NetworkX系列教程(11)-graph和其他数据格式转换

小书匠 Graph 图论 

学过线性代数的都了解矩阵,在矩阵上的文章可做的很多,什么特征矩阵,单位矩阵等.grpah存储可以使用矩阵,比如graph的邻接矩阵,权重矩阵等,这节主要是在等到graph后,如何快速得到这些信息.详细官方文档在这里

目录:

• 12.graph和其他数据格式转换
  • 12.1graph与字典(Dict)
  • 12.2graph与列表(List)
  • 12.3graph与numpy
  • 12.4graph与Scipy
  • 12.5graph与Pandas

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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.

12.graph和其他数据格式转换

12.1graph与字典(Dict)

1. #从字典生成图 
   

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2. dod = {0: {1: {'weight': 1}}}  
   

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3. G = nx.from_dict_of_dicts(dod) #或G=nx.Graph(dpl) 
   

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4. plt.subplots(1,1,figsize=(6,3)) 
   

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5. nx.draw(G, with_labels=True, font_weight='bold') 
   

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6. plt.axis('on') 
   

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7. plt.xticks([]) 
   

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8. plt.yticks([]) 
   

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9. plt.show() 
   

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10. ---

11. #图转换为字典 
    

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12. print(nx.to_dict_of_dicts(G)) 
    

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graph与字典(Dict)示例

输出:

{0: {1: {'weight': 1}}, 1: {0: {'weight': 1}}}

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12.2graph与列表(List)

1. #从列表中创建graph 
   

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2. dol = {0: [1,2,3]} 
   

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3. edgelist = [(0, 1),(0,3),(2,3)] 
   

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4. ---

5. G1 = nx.from_dict_of_lists(dol) #或G=nx.Graph(dol) 
   

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6. G2=nx.from_edgelist(edgelist) 
   

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7. ---

8. #显示graph 
   

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9. plt.subplots(1,2,figsize=(15,3)) 
   

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10. plt.subplot(121) 
    

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11. nx.draw(G1, with_labels=True, font_weight='bold') 
    

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12. plt.axis('on') 
    

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13. plt.xticks([]) 
    

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14. plt.yticks([]) 
    

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15. plt.subplot(122) 
    

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16. nx.draw(G2, with_labels=True, font_weight='bold') 
    

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17. plt.axis('on') 
    

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18. plt.xticks([]) 
    

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19. plt.yticks([]) 
    

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20. plt.show() 
    

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21. ---

22. #graph转list 
    

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23. print(nx.to_dict_of_lists(G1)) 
    

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24. print(nx.to_edgelist(G1)) 
    

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graph与列表(List)示例

输出:

{0: [1, 2, 3], 1: [0], 2: [0], 3: [0]}

[(0, 1, {}), (0, 2, {}), (0, 3, {})]

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12.3graph与numpy

1. #从numpy创建graph 
   

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2. import numpy as np 
   

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3. a = np.reshape(np.random.random_integers(0, 1, size=100), (10, 10)) 
   

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4. D = nx.DiGraph(a) 
   

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5. nx.draw(D, with_labels=True, font_weight='bold') 
   

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6. plt.axis('on') 
   

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7. plt.xticks([]) 
   

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8. plt.yticks([]) 
   

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9. plt.show() 
   

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10. ---

11. #graph返回numpy 
    

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12. G=nx.Graph() 
    

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13. G.add_edge(1, 2, weight=7.0, cost=5) 
    

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14. A1 = nx.to_numpy_matrix(G) 
    

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15. A2 = nx.to_numpy_recarray(G, dtype=[('weight', float), ('cost', int)]) 
    

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16. print(A1,A2) 
    

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graph与numpy示例

输出:

1. [[0. 7.] 
   

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2. [7. 0.]] [[(0., 0) (7., 5)] 
   

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3. [(7., 5) (0., 0)]] 
   

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12.4graph与Scipy

1. #从scipy创建graph 
   

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2. G.clear() 
   

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3. import scipy as sp 
   

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4. A = sp.sparse.eye(2, 2, 1) 
   

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5. G = nx.from_scipy_sparse_matrix(A) 
   

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6. nx.draw(D, with_labels=True, font_weight='bold') 
   

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7. plt.axis('on') 
   

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8. plt.xticks([]) 
   

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9. plt.yticks([]) 
   

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10. plt.show() 
    

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11. ---

12. #graph返回scipy 
    

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13. A = nx.to_scipy_sparse_matrix(G) 
    

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14. print(A.todense()) 
    

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graph与Scipy示例

输出:

1. [[0. 1.] 
   

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2. [1. 0.]] 
   

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12.5graph与Pandas

1. #从pandas创建graph 
   

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2. G.clear() 
   

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3. import pandas as pd 
   

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4. df = pd.DataFrame([[1, 1], [2, 1]]) 
   

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5. G = nx.from_pandas_adjacency(df) 
   

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6. nx.draw(D, with_labels=True, font_weight='bold') 
   

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7. plt.axis('on') 
   

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8. plt.xticks([]) 
   

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9. plt.yticks([]) 
   

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10. plt.show() 
    

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11. ---

12. #graph返回scipy 
    

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13. df = nx.to_pandas_adjacency(G) 
    

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14. print(df) 
    

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graph与Pandas示例

输出:

1. 0 1 
   

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2. 0 1.0 2.0 
   

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