Neo4j学习笔记1：增删改查

目录

• 安装
• 练习节点和关系结构
• 连接数据库
• 添加节点
• • [1. Cypher](#1. Cypher)
  • [2. Py2neo](#2. Py2neo)
• 添加关系
• • [1. Cypher](#1. Cypher)
  • [2. Py2neo](#2. Py2neo)
• 批量添加
• • [1. Cypher](#1. Cypher)
  • [2. Py2neo](#2. Py2neo)
• 查询
• • [1. Cypher](#1. Cypher)
  • [2. Py2neo](#2. Py2neo)
• 更新
• • [1. Cypher](#1. Cypher)
  • [2. Py2neo](#2. Py2neo)
• 删除
• • [1. Cypher](#1. Cypher)
  • [2. Py2neo](#2. Py2neo)

安装

这边直接下载了Neo4j Desktop+安装py2neo，安装后就可直接使用

pip install py2neo

练习节点和关系结构

类型	字段1	字段2	字段3	字段4
node	id	name	type	industry
link	source	relation	target	-

连接数据库

Desktop直接打开，用Neo4j Browser打开就行，很方便

下面这个是使用Py2neo库链接数据库

from py2neo import Graph, Node, Relationship

# 连接到Neo4j
graph = Graph("bolt://localhost:7687", auth=("neo4j", "password"))

添加节点

1. Cypher

在Neo4j Browser中可以直接输入命令

CREATE (n:Node {id:1, name:'Alice', type:'Person', industry:'software'})

2. Py2neo

# 创建节点
a = Node("Node", id=1, name="Alice", type="Person", industry="software")
graph.create(a)
b = Node("Node", id=2, name="Bom", type="Person", industry="hardware")
graph.create(b)

添加关系

1. Cypher

MATCH (a:Node),(b:Node)
WHERE a.name = 'Alice' AND b.name = 'Bom'
CREATE (a)-[r:KNOWS] -> (b)
RETURN r

2. Py2neo

这里的a,b是承接上一部分的添加节点的代码

# 创建关系
rel = Relationship(a, "KNOWS", b)
graph.create(rel)

批量添加

本人测试下来，这两种方法，后者速度更快

1. Cypher

这边的文件得放在neo4j文件夹下的import文件夹里

node

LOAD CSV WITH HEADERS FROM 'file:///nodes.csv' AS row
CREATE (n:Node {id: row.id, name: row.name, type: row.type, industry: row.industry});

relation

LOAD CSV WITH HEADERS FROM 'file:///links.csv' AS row
MATCH (source:Node {id: row.source}), (target:Node {id: row.target})
MERGE (source)-[r:RELATIONSHIP {type: row.relation}]->(target);

2. Py2neo

node

import pandas as pd
from tqdm import tqdm

# 读取CSV文件
df = pd.read_csv("path/to/your/nodes.csv")

# 使用tqdm显示进度条
for index, row in tqdm(df.iterrows(), total=df.shape[0]):
    # 创建节点，这里假设CSV有'name', 'type', 'industry'列
    node = Node("Node", id=row['id'], name=row['name'], type=row['type'], industry=row['industry'])
    # 将节点添加到图中
    graph.create(node)

relation

# 读取关系CSV文件
df_relations = pd.read_csv("path/to/your/relations.csv")

for index, row in df_relations.iterrows():
    source_node = matcher.match("Node", id=row['source']).first()
    target_node = matcher.match("Node", id=row['target']).first()
    if source_node and target_node:
        rel = Relationship(source_node, row['relation'], target_node)
        graph.create(rel)

查询

1. Cypher

MATCH (n:Node) 
WHERE n.industry = 'software' 
RETURN n

2. Py2neo

# 查询所有类型为"Person"的节点
query = "MATCH (n:Node) WHERE n.industry = 'software' RETURN n"
nodes = graph.run(query)
for node in nodes:
    print(node)

更新

1. Cypher

MATCH (n:Node {name: 'Alice'})
SET n.industry = 'IT'
RETURN n

2. Py2neo

# 查找名为Alice的节点并更新其行业
alice = matcher.match("Node", name="Alice").first()
if alice:
    alice['industry'] = 'IT'
    graph.push(alice)

删除

1. Cypher

MATCH (n:Node)-[r:KNOWS]->()
DELETE r

2. Py2neo

方法一：查询节点后删除

# 删除名为Bom的节点
bom = graph.nodes.match("Node", name="Bom").first()
graph.delete(bom)

方法二：运行CYPHER语句

#删除特定属性的节点，例如删除所有industry为"hardware"的节点
query = "MATCH (n:Node {industry: 'hardware'}) DELETE n"
graph.run(query)

# 删除特定类型的关系，例如删除所有KNOWS关系
query = "MATCH ()-[r:KNOWS]-() DELETE r"
graph.run(query)

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