python将csv数据导入neo4j

参考链接：https://github.com/jm199504/Financial-Knowledge-Graphs/tree/master

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from pandas import DataFrame
from py2neo import Graph,Node,Relationship,NodeMatcher
import pandas as pd
import numpy as np
import os
# 连接Neo4j数据库
from py2neo import Graph, Node, Relationship, walk, NodeMatcher, RelationshipMatcher
import pandas as pd
import json
# 连接数据库 输入地址、用户名、密码
from py2neo import Graph

# 使用包含用户名和密码的 URI 连接到数据库
uri = "http://neo4j:neo4j@localhost:7474"
graph = Graph(uri)

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a = Node('Person',name='Tom')
graph.create(a)
b = Node('Person',name='Bob')
graph.create(b)

# 创建关系例子
r = Relationship(a,'KNOWS',b)
graph.create(r)

# 读取节点信息
node = DataFrame(graph.run('MATCH (n:`Person`) RETURN n LIMIT 25'))
# print(node)

# 读取关系信息
relation = DataFrame(graph.run('MATCH (n:`Person`)-[r]->(m:`Person`) return n,m,type(r)'))
# print(relation)

# 删除所有节点
graph.run('MATCH (n) OPTIONAL MATCH (n)-[r]-() DELETE n,r')

(No data)

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# 读取数据
stock = pd.read_csv('stock_basic.csv',encoding="gbk")
holder = pd.read_csv('stock_holders.csv',encoding="gbk")
concept_num = pd.read_csv('concept.csv',encoding="gbk")
concept = pd.read_csv('stock_concept.csv',encoding="gbk")
sh = pd.read_csv('sh.csv')
sz = pd.read_csv('sz.csv')
corr = pd.read_csv('corr.csv')

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stock.head()

| | Unnamed: 0 | TS代码 | 股票代码 | 股票名称 | 行业 |
| 0 | 0 | 000001.SZ | 1 | 平安银行 | 银行 |
| 1 | 1 | 000002.SZ | 2 | 万科A | 全国地产 |
| 2 | 2 | 000004.SZ | 4 | 国华网安 | 互联网 |
| 3 | 3 | 000005.SZ | 5 | 世纪星源 | 环境保护 |

4	4	000006.SZ	6	深振业A	区域地产

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holder.head()

| | Unnamed: 0 | ts_code | ann_date | end_date | holder_name | hold_amount | hold_ratio |
| 0 | 0 | 000001.SZ | 20190307 | 20181231 | 新华人寿保险股份有限公司-分红-个人分红-018L-FH002深 | 4.960350e+07 | 0.29 |
| 1 | 1 | 000001.SZ | 20190307 | 20181231 | 中国平安保险(集团)股份有限公司-集团本级-自有资金 | 8.510493e+09 | 49.56 |
| 2 | 2 | 000001.SZ | 20190307 | 20181231 | 中国平安人寿保险股份有限公司-自有资金 | 1.049463e+09 | 6.11 |
| 3 | 3 | 000001.SZ | 20190307 | 20181231 | 香港中央结算有限公司(陆股通) | 4.307515e+08 | 2.51 |

4	4	000001.SZ	20190307	20181231	中国证券金融股份有限公司	4.292327e+08	2.50

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concept_num.head()

| | Unnamed: 0 | code | name | src |
| 0 | 0 | TS0 | 密集调研 | ts |
| 1 | 1 | TS1 | 南北船合并 | ts |
| 2 | 2 | TS2 | 5G | ts |
| 3 | 3 | TS3 | 机场 | ts |

4	4	TS4	高价股	ts

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concept.head()

| | Unnamed: 0 | id | concept_name | ts_code | name |
| 0 | 0 | TS0 | 密集调研 | 000301.SZ | 东方盛虹 |
| 1 | 1 | TS0 | 密集调研 | 000401.SZ | 冀东水泥 |
| 2 | 2 | TS0 | 密集调研 | 000932.SZ | 华菱钢铁 |
| 3 | 3 | TS0 | 密集调研 | 002013.SZ | 中航机电 |

4	4	TS0	密集调研	002106.SZ	莱宝高科

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sh.head()

| | ts_code | hs_type | in_date | out_date | is_new |
| 0 | 601628.SH | SH | 20141117 | NaN | 1 |
| 1 | 601099.SH | SH | 20141117 | NaN | 1 |
| 2 | 601808.SH | SH | 20141117 | NaN | 1 |
| 3 | 601107.SH | SH | 20141117 | NaN | 1 |

4	601880.SH	SH	20141117	NaN	1

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sz.head()

| | ts_code | hs_type | in_date | out_date | is_new |
| 0 | 002910.SZ | SZ | 20171114 | NaN | 1 |
| 1 | 000016.SZ | SZ | 20180102 | NaN | 1 |
| 2 | 001872.SZ | SZ | 20180102 | NaN | 1 |
| 3 | 000040.SZ | SZ | 20180102 | NaN | 1 |

4	000401.SZ	SZ	20180102	NaN	1

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corr.head()

| | Unnamed: 0 | s1 | s2 | corr |
| 0 | 0 | 000001.SZ. | 000001.SZ. | 1.000000 |
| 1 | 1 | 000001.SZ. | 000002.SZ. | 0.648945 |
| 2 | 2 | 000001.SZ. | 000005.SZ. | 0.342920 |
| 3 | 3 | 000001.SZ. | 000009.SZ. | 0.297213 |

4	4	000001.SZ.	000010.SZ.	0.186165

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# 数据预处理
stock['行业'] = stock['行业'].fillna('未知')
holder = holder.drop_duplicates(subset=None, keep='first', inplace=False)

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# 创建实体（概念、股票、股东、股通）

sz = Node('深股通',名字='深股通')
graph.create(sz)  
 
sh = Node('沪股通',名字='沪股通')
graph.create(sh)  

for i in concept_num.values:
    a = Node('概念',概念代码=i[1],概念名称=i[2])
    # print('概念代码:'+str(i[1]),'概念名称:'+str(i[2]))
    graph.create(a)

for i in stock.values:
    a = Node('股票',TS代码=i[1],股票名称=i[3],行业=i[4])
    # print('TS代码:'+str(i[1]),'股票名称:'+str(i[3]),'行业:'+str(i[4]))
    graph.create(a)

for i in holder.values:
    a = Node('股东',TS代码=i[0],股东名称=i[1],持股数量=i[2],持股比例=i[3])
    # print('TS代码:'+str(i[0]),'股东名称:'+str(i[1]),'持股数量:'+str(i[2]))
    graph.create(a)

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# 创建关系（股票-股东、股票-概念、股票-公告、股票-股通）

matcher = NodeMatcher(graph) 
for i in holder.values:    
    a = matcher.match("股票",TS代码=i[0]).first()
    b = matcher.match("股东",TS代码=i[0])
    for j in b:
        r = Relationship(j,'参股',a)
        graph.create(r)
        print('TS',str(i[0]))
            
for i in concept.values:
    a = matcher.match("股票",TS代码=i[3]).first()
    b = matcher.match("概念",概念代码=i[1]).first()
    if a == None or b == None:
        continue
    r = Relationship(a,'概念属于',b)
    graph.create(r)

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