Neo4j in Python

Kaj potrebujemo (SUPB):

  • Neo4j (najenostavneje dbms.security.auth_enabled=false)

Kaj potrebujemo (Python, pip install ...)

  • py2neo
  • ipython-cypher
  • networkx

ipython-cypher

ipython-cypher ponuja direktivi %%cypher in %cypher za uporabo povpreševalnega jezika Cypher v delovnih zvezkih. %cypher je namenjen za enovrstične, %%cypher pa za večvrstične poizvedbe. Uporabljamo lahko izključno v delovnih zvezkih!

In [29]:
%load_ext cypher

The cypher extension is already loaded. To reload it, use:
  %reload_ext cypher

Izpis vseh številk (ID) vozlišč, njihovih oznak in lastnosti.

In [75]:
%%cypher 
MATCH (n) RETURN labels(n), ID(n), n

12 rows affected.
Out[75]:
labels(n) ID(n) n
['Person'] 18 {'age': 20, 'name': 'Drew'}
['Person'] 19 {'age': 24, 'name': 'Nicole'}
['Drink'] 20 {'calories': 0, 'name': 'Coke Zero'}
['Manufacturer'] 21 {'name': 'Coca Cola'}
['Manufacturer'] 22 {'name': 'Pepsi'}
['Drink'] 23 {'calories': 9000, 'name': 'Mountain Dew'}
['Drink'] 24 {'calories': 0, 'name': 'Coke Zero'}
['Person'] 25 {'age': 20, 'name': 'Drew'}
['Manufacturer'] 26 {'name': 'Pepsi'}
['Person'] 27 {'age': 24, 'name': 'Nicole'}
['Manufacturer'] 28 {'name': 'Coca Cola'}
['Drink'] 29 {'calories': 9000, 'name': 'Mountain Dew'}

py2neo: povezava na Neo4j z razredom Graph

In [33]:
from py2neo import Graph

graph = Graph() # Ob nastavitvi dbms.security.auth_enabled=false, sicer je potrebna avtentikacija 
                # v obliko povezovalnega niza
In [42]:
graph.delete_all()

Vozlišča

Kreiranje vozlišč z uporabo razreda Node. Prvi argument je oznaka (labela, tip) vozlišča. Ostali argumenti so poljubne lastnosti (properties) vozlišča v obliki parov ključ = vrednost.

In [65]:
from py2neo import Node

# Kreiranje vozlišč
nicole = Node("Person", name="Nicole", age=24)
drew = Node("Person", name="Drew", age=20)

mtdew = Node("Drink", name="Mountain Dew", calories=9000)
cokezero = Node("Drink", name="Coke Zero", calories=0)

coke = Node("Manufacturer", name="Coca Cola")
pepsi = Node("Manufacturer", name="Pepsi")

# Kreiranje grafa
graph.create(nicole | drew | mtdew | cokezero | coke | pepsi)
In [69]:
pepsi
Out[69]:
(pepsi:Manufacturer {name:"Pepsi"})

Povezave (razmerja)

Kreiranje povezav med vozlišči z uporabo razreda Relationship.

In [57]:
from py2neo import Relationship

graph.create(Relationship(nicole, "LIKES", cokezero))
graph.create(Relationship(nicole, "LIKES", mtdew))
graph.create(Relationship(drew, "LIKES", mtdew))
graph.create(Relationship(coke, "MAKES", cokezero))
graph.create(Relationship(pepsi, "MAKES", mtdew))
In [56]:
%%cypher
MATCH (person:Person)-[:LIKES]->(drink:Drink)
RETURN person.name, drink.name, drink.calories

3 rows affected.
Out[56]:
person.name drink.name drink.calories
Drew Mountain Dew 9000
Nicole Mountain Dew 9000
Nicole Coke Zero 0

py2neo in Cypher

Izvajanje Cypher poizvedb z uporabo metode Graph.cypher.execute.

In [59]:
query = """
MATCH (person:Person)-[:LIKES]->(drink:Drink)
RETURN person.name AS name, drink.name AS drink
"""

data = graph.run(query)

for d in data:
    print(d)

('name': 'Drew', 'drink': 'Mountain Dew')
('name': 'Nicole', 'drink': 'Mountain Dew')
('name': 'Nicole', 'drink': 'Coke Zero')

py2neo - parametrizirane Cypher poizvedbe

Ob klicu metode Graph.cypher.execute lahko podamo parametre v parov ključ=vrednost. Parametri v Cypher poizvedbi so poimenovani znotraj zavitih oklepajev.

In [60]:
query = """
MATCH (p:Person)-[:LIKES]->(drink:Drink)
WHERE p.name = {name}
RETURN p.name AS name, AVG(drink.calories) AS avg_calories
"""

data = graph.run(query, name="Nicole") # Parameter {name} dobi vrednost "Nicole" 

for d in data:
    print(d)

('name': 'Nicole', 'avg_calories': 4500.0)

NetworkX

Modul za delo z grafi (omrežji). Omogoča enostaven izris.

In [72]:
import networkx as nx
%matplotlib inline
import matplotlib.pyplot as plt
results = %cypher MATCH p = (:Person)-[:LIKES]->(:Drink) RETURN p

g = results.get_graph()

nx.draw_networkx(g, arrows=True); _=plt.axis('off')

3 rows affected.
In [73]:
g.nodes(data=True)
Out[73]:
[('19', {'age': 24, 'labels': ['Person'], 'name': 'Nicole'}),
 ('20', {'calories': 0, 'labels': ['Drink'], 'name': 'Coke Zero'}),
 ('23', {'calories': 9000, 'labels': ['Drink'], 'name': 'Mountain Dew'}),
 ('18', {'age': 20, 'labels': ['Person'], 'name': 'Drew'})]
In [74]:
nx.degree(g)
Out[74]:
{'18': 1, '19': 2, '20': 1, '23': 2}
In [ ]: