來源：https://towardsdatascience.com/neo4j-cypher-python-7a919a372be7

有一個問題，如何用python包去調(diào)用neo4j的查詢及算法包呢？

從本質(zhì)上來思考問題，python只需要與Noe4j的接口打通就行了，將所有的執(zhí)行命令傳入給neo4j就行。

session = self.__driver.session(database=db) if db is not None else self.__driver.session()
response = list(session.run(query))

只要將query語句傳遞過去就行了，調(diào)用的命令就一個session.run。

from neo4j import GraphDatabase class Neo4jConnection:def __init__(self, uri, user, pwd):self.__uri = uriself.__user = userself.__pwd = pwdself.__driver = Nonetry:self.__driver = GraphDatabase.driver(self.__uri, auth=(self.__user, self.__pwd))except Exception as e:print("Failed to create the driver:", e)def close(self):if self.__driver is not None:self.__driver.close()def query(self, query, db=None):assert self.__driver is not None, "Driver not initialized!"session = Noneresponse = Nonetry: session = self.__driver.session(database=db) if db is not None else self.__driver.session() response = list(session.run(query))except Exception as e:print("Query failed:", e)finally: if session is not None:session.close()return response

還可以直接將整個csv文檔導(dǎo)入到neo4j數(shù)據(jù)庫，前面還擔(dān)心用Python讀取csv文檔，然后再存儲到數(shù)據(jù)庫中，速度 會日常慢，相比較于直接導(dǎo)入csv文檔會慢很多，隨著數(shù)據(jù)增多，差異會越來越明顯。

query_string = ''' USING PERIODIC COMMIT 500 LOAD CSV WITH HEADERS FROM 'https://raw.githubusercontent.com/ngshya/datasets/master/cora/cora_content.csv' AS line FIELDTERMINATOR ',' CREATE (:Paper {id: line.paper_id, class: line.label}) ''' conn.query(query_string, db='coradb')

調(diào)用算法包

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query_string = ''' CALL gds.graph.create('coraGraph','Paper','CITES' ) ''' conn.query(query_string, db='coradb')query_string = ''' CALL gds.pageRank.write('coraGraph', {writeProperty: 'pagerank' }) YIELD nodePropertiesWritten, ranIterations ''' conn.query(query_string, db='coradb')query_string = ''' CALL gds.betweenness.write('coraGraph', { writeProperty: 'betweenness' }) YIELD minimumScore, maximumScore, scoreSum, nodePropertiesWritten ''' conn.query(query_string, db='coradb')from pandas import DataFrame query_string = ''' MATCH (p:Paper) RETURN DISTINCT p.id, p.class, p.pagerank, p.betweenness ''' dtf_data = DataFrame([dict(_) for _ in conn.query(query_string, db='coradb')]) dtf_data.sample(10)

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