python创建neo4j图库节点及节点之间的关系

系统管理员 2021-09-21 10:04 667阅读 0赞

### python创建neo4j节点及节点之间的关系 ###

*   *  （连接）打开neo4j本地服务器，python连接。
     *  （数据加载）需要创建的节点及其关系
     *  重点（neo4j与python代码演示）
     *   *  ①创建节点函数
         *  ②查询节点函数
         *  ③节点之间创建关系函数
         *  ④加载进neo4j图库
         *  ⑤neo4j图库结果展示
     *  完整代码

## （连接）打开neo4j本地服务器，python连接。 ##

![在这里插入图片描述][20200807111522128.png]  
**python连接代码**

username = ***  
    password = *** （自己设置的neo4j的密码）
    graph = Graph('http://localhost:7474',username=username,password=password)

## （数据加载）需要创建的节点及其关系 ##

为了简单演示：我是简单的创建了几个节点及关系。大家可自行更改。  
这里演示的数据如下：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70]

## 重点（neo4j与python代码演示） ##

### ①创建节点函数 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 1]

### ②查询节点函数 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 2]

### ③节点之间创建关系函数 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 3]

### ④加载进neo4j图库 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 4]

### ⑤neo4j图库结果展示 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 5]

## 完整代码 ##

下面展示一些 `内联代码片`。

import pandas as pd
    from py2neo import Node,Relationship,Graph,NodeMatcher,RelationshipMatcher
    # 创建节点
    def CreateNode(m_graph,m_label,m_attrs):
        m_n="_.name="+"\'"+m_attrs['name']+"\'"
        matcher = NodeMatcher(m_graph)
        re_value = matcher.match(m_label).where(m_n).first()
        print(re_value)
        if re_value is None:
            m_mode = Node(m_label,**m_attrs)
            n = graph.create(m_mode)
            return n 
        return None
    # 查询节点
    def MatchNode(m_graph,m_label,m_attrs):
        m_n="_.name="+"\'"+m_attrs['name']+"\'"
        matcher = NodeMatcher(m_graph)
        re_value = matcher.match(m_label).where(m_n).first()
        return re_value
    # 创建关系
    def CreateRelationship(m_graph,m_label1,m_attrs1,m_label2,m_attrs2,m_r_name):
        reValue1 = MatchNode(m_graph,m_label1,m_attrs1)
        reValue2 = MatchNode(m_graph,m_label2,m_attrs2)
        if reValue1 is None or reValue2 is None:
            return False
        m_r = Relationship(reValue1,m_r_name,reValue2)
        n = graph.create(m_r)
        return n 
    graph = Graph('http://localhost:7474',username='neo4j',password='yangZAI520')

Names = ["老师","超市","程序员","小卖部","阳仔公司"]
    action = ["传授","销售","敲","售卖","提供"]
    things = ["知识","日常用品","代码","日常用品","餐饮服务"]
    data = pd.DataFrame({"名称":Names,"字段":things,"方式":action})
    data

username = ***  
    password = *** （自己设置的neo4j的密码）
    graph = Graph('http://localhost:7474',username=username,password=password)

label1 = "Name"
    label2 = "things"
    for i,j in data.iterrows():
        # 名称
        attr1 = { "name":j.名称}
        CreateNode(graph,label1,attr1)
        # 产品
        attr2 = { "name":j.字段}
        CreateNode(graph,label2,attr2)
    
        m_r_name = j.方式
        reValue = CreateRelationship(graph,label1,attr1,label2,attr2,m_r_name)
        print(reValue)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 6]

[20200807111522128.png]: /images/20210920/99438917d9e54692b4a394640399d338.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70]: /images/20210920/b02d16abe2c543d488e3fc52085cc8f6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 1]: /images/20210920/6fb32503bf424b3682a11c0f342ce5d3.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 2]: /images/20210920/169e4ae7314147188402d31e64683eeb.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 3]: /images/20210920/077bfde14e094fc9b21ed1f222cad14a.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 4]: /images/20210920/72fb4b2fcfd944d2ab15a4d4c3296c89.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 5]: /images/20210920/19090612bac54ccda8f09ccb07e82bf9.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMjI1MDQ3_size_16_color_FFFFFF_t_70 6]: /images/20210920/a6451a0057324ccf88c9b1158fc3b728.png