py2neo查询节点和关系

记录一下使用py2neo进行查询，查询某演员出演的电影，返回三元组，可转为json用于前端显示，如下(以下在jupyter notebook中进行操作)：

一.查询数据

from py2neo import Graph,Node,Relationship,NodeMatcher,RelationshipMatcher,Path

class Movie():
    '''
    电影
    '''
    def __init__(self, id, name):
        self.id = id
        self.name = name

class Person():
    '''
    人
    '''
    def __init__(self, id, name):
        self.id = id
        self.name = name

def initNeo4j():
    '''
    初始化neo4j
    :return:
    '''
    graph = Graph(
        "http://127.0.0.1:7474",
        username="neo4j",
        password="123"
    )
    return graph

def get_person_actorof_movie(graph, p_id) -> list:
    '''
    查找演员参演的电影，并返回三元组
    :param graph:
    :param p_id:
    :return:
    '''
    data = graph.run("match path=(m:Movie)-[:ACTOR_OF]->(p:Person) "
                     "where p.id = $id return relationships(path) as relationships",
                     id = p_id)

    movieDict = {} #电影
    personDict = {} #演员
    person_actorof_movie = {} # 三元组
    while data.forward():
        cursor = data.current
        for relation in cursor['relationships']:
            source_node = relation.start_node
            target_node = relation.end_node
            source_node_id = source_node['id']
            target_node_id = target_node['id']
            relation_type = list(relation.types())[0]
            source_node_label = str(source_node.labels).strip(":")
            target_node_label = str(target_node.labels).strip(":")
            source_node_name = source_node['title']
            target_node_name = target_node['name']
            # 存储三元组关系
            if source_node_id in person_actorof_movie.keys():
                target_dict = person_actorof_movie.get(source_node_id)
                target_dict.setdefault(target_node_id,relation_type)
            else:
                target_dict = {target_node_id:relation_type}
                person_actorof_movie[source_node_id] = target_dict
            # 存储节点
            if ("Movie" == source_node_label) and (source_node_id not in movieDict.keys()):
                movie = Movie(source_node_id, source_node_name)
                movieDict[source_node_id] = movie
            if ("Person" == target_node_label) and (target_node_id not in personDict.keys()):
                person = Person(target_node_id, target_node_name)
                personDict[target_node_id] = person
           
    json_list = []
    for source_key, value in person_actorof_movie.items():
        for target_key, rel_type in value.items():
            person_movie_dict = {}
            person_movie_dict['source'] = movieDict.get(source_key).name
            person_movie_dict['rel_type'] = rel_type
            person_movie_dict['target'] = personDict.get(target_key).name
            json_list.append(person_movie_dict)
                
    print(len(json_list))
    for i in range(len(json_list)):
        print("{}-[{}]->{}".format(json_list[i]['source'], json_list[i]['rel_type'], json_list[i]['target']))

    return json_list

if __name__ == '__main__':
    graph = initNeo4j()
    json_list = get_person_actorof_movie(graph, 'e425fdb6bade4fc19d09754d1ae4670f')

二.展示出来

from pyecharts import options as opts
from pyecharts.charts import Graph

#明星主演的电影
movie_person_dict = set()
nodes = []
links = []
for i in range(len(json_list)):
    if json_list[i]['source'] not in movie_person_dict:
        movie_person_dict.add(json_list[i]['source'])
        nodes.append({'name':json_list[i]['source'], "symbolSize": 10, 'draggable': 'true'})
    if json_list[i]['target'] not in movie_person_dict:
        movie_person_dict.add(json_list[i]['target'])
        nodes.append({'name':json_list[i]['target'], "symbolSize": 10, 'draggable': 'true'})
    links.append({'source':json_list[i]['source'], 'target':json_list[i]['target']})
    
graph = Graph(init_opts=opts.InitOpts(width='1000px', height='800px'))
graph.add("", nodes, links, repulsion=8000, edge_symbol=['','arrow'])
graph.set_global_opts(title_opts=opts.TitleOpts(title="明星主演的电影"))

graph.render_notebook()

三.如下图