <em class="xref py py-obj">x2</em> in terms of array broadcasting.</p>
<p class="rubric">Examples</p>
<div class="try_examples_outer_container docutils container" id="f4d6c5fd-7f7e-4d15-92df-088af223d00b">
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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">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)</span>
<span class="go">5.0</span>
<span class="gp">>>> </span><span class="n">x1</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="mf">9.0</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x2</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="mf">3.0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
<span class="go">array([[ 0., 2., 4.],</span>
<span class="go"> [ 3., 5., 7.],</span>
<span class="go"> [ 6., 8., 10.]])</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">+</span></code> operator can be used as a shorthand for <code class="docutils literal notranslate"><span class="pre">np.add</span></code> on ndarrays.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x1</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="mf">9.0</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x2</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="mf">3.0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x1</span> <span class="o">+</span> <span class="n">x2</span>
<span class="go">array([[ 0., 2., 4.],</span>
<span class="go"> [ 3., 5., 7.],</span>
<span class="go"> [ 6., 8., 10.]])</span>
</pre></div>
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