edges_iter¶

MultiGraph.edges_iter(nbunch=None, data=False, keys=False, default=None)[source]¶

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_iter – An iterator of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.
Return type:	iterator

See also

edges(): return a list 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
>>> G.add_path([0,1,2])
>>> G.add_edge(2,3,weight=5)
>>> [e for e in G.edges_iter()]
[(0, 1), (1, 2), (2, 3)]
>>> list(G.edges_iter(data=True)) # default data is {} (empty dict)
[(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]
>>> list(G.edges_iter(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_iter([0,3]))
[(0, 1), (3, 2)]
>>> list(G.edges_iter(0))
[(0, 1)]