networkx.DiGraph.out_edges

DiGraph.out_edges

Return an iterator over the edges.

edges(self, nbunch=None, data=False, default=None)

The EdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, G.edges[u, v]['color'] provides the value of the color attribute for edge (u, v) while for (u, v, c) in G.edges(data='color', default='red'): iterates through all the edges yielding the color attribute.

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

Parameters:
  • nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
  • 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.
Returns:

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

iterator

See also

in_edges, out_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.DiGraph()   # or MultiDiGraph, etc
>>> 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([0, 2]))
[(0, 1), (2, 3)]
>>> list(G.edges(0))
[(0, 1)]