Neo4j使用py2neo从pandas数据框中创建节点和关系 [英] Neo4j create nodes and relationships from pandas dataframe with py2neo

问题描述

通过带有py2neo的Neo4j数据库上的密码查询在熊猫数据帧上获取结果非常简单，

Getting results on a pandas dataframe from a cypher query on a Neo4j database with py2neo is really straightforward, as:

>>> from pandas import DataFrame
>>> DataFrame(graph.data("MATCH (a:Person) RETURN a.name, a.born LIMIT 4"))
   a.born              a.name
0    1964        Keanu Reeves
1    1967    Carrie-Anne Moss
2    1961  Laurence Fishburne
3    1960        Hugo Weaving

现在，我正在尝试创建(或更好地合并)一组节点和从熊猫数据帧到具有py2neo的Neo4j数据库的关系.想象一下，我有一个像这样的数据框:

Now I am trying to create (or better MERGE) a set of nodes and relationships from a pandas dataframe into a Neo4j database with py2neo. Imagine I have a dataframe like:

LABEL1 LABEL2
p1 n1
p2 n1
p3 n2
p4 n2

其中标签是列标题，属性是值.我想为我的数据帧的每一行重现以下密码查询(以第一行为例):

where Labels are column header and properties as values. I would like to reproduce the following cypher query (for the first row as example), for every rows of my dataframe:

query="""
    MATCH (a:Label1 {property:p1))
    MERGE (a)-[r:R_TYPE]->(b:Label2 {property:n1))
"""

我知道我可以告诉py2neo只是graph.run(query)，甚至可以用相同的方式运行LOAD CSV密码脚本，但是我想知道是否可以遍历数据帧并在py2neo中逐行应用以上查询.

I know I can tell py2neo just to graph.run(query), or even run a LOAD CSV cypher script in the same way, but I wonder whether I can iterate through the dataframe and apply the above query row by row WITHIN py2neo.

推荐答案

您可以使用DataFrame.iterrows()遍历DataFrame并对每个行执行查询，并将该行中的值作为参数传递.

You can use DataFrame.iterrows() to iterate through the DataFrame and execute a query for each row, passing in the values from the row as parameters.

for index, row in df.iterrows():
    graph.run('''
      MATCH (a:Label1 {property:$label1})
      MERGE (a)-[r:R_TYPE]->(b:Label2 {property:$label2})
    ''', parameters = {'label1': row['label1'], 'label2': row['label2']})

这将每行执行一次事务.我们可以将多个查询批量处理到一个事务中，以提高性能.

That will execute one transaction per row. We can batch multiple queries into one transaction for better performance.

tx = graph.begin()
for index, row in df.iterrows():
    tx.evaluate('''
      MATCH (a:Label1 {property:$label1})
      MERGE (a)-[r:R_TYPE]->(b:Label2 {property:$label2})
    ''', parameters = {'label1': row['label1'], 'label2': row['label2']})
tx.commit()

通常，我们可以在一个事务中批量处理约20k个数据库操作.

Typically we can batch ~20k database operations in a single transaction.

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