Python Neo4j螺栓状态结果到数据框

基于

这里的

result

是数据类型

neo4j.v1.session.StatementResult

。如何在不显式迭代的情况下访问pandas dataframe中的此数据

pd.DataFrame.from_记录（结果）

似乎没有帮助

这是我用列表理解法得到的

resultlist = [[record['title'], record['name']] for record in result]
pd.DataFrame.from_records(resultlist, columns=['title', 'name'])

---

我所能想到的最好的理解是一个与您的理解类似的列表，但不太详细：

df = pd.DataFrame([r.values() for r in result], columns=result.keys())

这个包似乎更适合数据帧，因为返回字典列表非常简单。下面是使用

py2neo

的等效代码：

import py2neo

# Some of these keyword arguments are unnecessary, as they are the default values.
graph = py2neo.Graph(bolt=True, host='localhost', user='neo4j', password='neo4j')

graph.run("CREATE (a:Person {name:'Arthur', title:'King'})")

query = "MATCH (a:Person) WHERE a.name = 'Arthur' RETURN a.name AS name, a.title AS title"
df = pd.DataFrame(graph.data(query))

---

将结果记录转换到字典中可以实现以下技巧：

df = pd.DataFrame([dict(record) for record in result])

---

在

py2neo

的

V4

中，到pandas数据帧的转换更加容易

    import py2neo

    # Some of these keyword arguments are unnecessary, as they are the default values.
    graph = py2neo.Graph(uri, user=username, password=password)

    df = graph.run("Your query goes here").to_data_frame()

那么：

from neo4j.v1 import GraphDatabase
from pandas import DataFrame

uri = "bolt://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("",""))

get_instances = """MATCH (n)--(m)
                   RETURN n
                   LIMIT 10
                   """

with driver.session() as graphDB_Session:
    result = graphDB_Session.run(get_instances)
    df = DataFrame(result.records(), columns=result.keys())

---

对我来说很有用。

py2neo在调用

到resultWorks上的\u data\u frame

时，除了列表理解之外，没有做更多的事情！这应该被接受为答案。非常干净，整洁，像蟒蛇一样。

from neo4j.v1 import GraphDatabase
from pandas import DataFrame

uri = "bolt://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("",""))

get_instances = """MATCH (n)--(m)
                   RETURN n
                   LIMIT 10
                   """

with driver.session() as graphDB_Session:
    result = graphDB_Session.run(get_instances)
    df = DataFrame(result.records(), columns=result.keys())