NetworkX系列教程(5)-查看graph的信息

小书匠Graph图论

有时候graph建好后,我们并不清除该graph内节点的,边的信息,这就需要调用函数去查看了.

目录:

• 6.查看Graph的信息
  • 6.1查看graph内节点,边的
  • 6.2查看graph中的点,边
  • 6.3查看某些节点的度
  • 6.4查看节点&边信息
  • 6.5遍历一个有权图

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注意:如果代码出现找不库,请返回第一个教程,把库文件导入.

6.查看Graph的信息

6.1查看graph内节点,边的数量

1. #生成graph 
   

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2. G=nx.path_graph(8) 
   

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3. nx.draw(G,with_labels=True) 
   

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4. plt.axis('on') 
   

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5. plt.xticks([]) 
   

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6. plt.yticks([]) 
   

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7. plt.show() 
   

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8.  
   

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9. #查看节点和边的情况 
   

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10. print('number of nodes',G.number_of_nodes()) 
    

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11. print('number of edges',G.number_of_edges()) 
    

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例子图

输出:

1. number of nodes 8 
   

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2. number of edges 7 
   

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6.2查看graph中的点,边

1. #输出graph所有的点和边 
   

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2. print('all nodes of Graph',G.nodes()) 
   

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3. print('all edges of Graph',G.edges()) 
   

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输出:

1. all nodes of Graph [0, 1, 2, 3, 4, 5, 6, 7] 
   

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2. all edges of Graph [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)] 
   

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6.3查看某些节点的度

1. #查看节点2和3的度 
   

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2. print('degree of some nodes',G.degree([2, 3])) 
   

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输出:

1. degree of some nodes [(2, 2), (3, 2)] 
   

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6.4查看节点&边信息

1. #设置一些节点信息 
   

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2. G.nodes[1]['room'] = 714 
   

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3. G.nodes[1]['color'] = 'b' 
   

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4. #设置一些边信息 
   

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5. G[1][2]['weight'] = 4.7 
   

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6. G[1][2]['color'] = "blue" 
   

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7.  
   

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8. print('imformation of one nodes',G.nodes[1]) 
   

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9. print('imformation of all nodes',G.nodes.data()) 
   

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10.  
    

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11. print('imformation of all nodes',G.edges.data()) #边不支持[x]这样的下标访问 
    

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输出:

1. imformation of one nodes {'room': 714, 'color': 'b'} 
   

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2. imformation of all nodes [(0, {}), (1, {'room': 714, 'color': 'b'}), (2, {}), (3, {}), (4, {}), (5, {}), (6, {}), (7, {})] 
   

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3. imformation of all nodes [(0, 1, {}), (1, 2, {'weight': 4.7, 'color': 'blue'}), (2, 3, {}), (3, 4, {}), (4, 5, {}), (5, 6, {}), (6, 
   

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7, {})]

6.5遍历一个有权图

1. #定义一个有权无向图 
   

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2. FG = nx.Graph() 
   

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3. FG.add_weighted_edges_from([(1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2), (3, 4, 0.375)]) 
   

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4.  
   

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5. #遍历邻接矩阵 
   

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6. for n, nbrs in FG.adj.items(): 
   

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7. for nbr, eattr in nbrs.items(): 
   

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8. wt = eattr['weight'] 
   

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9. #权重小于0.5的输出 
   

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10. if wt < 0.5:  
    

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11. print('way1-(%d, %d, %.3f)' % (n, nbr, wt)) 
    

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12.  
    

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13. #遍历所有边 
    

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14. for (u, v, wt) in FG.edges.data('weight'): 
    

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15. #权重小于0.5的输出 
    

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16. if wt < 0.5:  
    

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17. print('way2-(%d, %d, %.3f)' % (u, v, wt)) 
    

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输出:

1. way1-(1, 2, 0.125) 
   

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2. way1-(2, 1, 0.125) 
   

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3. way1-(3, 4, 0.375) 
   

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4. way1-(4, 3, 0.375) 
   

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5. way2-(1, 2, 0.125) 
   

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6. way2-(3, 4, 0.375) 
   

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