NetworkX系列教程(11)-graph和其他數據格式轉換

小書匠  Graph  圖論 

學過線性代數的都了解矩陣,在矩陣上的文章可做的很多,什么特征矩陣,單位矩陣等.grpah存儲可以使用矩陣,比如graph的鄰接矩陣,權重矩陣等,這節主要是在等到graph后,如何快速得到這些信息.詳細官方文檔在這里

目錄:

• 12.graph和其他數據格式轉換
  • 12.1graph與字典(Dict)
  • 12.2graph與列表(List)
  • 12.3graph與numpy
  • 12.4graph與Scipy
  • 12.5graph與Pandas

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注意:如果代碼出現找不庫,請返回第一個教程,把庫文件導入.

12.graph和其他數據格式轉換

12.1graph與字典(Dict)

1. #從字典生成圖 

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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.  

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11. #圖轉換為字典 

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12. print(nx.to_dict_of_dicts(G)) 

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graph與字典(Dict)示例

輸出:

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

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12.2graph與列表(List)

1. #從列表中創建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.  

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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.  

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8. #顯示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.  

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22. #graph轉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與列表(List)示例

輸出:

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

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12.3graph與numpy

1. #從numpy創建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.  

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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與numpy示例

輸出:

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與Scipy

1. #從scipy創建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.  

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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與Scipy示例

輸出:

1. [[0. 1.] 

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2. [1. 0.]] 

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12.5graph與Pandas

1. #從pandas創建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.  

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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與Pandas示例

輸出:

1. 0 1 

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2. 0 1.0 2.0 

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