{"id":987862,"date":"2024-12-27T07:57:16","date_gmt":"2024-12-26T23:57:16","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/987862.html"},"modified":"2024-12-27T07:57:19","modified_gmt":"2024-12-26T23:57:19","slug":"python%e5%a6%82%e4%bd%95%e6%9e%84%e5%bb%ba%e4%ba%ba%e7%89%a9%e5%85%b3%e7%b3%bb","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/987862.html","title":{"rendered":"python\u5982\u4f55\u6784\u5efa\u4eba\u7269\u5173\u7cfb"},"content":{"rendered":"

\"python\u5982\u4f55\u6784\u5efa\u4eba\u7269\u5173\u7cfb\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u56fe\u6570\u636e\u7ed3\u6784\u3001\u7f51\u7edc\u5206\u6790\u5e93\uff08\u5982NetworkX\uff09\u548c\u6570\u636e\u5e93\uff08\u5982Neo4j\uff09\u6784\u5efa\u4eba\u7269\u5173\u7cfb\u3002\u5176\u4e2d\uff0c\u4f7f\u7528NetworkX\u5e93\u8fdb\u884c\u56fe\u7684\u6784\u5efa\u4e0e\u5206\u6790\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u3002<\/strong><\/p>\n<\/p>\n

Python\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u6784\u5efa\u548c\u5206\u6790\u4eba\u7269\u5173\u7cfb\uff0c\u4e3b\u8981\u5305\u62ec\u4f7f\u7528\u56fe\u6570\u636e\u7ed3\u6784\u3001\u7f51\u7edc\u5206\u6790\u5e93\u548c\u56fe\u6570\u636e\u5e93\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528NetworkX\u5e93\u6765\u5b9e\u73b0\u4eba\u7269\u5173\u7cfb\u7684\u6784\u5efa\u4e0e\u5206\u6790\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NETWORKX\u5e93\u6784\u5efa\u4eba\u7269\u5173\u7cfb<\/h3>\n<\/p>\n

NetworkX\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u7528\u4e8e\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u7814\u7a76\u56fe\u7ed3\u6784\uff08\u7f51\u7edc\uff09\u3002\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8f7b\u677e\u6784\u5efa\u4eba\u7269\u5173\u7cfb\u56fe\uff0c\u5e76\u8fdb\u884c\u5404\u79cd\u590d\u6742\u7684\u7f51\u7edc\u5206\u6790\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5NetworkX<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4f7f\u7528NetworkX\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7pip\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install networkx<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u56fe\u5bf9\u8c61<\/h4>\n<\/p>\n
NetworkX\u63d0\u4f9b\u4e86\u591a\u79cd\u56fe\u7c7b\u578b\uff0c\u5982\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u548c\u591a\u91cd\u56fe\u3002\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u56fe\u7c7b\u578b\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
DG = nx.DiGraph()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6dfb\u52a0\u8282\u70b9\u548c\u8fb9<\/h4>\n<\/p>\n
\u8282\u70b9\u548c\u8fb9\u662f\u56fe\u7684\u57fa\u672c\u7ec4\u6210\u90e8\u5206\u3002\u8282\u70b9\u4ee3\u8868\u4eba\u7269\uff0c\u800c\u8fb9\u4ee3\u8868\u4eba\u7269\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u5355\u4e2a\u8282\u70b9<\/p>\n
G.add_node("Alice")<\/p>\n
\u6dfb\u52a0\u591a\u4e2a\u8282\u70b9<\/strong><\/h2>\n
G.add_nodes_from(["Bob", "Charlie", "David"])<\/p>\n
\u6dfb\u52a0\u5355\u6761\u8fb9<\/strong><\/h2>\n
G.add_edge("Alice", "Bob")<\/p>\n
\u6dfb\u52a0\u591a\u6761\u8fb9<\/strong><\/h2>\n
G.add_edges_from([("Bob", "Charlie"), ("Charlie", "David")])<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u8bbe\u7f6e\u8282\u70b9\u548c\u8fb9\u7684\u5c5e\u6027<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4e3a\u8282\u70b9\u548c\u8fb9\u8bbe\u7f6e\u5c5e\u6027\uff0c\u4ee5\u5b58\u50a8\u989d\u5916\u7684\u4fe1\u606f\uff0c\u4f8b\u5982\u5e74\u9f84\u3001\u5173\u7cfb\u7c7b\u578b\u7b49\u3002<\/p>\n<\/p>\n
# \u8bbe\u7f6e\u8282\u70b9\u5c5e\u6027<\/p>\n
G.nodes["Alice"]["age"] = 30<\/p>\n
\u8bbe\u7f6e\u8fb9\u5c5e\u6027<\/strong><\/h2>\n
G.edges["Alice", "Bob"]["relationship"] = "friend"<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u4eba\u7269\u5173\u7cfb<\/h3>\n<\/p>\n
\u5728\u6784\u5efa\u597d\u4eba\u7269\u5173\u7cfb\u56fe\u540e\uff0c\u53ef\u4ee5\u8fdb\u884c\u5404\u79cd\u5206\u6790\u548c\u53ef\u89c6\u5316\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1. \u57fa\u672c\u5206\u6790<\/h4>\n<\/p>\n
NetworkX\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5206\u6790\u5de5\u5177\uff0c\u53ef\u4ee5\u8ba1\u7b97\u56fe\u7684\u5404\u79cd\u5c5e\u6027\uff0c\u5982\u8282\u70b9\u5ea6\u3001\u6700\u77ed\u8def\u5f84\u7b49\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u8282\u70b9\u7684\u5ea6\uff08\u4e0e\u5176\u4ed6\u8282\u70b9\u8fde\u63a5\u7684\u6570\u91cf\uff09<\/p>\n
degree = G.degree("Alice")<\/p>\n
\u8ba1\u7b97\u4e24\u4e2a\u8282\u70b9\u4e4b\u95f4\u7684\u6700\u77ed\u8def\u5f84<\/strong><\/h2>\n
shortest_path = nx.shortest_path(G, source="Alice", target="David")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u4f7f\u7528Matplotlib\u5e93\u53ef\u4ee5\u5bf9\u4eba\u7269\u5173\u7cfb\u56fe\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
nx.draw(G, with_labels=True)<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528NEO4J\u8fdb\u884c\u9ad8\u7ea7\u5206\u6790<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u9700\u6c42\uff0c\u5c24\u5176\u662f\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\uff0c\u53ef\u4ee5\u4f7f\u7528Neo4j\uff0c\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u56fe\u6570\u636e\u5e93\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u548c\u914d\u7f6eNeo4j<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u4e0b\u8f7d\u5e76\u5b89\u88c5Neo4j\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u901a\u8fc7\u6d4f\u89c8\u5668\u8bbf\u95ee\u5176Web\u754c\u9762\u8fdb\u884c\u914d\u7f6e\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528Py2neo\u5e93\u8fde\u63a5Neo4j<\/h4>\n<\/p>\n
Py2neo\u662fPython\u4e0eNeo4j\u4ea4\u4e92\u7684\u5e93\u3002<\/p>\n<\/p>\n
pip install py2neo<\/p>\n
<\/code><\/pre>\n<\/p>\n
from py2neo import Graph<\/p>\n
\u8fde\u63a5\u5230Neo4j\u6570\u636e\u5e93<\/strong><\/h2>\n
graph = Graph("http:\/\/localhost:7474", auth=("username", "password"))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6784\u5efa\u548c\u67e5\u8be2\u56fe<\/h4>\n<\/p>\n
\u4f7f\u7528Cypher\u67e5\u8be2\u8bed\u8a00\u5728Neo4j\u4e2d\u6784\u5efa\u548c\u67e5\u8be2\u56fe\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u8282\u70b9\u548c\u5173\u7cfb<\/p>\n
graph.run("CREATE (a:Person {name: 'Alice'})-[:FRIEND]->(b:Person {name: 'Bob'})")<\/p>\n
\u67e5\u8be2\u8282\u70b9<\/strong><\/h2>\n
result = graph.run("MATCH (a:Person)-[:FRIEND]->(b:Person) RETURN a.name, b.name")<\/p>\n
for record in result:<\/p>\n
    print(record)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7ed3\u5408PANDAS\u8fdb\u884c\u6570\u636e\u5904\u7406<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u7ed3\u5408Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u662f\u975e\u5e38\u6709\u6548\u7684\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528Pandas\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528Pandas\u8bfb\u53d6CSV\u3001Excel\u7b49\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\uff0c\u5feb\u901f\u5c06\u5176\u8f6c\u5316\u4e3aDataFrame\u8fdb\u884c\u5904\u7406\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv("people_relationships.csv")<\/p>\n
\u6253\u5370\u6570\u636e<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6570\u636e\u6e05\u6d17\u548c\u8f6c\u6362<\/h4>\n<\/p>\n
\u5728\u6784\u5efa\u4eba\u7269\u5173\u7cfb\u4e4b\u524d\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u8f6c\u6362\uff0c\u4ee5\u4fbf\u9002\u5e94\u56fe\u7ed3\u6784\u3002<\/p>\n<\/p>\n
# \u53bb\u9664\u91cd\u590d\u6570\u636e<\/p>\n
df.drop_duplicates(inplace=True)<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna("Unknown", inplace=True)<\/p>\n
\u5c06DataFrame\u8f6c\u6362\u4e3aNetworkX\u56fe<\/strong><\/h2>\n
G = nx.from_pandas_edgelist(df, source="PersonA", target="PersonB", edge_attr=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3\u4e0e\u6269\u5c55<\/h3>\n<\/p>\n
\u901a\u8fc7Python\u7684\u56fe\u6570\u636e\u7ed3\u6784\u548c\u5206\u6790\u5e93\uff0c\u5982NetworkX\u548cNeo4j\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u6784\u5efa\u548c\u5206\u6790\u590d\u6742\u7684\u4eba\u7269\u5173\u7cfb\u3002\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u9700\u6c42\uff0c\u53ef\u4ee5\u7ed3\u5408Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u6e05\u6d17\uff0c\u6216\u4f7f\u7528Neo4j\u8fdb\u884c\u5927\u89c4\u6a21\u56fe\u6570\u636e\u7684\u5b58\u50a8\u4e0e\u67e5\u8be2\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u901a\u8fc7\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u5bf9\u56fe\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u5206\u6790\uff0c\u5982\u793e\u533a\u53d1\u73b0\u3001\u8282\u70b9\u5206\u7c7b\u7b49\u3002<\/p>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u9002\u7528\u4e8e\u4eba\u7269\u5173\u7cfb\u7684\u6784\u5efa\u4e0e\u5206\u6790\uff0c\u8fd8\u53ef\u4ee5\u6269\u5c55\u5230\u5404\u79cd\u7f51\u7edc\u5173\u7cfb\u7684\u7814\u7a76\uff0c\u5982\u793e\u4ea4\u7f51\u7edc\u3001\u4ea4\u901a\u7f51\u7edc\u7b49\u3002\u901a\u8fc7\u4e0d\u65ad\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u638c\u63e1\u56fe\u6570\u636e\u7ed3\u6784\u7684\u5206\u6790\u6280\u5de7\uff0c\u4e3a\u6570\u636e\u79d1\u5b66\u548c\u4eba\u5de5\u667a\u80fd<\/a>\u9886\u57df\u7684\u7814\u7a76\u63d0\u4f9b\u6709\u529b\u652f\u6301\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u4eba\u7269\u5173\u7cfb\u7684\u6570\u636e\u7ed3\u6784\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5b57\u5178\u3001\u5217\u8868\u6216\u7c7b\u6765\u6784\u5efa\u4eba\u7269\u5173\u7cfb\u7684\u6570\u636e\u7ed3\u6784\u3002\u4f8b\u5982\uff0c\u5b57\u5178\u53ef\u4ee5\u7528\u6765\u8868\u793a\u4eba\u7269\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u952e\u4e3a\u4eba\u7269\u7684\u540d\u5b57\uff0c\u503c\u4e3a\u4e0e\u5176\u76f8\u5173\u8054\u7684\u5176\u4ed6\u4eba\u7269\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u4f60\u53ef\u4ee5\u7075\u6d3b\u5730\u589e\u52a0\u6216\u4fee\u6539\u4eba\u7269\u53ca\u5176\u5173\u7cfb\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5b9e\u73b0\u4eba\u7269\u5173\u7cfb\u7684\u56fe\u5f62\u5316\u5c55\u793a\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528\u7f51\u7edc\u56fe\u5f62\u5e93\u5982NetworkX\u548cMatplotlib\u6765\u53ef\u89c6\u5316\u4eba\u7269\u5173\u7cfb\u3002\u8fd9\u4e9b\u5e93\u5141\u8bb8\u4f60\u521b\u5efa\u56fe\u5f62\u8868\u793a\uff0c\u5c55\u793a\u4eba\u7269\u4e4b\u95f4\u7684\u8054\u7cfb\u548c\u4ea4\u4e92\uff0c\u901a\u8fc7\u8282\u70b9\u548c\u8fb9\u7684\u65b9\u5f0f\u6e05\u6670\u5730\u8868\u8fbe\u51fa\u5173\u7cfb\u7f51\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u590d\u6742\u7684\u4eba\u7269\u5173\u7cfb\uff0c\u4f8b\u5982\u591a\u5c42\u6b21\u6216\u591a\u7ef4\u5ea6\u7684\u5173\u7cfb\uff1f<\/strong>
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