{"id":936404,"date":"2024-12-26T19:24:38","date_gmt":"2024-12-26T11:24:38","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/936404.html"},"modified":"2024-12-26T19:24:40","modified_gmt":"2024-12-26T11:24:40","slug":"python%e5%a6%82%e4%bd%95%e6%9e%84%e9%80%a0%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/936404.html","title":{"rendered":"python\u5982\u4f55\u6784\u9020\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u6784\u9020\u56fe\"<\/p>\n

\u5728Python\u4e2d\u6784\u9020\u56fe\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u6700\u5e38\u89c1\u7684\u5e93\u662fNetworkX\u3001Matplotlib\u548cGraphviz\u3002NetworkX\u7528\u4e8e\u521b\u5efa\u590d\u6742\u7f51\u7edc\u56fe\u3001Matplotlib\u7528\u4e8e\u57fa\u672c\u56fe\u5f62\u7ed8\u5236\u3001Graphviz\u7528\u4e8e\u4e13\u4e1a\u7684\u56fe\u5f62\u53ef\u89c6\u5316<\/strong>\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u6765\u6784\u9020\u56fe\u5f62\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NETWORKX\u6784\u9020\u56fe<\/h3>\n<\/p>\n

NetworkX\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u4e13\u95e8\u7528\u4e8e\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u7814\u7a76\u590d\u6742\u7684\u7f51\u7edc\u548c\u56fe\u5f62\u3002\u5b83\u975e\u5e38\u9002\u5408\u4e8e\u9700\u8981\u5904\u7406\u8282\u70b9\u548c\u8fb9\u7684\u590d\u6742\u5173\u7cfb\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5\u4e0e\u57fa\u7840\u64cd\u4f5c<\/h4>\n<\/p>\n

\u8981\u4f7f\u7528NetworkX\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\u3002\u5728\u547d\u4ee4\u884c\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n

pip install networkx<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\uff1a<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u65e0\u5411\u56fe<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
\u6dfb\u52a0\u8282\u70b9<\/strong><\/h2>\n
G.add_node(1)<\/p>\n
G.add_node(2)<\/p>\n
\u6dfb\u52a0\u8fb9<\/strong><\/h2>\n
G.add_edge(1, 2)<\/p>\n
\u7ed8\u5236\u56fe<\/strong><\/h2>\n
nx.draw(G, with_labels=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u7a7a\u56fe\uff0c\u7136\u540e\u6dfb\u52a0\u4e86\u4e24\u4e2a\u8282\u70b9\u548c\u4e00\u6761\u8fb9\u3002\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528nx.draw<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u5e76\u663e\u793a\u56fe\u5f62\u3002<\/p>\n<\/p>\n
2. \u8282\u70b9\u4e0e\u8fb9\u7684\u64cd\u4f5c<\/h4>\n<\/p>\n
NetworkX\u652f\u6301\u591a\u79cd\u590d\u6742\u7684\u8282\u70b9\u548c\u8fb9\u7684\u64cd\u4f5c\uff0c\u5305\u62ec\u6dfb\u52a0\u5c5e\u6027\u3001\u5220\u9664\u8282\u70b9\u548c\u8fb9\u3001\u67e5\u8be2\u8282\u70b9\u548c\u8fb9\u7b49\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u591a\u4e2a\u8282\u70b9<\/p>\n
G.add_nodes_from([3, 4, 5])<\/p>\n
\u6dfb\u52a0\u591a\u6761\u8fb9<\/strong><\/h2>\n
G.add_edges_from([(3, 4), (4, 5)])<\/p>\n
\u8bbe\u7f6e\u8282\u70b9\u5c5e\u6027<\/strong><\/h2>\n
G.nodes[1]['color'] = 'red'<\/p>\n
\u8bbe\u7f6e\u8fb9\u5c5e\u6027<\/strong><\/h2>\n
G.edges[3, 4]['weight'] = 4.2<\/p>\n
\u83b7\u53d6\u8282\u70b9\u5c5e\u6027<\/strong><\/h2>\n
print(G.nodes[1])<\/p>\n
\u83b7\u53d6\u8fb9\u5c5e\u6027<\/strong><\/h2>\n
print(G.edges[3, 4])<\/p>\n
\u5220\u9664\u8282\u70b9<\/strong><\/h2>\n
G.remove_node(2)<\/p>\n
\u5220\u9664\u8fb9<\/strong><\/h2>\n
G.remove_edge(3, 4)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u6b64\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u6279\u91cf\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\uff0c\u5982\u4f55\u4e3a\u8282\u70b9\u548c\u8fb9\u6dfb\u52a0\u5c5e\u6027\uff0c\u4ee5\u53ca\u5982\u4f55\u5220\u9664\u8282\u70b9\u548c\u8fb9\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528MATPLOTLIB\u7ed8\u5236\u56fe<\/h3>\n<\/p>\n
Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u4e0d\u4ec5\u53ef\u4ee5\u7ed8\u5236\u57fa\u672c\u76842D\u56fe\u5f62\uff0c\u8fd8\u53ef\u4ee5\u4e0eNetworkX\u7ed3\u5408\u4f7f\u7528\u6765\u7ed8\u5236\u66f4\u590d\u6742\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n
1. \u7ed8\u5236\u57fa\u7840\u56fe\u5f62<\/h4>\n<\/p>\n
Matplotlib\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u7ed8\u5236\u7b80\u5355\u7684\u56fe\u5f62\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4]<\/p>\n
y = [10, 20, 25, 30]<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Sample Line Chart')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e9b\u7b80\u5355\u7684x\u548cy\u6570\u636e\uff0c\u7136\u540e\u4f7f\u7528plt.plot<\/code>\u51fd\u6570\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5e76\u6dfb\u52a0\u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u7b7e\u3002<\/p>\n<\/p>\n
2. \u4e0eNetworkX\u7ed3\u5408<\/h4>\n<\/p>\n
Matplotlib\u53ef\u4ee5\u4e0eNetworkX\u7ed3\u5408\u4f7f\u7528\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u56fe\u5f62\u5e03\u5c40\u548c\u5c55\u793a\u6548\u679c\u3002<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u56fe<\/strong><\/h2>\n
G = nx.karate_club_graph()<\/p>\n
\u4f7f\u7528spring\u5e03\u5c40<\/strong><\/h2>\n
pos = nx.spring_layout(G)<\/p>\n
\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
nx.draw(G, pos, with_labels=True, node_size=700, node_color='skyblue', font_size=12, font_color='black')<\/p>\n
plt.title('Karate Club Graph')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NetworkX\u63d0\u4f9b\u7684\u201ckarate club\u201d\u56fe\u4f5c\u4e3a\u793a\u4f8b\uff0c\u5e76\u4f7f\u7528spring\u5e03\u5c40\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u7ed3\u5408Matplotlib\u8fdb\u884c\u5c55\u793a\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528GRAPHVIZ\u8fdb\u884c\u4e13\u4e1a\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
Graphviz\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u56fe\u5f62\u53ef\u89c6\u5316\u8f6f\u4ef6\uff0c\u63d0\u4f9b\u4e86\u4e00\u5957\u7b80\u5355\u7684\u547d\u4ee4\u8bed\u8a00\u6765\u63cf\u8ff0\u56fe\u5f62\u7684\u7ed3\u6784\uff0cPython\u4e2d\u53ef\u4ee5\u901a\u8fc7PyGraphviz\u6216\u8005Graphviz\u6a21\u5757\u6765\u4f7f\u7528\u5b83\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Graphviz<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5Graphviz\u8f6f\u4ef6\uff0c\u53ef\u4ee5\u4eceGraphviz\u7684\u5b98\u65b9\u7f51\u7ad9\u4e0b\u8f7d\u5e76\u5b89\u88c5\u3002\u7136\u540e\u5b89\u88c5Python\u63a5\u53e3\uff1a<\/p>\n<\/p>\n
pip install graphviz<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Graphviz\u7ed8\u5236\u56fe<\/h4>\n<\/p>\n
Graphviz\u901a\u8fc7\u5b9a\u4e49\u56fe\u7684\u6587\u672c\u63cf\u8ff0\u6765\u751f\u6210\u56fe\u5f62\u3002<\/p>\n<\/p>\n
from graphviz import Digraph<\/p>\n
\u521b\u5efa\u6709\u5411\u56fe<\/strong><\/h2>\n
dot = Digraph(comment='The Round Table')<\/p>\n
\u6dfb\u52a0\u8282\u70b9<\/strong><\/h2>\n
dot.node('A', 'King Arthur')<\/p>\n
dot.node('B', 'Sir Bedevere the Wise')<\/p>\n
dot.node('L', 'Sir Lancelot the Brave')<\/p>\n
\u6dfb\u52a0\u8fb9<\/strong><\/h2>\n
dot.edges(['AB', 'AL'])<\/p>\n
dot.edge('B', 'L', constrAI<\/a>nt='false')<\/p>\n
\u4fdd\u5b58\u5e76\u6e32\u67d3<\/strong><\/h2>\n
dot.render('round-table.gv', view=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6709\u5411\u56fe\uff0c\u63cf\u8ff0\u4e86\u201c\u5706\u684c\u9a91\u58eb\u201d\u7684\u5173\u7cfb\uff0c\u5e76\u901a\u8fc7Graphviz\u7684\u8bed\u6cd5\u8fdb\u884c\u6e32\u67d3\u548c\u663e\u793a\u3002<\/p>\n<\/p>\n
\u56db\u3001\u590d\u6742\u56fe\u5f62\u5206\u6790\u4e0e\u7b97\u6cd5<\/h3>\n<\/p>\n
\u6784\u9020\u56fe\u53ea\u662f\u7b2c\u4e00\u6b65\uff0c\u5f88\u591a\u65f6\u5019\u9700\u8981\u5bf9\u56fe\u8fdb\u884c\u5206\u6790\u6216\u5e94\u7528\u4e00\u4e9b\u7b97\u6cd5\uff0c\u6bd4\u5982\u6700\u77ed\u8def\u5f84\u3001\u56fe\u7684\u904d\u5386\u7b49\u3002<\/p>\n<\/p>\n
1. \u6700\u77ed\u8def\u5f84\u7b97\u6cd5<\/h4>\n<\/p>\n
NetworkX\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6700\u77ed\u8def\u5f84\u7b97\u6cd5\uff0c\u5982Dijkstra\u7b97\u6cd5\u548cFloyd-Warshall\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
\u521b\u5efa\u56fe<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
\u6dfb\u52a0\u8fb9\u53ca\u5176\u6743\u91cd<\/strong><\/h2>\n
G.add_weighted_edges_from([(1, 2, 1.5), (1, 3, 2.5), (2, 4, 1.0), (3, 4, 2.0)])<\/p>\n
\u4f7f\u7528Dijkstra\u7b97\u6cd5\u5bfb\u627e\u6700\u77ed\u8def\u5f84<\/strong><\/h2>\n
path = nx.dijkstra_path(G, source=1, target=4)<\/p>\n
print("Shortest path from 1 to 4:", path)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u5e26\u6743\u56fe\uff0c\u5e76\u4f7f\u7528Dijkstra\u7b97\u6cd5\u8ba1\u7b97\u4ece\u8282\u70b91\u5230\u8282\u70b94\u7684\u6700\u77ed\u8def\u5f84\u3002<\/p>\n<\/p>\n
2. \u56fe\u7684\u904d\u5386<\/h4>\n<\/p>\n
\u56fe\u7684\u904d\u5386\u662f\u56fe\u8bba\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6982\u5ff5\uff0c\u5305\u62ec\u6df1\u5ea6\u4f18\u5148\u641c\u7d22\uff08DFS\uff09\u548c\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22\uff08BFS\uff09\u3002<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
\u521b\u5efa\u56fe<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4)])<\/p>\n
\u6df1\u5ea6\u4f18\u5148\u641c\u7d22<\/strong><\/h2>\n
dfs_edges = list(nx.dfs_edges(G, source=1))<\/p>\n
print("DFS edges:", dfs_edges)<\/p>\n
\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22<\/strong><\/h2>\n
bfs_edges = list(nx.bfs_edges(G, source=1))<\/p>\n
print("BFS edges:", bfs_edges)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NetworkX\u63d0\u4f9b\u7684dfs_edges<\/code>\u548cbfs_edges<\/code>\u51fd\u6570\u5206\u522b\u5b9e\u73b0\u6df1\u5ea6\u4f18\u5148\u641c\u7d22\u548c\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u56fe\u7684\u5e94\u7528\u6848\u4f8b<\/h3>\n<\/p>\n
\u56fe\u5728\u73b0\u5b9e\u4e16\u754c\u4e2d\u6709\u5f88\u591a\u5e94\u7528\u573a\u666f\uff0c\u4ece\u793e\u4ea4\u7f51\u7edc\u5206\u6790\u5230\u751f\u7269\u4fe1\u606f\u5b66\uff0c\u518d\u5230\u63a8\u8350\u7cfb\u7edf\u7b49\u3002<\/p>\n<\/p>\n
1. \u793e\u4ea4\u7f51\u7edc\u5206\u6790<\/h4>\n<\/p>\n
\u5728\u793e\u4ea4\u7f51\u7edc\u4e2d\uff0c\u8282\u70b9\u53ef\u4ee5\u4ee3\u8868\u7528\u6237\uff0c\u8fb9\u53ef\u4ee5\u4ee3\u8868\u7528\u6237\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u901a\u8fc7\u56fe\u5206\u6790\uff0c\u53ef\u4ee5\u8bc6\u522b\u5173\u952e\u7684\u5f71\u54cd\u8005\u3001\u793e\u533a\u7ed3\u6784\u7b49\u3002<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
\u521b\u5efa\u793e\u4ea4\u7f51\u7edc\u56fe<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
G.add_edges_from([<\/p>\n
    ('Alice', 'Bob'),<\/p>\n
    ('Alice', 'Charlie'),<\/p>\n
    ('Bob', 'David'),<\/p>\n
    ('Charlie', 'David'),<\/p>\n
    ('Charlie', 'Eve')<\/p>\n
])<\/p>\n
\u8ba1\u7b97\u8282\u70b9\u7684\u5ea6\u4e2d\u5fc3\u6027<\/strong><\/h2>\n
degree_centrality = nx.degree_centrality(G)<\/p>\n
print("Degree centrality:", degree_centrality)<\/p>\n
\u8bc6\u522b\u793e\u533a\u7ed3\u6784<\/strong><\/h2>\n
from networkx.algorithms import community<\/p>\n
communities = community.greedy_modularity_communities(G)<\/p>\n
print("Communities:", list(communities))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u91cc\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u793e\u4ea4\u7f51\u7edc\u56fe\uff0c\u5e76\u8ba1\u7b97\u4e86\u6bcf\u4e2a\u8282\u70b9\u7684\u5ea6\u4e2d\u5fc3\u6027\uff0c\u8bc6\u522b\u4e86\u793e\u533a\u7ed3\u6784\u3002<\/p>\n<\/p>\n
2. \u63a8\u8350\u7cfb\u7edf<\/h4>\n<\/p>\n
\u5728\u63a8\u8350\u7cfb\u7edf\u4e2d\uff0c\u56fe\u53ef\u4ee5\u7528\u4e8e\u5efa\u6a21\u7528\u6237\u548c\u7269\u54c1\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u901a\u8fc7\u56fe\u7b97\u6cd5\u8fdb\u884c\u63a8\u8350\u3002<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
\u521b\u5efa\u7528\u6237-\u7269\u54c1\u56fe<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
G.add_edges_from([<\/p>\n
    ('User1', 'ItemA'),<\/p>\n
    ('User1', 'ItemB'),<\/p>\n
    ('User2', 'ItemA'),<\/p>\n
    ('User2', 'ItemC'),<\/p>\n
    ('User3', 'ItemB'),<\/p>\n
    ('User3', 'ItemC')<\/p>\n
])<\/p>\n
\u4f7f\u7528\u8282\u70b9\u76f8\u4f3c\u6027\u8fdb\u884c\u63a8\u8350<\/strong><\/h2>\n
item_similarity = nx.jaccard_coefficient(G, [('User1', 'ItemC')])<\/p>\n
print("Item similarity for recommendation:", list(item_similarity))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8be5\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6784\u9020\u4e86\u4e00\u4e2a\u7528\u6237-\u7269\u54c1\u56fe\uff0c\u5e76\u4f7f\u7528Jaccard\u7cfb\u6570\u8ba1\u7b97\u76f8\u4f3c\u6027\u8fdb\u884c\u63a8\u8350\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u6784\u9020\u56fe\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u975e\u5e38\u591a\u6837\u5316\u3002\u901a\u8fc7NetworkX\u53ef\u4ee5\u5b9e\u73b0\u590d\u6742\u7f51\u7edc\u7684\u6784\u5efa\u4e0e\u5206\u6790\uff0cMatplotlib\u53ef\u4ee5\u7528\u4e8e\u57fa\u672c\u7684\u56fe\u5f62\u5c55\u793a\uff0c\u800cGraphviz\u5219\u63d0\u4f9b\u4e86\u4e13\u4e1a\u7684\u56fe\u5f62\u53ef\u89c6\u5316\u80fd\u529b\u3002\u7ed3\u5408\u8fd9\u4e9b\u5de5\u5177\uff0c\u53ef\u4ee5\u5728\u79d1\u5b66\u7814\u7a76\u3001\u5de5\u7a0b\u5e94\u7528\u548c\u5546\u4e1a\u5206\u6790\u4e2d\u5b9e\u73b0\u4e30\u5bcc\u7684\u56fe\u5f62\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\u3002\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u7b97\u6cd5\uff0c\u5c06\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u89e3\u51b3\u7279\u5b9a\u7684\u56fe\u5f62\u95ee\u9898<\/strong>\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u6784\u5efa\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u6784\u5efa\u56fe\u7684\u65b9\u5f0f\u591a\u79cd\u591a\u6837\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u60a8\u7684\u9700\u6c42\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecNetworkX\u3001Matplotlib\u548cGraph-tool\u7b49\u3002NetworkX\u975e\u5e38\u9002\u5408\u521b\u5efa\u548c\u64cd\u4f5c\u590d\u6742\u7f51\u7edc\uff0c\u800cMatplotlib\u53ef\u4ee5\u7528\u4e8e\u53ef\u89c6\u5316\u56fe\u5f62\u3002\u60a8\u53ef\u4ee5\u6839\u636e\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u5e93\u6765\u6784\u9020\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u6216\u52a0\u6743\u56fe\u7b49\u3002<\/p>\n
Python\u4e2d\u6784\u5efa\u56fe\u7684\u6700\u4f73\u5b9e\u8df5\u662f\u4ec0\u4e48\uff1f<\/strong>
\u5728\u6784\u5efa\u56fe\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u7ed3\u6784\u7684\u9009\u62e9\u7b26\u5408\u9879\u76ee\u9700\u6c42\u81f3\u5173\u91cd\u8981\u3002\u4f8b\u5982\uff0c\u5982\u679c\u56fe\u7684\u8fb9\u548c\u8282\u70b9\u6570\u91cf\u975e\u5e38\u5e9e\u5927\uff0c\u4f7f\u7528\u90bb\u63a5\u8868\u53ef\u80fd\u66f4\u9ad8\u6548\u3002\u6b64\u5916\uff0c\u6ce8\u91cd\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u53ef\u7ef4\u62a4\u6027\uff0c\u4f7f\u7528\u6e05\u6670\u7684\u547d\u540d\u548c\u6ce8\u91ca\u53ef\u4ee5\u5e2e\u52a9\u5176\u4ed6\u5f00\u53d1\u8005\u7406\u89e3\u4ee3\u7801\u903b\u8f91\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u53ef\u89c6\u5316\u6784\u5efa\u7684\u56fe\uff1f<\/strong>
\u53ef\u89c6\u5316\u662f\u7406\u89e3\u56fe\u5f62\u7ed3\u6784\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7ed3\u5408NetworkX\u8fdb\u884c\u56fe\u5f62\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u8c03\u7528NetworkX\u7684\u7ed8\u56fe\u51fd\u6570\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u56fe\u5f62\u6e32\u67d3\u51fa\u6765\uff0c\u5e76\u901a\u8fc7\u8c03\u6574\u8282\u70b9\u548c\u8fb9\u7684\u6837\u5f0f\u6765\u589e\u5f3a\u53ef\u8bfb\u6027\u3002\u8fd8\u53ef\u4ee5\u5229\u7528\u5176\u4ed6\u53ef\u89c6\u5316\u5e93\u5982Plotly\u548cSeaborn\uff0c\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u6784\u9020\u56fe\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u6700\u5e38\u89c1\u7684\u5e93\u662fNetworkX\u3001Matplotlib\u548cGraphvi […]","protected":false},"author":3,"featured_media":936407,"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\/936404"}],"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=936404"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/936404\/revisions"}],"predecessor-version":[{"id":936408,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/936404\/revisions\/936408"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/936407"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=936404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=936404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=936404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}