update(self, edges=None, nodes=None)
Like dict.update, this method takes a graph as input, adding the graph's nodes and edges to this graph. It can also take two inputs: edges and nodes. Finally it can take either edges or nodes. To specify only nodes the keyword nodes
must be used.
The collections of edges and nodes are treated similarly to the add_edges_from/add_nodes_from methods. When iterated, they should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).
It you want to update the graph using an adjacency structure it is straightforward to obtain the edges/nodes from adjacency. The following examples provide common cases, your adjacency may be slightly different and require tweaks of these examples:
>>> # dict-of-set/list/tuple
>>> adj = {1: {2, 3}, 2: {1, 3}, 3: {1, 2}}
>>> e = [(u, v) for u, nbrs in adj.items() for v in nbrs]
>>> G.update(edges=e, nodes=adj)
>>> DG = nx.DiGraph()
>>> # dict-of-dict-of-attribute
>>> adj = {1: {2: 1.3, 3: 0.7}, 2: {1: 1.4}, 3: {1: 0.7}}
>>> e = [
... (u, v, {"weight": d})
... for u, nbrs in adj.items()
... for v, d in nbrs.items()
... ]
>>> DG.update(edges=e, nodes=adj)
>>> # dict-of-dict-of-dict
>>> adj = {1: {2: {"weight": 1.3}, 3: {"color": 0.7, "weight": 1.2}}}
>>> e = [
... (u, v, {"weight": d})
... for u, nbrs in adj.items()
... for v, d in nbrs.items()
... ]
>>> DG.update(edges=e, nodes=adj)
>>> # predecessor adjacency (dict-of-set)
>>> pred = {1: {2, 3}, 2: {3}, 3: {3}}
>>> e = [(v, u) for u, nbrs in pred.items() for v in nbrs]
>>> # MultiGraph dict-of-dict-of-dict-of-attribute
>>> MDG = nx.MultiDiGraph()
>>> adj = {
... 1: {2: {0: {"weight": 1.3}, 1: {"weight": 1.2}}},
... 3: {2: {0: {"weight": 0.7}}},
... }
>>> e = [
... (u, v, ekey, d)
... for u, nbrs in adj.items()
... for v, keydict in nbrs.items()
... for ekey, d in keydict.items()
... ]
>>> MDG.update(edges=e)
The first parameter can be a graph or some edges. If it has attributes nodes
and edges
, then it is taken to be a Graph-like object and those attributes are used as collections of nodes and edges to be added to the graph. If the first parameter does not have those attributes, it is treated as a collection of edges and added to the graph. If the first argument is None, no edges are added.
The second parameter is treated as a collection of nodes to be added to the graph unless it is None. If :None:None:`edges is None` and :None:None:`nodes is None` an exception is raised. If the first parameter is a Graph, then nodes
is ignored.
Update the graph using nodes/edges/graphs as input.
add_edges_from
add multiple edges to a graph
add_nodes_from
add multiple nodes to a graph
>>> G = nx.path_graph(5)See :
... G.update(nx.complete_graph(range(4, 10)))
... from itertools import combinations
... edges = (
... (u, v, {"power": u * v})
... for u, v in combinations(range(10, 20), 2)
... if u * v < 225
... )
... nodes = [1000] # for singleton, use a container
... G.update(edges, nodes)
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.classes.graph.Graph.update
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