Neo4j create nodes and relationships from pandas dataframe with py2neo

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萌比男神i
萌比男神i 2021-02-04 15:43

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

>>> from pandas import DataFram         


        
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  • 2021-02-04 16:37

    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']})
    

    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()
    

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

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  • 2021-02-04 16:43

    I found out that the proposed solution doesn't work for me. The code above creates new nodes even though the nodes already exist. To make sure you don't create any duplicates, I suggest matching both a and b node before merge:

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

    Also in my case, I had to add relationship properties simultaneously (see the code below). Moreover, I had 500k+ relationships to add, so I expectedly run into the java heap memory error. I solved the problem by placing begin() and commit() inside the loop, so for each new relationship a new transaction is created:

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