add_edges_from(self, ebunch_to_add, **attr)
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 an ebunch take precedence over attributes specified via keyword arguments.
Default keys are generated using the method new_edge_key()
. This method can be overridden by subclassing the base class and providing a custom new_edge_key()
method.
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 data dict d, or
3-tuples (u, v, k) for not iterable key k, or
4-tuples (u, v, k, d) for an edge with data and key k
Edge data (or labels or objects) can be assigned using keyword arguments.
Add all the edges in ebunch_to_add.
add_edge
add a single edge
add_weighted_edges_from
convenient way to add weighted edges
>>> 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)See :
... G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.classes.multidigraph.MultiDiGraph.remove_edge
networkx.classes.multidigraph.MultiDiGraph
networkx.classes.multigraph.MultiGraph.remove_edge
networkx.classes.multidigraph.MultiDiGraph.add_edge
networkx.classes.multigraph.MultiGraph
networkx.classes.multigraph.MultiGraph.add_edge
networkx.classes.multigraph.MultiGraph.to_undirected
networkx.classes.multigraph.MultiGraph.remove_edges_from
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them