Neo4j create nodes and relationships from pandas dataframe with py2neo

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.

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()