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<section id="tutorial">
<h1>Tutorial<a class="headerlink" href="#tutorial" title="Permalink to this heading">#</a></h1>
<p>This guide can help you start working with NetworkX.</p>
<section id="creating-a-graph">
<h2>Creating a graph<a class="headerlink" href="#creating-a-graph" title="Permalink to this heading">#</a></h2>
<p>Create an empty graph with no nodes and no edges.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<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">Graph</span><span class="p">()</span>
</pre></div>
</div>
<p>By definition, a <a class="reference internal" href="reference/classes/graph.html#networkx.Graph" title="networkx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> is a collection of nodes (vertices) along with
identified pairs of nodes (called edges, links, etc). In NetworkX, nodes can
be any <a class="reference external" href="https://docs.python.org/3/glossary.html#term-hashable" title="(in Python v3.11)"><span class="xref std std-term">hashable</span></a> object e.g., a text string, an image, an XML object,
another Graph, a customized node object, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Python’s <code class="docutils literal notranslate"><span class="pre">None</span></code> object is not allowed to be used as a node. It
determines whether optional function arguments have been assigned in many
functions.</p>
</div>
</section>
<section id="nodes">
<h2>Nodes<a class="headerlink" href="#nodes" title="Permalink to this heading">#</a></h2>
<p>The graph <code class="docutils literal notranslate"><span class="pre">G</span></code> can be grown in several ways. NetworkX includes many
<a class="reference internal" href="reference/generators.html"><span class="doc">graph generator functions</span></a> and
<a class="reference internal" href="reference/readwrite/index.html"><span class="doc">facilities to read and write graphs in many formats</span></a>.
To get started though we’ll look at simple manipulations. You can add one node
at a time,</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>or add nodes from any <a class="reference external" href="https://docs.python.org/3/glossary.html#term-iterable" title="(in Python v3.11)"><span class="xref std std-term">iterable</span></a> container, such as a list</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
</pre></div>
</div>
<p>You can also add nodes along with node
attributes if your container yields 2-tuples of the form
<code class="docutils literal notranslate"><span class="pre">(node,</span> <span class="pre">node_attribute_dict)</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">([</span>
<span class="gp">... </span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="p">{</span><span class="s2">"color"</span><span class="p">:</span> <span class="s2">"red"</span><span class="p">}),</span>
<span class="gp">... </span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="p">{</span><span class="s2">"color"</span><span class="p">:</span> <span class="s2">"green"</span><span class="p">}),</span>
<span class="gp">... </span><span class="p">])</span>
</pre></div>
</div>
<p>Node attributes are discussed further <a class="reference internal" href="#attributes"><span class="std std-ref">below</span></a>.</p>
<p>Nodes from one graph can be incorporated into another:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">H</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">path_graph</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">H</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">G</span></code> now contains the nodes of <code class="docutils literal notranslate"><span class="pre">H</span></code> as nodes of <code class="docutils literal notranslate"><span class="pre">G</span></code>.
In contrast, you could use the graph <code class="docutils literal notranslate"><span class="pre">H</span></code> as a node in <code class="docutils literal notranslate"><span class="pre">G</span></code>.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">H</span><span class="p">)</span>
</pre></div>
</div>
<p>The graph <code class="docutils literal notranslate"><span class="pre">G</span></code> now contains <code class="docutils literal notranslate"><span class="pre">H</span></code> as a node. This flexibility is very powerful as
it allows graphs of graphs, graphs of files, graphs of functions and much more.
It is worth thinking about how to structure your application so that the nodes
are useful entities. Of course you can always use a unique identifier in <code class="docutils literal notranslate"><span class="pre">G</span></code>
and have a separate dictionary keyed by identifier to the node information if
you prefer.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You should not change the node object if the hash depends
on its contents.</p>
</div>
</section>
<section id="edges">
<h2>Edges<a class="headerlink" href="#edges" title="Permalink to this heading">#</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">G</span></code> can also be grown by adding one edge at a time,</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">e</span> <span class="o">=</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">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="o">*</span><span class="n">e</span><span class="p">)</span> <span class="c1"># unpack edge tuple*</span>
</pre></div>
</div>
<p>by adding a list of edges,</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</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">3</span><span class="p">)])</span>
</pre></div>
</div>
<p>or by adding any <a class="reference internal" href="reference/glossary.html#term-ebunch"><span class="xref std std-term">ebunch</span></a> of edges. An <em>ebunch</em> is any iterable
container of edge-tuples. An edge-tuple can be a 2-tuple of nodes or a 3-tuple
with 2 nodes followed by an edge attribute dictionary, e.g.,
<code class="docutils literal notranslate"><span class="pre">(2,</span> <span class="pre">3,</span> <span class="pre">{'weight':</span> <span class="pre">3.1415})</span></code>. Edge attributes are discussed further
<a class="reference internal" href="#attributes"><span class="std std-ref">below</span></a>.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span><span class="n">H</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span>
</pre></div>
</div>
<p>There are no complaints when adding existing nodes or edges. For example,
after removing all nodes and edges,</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
</pre></div>
</div>
<p>we add new nodes/edges and NetworkX quietly ignores any that are
already present.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</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">3</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="mi">1</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">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"spam"</span><span class="p">)</span> <span class="c1"># adds node "spam"</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="s2">"spam"</span><span class="p">)</span> <span class="c1"># adds 4 nodes: 's', 'p', 'a', 'm'</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">3</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">)</span>
</pre></div>
</div>
<p>At this stage the graph <code class="docutils literal notranslate"><span class="pre">G</span></code> consists of 8 nodes and 3 edges, as can be seen by:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()</span>
<span class="go">8</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">number_of_edges</span><span class="p">()</span>
<span class="go">3</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The order of adjacency reporting (e.g., <a class="reference internal" href="reference/classes/generated/networkx.Graph.adj.html#networkx.Graph.adj" title="networkx.Graph.adj"><code class="xref py py-meth docutils literal notranslate"><span class="pre">G.adj</span></code></a>,
<a class="reference internal" href="reference/classes/generated/networkx.DiGraph.successors.html#networkx.DiGraph.successors" title="networkx.DiGraph.successors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">G.successors</span></code></a>,
<a class="reference internal" href="reference/classes/generated/networkx.DiGraph.predecessors.html#networkx.DiGraph.predecessors" title="networkx.DiGraph.predecessors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">G.predecessors</span></code></a>) is the order of
edge addition. However, the order of G.edges is the order of the adjacencies
which includes both the order of the nodes and each
node’s adjacencies. See example below:</p>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">DG</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">DG</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">1</span><span class="p">)</span> <span class="c1"># adds the nodes in order 2, 1</span>
<span class="gp">>>> </span><span class="n">DG</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">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">DG</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">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">DG</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">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">assert</span> <span class="nb">list</span><span class="p">(</span><span class="n">DG</span><span class="o">.</span><span class="n">successors</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">==</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="gp">>>> </span><span class="k">assert</span> <span class="nb">list</span><span class="p">(</span><span class="n">DG</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span> <span class="o">==</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="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</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="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
</pre></div>
</div>
</section>
<section id="examining-elements-of-a-graph">
<h2>Examining elements of a graph<a class="headerlink" href="#examining-elements-of-a-graph" title="Permalink to this heading">#</a></h2>
<p>We can examine the nodes and edges. Four basic graph properties facilitate
reporting: <code class="docutils literal notranslate"><span class="pre">G.nodes</span></code>, <code class="docutils literal notranslate"><span class="pre">G.edges</span></code>, <code class="docutils literal notranslate"><span class="pre">G.adj</span></code> and <code class="docutils literal notranslate"><span class="pre">G.degree</span></code>. These
are set-like views of the nodes, edges, neighbors (adjacencies), and degrees
of nodes in a graph. They offer a continually updated read-only view into
the graph structure. They are also dict-like in that you can look up node
and edge data attributes via the views and iterate with data attributes
using methods <code class="docutils literal notranslate"><span class="pre">.items()</span></code>, <code class="docutils literal notranslate"><span class="pre">.data()</span></code>.
If you want a specific container type instead of a view, you can specify one.
Here we use lists, though sets, dicts, tuples and other containers may be
better in other contexts.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">)</span>
<span class="go">[1, 2, 3, 'spam', 's', 'p', 'a', 'm']</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span>
<span class="go">[(1, 2), (1, 3), (3, 'm')]</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">adj</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="c1"># or list(G.neighbors(1))</span>
<span class="go">[2, 3]</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">degree</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># the number of edges incident to 1</span>
<span class="go">2</span>
</pre></div>
</div>
<p>One can specify to report the edges and degree from a subset of all nodes
using an <a class="reference internal" href="reference/glossary.html#term-nbunch"><span class="xref std std-term">nbunch</span></a>. An <em>nbunch</em> is any of: <code class="docutils literal notranslate"><span class="pre">None</span></code> (meaning all nodes),
a node, or an iterable container of nodes that is not itself a node in the
graph.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">])</span>
<span class="go">EdgeDataView([(2, 1), ('m', 3)])</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">degree</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="go">DegreeView({2: 1, 3: 2})</span>
</pre></div>
</div>
</section>
<section id="removing-elements-from-a-graph">
<h2>Removing elements from a graph<a class="headerlink" href="#removing-elements-from-a-graph" title="Permalink to this heading">#</a></h2>
<p>One can remove nodes and edges from the graph in a similar fashion to adding.
Use methods
<a class="reference internal" href="reference/classes/generated/networkx.Graph.remove_node.html#networkx.Graph.remove_node" title="networkx.Graph.remove_node"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.remove_node()</span></code></a>,
<a class="reference internal" href="reference/classes/generated/networkx.Graph.remove_nodes_from.html#networkx.Graph.remove_nodes_from" title="networkx.Graph.remove_nodes_from"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.remove_nodes_from()</span></code></a>,
<a class="reference internal" href="reference/classes/generated/networkx.Graph.remove_edge.html#networkx.Graph.remove_edge" title="networkx.Graph.remove_edge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.remove_edge()</span></code></a>
and
<a class="reference internal" href="reference/classes/generated/networkx.Graph.remove_edges_from.html#networkx.Graph.remove_edges_from" title="networkx.Graph.remove_edges_from"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.remove_edges_from()</span></code></a>, e.g.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">remove_node</span><span class="p">(</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">remove_nodes_from</span><span class="p">(</span><span class="s2">"spam"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">)</span>
<span class="go">[1, 3, 'spam']</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">remove_edge</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="using-the-graph-constructors">
<h2>Using the graph constructors<a class="headerlink" href="#using-the-graph-constructors" title="Permalink to this heading">#</a></h2>
<p>Graph objects do not have to be built up incrementally - data specifying
graph structure can be passed directly to the constructors of the various
graph classes.
When creating a graph structure by instantiating one of the graph
classes you can specify data in several formats.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">H</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="c1"># create a DiGraph using the connections from G</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">H</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span>
<span class="go">[(1, 2), (2, 1)]</span>
<span class="gp">>>> </span><span class="n">edgelist</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">2</span><span class="p">),</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">H</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">edgelist</span><span class="p">)</span> <span class="c1"># create a graph from an edge list</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">H</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span>
<span class="go">[(0, 1), (1, 2), (2, 3)]</span>
<span class="gp">>>> </span><span class="n">adjacency_dict</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</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">1</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="mi">2</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">H</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">adjacency_dict</span><span class="p">)</span> <span class="c1"># create a Graph dict mapping nodes to nbrs</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">H</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span>
<span class="go">[(0, 1), (0, 2), (1, 2)]</span>
</pre></div>
</div>
</section>
<section id="what-to-use-as-nodes-and-edges">
<h2>What to use as nodes and edges<a class="headerlink" href="#what-to-use-as-nodes-and-edges" title="Permalink to this heading">#</a></h2>
<p>You might notice that nodes and edges are not specified as NetworkX
objects. This leaves you free to use meaningful items as nodes and
edges. The most common choices are numbers or strings, but a node can
be any hashable object (except <code class="docutils literal notranslate"><span class="pre">None</span></code>), and an edge can be associated
with any object <code class="docutils literal notranslate"><span class="pre">x</span></code> using <code class="docutils literal notranslate"><span class="pre">G.add_edge(n1,</span> <span class="pre">n2,</span> <span class="pre">object=x)</span></code>.</p>
<p>As an example, <code class="docutils literal notranslate"><span class="pre">n1</span></code> and <code class="docutils literal notranslate"><span class="pre">n2</span></code> could be protein objects from the RCSB Protein
Data Bank, and <code class="docutils literal notranslate"><span class="pre">x</span></code> could refer to an XML record of publications detailing
experimental observations of their interaction.</p>
<p>We have found this power quite useful, but its abuse
can lead to surprising behavior unless one is familiar with Python.
If in doubt, consider using <a class="reference internal" href="reference/generated/networkx.relabel.convert_node_labels_to_integers.html#networkx.relabel.convert_node_labels_to_integers" title="networkx.relabel.convert_node_labels_to_integers"><code class="xref py py-func docutils literal notranslate"><span class="pre">convert_node_labels_to_integers()</span></code></a> to obtain
a more traditional graph with integer labels.</p>
</section>
<section id="accessing-edges-and-neighbors">
<h2>Accessing edges and neighbors<a class="headerlink" href="#accessing-edges-and-neighbors" title="Permalink to this heading">#</a></h2>
<p>In addition to the views <a class="reference internal" href="reference/classes/generated/networkx.Graph.edges.html#networkx.Graph.edges" title="networkx.Graph.edges"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Graph.edges</span></code></a>, and <a class="reference internal" href="reference/classes/generated/networkx.Graph.adj.html#networkx.Graph.adj" title="networkx.Graph.adj"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Graph.adj</span></code></a>,
access to edges and neighbors is possible using subscript notation.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><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">Graph</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="s2">"color"</span><span class="p">:</span> <span class="s2">"yellow"</span><span class="p">})])</span>
<span class="gp">>>> </span><span class="n">G</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># same as G.adj[1]</span>
<span class="go">AtlasView({2: {'color': 'yellow'}})</span>
<span class="gp">>>> </span><span class="n">G</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">{'color': 'yellow'}</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</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">{'color': 'yellow'}</span>
</pre></div>
</div>
<p>You can get/set the attributes of an edge using subscript notation
if the edge already exists.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</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="s1">'color'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"blue"</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</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="s1">'color'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"red"</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</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">{'color': 'red'}</span>
</pre></div>
</div>
<p>Fast examination of all (node, adjacency) pairs is achieved using
<code class="docutils literal notranslate"><span class="pre">G.adjacency()</span></code>, or <code class="docutils literal notranslate"><span class="pre">G.adj.items()</span></code>.
Note that for undirected graphs, adjacency iteration sees each edge twice.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">FG</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">FG</span><span class="o">.</span><span class="n">add_weighted_edges_from</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="mf">0.125</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="mf">0.75</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">1.2</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="mf">0.375</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">nbrs</span> <span class="ow">in</span> <span class="n">FG</span><span class="o">.</span><span class="n">adj</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">nbr</span><span class="p">,</span> <span class="n">eattr</span> <span class="ow">in</span> <span class="n">nbrs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="gp">... </span> <span class="n">wt</span> <span class="o">=</span> <span class="n">eattr</span><span class="p">[</span><span class="s1">'weight'</span><span class="p">]</span>
<span class="gp">... </span> <span class="k">if</span> <span class="n">wt</span> <span class="o"><</span> <span class="mf">0.5</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"(</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">nbr</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">wt</span><span class="si">:</span><span class="s2">.3</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
<span class="go">(1, 2, 0.125)</span>
<span class="go">(2, 1, 0.125)</span>
<span class="go">(3, 4, 0.375)</span>
<span class="go">(4, 3, 0.375)</span>
</pre></div>
</div>
<p>Convenient access to all edges is achieved with the edges property.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">wt</span><span class="p">)</span> <span class="ow">in</span> <span class="n">FG</span><span class="o">.</span><span class="n">edges</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">if</span> <span class="n">wt</span> <span class="o"><</span> <span class="mf">0.5</span><span class="p">:</span>
<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"(</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">wt</span><span class="si">:</span><span class="s2">.3</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
<span class="go">(1, 2, 0.125)</span>
<span class="go">(3, 4, 0.375)</span>
</pre></div>
</div>
</section>
<section id="adding-attributes-to-graphs-nodes-and-edges">
<span id="attributes"></span><h2>Adding attributes to graphs, nodes, and edges<a class="headerlink" href="#adding-attributes-to-graphs-nodes-and-edges" title="Permalink to this heading">#</a></h2>
<p>Attributes such as weights, labels, colors, or whatever Python object you like,
can be attached to graphs, nodes, or edges.</p>
<p>Each graph, node, and edge can hold key/value attribute pairs in an associated
attribute dictionary (the keys must be hashable). By default these are empty,
but attributes can be added or changed using <code class="docutils literal notranslate"><span class="pre">add_edge</span></code>, <code class="docutils literal notranslate"><span class="pre">add_node</span></code> or direct
manipulation of the attribute dictionaries named <code class="docutils literal notranslate"><span class="pre">G.graph</span></code>, <code class="docutils literal notranslate"><span class="pre">G.nodes</span></code>, and
<code class="docutils literal notranslate"><span class="pre">G.edges</span></code> for a graph <code class="docutils literal notranslate"><span class="pre">G</span></code>.</p>
<section id="graph-attributes">
<h3>Graph attributes<a class="headerlink" href="#graph-attributes" title="Permalink to this heading">#</a></h3>
<p>Assign graph attributes when creating a new graph</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><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">Graph</span><span class="p">(</span><span class="n">day</span><span class="o">=</span><span class="s2">"Friday"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">graph</span>
<span class="go">{'day': 'Friday'}</span>
</pre></div>
</div>
<p>Or you can modify attributes later</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">graph</span><span class="p">[</span><span class="s1">'day'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"Monday"</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">graph</span>
<span class="go">{'day': 'Monday'}</span>
</pre></div>
</div>
</section>
<section id="node-attributes">
<h3>Node attributes<a class="headerlink" href="#node-attributes" title="Permalink to this heading">#</a></h3>
<p>Add node attributes using <code class="docutils literal notranslate"><span class="pre">add_node()</span></code>, <code class="docutils literal notranslate"><span class="pre">add_nodes_from()</span></code>, or <code class="docutils literal notranslate"><span class="pre">G.nodes</span></code></p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="s1">'5pm'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">([</span><span class="mi">3</span><span class="p">],</span> <span class="n">time</span><span class="o">=</span><span class="s1">'2pm'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="go">{'time': '5pm'}</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="s1">'room'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">714</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">data</span><span class="p">()</span>
<span class="go">NodeDataView({1: {'time': '5pm', 'room': 714}, 3: {'time': '2pm'}})</span>
</pre></div>
</div>
<p>Note that adding a node to <code class="docutils literal notranslate"><span class="pre">G.nodes</span></code> does not add it to the graph, use
<code class="docutils literal notranslate"><span class="pre">G.add_node()</span></code> to add new nodes. Similarly for edges.</p>
</section>
<section id="edge-attributes">
<h3>Edge Attributes<a class="headerlink" href="#edge-attributes" title="Permalink to this heading">#</a></h3>
<p>Add/change edge attributes using <code class="docutils literal notranslate"><span class="pre">add_edge()</span></code>, <code class="docutils literal notranslate"><span class="pre">add_edges_from()</span></code>,
or subscript notation.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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">2</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mf">4.7</span> <span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</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="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</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="s1">'color'</span><span class="p">:</span> <span class="s1">'blue'</span><span class="p">}),</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="s1">'weight'</span><span class="p">:</span> <span class="mi">8</span><span class="p">})])</span>
<span class="gp">>>> </span><span class="n">G</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="s1">'weight'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">4.7</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">edges</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="s1">'weight'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">4.2</span>
</pre></div>
</div>
<p>The special attribute <code class="docutils literal notranslate"><span class="pre">weight</span></code> should be numeric as it is used by
algorithms requiring weighted edges.</p>
</section>
</section>
<section id="directed-graphs">
<h2>Directed graphs<a class="headerlink" href="#directed-graphs" title="Permalink to this heading">#</a></h2>
<p>The <a class="reference internal" href="reference/classes/digraph.html#networkx.DiGraph" title="networkx.DiGraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">DiGraph</span></code></a> class provides additional methods and properties specific
to directed edges, e.g.,
<a class="reference internal" href="reference/classes/generated/networkx.DiGraph.out_edges.html#networkx.DiGraph.out_edges" title="networkx.DiGraph.out_edges"><code class="xref py py-attr docutils literal notranslate"><span class="pre">DiGraph.out_edges</span></code></a>, <a class="reference internal" href="reference/classes/generated/networkx.DiGraph.in_degree.html#networkx.DiGraph.in_degree" title="networkx.DiGraph.in_degree"><code class="xref py py-attr docutils literal notranslate"><span class="pre">DiGraph.in_degree</span></code></a>,
<a class="reference internal" href="reference/classes/generated/networkx.DiGraph.predecessors.html#networkx.DiGraph.predecessors" title="networkx.DiGraph.predecessors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DiGraph.predecessors</span></code></a>, <a class="reference internal" href="reference/classes/generated/networkx.DiGraph.successors.html#networkx.DiGraph.successors" title="networkx.DiGraph.successors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DiGraph.successors</span></code></a> etc.
To allow algorithms to work with both classes easily, the directed versions of
<a class="reference internal" href="reference/classes/generated/networkx.DiGraph.neighbors.html#networkx.DiGraph.neighbors" title="networkx.DiGraph.neighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">neighbors</span></code></a> is equivalent to
<a class="reference internal" href="reference/classes/generated/networkx.DiGraph.successors.html#networkx.DiGraph.successors" title="networkx.DiGraph.successors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">successors</span></code></a> while <a class="reference internal" href="reference/classes/generated/networkx.DiGraph.degree.html#networkx.DiGraph.degree" title="networkx.DiGraph.degree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">degree</span></code></a> reports the sum
of <a class="reference internal" href="reference/classes/generated/networkx.DiGraph.in_degree.html#networkx.DiGraph.in_degree" title="networkx.DiGraph.in_degree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">in_degree</span></code></a> and <a class="reference internal" href="reference/classes/generated/networkx.DiGraph.out_degree.html#networkx.DiGraph.out_degree" title="networkx.DiGraph.out_degree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">out_degree</span></code></a> even though that may feel inconsistent at times.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">DG</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">DG</span><span class="o">.</span><span class="n">add_weighted_edges_from</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="mf">0.5</span><span class="p">),</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="mf">0.75</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">DG</span><span class="o">.</span><span class="n">out_degree</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="s1">'weight'</span><span class="p">)</span>
<span class="go">0.5</span>
<span class="gp">>>> </span><span class="n">DG</span><span class="o">.</span><span class="n">degree</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="s1">'weight'</span><span class="p">)</span>
<span class="go">1.25</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">DG</span><span class="o">.</span><span class="n">successors</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="go">[2]</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">DG</span><span class="o">.</span><span class="n">neighbors</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="go">[2]</span>
</pre></div>
</div>
<p>Some algorithms work only for directed graphs and others are not well
defined for directed graphs. Indeed the tendency to lump directed
and undirected graphs together is dangerous. If you want to treat
a directed graph as undirected for some measurement you should probably
convert it using <a class="reference internal" href="reference/classes/generated/networkx.Graph.to_undirected.html#networkx.Graph.to_undirected" title="networkx.Graph.to_undirected"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.to_undirected()</span></code></a> or with</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">H</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="c1"># create an undirected graph H from a directed graph G</span>
</pre></div>
</div>
</section>
<section id="multigraphs">
<h2>Multigraphs<a class="headerlink" href="#multigraphs" title="Permalink to this heading">#</a></h2>
<p>NetworkX provides classes for graphs which allow multiple edges
between any pair of nodes. The <a class="reference internal" href="reference/classes/multigraph.html#networkx.MultiGraph" title="networkx.MultiGraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiGraph</span></code></a> and
<a class="reference internal" href="reference/classes/multidigraph.html#networkx.MultiDiGraph" title="networkx.MultiDiGraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiDiGraph</span></code></a>
classes allow you to add the same edge twice, possibly with different
edge data. This can be powerful for some applications, but many
algorithms are not well defined on such graphs.
Where results are well defined,
e.g., <a class="reference internal" href="reference/classes/generated/networkx.MultiGraph.degree.html#networkx.MultiGraph.degree" title="networkx.MultiGraph.degree"><code class="xref py py-meth docutils literal notranslate"><span class="pre">MultiGraph.degree()</span></code></a> we provide the function. Otherwise you
should convert to a standard graph in a way that makes the measurement
well defined.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MG</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">MultiGraph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">MG</span><span class="o">.</span><span class="n">add_weighted_edges_from</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="mf">0.5</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="mf">0.75</span><span class="p">),</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="mf">0.5</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="nb">dict</span><span class="p">(</span><span class="n">MG</span><span class="o">.</span><span class="n">degree</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="s1">'weight'</span><span class="p">))</span>
<span class="go">{1: 1.25, 2: 1.75, 3: 0.5}</span>
<span class="gp">>>> </span><span class="n">GG</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">nbrs</span> <span class="ow">in</span> <span class="n">MG</span><span class="o">.</span><span class="n">adjacency</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">nbr</span><span class="p">,</span> <span class="n">edict</span> <span class="ow">in</span> <span class="n">nbrs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="gp">... </span> <span class="n">minvalue</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="n">d</span><span class="p">[</span><span class="s1">'weight'</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">edict</span><span class="o">.</span><span class="n">values</span><span class="p">()])</span>
<span class="gp">... </span> <span class="n">GG</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">nbr</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="n">minvalue</span><span class="p">)</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">shortest_path</span><span class="p">(</span><span class="n">GG</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">[1, 2, 3]</span>
</pre></div>
</div>
</section>
<section id="graph-generators-and-graph-operations">
<h2>Graph generators and graph operations<a class="headerlink" href="#graph-generators-and-graph-operations" title="Permalink to this heading">#</a></h2>
<p>In addition to constructing graphs node-by-node or edge-by-edge, they
can also be generated by</p>
<section id="applying-classic-graph-operations-such-as">
<h3>1. Applying classic graph operations, such as:<a class="headerlink" href="#applying-classic-graph-operations-such-as" title="Permalink to this heading">#</a></h3>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.classes.function.subgraph.html#networkx.classes.function.subgraph" title="networkx.classes.function.subgraph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">subgraph</span></code></a>(G, nbunch)</p></td>
<td><p>Returns the subgraph induced on nodes in nbunch.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/algorithms/generated/networkx.algorithms.operators.binary.union.html#networkx.algorithms.operators.binary.union" title="networkx.algorithms.operators.binary.union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">union</span></code></a>(G, H[, rename])</p></td>
<td><p>Combine graphs G and H.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/algorithms/generated/networkx.algorithms.operators.binary.disjoint_union.html#networkx.algorithms.operators.binary.disjoint_union" title="networkx.algorithms.operators.binary.disjoint_union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">disjoint_union</span></code></a>(G, H)</p></td>
<td><p>Combine graphs G and H.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/algorithms/generated/networkx.algorithms.operators.product.cartesian_product.html#networkx.algorithms.operators.product.cartesian_product" title="networkx.algorithms.operators.product.cartesian_product"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cartesian_product</span></code></a>(G, H)</p></td>
<td><p>Returns the Cartesian product of G and H.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/algorithms/generated/networkx.algorithms.operators.binary.compose.html#networkx.algorithms.operators.binary.compose" title="networkx.algorithms.operators.binary.compose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose</span></code></a>(G, H)</p></td>
<td><p>Compose graph G with H by combining nodes and edges into a single graph.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/algorithms/generated/networkx.algorithms.operators.unary.complement.html#networkx.algorithms.operators.unary.complement" title="networkx.algorithms.operators.unary.complement"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complement</span></code></a>(G)</p></td>
<td><p>Returns the graph complement of G.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.classes.function.create_empty_copy.html#networkx.classes.function.create_empty_copy" title="networkx.classes.function.create_empty_copy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_empty_copy</span></code></a>(G[, with_data])</p></td>
<td><p>Returns a copy of the graph G with all of the edges removed.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/generated/networkx.classes.function.to_undirected.html#networkx.classes.function.to_undirected" title="networkx.classes.function.to_undirected"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_undirected</span></code></a>(graph)</p></td>
<td><p>Returns an undirected view of the graph <code class="xref py py-obj docutils literal notranslate"><span class="pre">graph</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.classes.function.to_directed.html#networkx.classes.function.to_directed" title="networkx.classes.function.to_directed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_directed</span></code></a>(graph)</p></td>
<td><p>Returns a directed view of the graph <code class="xref py py-obj docutils literal notranslate"><span class="pre">graph</span></code>.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="using-a-call-to-one-of-the-classic-small-graphs-e-g">
<h3>2. Using a call to one of the classic small graphs, e.g.,<a class="headerlink" href="#using-a-call-to-one-of-the-classic-small-graphs-e-g" title="Permalink to this heading">#</a></h3>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.generators.small.petersen_graph.html#networkx.generators.small.petersen_graph" title="networkx.generators.small.petersen_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">petersen_graph</span></code></a>([create_using])</p></td>
<td><p>Returns the Petersen graph.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/generated/networkx.generators.small.tutte_graph.html#networkx.generators.small.tutte_graph" title="networkx.generators.small.tutte_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tutte_graph</span></code></a>([create_using])</p></td>
<td><p>Returns the Tutte graph.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.generators.small.sedgewick_maze_graph.html#networkx.generators.small.sedgewick_maze_graph" title="networkx.generators.small.sedgewick_maze_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sedgewick_maze_graph</span></code></a>([create_using])</p></td>
<td><p>Return a small maze with a cycle.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/generated/networkx.generators.small.tetrahedral_graph.html#networkx.generators.small.tetrahedral_graph" title="networkx.generators.small.tetrahedral_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tetrahedral_graph</span></code></a>([create_using])</p></td>
<td><p>Returns the 3-regular Platonic Tetrahedral graph.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="using-a-constructive-generator-for-a-classic-graph-e-g">
<h3>3. Using a (constructive) generator for a classic graph, e.g.,<a class="headerlink" href="#using-a-constructive-generator-for-a-classic-graph-e-g" title="Permalink to this heading">#</a></h3>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.generators.classic.complete_graph.html#networkx.generators.classic.complete_graph" title="networkx.generators.classic.complete_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complete_graph</span></code></a>(n[, create_using])</p></td>
<td><p>Return the complete graph <code class="xref py py-obj docutils literal notranslate"><span class="pre">K_n</span></code> with n nodes.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/algorithms/generated/networkx.algorithms.bipartite.generators.complete_bipartite_graph.html#networkx.algorithms.bipartite.generators.complete_bipartite_graph" title="networkx.algorithms.bipartite.generators.complete_bipartite_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complete_bipartite_graph</span></code></a>(n1, n2[, create_using])</p></td>
<td><p>Returns the complete bipartite graph <code class="xref py py-obj docutils literal notranslate"><span class="pre">K_{n_1,n_2}</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.generators.classic.barbell_graph.html#networkx.generators.classic.barbell_graph" title="networkx.generators.classic.barbell_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">barbell_graph</span></code></a>(m1, m2[, create_using])</p></td>
<td><p>Returns the Barbell Graph: two complete graphs connected by a path.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/generated/networkx.generators.classic.lollipop_graph.html#networkx.generators.classic.lollipop_graph" title="networkx.generators.classic.lollipop_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lollipop_graph</span></code></a>(m, n[, create_using])</p></td>
<td><p>Returns the Lollipop Graph; <code class="xref py py-obj docutils literal notranslate"><span class="pre">K_m</span></code> connected to <code class="xref py py-obj docutils literal notranslate"><span class="pre">P_n</span></code>.</p></td>
</tr>
</tbody>
</table>
<p>like so:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">K_5</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_graph</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">K_3_5</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_bipartite_graph</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">barbell</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barbell_graph</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lollipop</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">lollipop_graph</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="using-a-stochastic-graph-generator-e-g">
<h3>4. Using a stochastic graph generator, e.g,<a class="headerlink" href="#using-a-stochastic-graph-generator-e-g" title="Permalink to this heading">#</a></h3>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.generators.random_graphs.erdos_renyi_graph.html#networkx.generators.random_graphs.erdos_renyi_graph" title="networkx.generators.random_graphs.erdos_renyi_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">erdos_renyi_graph</span></code></a>(n, p[, seed, directed])</p></td>
<td><p>Returns a <span class="math notranslate nohighlight">\(G_{n,p}\)</span> random graph, also known as an Erdős-Rényi graph or a binomial graph.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/generated/networkx.generators.random_graphs.watts_strogatz_graph.html#networkx.generators.random_graphs.watts_strogatz_graph" title="networkx.generators.random_graphs.watts_strogatz_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">watts_strogatz_graph</span></code></a>(n, k, p[, seed])</p></td>
<td><p>Returns a Watts–Strogatz small-world graph.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="reference/generated/networkx.generators.random_graphs.barabasi_albert_graph.html#networkx.generators.random_graphs.barabasi_albert_graph" title="networkx.generators.random_graphs.barabasi_albert_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">barabasi_albert_graph</span></code></a>(n, m[, seed, ...])</p></td>
<td><p>Returns a random graph using Barabási–Albert preferential attachment</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="reference/generated/networkx.generators.random_graphs.random_lobster.html#networkx.generators.random_graphs.random_lobster" title="networkx.generators.random_graphs.random_lobster"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_lobster</span></code></a>(n, p1, p2[, seed])</p></td>
<td><p>Returns a random lobster graph.</p></td>
</tr>
</tbody>
</table>
<p>like so:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">er</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">erdos_renyi_graph</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">ws</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">watts_strogatz_graph</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">ba</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barabasi_albert_graph</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">red</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">random_lobster</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="reading-a-graph-stored-in-a-file-using-common-graph-formats">
<h3>5. Reading a graph stored in a file using common graph formats<a class="headerlink" href="#reading-a-graph-stored-in-a-file-using-common-graph-formats" title="Permalink to this heading">#</a></h3>
<p>NetworkX supports many popular formats, such as edge lists, adjacency lists,
GML, GraphML, LEDA and others.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">write_gml</span><span class="p">(</span><span class="n">red</span><span class="p">,</span> <span class="s2">"path.to.file"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mygraph</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">read_gml</span><span class="p">(</span><span class="s2">"path.to.file"</span><span class="p">)</span>
</pre></div>
</div>
<p>For details on graph formats see <a class="reference internal" href="reference/readwrite/index.html"><span class="doc">Reading and writing graphs</span></a>
and for graph generator functions see <a class="reference internal" href="reference/generators.html"><span class="doc">Graph generators</span></a></p>
</section>
</section>
<section id="analyzing-graphs">
<h2>Analyzing graphs<a class="headerlink" href="#analyzing-graphs" title="Permalink to this heading">#</a></h2>
<p>The structure of <code class="docutils literal notranslate"><span class="pre">G</span></code> can be analyzed using various graph-theoretic
functions such as:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><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">Graph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</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">3</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">"spam"</span><span class="p">)</span> <span class="c1"># adds node "spam"</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">connected_components</span><span class="p">(</span><span class="n">G</span><span class="p">))</span>
<span class="go">[{1, 2, 3}, {'spam'}]</span>
<span class="gp">>>> </span><span class="nb">sorted</span><span class="p">(</span><span class="n">d</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">degree</span><span class="p">())</span>
<span class="go">[0, 1, 1, 2]</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">clustering</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="go">{1: 0, 2: 0, 3: 0, 'spam': 0}</span>
</pre></div>
</div>
<p>Some functions with large output iterate over (node, value) 2-tuples.
These are easily stored in a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dict</span></code></a> structure if you desire.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sp</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">all_pairs_shortest_path</span><span class="p">(</span><span class="n">G</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">sp</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="go">{3: [3], 1: [3, 1], 2: [3, 1, 2]}</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="reference/algorithms/index.html"><span class="doc">Algorithms</span></a> for details on graph algorithms
supported.</p>
</section>
<section id="drawing-graphs">
<h2>Drawing graphs<a class="headerlink" href="#drawing-graphs" title="Permalink to this heading">#</a></h2>
<p>NetworkX is not primarily a graph drawing package but basic drawing with
Matplotlib as well as an interface to use the open source Graphviz software
package are included. These are part of the <a class="reference internal" href="reference/drawing.html"><span class="doc">networkx.drawing</span></a>
module and will be imported if possible.</p>
<p>First import Matplotlib’s plot interface (pylab works too)</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
</pre></div>
</div>
<p>To test if the import of <a class="reference internal" href="reference/drawing.html#module-networkx.drawing.nx_pylab" title="networkx.drawing.nx_pylab"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nx_pylab</span></code></a> was successful draw <code class="docutils literal notranslate"><span class="pre">G</span></code>
using one of</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><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">petersen_graph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">subax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">font_weight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">subax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_shell</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nlist</span><span class="o">=</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)],</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">font_weight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href=".//tutorial-34.png">png</a>, <a class="reference external" href=".//tutorial-34.hires.png">hires.png</a>, <a class="reference external" href=".//tutorial-34.pdf">pdf</a>)</p>
<figure class="align-default">
<img alt="_images/tutorial-34.png" src="_images/tutorial-34.png" />
</figure>
<p>when drawing to an interactive display. Note that you may need to issue a
Matplotlib</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p>command if you are not using matplotlib in interactive mode.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">options</span> <span class="o">=</span> <span class="p">{</span>
<span class="gp">... </span> <span class="s1">'node_color'</span><span class="p">:</span> <span class="s1">'black'</span><span class="p">,</span>
<span class="gp">... </span> <span class="s1">'node_size'</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
<span class="gp">... </span> <span class="s1">'width'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="gp">... </span><span class="p">}</span>
<span class="gp">>>> </span><span class="n">subax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">221</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_random</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">subax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">222</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_circular</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">subax3</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">223</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_spectral</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">subax4</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">224</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_shell</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nlist</span><span class="o">=</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)],</span> <span class="o">**</span><span class="n">options</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href=".//tutorial-35.png">png</a>, <a class="reference external" href=".//tutorial-35.hires.png">hires.png</a>, <a class="reference external" href=".//tutorial-35.pdf">pdf</a>)</p>
<figure class="align-default">
<img alt="_images/tutorial-35.png" src="_images/tutorial-35.png" />
</figure>
<p>You can find additional options via <a class="reference internal" href="reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#networkx.drawing.nx_pylab.draw_networkx" title="networkx.drawing.nx_pylab.draw_networkx"><code class="xref py py-func docutils literal notranslate"><span class="pre">draw_networkx()</span></code></a> and
layouts via the <a class="reference internal" href="reference/drawing.html#module-networkx.drawing.layout" title="networkx.drawing.layout"><code class="xref py py-mod docutils literal notranslate"><span class="pre">layout</span> <span class="pre">module</span></code></a>.
You can use multiple shells with <a class="reference internal" href="reference/generated/networkx.drawing.nx_pylab.draw_shell.html#networkx.drawing.nx_pylab.draw_shell" title="networkx.drawing.nx_pylab.draw_shell"><code class="xref py py-func docutils literal notranslate"><span class="pre">draw_shell()</span></code></a>.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><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">dodecahedral_graph</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">shells</span> <span class="o">=</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="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</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">19</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">13</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw_shell</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">nlist</span><span class="o">=</span><span class="n">shells</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href=".//tutorial-36.png">png</a>, <a class="reference external" href=".//tutorial-36.hires.png">hires.png</a>, <a class="reference external" href=".//tutorial-36.pdf">pdf</a>)</p>
<figure class="align-default">
<img alt="_images/tutorial-36.png" src="_images/tutorial-36.png" />
</figure>
<p>To save drawings to a file, use, for example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">"path.png"</span><span class="p">)</span>
</pre></div>
</div>
<p>This function writes to the file <code class="docutils literal notranslate"><span class="pre">path.png</span></code> in the local directory. If Graphviz and
PyGraphviz or pydot, are available on your system, you can also use
<a class="reference internal" href="reference/generated/networkx.drawing.nx_agraph.graphviz_layout.html#networkx.drawing.nx_agraph.graphviz_layout" title="networkx.drawing.nx_agraph.graphviz_layout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.drawing.nx_agraph.graphviz_layout</span></code></a> or
<a class="reference internal" href="reference/generated/networkx.drawing.nx_pydot.graphviz_layout.html#networkx.drawing.nx_pydot.graphviz_layout" title="networkx.drawing.nx_pydot.graphviz_layout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.drawing.nx_pydot.graphviz_layout</span></code></a> to get the node positions, or write
the graph in dot format for further processing.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">networkx.drawing.nx_pydot</span> <span class="kn">import</span> <span class="n">write_dot</span>
<span class="gp">>>> </span><span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">nx_agraph</span><span class="o">.</span><span class="n">graphviz_layout</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">write_dot</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="s1">'file.dot'</span><span class="p">)</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="reference/drawing.html"><span class="doc">Drawing</span></a> for additional details.</p>
<ul class="simple">
<li><p><a class="reference download internal" href="tutorial.py">Download this page as a Python code file</a>;</p></li>
<li><p><a class="reference download internal" href="tutorial.ipynb">Download this page as a Jupyter notebook (no outputs)</a>;</p></li>
<li><p><a class="reference download internal" href="tutorial_full.ipynb">Download this page as a Jupyter notebook (with outputs)</a>.</p></li>
</ul>
</section>
<section id="nx-guides">
<h2>NX-Guides<a class="headerlink" href="#nx-guides" title="Permalink to this heading">#</a></h2>
<p>If you are interested in learning more about NetworkX, graph theory and network analysis
then you should check out <a class="reference external" href="https://networkx.org/nx-guides/index.html" title="(in nx-guides)"><span class="xref std std-doc">nx-guides</span></a>. There you can find tutorials,
real-world applications and in-depth examinations of graphs and network algorithms.
All the material is official and was developed and curated by the NetworkX community.</p>
</section>
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Drawing graphs
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