# edges_iter¶

MultiGraph.edges_iter(nbunch=None, data=False, keys=False)[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 : bool, optional (default=False) If True, return edge attribute dict with each edge. keys : bool, optional (default=False) If True, return edge keys with each edge. edge_iter : iterator An iterator of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.

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
>>> [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, {})]
>>> 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, {})]
>>> list(G.edges_iter([0,3]))
[(0, 1), (3, 2)]
>>> list(G.edges_iter(0))
[(0, 1)]