Networkx study notes(0)

Networkx Class	Type	Self-loops allowed	Parallel edges allowed
Graph	undirected	Yes	No
DiGraph	directed	Yes	No
MultiGraph	undirected	Yes	Yes
MultiDiGraph	directed	Yes	Yes

　　There are two methods to grow the Graph :

import networkx as nx
import matplotlib.pyplot as plt

G = nx.Graph()
G.add_node(1, features = 10)
G.add_node(1, features = 20)
G.add_node(2, features = 'bobo')

nodes = [(3, {'features' : 'aoao'}), (4, {'features' : 9})]
G.add_nodes_from(nodes)

G.nodes[1]['feature'] = 'nice'
print(G.nodes)
print(G.nodes(data = True))

#result
#[1, 2, 3, 4]
#[(1, {'features': 'nice'}), (2, {'features': 'bobo'}), (3, {'features': 'aoao'}), (4, {'features':9})]

G.add_edge(0, 1, distance = 100)
G.add_edge(0, 1, distance = 200)

edges = [(1, 3, {'distance': 142}), (2, 3, {'distance': 19})]
G.add_edges_from(edges)

G.edges[0, 1]['distance'] = 999
G[0][1]['distance'] = 1999

print(G.edges)
print(G.edges(data = True))

#result
#[(1, 0), (1, 3), (2, 3)]
#[(1, 0, {'distance': 1999}), (1, 3, {'distance': 142}), (2, 3, {'distance': 19})]

　　the other useful methods to report something :（neighbors and adj are not useful almostly,because we could access the Graph index directly）

for n in G.neighbors(1):
    print(n)
for n in G[1]:
    print(n)
for nbr, datadict in G.adj[2].items():
    print(nbr, datadict)
for nbr, datadict in G[2].items():
    print(nbr, datadict)
print(G.has_node(0))
print(G.has_node(5))
print(G.has_edge(0,1))
print(G.has_edge(2,1))

#0
#3
#0
#3
#3 {'distance': 19}
#3 {'distance': 19}
#True
#False
#True
#False

　　how to use the data of the attributes of nodes and edges :

for node_index, node_feature in G.nodes(data=True):
    print(node_index, node_feature)
for receiver, sender, features in G.edges(data=True):
    print(receiver, sender, features)

1 {'features': 'nice'}
2 {'features': 'bobo'}
3 {'features': 'aoao'}
4 {'features': 9}
0 {}
1 0 {'distance': 1999}
1 3 {'distance': 142}
2 3 {'distance': 19}

　　there are two methods to get a directed graph like G, they have the same nodes,name ,but with each edge (u, v, data) replaced by two directed edges (u, v, data) and (v, u, data), the difference between of them is that '.to_directed()' is a deep copy , nx.DiGraph is a shallow copy 　　　　　　　　

diG = G.to_directed()
for receiver, sender, features in diG.edges(data=True):
    print(receiver, sender, features)
diG = nx.DiGraph(G)

1 0 {'distance': 1999}
1 3 {'distance': 142}
2 3 {'distance': 19}
3 1 {'distance': 142}
3 2 {'distance': 19}
0 1 {'distance': 1999}

原文地址：https://www.cnblogs.com/bobo-aoao/p/11881228.html