Module Expr Str ImportFrom alias Import alias identifier ImportFrom alias ImportFrom alias Assign NameStore Call AttributeLoad Str attr ListLoad Str Str Str ClassDef bases NameLoad body Expr Str Assign NameStore NameLoad FunctionDef arguments args NameParam NameParam defaults NameLoad kwarg body Assign AttributeStore NameLoad attr AttributeLoad NameLoad attr Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad kwarg body Expr Str If CompareIs NameLoad NameLoad body Assign NameStore NameLoad orelse TryExcept body Expr Call AttributeLoad NameLoad attr NameLoad handlers ExceptHandler type NameLoad body Raise Call NameLoad Str If CompareNotIn NameLoad AttributeLoad NameLoad attr body Assign SubscriptStore AttributeLoad NameLoad attr Index NameLoad Call AttributeLoad NameLoad attr Assign SubscriptStore AttributeLoad NameLoad attr Index NameLoad Dict If 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NameLoad Assign NameStore Dict Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad body Expr Str TryExcept body Assign NameStore SubscriptLoad SubscriptLoad AttributeLoad NameLoad attr Index NameLoad Index NameLoad handlers ExceptHandler type NameLoad body Raise Call NameLoad BinOpMod Str TupleLoad NameLoad NameLoad If CompareIs NameLoad NameLoad body Expr Call AttributeLoad NameLoad attr orelse TryExcept body Delete SubscriptDel NameLoad Index NameLoad handlers ExceptHandler type NameLoad body Raise Call NameLoad BinOpMod Str TupleLoad NameLoad NameLoad NameLoad If CompareEq Call NameLoad NameLoad Num body Delete SubscriptDel SubscriptLoad AttributeLoad NameLoad attr Index NameLoad Index NameLoad If CompareNotEq NameLoad NameLoad body Delete SubscriptDel SubscriptLoad 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N/A N/A Base class for MultiGraph. copy deepcopy N/A networkx nx networkx.classes.graph Graph networkx NetworkXError N/A __author__ N/A N/A join N/A Aric Hagberg (hagberg@lanl.gov) Pieter Swart (swart@lanl.gov) Dan Schult(dschult@colgate.edu) MultiGraph N/A Graph N/A N/A 
    An undirected graph class that can store multiedges.

    Multiedges are multiple edges between two nodes.  Each edge
    can hold optional data or attributes.

    A MultiGraph holds undirected edges.  Self loops are allowed.

    Nodes can be arbitrary (hashable) Python objects with optional
    key/value attributes.

    Edges are represented as links between nodes with optional
    key/value attributes.

    Parameters
    ----------
    data : input graph
        Data to initialize graph. If data=None (default) an empty
        graph is created.  The data can be any format that is supported
        by the to_networkx_graph() function, currently including edge list,
        dict of dicts, dict of lists, NetworkX graph, NumPy matrix
        or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.

    attr : keyword arguments, optional (default= no attributes)
        Attributes to add to graph as key=value pairs.

    See Also
    --------
    Graph
    DiGraph
    MultiDiGraph

    Examples
    --------
    Create an empty graph structure (a "null graph") with no nodes and
    no edges.

    >>> G = nx.MultiGraph()

    G can be grown in several ways.

    **Nodes:**

    Add one node at a time:

    >>> G.add_node(1)

    Add the nodes from any container (a list, dict, set or
    even the lines from a file or the nodes from another graph).

    >>> G.add_nodes_from([2,3])
    >>> G.add_nodes_from(range(100,110))
    >>> H=nx.path_graph(10)
    >>> G.add_nodes_from(H)

    In addition to strings and integers any hashable Python object
    (except None) can represent a node, e.g. a customized node object,
    or even another Graph.

    >>> G.add_node(H)

    **Edges:**

    G can also be grown by adding edges.

    Add one edge,

    >>> G.add_edge(1, 2)

    a list of edges,

    >>> G.add_edges_from([(1,2),(1,3)])

    or a collection of edges,

    >>> G.add_edges_from(list(H.edges()))

    If some edges connect nodes not yet in the graph, the nodes
    are added automatically.  If an edge already exists, an additional
    edge is created and stored using a key to identify the edge.
    By default the key is the lowest unused integer.

    >>> G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))])
    >>> G[4]
    {3: {0: {}}, 5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}}

    **Attributes:**

    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 can be added or changed using
    add_edge, add_node or direct manipulation of the attribute
    dictionaries named graph, node and edge respectively.

    >>> G = nx.MultiGraph(day="Friday")
    >>> G.graph
    {'day': 'Friday'}

    Add node attributes using add_node(), add_nodes_from() or G.node

    >>> G.add_node(1, time='5pm')
    >>> G.add_nodes_from([3], time='2pm')
    >>> G.node[1]
    {'time': '5pm'}
    >>> G.node[1]['room'] = 714
    >>> del G.node[1]['room'] # remove attribute
    >>> list(G.nodes(data=True))
    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]

    Warning: adding a node to G.node does not add it to the graph.

    Add edge attributes using add_edge(), add_edges_from(), subscript
    notation, or G.edge.

    >>> G.add_edge(1, 2, weight=4.7 )
    >>> G.add_edges_from([(3,4),(4,5)], color='red')
    >>> G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
    >>> G[1][2][0]['weight'] = 4.7
    >>> G.edge[1][2][0]['weight'] = 4

    **Shortcuts:**

    Many common graph features allow python syntax to speed reporting.

    >>> 1 in G     # check if node in graph
    True
    >>> [n for n in G if n<3]   # iterate through nodes
    [1, 2]
    >>> len(G)  # number of nodes in graph
    5
    >>> G[1] # adjacency dict keyed by neighbor to edge attributes
    ...            # Note: you should not change this dict manually!
    {2: {0: {'weight': 4}, 1: {'color': 'blue'}}}

    The fastest way to traverse all edges of a graph is via
    adjacency(), but the edges() method is often more convenient.

    >>> for n,nbrsdict in G.adjacency():
    ...     for nbr,keydict in nbrsdict.items():
    ...        for key,eattr in keydict.items():
    ...            if 'weight' in eattr:
    ...                (n,nbr,key,eattr['weight'])
    (1, 2, 0, 4)
    (2, 1, 0, 4)
    (2, 3, 0, 8)
    (3, 2, 0, 8)
    >>> list(G.edges(data='weight', keys=True))
    [(1, 2, 0, 4), (1, 2, 1, None), (2, 3, 0, 8), (3, 4, 0, None), (4, 5, 0, None)]

    **Reporting:**

    Simple graph information is obtained using methods.
    Reporting methods usually return iterators instead of containers
    to reduce memory usage.
    Methods exist for reporting nodes(), edges(), neighbors() and degree()
    as well as the number of nodes and edges.

    For details on these and other miscellaneous methods, see below.

    **Subclasses (Advanced):**

    The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.
    The outer dict (node_dict) holds adjacency information keyed by node.
    The next dict (adjlist_dict) represents the adjacency information and holds
    edge_key dicts keyed by neighbor. The edge_key dict holds each edge_attr
    dict keyed by edge key. The inner dict (edge_attr_dict) represents
    the edge data and holds edge attribute values keyed by attribute names.

    Each of these four dicts in the dict-of-dict-of-dict-of-dict
    structure can be replaced by a user defined dict-like object.
    In general, the dict-like features should be maintained but
    extra features can be added. To replace one of the dicts create
    a new graph class by changing the class(!) variable holding the
    factory for that dict-like structure. The variable names
    are node_dict_factory, adjlist_dict_factory, edge_key_dict_factory
    and edge_attr_dict_factory.

    node_dict_factory : function, (default: dict)
        Factory function to be used to create the outer-most dict
        in the data structure that holds adjacency info keyed by node.
        It should require no arguments and return a dict-like object.

    adjlist_dict_factory : function, (default: dict)
        Factory function to be used to create the adjacency list
        dict which holds multiedge key dicts keyed by neighbor.
        It should require no arguments and return a dict-like object.

    edge_key_dict_factory : function, (default: dict)
        Factory function to be used to create the edge key dict
        which holds edge data keyed by edge key.
        It should require no arguments and return a dict-like object.

    edge_attr_dict_factory : function, (default: dict)
        Factory function to be used to create the edge attribute
        dict which holds attrbute values keyed by attribute name.
        It should require no arguments and return a dict-like object.

    Examples
    --------
    Create a multigraph subclass that tracks the order nodes are added.

    >>> from collections import OrderedDict
    >>> class OrderedGraph(nx.MultiGraph):
    ...    node_dict_factory = OrderedDict
    >>> G = OrderedGraph()
    >>> G.add_nodes_from( (2,1) )
    >>> list(G.nodes())
    [2, 1]
    >>> G.add_edges_from( ((2,2), (2,1), (2,1), (1,1)) )
    >>> list(G.edges())
    [(2, 1), (2, 1), (2, 2), (1, 1)]

    Create a multgraph object that tracks the order nodes are added
    and for each node track the order that neighbors are added and for
    each neighbor tracks the order that multiedges are added.

    >>> class OrderedGraph(nx.MultiGraph):
    ...    node_dict_factory = OrderedDict
    ...    adjlist_dict_factory = OrderedDict
    ...    edge_key_dict_factory = OrderedDict
    >>> G = OrderedGraph()
    >>> G.add_nodes_from( (2,1) )
    >>> list(G.nodes())
    [2, 1]
    >>> G.add_edges_from( ((2,2), (2,1,2,{'weight':0.1}), (2,1,1,{'weight':0.2}), (1,1)) )
    >>> list(G.edges(keys=True))
    [(2, 2, 0), (2, 1, 2), (2, 1, 1), (1, 1, 0)]

     N/A edge_key_dict_factory dict __init__ N/A N/A self data N/A None attr N/A N/A N/A self edge_key_dict_factory N/A self edge_key_dict_factory N/A N/A N/A Graph __init__ self data attr N/A add_edge N/A N/A self u v key attr_dict N/A None None attr N/A N/A Add an edge between u and v.

        The nodes u and v will be automatically added if they are
        not already in the graph.

        Edge attributes can be specified with keywords or by providing
        a dictionary with key/value pairs.  See examples below.

        Parameters
        ----------
        u, v : nodes
            Nodes can be, for example, strings or numbers.
            Nodes must be hashable (and not None) Python objects.
        key : hashable identifier, optional (default=lowest unused integer)
            Used to distinguish multiedges between a pair of nodes.
        attr_dict : dictionary, optional (default= no attributes)
            Dictionary of edge attributes.  Key/value pairs will
            update existing data associated with the edge.
        attr : keyword arguments, optional
            Edge data (or labels or objects) can be assigned using
            keyword arguments.

        See Also
        --------
        add_edges_from : add a collection of edges

        Notes
        -----
        To replace/update edge data, use the optional key argument
        to identify a unique edge.  Otherwise a new edge will be created.

        NetworkX algorithms designed for weighted graphs cannot use
        multigraphs directly because it is not clear how to handle
        multiedge weights.  Convert to Graph using edge attribute
        'weight' to enable weighted graph algorithms.

        Examples
        --------
        The following all add the edge e=(1,2) to graph G:

        >>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> e = (1,2)
        >>> G.add_edge(1, 2)           # explicit two-node form
        >>> G.add_edge(*e)             # single edge as tuple of two nodes
        >>> G.add_edges_from( [(1,2)] ) # add edges from iterable container

        Associate data to edges using keywords:

        >>> G.add_edge(1, 2, weight=3)
        >>> G.add_edge(1, 2, key=0, weight=4)   # update data for key=0
        >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
         N/A N/A attr_dict None N/A N/A attr_dict attr N/A N/A N/A N/A N/A N/A attr_dict update attr N/A N/A N/A AttributeError N/A N/A N/A NetworkXError The attr_dict argument must be a dictionary. N/A N/A u N/A self adj N/A N/A N/A N/A self adj N/A u N/A N/A self adjlist_dict_factory N/A N/A N/A self node N/A u N/A N/A N/A v N/A self adj N/A N/A N/A N/A self adj N/A v N/A N/A self adjlist_dict_factory N/A N/A N/A self node N/A v N/A N/A N/A v N/A N/A self adj N/A u N/A N/A keydict N/A N/A N/A self adj N/A u N/A v N/A N/A key None N/A N/A key N/A len keydict N/A N/A key keydict N/A N/A key 1 N/A datadict N/A N/A keydict get key N/A N/A self edge_attr_dict_factory N/A N/A N/A datadict update attr_dict N/A N/A keydict N/A key datadict N/A N/A N/A key None N/A N/A key 0 N/A datadict N/A N/A self edge_attr_dict_factory N/A N/A N/A datadict update attr_dict N/A keydict N/A N/A self edge_key_dict_factory N/A N/A keydict N/A key datadict N/A N/A N/A N/A self adj N/A u N/A v keydict N/A N/A N/A N/A self adj N/A v N/A u keydict N/A add_edges_from N/A N/A self ebunch attr_dict N/A None attr N/A N/A Add all the edges in ebunch.

        Parameters
        ----------
        ebunch : container of edges
            Each edge given in the container will be added to the
            graph. The edges can be:

                - 2-tuples (u,v) or
                - 3-tuples (u,v,d) for an edge attribute dict d, or
                - 4-tuples (u,v,k,d) for an edge identified by key k

        attr_dict : dictionary, optional  (default= no attributes)
            Dictionary of edge attributes.  Key/value pairs will
            update existing data associated with each edge.
        attr : keyword arguments, optional
            Edge data (or labels or objects) can be assigned using
            keyword arguments.

        See Also
        --------
        add_edge : add a single edge
        add_weighted_edges_from : convenient way to add weighted edges

        Notes
        -----
        Adding the same edge twice has no effect but any edge data
        will be updated when each duplicate edge is added.

        Edge attributes specified in edges take precedence
        over attributes specified generally.

        Examples
        --------
        >>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> G.add_edges_from([(0,1),(1,2)]) # using a list of edge tuples
        >>> e = zip(range(0,3),range(1,4))
        >>> G.add_edges_from(e) # Add the path graph 0-1-2-3

        Associate data to edges

        >>> G.add_edges_from([(1,2),(2,3)], weight=3)
        >>> G.add_edges_from([(3,4),(1,4)], label='WN2898')
         N/A N/A attr_dict None N/A N/A attr_dict attr N/A N/A N/A N/A N/A N/A attr_dict update attr N/A N/A N/A AttributeError N/A N/A N/A NetworkXError The attr_dict argument must be a dictionary. N/A e ebunch N/A N/A ne N/A len e N/A N/A ne 4 N/A N/A N/A u v key dd e N/A N/A N/A ne 3 N/A N/A N/A u v dd e N/A key None N/A N/A N/A ne 2 N/A N/A N/A u v e N/A dd N/A N/A key None N/A N/A N/A NetworkXError N/A Edge tuple %s must be a 2-tuple, 3-tuple or 4-tuple. N/A e N/A ddd N/A N/A N/A N/A ddd update attr_dict N/A N/A N/A ddd update dd N/A N/A N/A self add_edge u v key ddd N/A remove_edge N/A N/A self u v key N/A None N/A N/A Remove an edge between u and v.

        Parameters
        ----------
        u, v : nodes
            Remove an edge between nodes u and v.
        key : hashable identifier, optional (default=None)
            Used to distinguish multiple edges between a pair of nodes.
            If None remove a single (arbitrary) edge between u and v.

        Raises
        ------
        NetworkXError
            If there is not an edge between u and v, or
            if there is no edge with the specified key.

        See Also
        --------
        remove_edges_from : remove a collection of edges

        Examples
        --------
        >>> G = nx.MultiGraph()
        >>> nx.add_path(G, [0, 1, 2, 3])
        >>> G.remove_edge(0,1)
        >>> e = (1,2)
        >>> G.remove_edge(*e) # unpacks e from an edge tuple

        For multiple edges

        >>> G = nx.MultiGraph()   # or MultiDiGraph, etc
        >>> G.add_edges_from([(1,2),(1,2),(1,2)])
        >>> G.remove_edge(1,2) # remove a single (arbitrary) edge

        For edges with keys

        >>> G = nx.MultiGraph()   # or MultiDiGraph, etc
        >>> G.add_edge(1,2,key='first')
        >>> G.add_edge(1,2,key='second')
        >>> G.remove_edge(1,2,key='second')

         N/A N/A N/A d N/A N/A N/A self adj N/A u N/A v N/A N/A N/A KeyError N/A N/A N/A NetworkXError N/A The edge %s-%s is not in the graph. N/A u v N/A N/A key None N/A N/A N/A N/A d popitem N/A N/A N/A N/A N/A d N/A key N/A N/A N/A KeyError N/A N/A N/A NetworkXError N/A The edge %s-%s with key %s is not in the graph. N/A u v key N/A N/A N/A len d 0 N/A N/A N/A N/A N/A self adj N/A u N/A v N/A N/A u v N/A N/A N/A N/A N/A self adj N/A v N/A u N/A remove_edges_from N/A N/A self ebunch N/A N/A N/A Remove all edges specified in ebunch.

        Parameters
        ----------
        ebunch: list or container of edge tuples
            Each edge given in the list or container will be removed
            from the graph. The edges can be:

                - 2-tuples (u,v) All edges between u and v are removed.
                - 3-tuples (u,v,key) The edge identified by key is removed.
                - 4-tuples (u,v,key,data) where data is ignored.

        See Also
        --------
        remove_edge : remove a single edge

        Notes
        -----
        Will fail silently if an edge in ebunch is not in the graph.

        Examples
        --------
        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> ebunch=[(1,2),(2,3)]
        >>> G.remove_edges_from(ebunch)

        Removing multiple copies of edges

        >>> G = nx.MultiGraph()
        >>> G.add_edges_from([(1,2),(1,2),(1,2)])
        >>> G.remove_edges_from([(1,2),(1,2)])
        >>> list(G.edges())
        [(1, 2)]
        >>> G.remove_edges_from([(1,2),(1,2)]) # silently ignore extra copy
        >>> list(G.edges()) # now empty graph
        []
         N/A e ebunch N/A N/A N/A N/A N/A N/A self remove_edge N/A e N/A 3 N/A N/A N/A NetworkXError N/A N/A N/A has_edge N/A N/A self u v key N/A None N/A N/A Return True if the graph has an edge between nodes u and v.

        Parameters
        ----------
        u, v : nodes
            Nodes can be, for example, strings or numbers.

        key : hashable identifier, optional (default=None)
            If specified return True only if the edge with
            key is found.

        Returns
        -------
        edge_ind : bool
            True if edge is in the graph, False otherwise.

        Examples
        --------
        Can be called either using two nodes u,v, an edge tuple (u,v),
        or an edge tuple (u,v,key).

        >>> G = nx.MultiGraph()   # or MultiDiGraph
        >>> nx.add_path(G, [0, 1, 2, 3])
        >>> G.has_edge(0,1)  # using two nodes
        True
        >>> e = (0,1)
        >>> G.has_edge(*e)  #  e is a 2-tuple (u,v)
        True
        >>> G.add_edge(0,1,key='a')
        >>> G.has_edge(0,1,key='a')  # specify key
        True
        >>> e=(0,1,'a')
        >>> G.has_edge(*e) # e is a 3-tuple (u,v,'a')
        True

        The following syntax are equivalent:

        >>> G.has_edge(0,1)
        True
        >>> 1 in G[0]  # though this gives :exc:`KeyError` if 0 not in G
        True



         N/A N/A N/A N/A key None N/A N/A N/A v N/A N/A self adj N/A u N/A N/A N/A key N/A N/A N/A self adj N/A u N/A v N/A N/A N/A KeyError N/A N/A False N/A edges N/A N/A self nbunch data keys default N/A None False False None N/A N/A Return an iterator over the edges.

        Edges are returned as tuples with optional data and keys
        in the order (node, neighbor, key, data).

        Parameters
        ----------
        nbunch : iterable container, optional (default= all nodes)
            A container of nodes.  The container will be iterated
            through once.
        data : string or bool, optional (default=False)
            The edge attribute returned in 3-tuple (u,v,ddict[data]).
            If True, return edge attribute dict in 3-tuple (u,v,ddict).
            If False, return 2-tuple (u,v).
        default : value, optional (default=None)
            Value used for edges that dont have the requested attribute.
            Only relevant if data is not True or False.
        keys : bool, optional (default=False)
            If True, return edge keys with each edge.

        Returns
        -------
        edge : iterator
            An iterator over (u,v), (u,v,d) or (u,v,key,d) tuples of edges.

        Notes
        -----
        Nodes in nbunch that are not in the graph will be (quietly) ignored.
        For directed graphs this returns the out-edges.

        Examples
        --------
        >>> G = nx.MultiGraph()   # or MultiDiGraph
        >>> nx.add_path(G, [0, 1, 2])
        >>> G.add_edge(2,3,weight=5)
        >>> [e for e in G.edges()]
        [(0, 1), (1, 2), (2, 3)]
        >>> list(G.edges(data=True)) # default data is {} (empty dict)
        [(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]
        >>> list(G.edges(data='weight', default=1))
        [(0, 1, 1), (1, 2, 1), (2, 3, 5)]
        >>> list(G.edges(keys=True)) # default keys are integers
        [(0, 1, 0), (1, 2, 0), (2, 3, 0)]
        >>> list(G.edges(data=True,keys=True)) # default keys are integers
        [(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})]
        >>> list(G.edges(data='weight',default=1,keys=True))
        [(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)]
        >>> list(G.edges([0,3]))
        [(0, 1), (3, 2)]
        >>> list(G.edges(0))
        [(0, 1)]

         N/A seen N/A N/A N/A nbunch None N/A N/A nodes_nbrs N/A N/A N/A self adj items N/A N/A nodes_nbrs N/A N/A n N/A N/A self adj N/A n N/A n N/A N/A self nbunch_iter nbunch N/A N/A data True N/A N/A N/A n nbrs nodes_nbrs N/A N/A N/A nbr keydict N/A N/A nbrs items N/A N/A N/A nbr seen N/A N/A N/A key ddict N/A N/A keydict items N/A N/A N/A N/A keys N/A n nbr key ddict N/A n nbr ddict N/A N/A seen N/A n 1 N/A N/A N/A data False N/A N/A N/A n nbrs nodes_nbrs N/A N/A N/A nbr keydict N/A N/A nbrs items N/A N/A N/A nbr seen N/A N/A N/A key ddict N/A N/A keydict items N/A N/A d N/A N/A data ddict N/A ddict N/A data default N/A N/A N/A keys N/A n nbr key d N/A n nbr d N/A N/A seen N/A n 1 N/A N/A N/A n nbrs nodes_nbrs N/A N/A N/A nbr keydict N/A N/A nbrs items N/A N/A N/A nbr seen N/A N/A key keydict N/A N/A N/A N/A keys N/A n nbr key N/A n nbr N/A N/A seen N/A n 1 N/A seen N/A get_edge_data N/A N/A self u v key default N/A None None N/A N/A Return the attribute dictionary associated with edge (u,v).

        Parameters
        ----------
        u, v : nodes

        default :  any Python object (default=None)
            Value to return if the edge (u,v) is not found.

        key : hashable identifier, optional (default=None)
            Return data only for the edge with specified key.

        Returns
        -------
        edge_dict : dictionary
            The edge attribute dictionary.

        Notes
        -----
        It is faster to use G[u][v][key].

        >>> G = nx.MultiGraph() # or MultiDiGraph
        >>> G.add_edge(0,1,key='a',weight=7)
        >>> G[0][1]['a']  # key='a'
        {'weight': 7}

        Warning: Assigning G[u][v][key] corrupts the graph data structure.
        But it is safe to assign attributes to that dictionary,

        >>> G[0][1]['a']['weight'] = 10
        >>> G[0][1]['a']['weight']
        10
        >>> G[1][0]['a']['weight']
        10

        Examples
        --------
        >>> G = nx.MultiGraph() # or MultiDiGraph
        >>> nx.add_path(G, [0, 1, 2, 3])
        >>> G.get_edge_data(0,1)
        {0: {}}
        >>> e = (0,1)
        >>> G.get_edge_data(*e) # tuple form
        {0: {}}
        >>> G.get_edge_data('a','b',default=0) # edge not in graph, return 0
        0
         N/A N/A N/A N/A key None N/A N/A N/A N/A N/A self adj N/A u N/A v N/A N/A N/A N/A N/A N/A self adj N/A u N/A v N/A key N/A N/A N/A KeyError N/A N/A default N/A degree N/A N/A self nbunch weight N/A None None N/A N/A Return an iterator for (node, degree) or degree for single node.

        The node degree is the number of edges adjacent to the node.
        This function returns the degree for a single node or an iterator
        for a bunch of nodes or if nothing is passed as argument.

        Parameters
        ----------
        nbunch : iterable container, optional (default=all nodes)
            A container of nodes.  The container will be iterated
            through once.

        weight : string or None, optional (default=None)
           The edge attribute that holds the numerical value used
           as a weight.  If None, then each edge has weight 1.
           The degree is the sum of the edge weights adjacent to the node.

        Returns
        -------
        If a single node is requested
        deg : int
            Degree of the node, if a single node is passed as argument.

        OR if multiple nodes are requested
        nd_iter : iterator
            The iterator returns two-tuples of (node, degree).

        Examples
        --------
        >>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> nx.add_path(G, [0, 1, 2, 3])
        >>> G.degree(0) # node 0 with degree 1
        1
        >>> list(G.degree([0,1]))
        [(0, 1), (1, 2)]

         N/A N/A nbunch self N/A N/A nbrs N/A N/A self adj N/A nbunch N/A N/A weight None N/A N/A N/A N/A sum N/A N/A len data N/A data N/A N/A nbrs values N/A N/A nbunch nbrs N/A len N/A nbrs N/A nbunch N/A deg N/A sum N/A N/A N/A d get weight 1 N/A data N/A N/A nbrs values N/A d N/A N/A data values N/A N/A nbunch nbrs N/A N/A deg N/A sum N/A N/A N/A d get weight 1 N/A N/A key d N/A N/A N/A nbrs N/A nbunch items N/A deg N/A N/A nbunch None N/A N/A nodes_nbrs N/A N/A N/A self adj items N/A N/A nodes_nbrs N/A N/A n N/A N/A self adj N/A n N/A n N/A N/A self nbunch_iter nbunch N/A N/A weight None N/A d_iter N/A N/A N/A N/A N/A N/A n nbrs nodes_nbrs N/A N/A deg N/A sum N/A N/A len data N/A data N/A N/A nbrs values N/A N/A N/A n N/A deg N/A N/A n nbrs N/A len N/A nbrs N/A n N/A N/A d_iter N/A N/A N/A N/A N/A N/A n nbrs nodes_nbrs N/A N/A deg N/A sum N/A N/A N/A d get weight 1 N/A data N/A N/A nbrs values N/A d N/A N/A data values N/A N/A n nbrs N/A N/A deg N/A sum N/A N/A N/A d get weight 1 N/A N/A key d N/A N/A N/A nbrs N/A n items N/A N/A N/A n deg N/A N/A N/A d_iter N/A is_multigraph N/A N/A self N/A N/A N/A Return True if graph is a multigraph, False otherwise. N/A True N/A is_directed N/A N/A self N/A N/A N/A Return True if graph is directed, False otherwise. N/A False N/A to_directed N/A N/A self N/A N/A N/A Return a directed representation of the graph.

        Returns
        -------
        G : MultiDiGraph
            A directed graph with the same name, same nodes, and with
            each edge (u,v,data) replaced by two directed edges
            (u,v,data) and (v,u,data).

        Notes
        -----
        This returns a "deepcopy" of the edge, node, and
        graph attributes which attempts to completely copy
        all of the data and references.

        This is in contrast to the similar D=DiGraph(G) which returns a
        shallow copy of the data.

        See the Python copy module for more information on shallow
        and deep copies, http://docs.python.org/library/copy.html.

        Warning: If you have subclassed MultiGraph to use dict-like objects
        in the data structure, those changes do not transfer to the MultiDiGraph
        created by this method.

        Examples
        --------
        >>> G = nx.Graph()   # or MultiGraph, etc
        >>> G.add_edge(0, 1)
        >>> H = G.to_directed()
        >>> list(H.edges())
        [(0, 1), (1, 0)]

        If already directed, return a (deep) copy

        >>> G = nx.DiGraph()   # or MultiDiGraph, etc
        >>> G.add_edge(0, 1)
        >>> H = G.to_directed()
        >>> list(H.edges())
        [(0, 1)]
         networkx.classes.multidigraph MultiDiGraph N/A G N/A MultiDiGraph N/A N/A N/A G add_nodes_from self N/A N/A N/A G add_edges_from N/A N/A u v key N/A deepcopy datadict N/A N/A u nbrs N/A N/A self adjacency N/A N/A v keydict N/A N/A nbrs items N/A N/A key datadict N/A N/A keydict items N/A N/A G graph N/A deepcopy N/A self graph N/A N/A G node N/A deepcopy N/A self node N/A G N/A selfloop_edges N/A N/A self data keys default N/A False False None N/A N/A Return a list of selfloop edges.

        A selfloop edge has the same node at both ends.

        Parameters
        ----------
        data : bool, optional (default=False)
            Return selfloop edges as two tuples (u,v) (data=False)
            or three-tuples (u,v,datadict) (data=True)
            or three-tuples (u,v,datavalue) (data='attrname')
        default : value, optional (default=None)
            Value used for edges that dont have the requested attribute.
            Only relevant if data is not True or False.
        keys : bool, optional (default=False)
            If True, return edge keys with each edge.

        Returns
        -------
        edgelist : list of edge tuples
            A list of all selfloop edges.

        See Also
        --------
        nodes_with_selfloops, number_of_selfloops

        Examples
        --------
        >>> G = nx.MultiGraph()   # or MultiDiGraph
        >>> G.add_edge(1,1)
        >>> G.add_edge(1,2)
        >>> list(G.selfloop_edges())
        [(1, 1)]
        >>> list(G.selfloop_edges(data=True))
        [(1, 1, {})]
        >>> list(G.selfloop_edges(keys=True))
        [(1, 1, 0)]
        >>> list(G.selfloop_edges(keys=True, data=True))
        [(1, 1, 0, {})]
         N/A N/A data True N/A N/A keys N/A N/A N/A N/A n n k d N/A N/A n nbrs N/A N/A N/A self adj items N/A n nbrs N/A N/A k d N/A N/A N/A nbrs N/A n items N/A N/A N/A N/A n n d N/A N/A n nbrs N/A N/A N/A self adj items N/A n nbrs N/A d N/A N/A N/A nbrs N/A n values N/A N/A N/A data False N/A N/A keys N/A N/A N/A N/A n n k N/A N/A d get data default N/A N/A n nbrs N/A N/A N/A self adj items N/A n nbrs N/A N/A k d N/A N/A N/A nbrs N/A n items N/A N/A N/A N/A n n N/A N/A d get data default N/A N/A n nbrs N/A N/A N/A self adj items N/A n nbrs N/A d N/A N/A N/A nbrs N/A n values N/A N/A keys N/A N/A N/A N/A n n k N/A N/A n nbrs N/A N/A N/A self adj items N/A n nbrs N/A k N/A N/A N/A nbrs N/A n keys N/A N/A N/A N/A n n N/A N/A n nbrs N/A N/A N/A self adj items N/A n nbrs N/A d N/A N/A N/A nbrs N/A n values N/A number_of_edges N/A N/A self u v N/A None None N/A N/A Return the number of edges between two nodes.

        Parameters
        ----------
        u, v : nodes, optional (default=all edges)
            If u and v are specified, return the number of edges between
            u and v. Otherwise return the total number of all edges.

        Returns
        -------
        nedges : int
            The number of edges in the graph.  If nodes u and v are specified
            return the number of edges between those nodes.

        See Also
        --------
        size

        Examples
        --------
        >>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> nx.add_path(G, [0, 1, 2, 3])
        >>> G.number_of_edges()
        3
        >>> G.number_of_edges(0,1)
        1
        >>> e = (0,1)
        >>> G.number_of_edges(*e)
        1
         N/A N/A u None N/A N/A N/A N/A self size N/A N/A N/A edgedata N/A N/A N/A self adj N/A u N/A v N/A N/A N/A KeyError N/A N/A 0 N/A N/A len edgedata N/A subgraph N/A N/A self nbunch N/A N/A N/A Return the subgraph induced on nodes in nbunch.

        The induced subgraph of the graph contains the nodes in nbunch
        and the edges between those nodes.

        Parameters
        ----------
        nbunch : list, iterable
            A container of nodes which will be iterated through once.

        Returns
        -------
        G : Graph
            A subgraph of the graph with the same edge attributes.

        Notes
        -----
        The graph, edge or node attributes just point to the original graph.
        So changes to the node or edge structure will not be reflected in
        the original graph while changes to the attributes will.

        To create a subgraph with its own copy of the edge/node attributes use:
        nx.Graph(G.subgraph(nbunch))

        If edge attributes are containers, a deep copy can be obtained using:
        G.subgraph(nbunch).copy()

        For an inplace reduction of a graph to a subgraph you can remove nodes:
        G.remove_nodes_from([ n in G if n not in set(nbunch)])

        Examples
        --------
        >>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
        >>> nx.add_path(G, [0, 1, 2, 3])
        >>> H = G.subgraph([0,1,2])
        >>> list(H.edges())
        [(0, 1), (1, 2)]
         N/A bunch N/A N/A self nbunch_iter nbunch N/A H N/A N/A self __class__ N/A n bunch N/A N/A N/A N/A H node N/A n N/A N/A self node N/A n N/A H_adj N/A H adj N/A self_adj N/A self adj N/A n H N/A N/A Hnbrs N/A N/A H adjlist_dict_factory N/A N/A H_adj N/A n Hnbrs N/A N/A nbr edgedict N/A N/A N/A self_adj N/A n items N/A N/A N/A nbr H_adj N/A N/A ed N/A N/A edgedict copy N/A N/A Hnbrs N/A nbr ed N/A N/A N/A H_adj N/A nbr N/A n ed N/A N/A H graph N/A self graph N/A H N/A edge_subgraph N/A N/A self edges N/A N/A N/A Returns the subgraph induced by the specified edges.

        The induced subgraph contains each edge in `edges` and each
        node incident to any one of those edges.

        Parameters
        ----------
        edges : iterable
            An iterable of edges in this graph.

        Returns
        -------
        G : Graph
            An edge-induced subgraph of this graph with the same edge
            attributes.

        Notes
        -----
        The graph, edge, and node attributes in the returned subgraph
        are references to the corresponding attributes in the original
        graph. Thus changes to the node or edge structure of the
        returned graph will not be reflected in the original graph, but
        changes to the attributes will.

        To create a subgraph with its own copy of the edge or node
        attributes, use::

            >>> nx.MultiGraph(G.edge_subgraph(edges))  # doctest: +SKIP

        If edge attributes are containers, a deep copy of the attributes
        can be obtained using::

            >>> G.edge_subgraph(edges).copy()  # doctest: +SKIP

        Examples
        --------
        Get a subgraph induced by only those edges that have a certain
        attribute::

            >>> # Create a graph in which some edges are "good" and some "bad".
            >>> G = nx.MultiGraph()
            >>> G.add_edge(0, 1, key=0, good=True)
            >>> G.add_edge(0, 1, key=1, good=False)
            >>> G.add_edge(1, 2, key=0, good=False)
            >>> G.add_edge(1, 2, key=1, good=True)
            >>> # Keep only those edges that are marked as "good".
            >>> edges = G.edges(keys=True, data='good')
            >>> edges = ((u, v, k) for (u, v, k, good) in edges if good)
            >>> H = G.edge_subgraph(edges)
            >>> list(H.edges(keys=True, data=True))
            [(0, 1, 0, {'good': True}), (1, 2, 1, {'good': True})]

         N/A H N/A N/A self __class__ N/A adj N/A self adj is_in_graph N/A N/A u v k N/A N/A N/A N/A N/A u adj N/A v N/A adj N/A u N/A k N/A N/A adj N/A u N/A v N/A N/A edges N/A e N/A e edges N/A is_in_graph e N/A N/A u v k edges N/A N/A N/A u N/A H node N/A N/A N/A N/A H node N/A u N/A N/A self node N/A u N/A N/A v N/A H node N/A N/A N/A N/A H node N/A v N/A N/A self node N/A v N/A N/A u N/A H adj N/A N/A N/A N/A H adj N/A u N/A N/A H adjlist_dict_factory N/A N/A v N/A H adj N/A N/A N/A N/A H adj N/A v N/A N/A H adjlist_dict_factory N/A N/A v N/A N/A H adj N/A u N/A N/A N/A N/A N/A H adj N/A u N/A v N/A N/A H edge_key_dict_factory N/A N/A u N/A N/A H adj N/A v N/A N/A N/A N/A N/A H adj N/A v N/A u N/A N/A H edge_key_dict_factory N/A N/A N/A N/A N/A H edge N/A u N/A v N/A k N/A N/A N/A N/A self edge N/A u N/A v N/A k N/A N/A N/A N/A N/A H edge N/A v N/A u N/A k N/A N/A N/A N/A self edge N/A v N/A u N/A k N/A N/A H graph N/A self graph N/A H N/A N/A