Python怎样操作Neo4j图数据库？py2neo

py2neo

py2neo

pip install py2neo

from py2neo import Graph, Node, Relationship, Subgraph
from py2neo.matching import NodeMatcher

# 连接到Neo4j数据库
# 请替换为你的Neo4j连接信息
try:
    graph = Graph("bolt://localhost:7687", auth=("neo4j", "your_password"))
    print("成功连接到Neo4j数据库。")
except Exception as e:
    print(f"连接Neo4j失败: {e}")
    # 在实际应用中，这里可能需要更复杂的错误处理
    exit()

# 清空数据库，仅用于演示，请勿在生产环境随意执行！
graph.run("MATCH (n) DETACH DELETE n")
print("数据库已清空（仅用于演示）。")

# 1. 创建节点 (Nodes)
# py2neo允许我们像创建Python对象一样创建图节点
alice = Node("Person", name="Alice", age=30, city="New York")
bob = Node("Person", name="Bob", age=25, city="London")
charlie = Node("Person", name="Charlie", age=35, city="New York")
project_x = Node("Project", name="Project X", status="Active")
product_a = Node("Product", name="Product A", version="1.0")

# 使用graph.create()将节点保存到数据库
# 这里的create方法非常方便，它可以接受单个节点、关系或一个Subgraph对象
graph.create(alice)
graph.create(bob)
graph.create(charlie)
graph.create(project_x)
graph.create(product_a)
print("\n创建了多个节点。")

# 2. 创建关系 (Relationships)
# 关系也是一等公民，可以带有属性
rel_alice_knows_bob = Relationship(alice, "KNOWS", bob, since=2018)
rel_bob_friends_charlie = Relationship(bob, "FRIENDS_WITH", charlie, mutual_interest="Coding")
rel_alice_works_on_project = Relationship(alice, "WORKS_ON", project_x, role="Lead Developer")
rel_bob_works_on_project = Relationship(bob, "WORKS_ON", project_x, role="Developer")
rel_project_uses_product = Relationship(project_x, "USES", product_a)

# 同样使用graph.create()保存关系
graph.create(rel_alice_knows_bob)
graph.create(rel_bob_friends_charlie)
graph.create(rel_alice_works_on_project)
graph.create(rel_bob_works_on_project)
graph.create(rel_project_uses_product)
print("创建了多个关系。")

# 3. 查询数据 (Querying Data)
# py2neo最强大的地方在于它能直接执行Cypher查询，并以Python友好的方式返回结果
print("\n查询数据:")

# 查找所有Person节点及其名称和年龄
query_persons = "MATCH (p:Person) RETURN p.name, p.age"
result_persons = graph.run(query_persons).data()
print("所有人员信息:", result_persons)

# 查找Alice认识的人
query_alice_friends = "MATCH (alice:Person)-[:KNOWS]->(friend) WHERE alice.name = 'Alice' RETURN friend.name AS FriendName"
result_alice_friends = graph.run(query_alice_friends).data()
print("Alice认识的人:", result_alice_friends)

# 查找在New York的人以及他们参与的项目
query_ny_projects = """
MATCH (p:Person {city: 'New York'})-[:WORKS_ON]->(proj:Project)
RETURN p.name AS PersonName, proj.name AS ProjectName
"""
result_ny_projects = graph.run(query_ny_projects).data()
print("New York的人及其项目:", result_ny_projects)

# py2neo也提供了NodeMatcher来通过属性匹配节点，虽然底层还是Cypher
matcher = NodeMatcher(graph)
found_alice = matcher.match("Person", name="Alice").first()
if found_alice:
    print(f"通过Matcher找到Alice: {found_alice['name']}, {found_alice['age']}")

# 4. 更新数据 (Updating Data)
# 更新通常通过Cypher的SET子句完成
print("\n更新数据:")
graph.run("MATCH (p:Person {name: 'Alice'}) SET p.age = 31, p.status = 'Active'")
print("Alice的年龄和状态已更新。")

# 更新关系属性
graph.run("""
MATCH (a:Person {name: 'Alice'})-[r:WORKS_ON]->(p:Project {name: 'Project X'})
SET r.hours_per_week = 40
""")
print("Alice在项目X上的工作时长已更新。")

# 验证更新
updated_alice_info = graph.run("MATCH (p:Person {name: 'Alice'}) RETURN p.name, p.age, p.status").data()
print("更新后的Alice信息:", updated_alice_info)

# 5. 删除数据 (Deleting Data)
# 删除节点或关系，同样使用Cypher的DELETE或DETACH DELETE
print("\n删除数据:")
# 删除Charlie节点及其所有关系
graph.run("MATCH (c:Person {name: 'Charlie'}) DETACH DELETE c")
print("Charlie节点及其相关关系已删除。")

# 验证删除
remaining_persons = graph.run("MATCH (p:Person) RETURN p.name").data()
print("删除Charlie后剩余的人员:", remaining_persons)

py2neo

py2neo

graph.create(node)

graph.run("CREATE (n:Label {prop: 'value'})")

py2neo

graph.create()

Node

Relationship

Subgraph

graph.run()

CREATE

MATCH (n) RETURN n

CREATE INDEX ON :Person(name)

EXPLAIN

PROFILE

with graph.begin() as tx:

CREATE

run

py2neo

neo4j-driver

py2neo

Node

Relationship

graph.create()

py2neo

neo4j-driver

neo4j-driver

py2neo

neo4j-driver

py2neo

py2neo

graph.run()