py2neo框架学习全网最详细教程

py2neo框架学习

1.公式模式是语法范式

2.代码模式是实例，可按顺序复制执行
3.该教程全部使用py2neo的内置方法，不使用run运行neo4j
4.涉及所有可能的节点、节点类型、属性，关系，关系类型、属性的增删改查

1.连接图模型

import py2neo
from py2neo import Graph, Node, Relationship, RelationshipMatcher, NodeMatcher
graph = Graph("http://localhost:7474",user ="",password='')
# 用pycharm打开neo4j工作台连接，默认的端口号就是7474，输入自己的账号密码

2.节点规则

$$节点=Node(节点标签,属性名1=属性值1,属性名2=属性值2)$$

node1 = Node('Person', name='tony', age=25, weight=55)
node2 = Node('Person', name='tom', age=23, weight=50, hight=170)
node3 = Node('Person','male','Suspect', name='peter', age=22, weight=48, hight=165)
node4 = Node('Person','female',name='lisa', age=21, weight=46)
node5 = Node('Person')

3.关系规则

$$关系=Relationship(节点1,关系类型,节点2,属性1=属性值1,属性2=属性值2)$$

relation1 = Relationship(node1, 'KNOW', node2)
relation2 = Relationship(node1, 'KNOW', node3, strong=85)
relation3 = Relationship(node1, 'COUPLE', node4, value=90)
relation4 = Relationship(node4, 'MATE', node2, strong=40)
relation5 = Relationship(node2, 'KNOW', node3, strong=60)

4.语法

i 节点添加

$$graph.create(节点)$$

graph.create(node1)
graph.create(node2)
graph.create(node3)
graph.create(node4)
graph.create(node5)

ii 关系添加

$$graph.create(关系)$$

graph.create(relation1)
graph.create(relation2)
graph.create(relation3)
graph.create(relation4)
graph.create(relation5)

iii 节点修改

a 增加/修改属性

$$\begin{gathered} 节点[属性名]=属性值 \\ graph.push(节点) \end{gathered}$$

node1['hight']=190
node1['age']=26
graph.push(node1)

b 删除属性

$$\begin{gathered} del节点[属性名] \\ graph.push(节点) \end{gathered}$$

del node1['age']
graph.push(node1)

c 增加/移除标签

$$\begin{gathered} 节点.add\_label(标签1) \\ graph.push(node1) \\ 节点.remove\_label(标签1) \\ graph.push(node1) \end{gathered}$$

node1.add_label('male')
graph.push(node1)
node3.remove_label('Suspect')
graph.push(node3)

d 批量增加节点标签

$$\begin{gathered} 节点.update\_labels([标签1,标签2]) \\ graph.push(节点) \end{gathered}$$

node2.update_labels(['male','Suspect'])
graph.push(node2)

e 删除节点

$$graph.delete(节点)\#把关系一同删除$$

graph.delete(node5)

iv 关系修改

a 增加/修改属性

$$\begin{gathered} 关系[属性名]=属性值 \\ graph.push(关系) \end{gathered}$$

relation2['strong']=90
graph.push(relation2)

b 删除属性

$$\begin{gathered} del关系[属性名] \\ graph.push(关系) \end{gathered}$$

del relation3['value']
graph.push(relation2)

c 删除关系

$$\begin{gathered} relation=RelationshipMatcher(graph) \\ 关系组=relation.match(nodes=[节点1,节点2],r\_type=关系类型,limit=None) \\ for关系in关系组: \\ graph.seperate(关系) \end{gathered}$$

relation_matcher = RelationshipMatcher(graph) # 生成关系匹配器
#；例子1 删除所有couple关系
relations = relation_matcher.match(r_type='COUPLE')  # 可迭代对象
for relation in relations:
    graph.separate(relation)
# 例子2 删除node1和node2的关系
relations = relation_matcher.match([node1,node2])  # 可迭代对象
for relation in relations:
    graph.separate(relation)

d 删除关系与两端节点

$$graph.delete(关系)$$

graph.delete(relation2) # 删除tony和peter关系和节点
# 这个就不示范了

v 查询

a 节点查询

(1) 根据特定属性值查询，结果为字典构成的可迭代：
$$\begin{gathered} node\_matcher=NodeMatcher(graph) \\ node\_matcher.match(节点类型1,节点类型2,属性1=属性值1,属性2=属性值2) \end{gathered}$$

node_matcher = NodeMatcher(graph)  # 节点匹配器
a = node_matcher.match('female',age=21)  # 提取满足属性值的节点

(2) 根据复杂规则遍历查找特定条件的节点，结果为字典构成的可迭代：
$$\begin{gathered} nodes=node\_matcher.match() \\ fornodeinnodes: \\ 规则 \end{gathered}$$

nodes = node_matcher.match()   # 直接提取所有节点
for node in nodes:
    if node['weight'] < 50:
        print(node)

b 关系查询

(1) 根据关系类型、属性

根据关系属性特定值匹配，匹配结果为字典构成的可迭代
$$\begin{gathered} relation\_matcher=RelationshipMatcher(graph) \\ 关系=relation\_matcher.match(r\_type=关系类型,属性1=属性值1) \end{gathered}$$

relation_matcher = RelationshipMatcher(graph)  # 关系匹配器
relations = relation_matcher.match(r_type='KNOW', strong=60) # 提取特别属性的关系

根据复杂规则遍历查找特定条件关系，匹配结果为字典构成的可迭代
$$\begin{gathered} relations=relation\_matcher.match() \\ forrelationinrelations: \\ 规则 \end{gathered}$$

relations = relation_matcher.match()  # 提取所有关系
for relation in relations:
    if relation['strong'] > 20:
        print(relation)

(2) 根据节点、关系类型

$$graph.match(nodes=[节点1,节点2],r\_type=关系类型,limit=None)$$

graph.match(nodes=[node1, node3], r_type='KNOW', limit=None)