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<div class="section" id="to-numpy-matrix">
<h1>to_numpy_matrix<a class="headerlink" href="#to-numpy-matrix" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="networkx.convert.to_numpy_matrix">
<tt class="descname">to_numpy_matrix</tt><big>(</big><em>G</em>, <em>nodelist=None</em>, <em>dtype=None</em>, <em>order=None</em>, <em>multigraph_weight=<built-in function sum></em>, <em>weight='weight'</em><big>)</big><a class="reference internal" href="../../_modules/networkx/convert.html#to_numpy_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#networkx.convert.to_numpy_matrix" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the graph adjacency matrix as a NumPy matrix.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>G</strong> : graph</p>
<blockquote>
<div><p>The NetworkX graph used to construct the NumPy matrix.</p>
</div></blockquote>
<p><strong>nodelist</strong> : list, optional</p>
<blockquote>
<div><p>The rows and columns are ordered according to the nodes in <span class="math">nodelist</span>.
If <span class="math">nodelist</span> is None, then the ordering is produced by G.nodes().</p>
</div></blockquote>
<p><strong>dtype</strong> : NumPy data type, optional</p>
<blockquote>
<div><p>A valid single NumPy data type used to initialize the array.
This must be a simple type such as int or numpy.float64 and
not a compound data type (see to_numpy_recarray)
If None, then the NumPy default is used.</p>
</div></blockquote>
<p><strong>order</strong> : {‘C’, ‘F’}, optional</p>
<blockquote>
<div><p>Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. If None, then the NumPy default
is used.</p>
</div></blockquote>
<p><strong>multigraph_weight</strong> : {sum, min, max}, optional</p>
<blockquote>
<div><p>An operator that determines how weights in multigraphs are handled.
The default is to sum the weights of the multiple edges.</p>
</div></blockquote>
<p><strong>weight</strong> : string or None optional (default=’weight’)</p>
<blockquote>
<div><p>The edge attribute that holds the numerical value used for
the edge weight. If None then all edge weights are 1.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>M</strong> : NumPy matrix</p>
<blockquote class="last">
<div><p>Graph adjacency matrix.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition-see-also admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="networkx.convert.to_numpy_recarray.html#networkx.convert.to_numpy_recarray" title="networkx.convert.to_numpy_recarray"><tt class="xref py py-obj docutils literal"><span class="pre">to_numpy_recarray</span></tt></a>, <a class="reference internal" href="networkx.convert.from_numpy_matrix.html#networkx.convert.from_numpy_matrix" title="networkx.convert.from_numpy_matrix"><tt class="xref py py-obj docutils literal"><span class="pre">from_numpy_matrix</span></tt></a></p>
</div>
<p class="rubric">Notes</p>
<p>The matrix entries are assigned with weight edge attribute. When
an edge does not have the weight attribute, the value of the entry is 1.
For multiple edges, the values of the entries are the sums of the edge
attributes for each edge.</p>
<p>When <span class="math">nodelist</span> does not contain every node in <span class="math">G</span>, the matrix is built
from the subgraph of <span class="math">G</span> that is induced by the nodes in <span class="math">nodelist</span>.</p>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">MultiDiGraph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</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">weight</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</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">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">weight</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">to_numpy_matrix</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nodelist</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">2</span><span class="p">])</span>
<span class="go">matrix([[ 0., 2., 0.],</span>
<span class="go"> [ 1., 0., 0.],</span>
<span class="go"> [ 0., 0., 4.]])</span>
</pre></div>
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