{"id":1044077,"date":"2024-12-31T13:11:29","date_gmt":"2024-12-31T05:11:29","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1044077.html"},"modified":"2024-12-31T13:11:31","modified_gmt":"2024-12-31T05:11:31","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%be%97%e5%88%b0%e7%bd%91%e7%bb%9c%e7%9a%84%e8%be%b9%e6%9d%83","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1044077.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u5f97\u5230\u7f51\u7edc\u7684\u8fb9\u6743"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u5f97\u5230\u7f51\u7edc\u7684\u8fb9\u6743\"<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u56fe\u8bba\u5e93\u5982NetworkX\u6765\u83b7\u53d6\u7f51\u7edc\u7684\u8fb9\u6743\u3002\u901a\u8fc7NetworkX\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u548c\u64cd\u4f5c\u590d\u6742\u7684\u7f51\u7edc\uff0c\u5e76\u83b7\u53d6\u8fb9\u6743\u91cd\u3001\u8282\u70b9\u5c5e\u6027\u7b49\u4fe1\u606f\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528NetworkX\u83b7\u53d6\u7f51\u7edc\u8fb9\u6743\u7684\u4e00\u4e9b\u65b9\u6cd5\uff1a\u521b\u5efa\u56fe\uff0c\u6dfb\u52a0\u52a0\u6743\u8fb9\uff0c\u8bbf\u95ee\u8fb9\u6743\u91cd\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u521b\u5efa\u56fe<\/h2>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u4f7f\u7528NetworkX\u5e93\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u3002NetworkX\u652f\u6301\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u3001\u5e26\u6743\u56fe\u7b49\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165NetworkX\u5e93\uff0c\u5e76\u521b\u5efa\u4e00\u4e2a\u56fe\u5bf9\u8c61\u3002<\/p>\n<\/p>\n

import networkx as nx<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u65e0\u5411\u56fe<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6709\u5411\u56fe<\/strong><\/h2>\n
G = nx.DiGraph()<\/strong><\/h2>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6dfb\u52a0\u52a0\u6743\u8fb9<\/h2>\n<\/p>\n
\u5728\u521b\u5efa\u4e86\u56fe\u5bf9\u8c61\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5411\u56fe\u4e2d\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\u3002\u8fb9\u53ef\u4ee5\u5e26\u6709\u6743\u91cd\uff08weight\uff09\uff0c\u6743\u91cd\u901a\u5e38\u8868\u793a\u8fb9\u7684\u67d0\u79cd\u5c5e\u6027\uff0c\u5982\u8ddd\u79bb\u3001\u65f6\u95f4\u3001\u6210\u672c\u7b49\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u5e26\u6743\u91cd\u7684\u8fb9<\/p>\n
G.add_edge('A', 'B', weight=4)<\/p>\n
G.add_edge('A', 'C', weight=2)<\/p>\n
G.add_edge('B', 'C', weight=5)<\/p>\n
G.add_edge('B', 'D', weight=10)<\/p>\n
G.add_edge('C', 'D', weight=3)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5411\u56fe\u5bf9\u8c61G\u4e2d\u6dfb\u52a0\u4e86\u5e26\u6709\u6743\u91cd\u7684\u8fb9\u3002\u6bcf\u6761\u8fb9\u901a\u8fc7\u8282\u70b9\u5bf9\u548c\u4e00\u4e2a\u6743\u91cd\u503c\u8fdb\u884c\u5b9a\u4e49\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u8bbf\u95ee\u8fb9\u6743\u91cd<\/h2>\n<\/p>\n
\u4e00\u65e6\u6211\u4eec\u521b\u5efa\u4e86\u5e26\u6743\u91cd\u7684\u56fe\uff0c\u4fbf\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8bbf\u95ee\u8fb9\u7684\u6743\u91cd\u3002\u4ee5\u4e0b\u662f\u51e0\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u901a\u8fc7get_edge_data<\/code><\/h3>\n<\/p>\n
\u4f7f\u7528get_edge_data<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u8bbf\u95ee\u8fb9\u7684\u5c5e\u6027\uff0c\u5305\u62ec\u6743\u91cd\u3002<\/p>\n<\/p>\n
# \u83b7\u53d6\u8fb9 ('A', 'B') \u7684\u6743\u91cd<\/p>\n
edge_data = G.get_edge_data('A', 'B')<\/p>\n
weight = edge_data['weight']<\/p>\n
print(f"Weight of edge ('A', 'B'): {weight}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u901a\u8fc7edges<\/code>\u65b9\u6cd5<\/h3>\n<\/p>\n
edges<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8fd4\u56de\u6240\u6709\u8fb9\u7684\u4fe1\u606f\uff0c\u5305\u542b\u6743\u91cd\u503c\u3002<\/p>\n<\/p>\n
# \u83b7\u53d6\u6240\u6709\u8fb9\u53ca\u5176\u6743\u91cd<\/p>\n
for u, v, data in G.edges(data=True):<\/p>\n
    print(f"Edge ({u}, {v}) has weight: {data['weight']}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u901a\u8fc7\u5b50\u56fe\u8ba1\u7b97<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u9700\u8981\u8ba1\u7b97\u5b50\u56fe\u7684\u8fb9\u6743\u91cd\u3002\u53ef\u4ee5\u4f7f\u7528NetworkX\u7684\u5b50\u56fe\u529f\u80fd\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
# \u83b7\u53d6\u5b50\u56fe<\/p>\n
subgraph = G.subgraph(['A', 'B', 'C'])<\/p>\n
for u, v, data in subgraph.edges(data=True):<\/p>\n
    print(f"Subgraph edge ({u}, {v}) has weight: {data['weight']}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u901a\u8fc7\u90bb\u63a5\u77e9\u9635<\/h3>\n<\/p>\n
NetworkX\u8fd8\u652f\u6301\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff0c\u77e9\u9635\u4e2d\u7684\u503c\u8868\u793a\u8fb9\u7684\u6743\u91cd\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u83b7\u53d6\u90bb\u63a5\u77e9\u9635<\/strong><\/h2>\n
adj_matrix = nx.adjacency_matrix(G).todense()<\/p>\n
print("Adjacency Matrix:")<\/p>\n
print(np.array(adj_matrix))<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u901a\u8fc7Pandas DataFrame<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u66f4\u503e\u5411\u4e8e\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u4e5f\u53ef\u4ee5\u5c06\u56fe\u7684\u6570\u636e\u8f6c\u6362\u4e3aDataFrame\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u5c06\u8fb9\u6570\u636e\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
edge_df = nx.to_pandas_edgelist(G)<\/p>\n
print(edge_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f<\/h2>\n<\/p>\n
\u4e86\u89e3\u5982\u4f55\u5728Python\u4e2d\u83b7\u53d6\u7f51\u7edc\u8fb9\u6743\u91cd\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u89e3\u51b3\u8bb8\u591a\u5b9e\u9645\u95ee\u9898\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
1\u3001\u4ea4\u901a\u7f51\u7edc<\/h3>\n<\/p>\n
\u5728\u4ea4\u901a\u7f51\u7edc\u4e2d\uff0c\u8fb9\u6743\u91cd\u53ef\u80fd\u8868\u793a\u8ddd\u79bb\u6216\u65f6\u95f4\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u6743\u91cd\u6765\u8ba1\u7b97\u6700\u77ed\u8def\u5f84\u6216\u6700\u4f18\u8def\u5f84\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6700\u77ed\u8def\u5f84<\/p>\n
shortest_path = nx.shortest_path(G, source='A', target='D', weight='weight')<\/p>\n
print(f"Shortest path from A to D: {shortest_path}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u793e\u4ea4\u7f51\u7edc<\/h3>\n<\/p>\n
\u5728\u793e\u4ea4\u7f51\u7edc\u4e2d\uff0c\u8fb9\u6743\u91cd\u53ef\u80fd\u8868\u793a\u5173\u7cfb\u7684\u5f3a\u5ea6\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u6743\u91cd\u6765\u5206\u6790\u793e\u4ea4\u7f51\u7edc\u7684\u7ed3\u6784\u548c\u5c5e\u6027\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u8282\u70b9\u7684\u5ea6\u4e2d\u5fc3\u6027<\/p>\n
degree_centrality = nx.degree_centrality(G)<\/p>\n
print("Degree Centrality:")<\/p>\n
print(degree_centrality)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f9b\u5e94\u94fe\u7f51\u7edc<\/h3>\n<\/p>\n
\u5728\u4f9b\u5e94\u94fe\u7f51\u7edc\u4e2d\uff0c\u8fb9\u6743\u91cd\u53ef\u80fd\u8868\u793a\u6210\u672c\u6216\u8fd0\u8f93\u65f6\u95f4\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u6743\u91cd\u6765\u4f18\u5316\u4f9b\u5e94\u94fe\u7ba1\u7406\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6700\u5c0f\u751f\u6210\u6811<\/p>\n
mst = nx.minimum_spanning_tree(G, weight='weight')<\/p>\n
print("Minimum Spanning Tree Edges:")<\/p>\n
print(mst.edges(data=True))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u4f7f\u7528NetworkX\u5e93\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u548c\u64cd\u4f5c\u5e26\u6743\u56fe\u3002\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u83b7\u53d6\u8fb9\u7684\u6743\u91cd\uff0c\u5e76\u5c06\u5176\u5e94\u7528\u5230\u5404\u79cd\u5b9e\u9645\u573a\u666f\u4e2d\u3002\u65e0\u8bba\u662f\u4ea4\u901a\u7f51\u7edc\u3001\u793e\u4ea4\u7f51\u7edc\u8fd8\u662f\u4f9b\u5e94\u94fe\u7f51\u7edc\uff0c\u4e86\u89e3\u5982\u4f55\u83b7\u53d6\u548c\u64cd\u4f5c\u8fb9\u6743\u91cd\u90fd\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f18\u5316\u590d\u6742\u7cfb\u7edf\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u5e0c\u671b\u5927\u5bb6\u80fd\u591f\u638c\u63e1\u5728Python\u4e2d\u83b7\u53d6\u7f51\u7edc\u8fb9\u6743\u91cd\u7684\u65b9\u6cd5\uff0c\u5e76\u80fd\u591f\u5c06\u5176\u5e94\u7528\u5230\u5b9e\u9645\u95ee\u9898\u4e2d\u3002\u5982\u679c\u4f60\u5bf9\u56fe\u8bba\u548c\u7f51\u7edc\u5206\u6790\u611f\u5174\u8da3\uff0c\u5efa\u8bae\u8fdb\u4e00\u6b65\u5b66\u4e60NetworkX\u5e93\u7684\u5176\u4ed6\u529f\u80fd\u548c\u9ad8\u7ea7\u7528\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u5e93\u83b7\u53d6\u7f51\u7edc\u8fb9\u6743\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u7f51\u7edc\u79d1\u5b66\u5e93\u5982NetworkX\u6765\u521b\u5efa\u548c\u64cd\u4f5c\u7f51\u7edc\u56fe\u3002\u53ef\u4ee5\u901a\u8fc7\u4e3a\u6bcf\u6761\u8fb9\u6dfb\u52a0\u6743\u91cd\u5c5e\u6027\u6765\u8f7b\u677e\u83b7\u53d6\u8fb9\u6743\u3002\u4f8b\u5982\uff0c\u4f7f\u7528G.add_edge(node1, node2, weight=\u8fb9\u6743\u503c)<\/code>\u6765\u6dfb\u52a0\u5e26\u6743\u91cd\u7684\u8fb9\u3002\u4e4b\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7G[node1][node2]['weight']<\/code>\u6765\u83b7\u53d6\u7279\u5b9a\u8fb9\u7684\u6743\u91cd\u3002<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u7f51\u7edc\u65f6\uff0c\u5982\u4f55\u9ad8\u6548\u83b7\u53d6\u8fb9\u6743\uff1f<\/strong>
\u5bf9\u4e8e\u5927\u89c4\u6a21\u7f51\u7edc\uff0c\u4f7f\u7528\u90bb\u63a5\u77e9\u9635\u6216\u90bb\u63a5\u8868\u7684\u65b9\u5f0f\u5b58\u50a8\u8fb9\u6743\u4f1a\u66f4\u9ad8\u6548\u3002\u53ef\u4ee5\u901a\u8fc7NetworkX\u7684edges(data=True)<\/code>\u51fd\u6570\u83b7\u53d6\u6240\u6709\u8fb9\u53ca\u5176\u6743\u91cd\uff0c\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u8fb9\u548c\u6743\u91cd\u7684\u5217\u8868\uff0c\u4fbf\u4e8e\u8fdb\u884c\u6279\u91cf\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n
\u5982\u4f55\u5728\u56fe\u4e2d\u4fee\u6539\u5df2\u6709\u8fb9\u7684\u6743\u91cd\uff1f<\/strong>
\u5728NetworkX\u4e2d\uff0c\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u8bbe\u7f6e\u8fb9\u7684\u6743\u91cd\u5c5e\u6027\u6765\u4fee\u6539\u5df2\u6709\u8fb9\u7684\u6743\u91cd\u3002\u4f8b\u5982\uff0c\u4f7f\u7528G[node1][node2]['weight'] = \u65b0\u6743\u91cd<\/code>\u6765\u66f4\u65b0\u7279\u5b9a\u8fb9\u7684\u6743\u91cd\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u76f4\u89c2\uff0c\u9002\u5408\u5728\u9700\u8981\u52a8\u6001\u8c03\u6574\u8fb9\u6743\u7684\u573a\u666f\u4e2d\u4f7f\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u56fe\u8bba\u5e93\u5982NetworkX\u6765\u83b7\u53d6\u7f51\u7edc\u7684\u8fb9\u6743\u3002\u901a\u8fc7NetworkX\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u548c\u64cd […]","protected":false},"author":3,"featured_media":1044085,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1044077"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1044077"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1044077\/revisions"}],"predecessor-version":[{"id":1044090,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1044077\/revisions\/1044090"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1044085"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1044077"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1044077"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1044077"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}