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import py2neo -- Quickstart

As everyone knows:

python == best

neo4j == amazing

both.gif

python + neo4j = Py2Neo <3

We are so grateful to https://github.com/technige for combining them as https://github.com/technige/py2neo.

---

This guide exists to demonstrate how to run the cypher in the built-in Neo4j tutorial "movie demo database" using technige's Py2Neo.

---

Both the python driver for neo4j and py2neo are actively being developed. This guide was created based on versions:

  • Py2neo v5
  • Neo4j 4.0

This is not a beginner-beginner tutorial, but it's pretty close to the beginning. We'll be assuming that you know what both python and Neo4j/cypher are and have them both running and have played with them a bit and are maybe still learning.

... hey, that's a bit too fast ...

If you'd like to know more about ``Python``: the programming language: there is a whole internet of resources, this is the official we like guide to kicking off: https://wiki.python.org/moin/BeginnersGuide/Download.

If you'd like to know more about ``Neo4j``: the awesome-sauce property graph database. Take your first steps using the docs here: https://neo4j.com/developer/get-started/.

Going forward we're going to use jargon and assume you are already familiar with these <3

... hey, that's a bit too slow ...

This document is to replay the basic cypher tutorial "Movie Graph", as Py2Neo. This would potentially be useful to understand interacting with a graph using python. This is just some demo code, not a whole project.

If you'd like something heavier, say to deploy this graph in a project, there's the official python-flask demo project here: https://github.com/neo4j-examples/movies-python-bolt

---

If you're cool with this, you're ready for this quickstart!

Where we're starting from ...

The built-in Neo4j tutorial "movie demo database" is one of the first things you see when you first open the Neo4j browser interface (the instructions to install this are here: `https://neo4j.com/developer/guide-neo4j-browser/`_).

Once you install and start Neo4j the defaul neo4j-browser url is: http://localhost:7474/browser/. From here you can see the big friendly "Jump into code: Write Code" :play write-code.

The official description of this demo is:

The Movie Graph is a mini graph application containing actors and directors that are related through the movies they've collaborated on.

This guide will show you how to:

  1. Create: insert movie data into the graph
  2. Find: retrieve individual movies and actors
  3. Query: discover related actors and directors
  4. Solve: the Bacon Path

In this document we going to replicate this :play movie-graph using Py2Neo.

All code examples presented are also here: Movie Graph as Py2Neo Jupyter Notebook

---

Contents

Create

This is a sample of the cypher from the tutorial, hopefully this looks familiar. This is what we'll convert to python.

cypher:

// Nodes
CREATE (TheMatrix:Movie {title:'The Matrix', released:1999, tagline:'Welcome to the Real World'})
CREATE (Keanu:Person {name:'Keanu Reeves', born:1964})
CREATE (Carrie:Person {name:'Carrie-Anne Moss', born:1967})
CREATE (Laurence:Person {name:'Laurence Fishburne', born:1961})
CREATE (Hugo:Person {name:'Hugo Weaving', born:1960})
CREATE (LillyW:Person {name:'Lilly Wachowski', born:1967})
CREATE (LanaW:Person {name:'Lana Wachowski', born:1965})
CREATE (JoelS:Person {name:'Joel Silver', born:1952})
CREATE (Emil:Person {name:"Emil Eifrem", born:1978})
CREATE (Emil)-[:ACTED_IN {roles:["Emil"]}]->(TheMatrix)

// Relationships
CREATE
  (Keanu)-[:ACTED_IN {roles:['Neo']}]->(TheMatrix),
  (Carrie)-[:ACTED_IN {roles:['Trinity']}]->(TheMatrix),
  (Laurence)-[:ACTED_IN {roles:['Morpheus']}]->(TheMatrix),
  (Hugo)-[:ACTED_IN {roles:['Agent Smith']}]->(TheMatrix),
  (LillyW)-[:DIRECTED]->(TheMatrix),
  (LanaW)-[:DIRECTED]->(TheMatrix),
  (JoelS)-[:PRODUCED]->(TheMatrix)

You'll notice that this is effectivley 1 step, where you create:

Step 1. CREATE nodes, then CREATE relationships.

You don't really think about committing this transaction.

---

Using the python driver/py2neo you must specifically think about both:

  • Connecting to your Graph DB
  • Committing the transaction

So using py2neo there are 3 steps.

Step 0: Connect to your GraphDB

Step 1: Create your Node and Relationship objects

Step 2: Commit your Subgraphs (https://py2neo.org/v5/data.html#subgraph-objects)

--

Step 0: Connect to Graph using Py2Neo

from py2neo import Graph

my_graph = Graph(password='[mysekretpasswordhere]')

There are plenty of options for connecting to your database if this implementation doesn't work for you.

For example, the following are all equivalent:

my_graph0 = Graph()
my_graph1 = Graph(host="localhost")
my_graph2 = Graph("bolt://localhost:7687")

my_graph0 == my_graph1 == my_graph2

See the reference here: https://py2neo.org/v5/database.html#py2neo.database.Graph

Note that as of Neo4j 4.0+: if you have multiple graphs databases, you can choose which database you connect to using the name argument, see the docs above. Multi-database support is still in active development at the database level: https://neo4j.com/developer/manage-multiple-databases/

A full list of database names can be shown through the Cypher:

cypher:

// switch to system database
:use system
SHOW DATABASES

---

Step 1: Create Node and Relationship Subgraphs using Py2Neo

Full Node and Relationship reference: https://py2neo.org/v5/data.html

python:

from py2neo import Node, Relationship

# Nodes
TheMatrix = Node("Movie", title='The Matrix', released=1999, tagline='Welcome to the Real World')
Keanu = Node("Person", name='Keanu Reeves', born=1964)
Carrie = Node("Person", name='Carrie-Anne Moss', born=1967)
Laurence = Node("Person", name='Laurence Fishburne', born=1961)
Hugo = Node("Person", name='Hugo Weaving', born=1960)
LillyW = Node("Person", name='Lilly Wachowski', born=1967)
LanaW = Node("Person", name='Lana Wachowski', born=1965)
JoelS = Node("Person", name='Joel Silver', born=1952)
Emil = Node("Person", name="Emil Eifrem", born=1978)

# Relationships
LillyWTheMatrix = Relationship(LillyW, "DIRECTED", TheMatrix)
LanaWTheMatrix = Relationship(LanaW, "DIRECTED", TheMatrix)
JoelSTheMatrix = Relationship(JoelS, "PRODUCED", TheMatrix)
KeanuTheMatrix = Relationship(Keanu, "ACTED_IN", TheMatrix)
KeanuTheMatrix['roles'] = ['Neo']
CarrieTheMatrix = Relationship(Carrie, "ACTED_IN", TheMatrix)
CarrieTheMatrix['roles'] = ['Trinity']
LaurenceTheMatrix = Relationship(Laurence, "ACTED_IN", TheMatrix)
LaurenceTheMatrix['roles'] = ['Morpheus']
HugoTheMatrix = Relationship(Hugo, "ACTED_IN", TheMatrix)
HugoTheMatrix['roles'] = ['Agent Smith']
EmilTheMatrix = Relationship(Emil, "ACTED_IN", TheMatrix)
EmilTheMatrix['roles'] = ['Emil']

Note: This looks great but YOUR DB OBJECTS DO NOT EXIST YET!

They need to committed to the database.

Step 2: Commit using Py2Neo

python:

# Commit the transactions

tx = graph.begin()
tx.create(TheMatrix)
tx.create(Keanu)
tx.create(Carrie)
tx.create(Laurence)
tx.create(Hugo)
tx.create(LillyW)
tx.create(LanaW)
tx.create(JoelS)
tx.create(Emil)
tx.create(KeanuTheMatrix)
tx.create(CarrieTheMatrix)
tx.create(LaurenceTheMatrix)
tx.create(HugoTheMatrix)
tx.create(LillyWTheMatrix)
tx.create(LanaWTheMatrix)
tx.create(JoelSTheMatrix)
tx.create(EmilTheMatrix)
tx.commit()

This is just a sample from the more detailed example database provided at: https://neo4j.com/developer/movie-database/. The gist of the full dataset can be found here: https://gist.github.com/elena/733275bd55fba0a48cd885fe0427e5d4

The full set is also with the code examples that go along with this here: Movie Graph as Py2Neo Jupyter Notebook

---

Find

Example queries for finding individual nodes.

Connect to the database:

from py2neo import Graph
graph = Graph(password='[yoursekretpasswordhere]')

There are multiple methods of instantiating NodeMatcher.

nodes_matcher = NodeMatcher(graph)
nodes_matcher.match()

# this is the same as:

graph.nodes.match()

https://py2neo.org/v5/matching.html#py2neo.matching.NodeMatcher https://py2neo.org/v5/database.html#py2neo.database.Graph.nodes

Quick demo:

keanu = graph.nodes.match("Person", name="Keanu Reeves").first()

Out[]: Node('Person', born=1964, name='Keanu Reeves')
match_using_matcher = node_matcher.match(name="Keanu Reeves").first()
match_using_graphnodes = graph.nodes.match(name="Keanu Reeves").first()

match_using_matcher == match_using_graphnodes

Out[]: True

---

Demo from https://py2neo.org/v5/database.html#py2neo.database.Graph.nodes:

keanu0 = graph.nodes[1]
keanu1 = graph.nodes.get(1)
keanu2 = graph.nodes.match("Person", name="Keanu Reeves").first()

keanu0 == keanu1 == keanu2

Out[]: True
len(graph.nodes.match("Person"))

Out[]: 145

Note, the full set of data has been loaded, you can see this:

---

Find the actor named "Tom Hanks"...

cypher:

MATCH (tom {name: "Tom Hanks"}) RETURN tom

python:

node_matcher.match(name="Tom Hanks").first()

Out[]: Node('Person', born=1956, name='Tom Hanks')

Note: don't forget the ``.first()``. Without it you get a NodeMatch object, which is probably not what you want.

There may be performance differences based upon your use case. As a general rule it's better to be specific in queries (in this case using the label "Person" would assist performance).

Find the movie with title "Cloud Atlas"...

cypher:

MATCH (cloudAtlas {title: "Cloud Atlas"}) RETURN cloudAtlas

python:

node_matcher.match(title="Cloud Atlas").first()

Out[]: Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas')

Find 10 people...

cypher:

MATCH (people:Person) RETURN people.name LIMIT 10

python:

node_matcher.match("Person").limit(10).all()

Out[]: [Node('Person', born=1964, name='Keanu Reeves'),
        Node('Person', born=1967, name='Carrie-Anne Moss'),
        Node('Person', born=1961, name='Laurence Fishburne'),
        Node('Person', born=1960, name='Hugo Weaving'),
        Node('Person', born=1967, name='Lilly Wachowski'),
        Node('Person', born=1965, name='Lana Wachowski'),
        Node('Person', born=1952, name='Joel Silver'),
        Node('Person', born=1978, name='Emil Eifrem'),
        Node('Person', born=1964, name='Keanu Reeves'),
        Node('Person', born=1967, name='Carrie-Anne Moss')]

Note: don't forget the ``.all()``. Without it you get a NodeMatch object, which is probably not what you want.

Find movies released in the 1990s...

cypher:

MATCH (nineties:Movie) WHERE nineties.released >= 1990 AND nineties.released < 2000 RETURN nineties.title

python:

There are a list of standard operators available such as =, <>, etc. See the full list here: https://py2neo.org/v5/matching.html#node-matching

node_matcher.match("Movie").where('_.released >= 1990', '_.released < 2000')

Out[] = [Node('Movie', released=1999, tagline='Welcome to the Real World', title='The Matrix'),
         Node('Movie', released=1992, tagline="In the heart of the nation's capital, in a courthouse of the U.S. government, one man will stop at nothing to keep his honor, and one will stop at nothing to find the truth.", title='A Few Good Men'),
         Node('Movie', released=1992, tagline='Once in a lifetime you get a chance to do something different.', title='A League of Their Own'),
         Node('Movie', released=1999, tagline='First loves last. Forever.', title='Snow Falling on Cedars'),
         Node('Movie', released=1996, tagline='In every life there comes a time when that thing you dream becomes that thing you do', title='That Thing You Do'),
         Node('Movie', released=1998, tagline='After life there is more. The end is just the beginning.', title='What Dreams May Come'),
         ...
         Node('Movie', released=1998, tagline='At odds in life... in love on-line.', title='When Harry Met Sally'),

Watch the prefix `"_."` in the where statement.

https://py2neo.org/v5/matching.html#py2neo.matching.NodeMatch.where

---

Query

Finding patterns within the graph.

  1. Actors are people who acted in movies
  2. Directors are people who directed a movie
  3. What other relationships exist?

Connect to the database:

from py2neo import Graph
graph = Graph(password='[yoursekretpasswordhere]')

There are multiple methods of instantiating RelationshipMatcher.

relationship_matcher = RelationshipMatcher(graph)
relationship_matcher.match()

# this is the same as:

graph.relationships.match()

https://py2neo.org/v5/matching.html#py2neo.matching.RelationshipMatch https://py2neo.org/v5/database.html#py2neo.database.Graph.match

---

List all Tom Hanks movies...

cypher:

MATCH (tom:Person {name: "Tom Hanks"})-[:ACTED_IN]->(tomHanksMovies) RETURN tom,tomHanksMovies

python:

graph.nodes.match(name="Tom Hanks").first()
graph.match(nodes=[tom], r_type="ACTED_IN").all()

Out[]: [ACTED_IN(Node('Person', born=1956, name='Tom Hanks'), Node('Movie', released=2006, tagline='Break The Codes', title='The Da Vinci Code'), roles=['Dr. Robert Langdon']),
        ACTED_IN(Node('Person', born=1956, name='Tom Hanks'), Node('Movie', released=1990, tagline='A story of love, lava and burning desire.', title='Joe Versus the Volcano'), roles=['Joe Banks']),
        ACTED_IN(Node('Person', born=1956, name='Tom Hanks'), Node('Movie', released=1999, tagline="Walk a mile you'll never forget.", title='The Green Mile'), roles=['Paul Edgecomb']),
        ...
        ACTED_IN(Node('Person', born=1956, name='Tom Hanks'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'), roles=['Zachry', 'Dr. Henry Goose', 'Isaac Sachs', 'Dermot Hoggins']),
        ACTED_IN(Node('Person', born=1956, name='Tom Hanks'), Node('Movie', released=2004, tagline='This Holiday Season… Believe', title='The Polar Express'), roles=['Hero Boy', 'Father', 'Conductor', 'Hobo', 'Scrooge', 'Santa Claus']),
        ACTED_IN(Node('Person', born=1956, name='Tom Hanks'), Node('Movie', released=1996, tagline='In every life there comes a time when that thing you dream becomes that thing you do', title='That Thing You Do'), roles=['Mr. White'])]

Who directed "Cloud Atlas"?

cypher:

MATCH (cloudAtlas {title: "Cloud Atlas"})<-[:DIRECTED]-(directors) RETURN directors.name

This is possible, but getting out of the scope of py2neo, the following are all cases where falling back to native cypher is probably best.

python:

results = graph.run('MATCH (cloudAtlas {title: "Cloud Atlas"})<-[:DIRECTED]-(directors) RETURN directors.name')
results.data()

Out[]: [{'directors.name': 'Tom Tykwer'},
        {'directors.name': 'Lilly Wachowski'},
        {'directors.name': 'Lana Wachowski'}]

The following will produce the same result, although is less elegant:

python:

cloudAtlas = matcher.match(title="Cloud Atlas").first()
directors = graph.match(r_type="DIRECTED", nodes=(None, cloudAtlas)) # << see notes about use of nodes=() here
for director in directors:
     print(director.nodes[0]['name'])
Tom Tykwer
Lilly Wachowski
Lana Wachowski

There are several important things to note here:

  • r_type is a kwarg to .match()
  • nodes is a set, of: (NodeTo, NodeFrom) -- in this case, the "from" Node is None, because that's the undefined data that we want to find.

In the "List all Tom Hanks movies..." example above only one of the nodes set is defined -- we were less e xplicit with our requirements. For this kwarg the correct number of inputs in the set is one or two, in a particular order.

Tom Hanks' co-actors...

cypher:

MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors) RETURN coActors.name

python:

results = graph.run('MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors) RETURN coActors.name')
results.data()

Out[]: [{'coActors.name': 'Bill Paxton'},
        {'coActors.name': 'Madonna'},
        {'coActors.name': 'Geena Davis'},
        {'coActors.name': 'Lori Petty'},
        {'coActors.name': 'Philip Seymour Hoffman'},
        ...
        {'coActors.name': 'Meg Ryan'},
        {'coActors.name': 'Parker Posey'},
        {'coActors.name': 'Dave Chappelle'},
        {'coActors.name': 'Greg Kinnear'},
        {'coActors.name': 'Meg Ryan'}]
How people are related to "Cloud Atlas"...

cypher:

MATCH (people:Person)-[relatedTo]-(:Movie {title: "Cloud Atlas"}) RETURN people.name, Type(relatedTo), relatedTo

python:

results = graph.run('MATCH (people:Person)-[relatedTo]-(:Movie {title: "Cloud Atlas"}) RETURN people.name, Type(relatedTo), relatedTo')
results.data()

Out[]: [{'people.name': 'Halle Berry',
         'Type(relatedTo)': 'ACTED_IN',
         'relatedTo': ACTED_IN(Node('Person', born=1966, name='Halle Berry'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'), roles=['Luisa Rey', 'Jocasta Ayrs', 'Ovid', 'Meronym'])},
        {'people.name': 'Stefan Arndt',
         'Type(relatedTo)': 'PRODUCED',
         'relatedTo': PRODUCED(Node('Person', born=1961, name='Stefan Arndt'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'))},
        {'people.name': 'Hugo Weaving',
         'Type(relatedTo)': 'ACTED_IN',
         'relatedTo': ACTED_IN(Node('Person', born=1960, name='Hugo Weaving'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'), roles=['Bill Smoke', 'Haskell Moore', 'Tadeusz Kesselring', 'Nurse Noakes', 'Boardman Mephi', 'Old Georgie'])},
        {'people.name': 'Lilly Wachowski',
         'Type(relatedTo)': 'DIRECTED',
         'relatedTo': DIRECTED(Node('Person', born=1967, name='Lilly Wachowski'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'))},
        {'people.name': 'Tom Tykwer',
         'Type(relatedTo)': 'DIRECTED',
         'relatedTo': DIRECTED(Node('Person', born=1965, name='Tom Tykwer'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'))},
        {'people.name': 'Tom Hanks',
         'Type(relatedTo)': 'ACTED_IN',
         'relatedTo': ACTED_IN(Node('Person', born=1956, name='Tom Hanks'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'), roles=['Zachry', 'Dr. Henry Goose', 'Isaac Sachs', 'Dermot Hoggins'])},
        {'people.name': 'Jim Broadbent',
         'Type(relatedTo)': 'ACTED_IN',
         'relatedTo': ACTED_IN(Node('Person', born=1949, name='Jim Broadbent'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'), roles=['Vyvyan Ayrs', 'Captain Molyneux', 'Timothy Cavendish'])},
        {'people.name': 'Lana Wachowski',
         'Type(relatedTo)': 'DIRECTED',
         'relatedTo': DIRECTED(Node('Person', born=1965, name='Lana Wachowski'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'))},
        {'people.name': 'David Mitchell',
         'Type(relatedTo)': 'WROTE',
         'relatedTo': WROTE(Node('Person', born=1969, name='David Mitchell'), Node('Movie', released=2012, tagline='Everything is connected', title='Cloud Atlas'))}]

Python has strengths far beyond cypher, though cypher is also magically strong, so we're not too fussed by dropping back to native cypher here. We get the best of both worlds.

For example:

>>> results.to_table()
 people.name     | Type(relatedTo) | relatedTo
-----------------|-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------
 Halle Berry     | ACTED_IN        | (Halle Berry)-[:ACTED_IN {roles: ['Luisa Rey', 'Jocasta Ayrs', 'Ovid', 'Meronym']}]->(_64)
 Stefan Arndt    | PRODUCED        | (Stefan Arndt)-[:PRODUCED {}]->(_64)
 Hugo Weaving    | ACTED_IN        | (Hugo Weaving)-[:ACTED_IN {roles: ['Bill Smoke', 'Haskell Moore', 'Tadeusz Kesselring', 'Nurse Noakes', 'Boardman Mephi', 'Old Georgie']}]->(_64)
 Lilly Wachowski | DIRECTED        | (Lilly Wachowski)-[:DIRECTED {}]->(_64)
 Tom Tykwer      | DIRECTED        | (Tom Tykwer)-[:DIRECTED {}]->(_64)
 Tom Hanks       | ACTED_IN        | (Tom Hanks)-[:ACTED_IN {roles: ['Zachry', 'Dr. Henry Goose', 'Isaac Sachs', 'Dermot Hoggins']}]->(_64)
 Jim Broadbent   | ACTED_IN        | (Jim Broadbent)-[:ACTED_IN {roles: ['Vyvyan Ayrs', 'Captain Molyneux', 'Timothy Cavendish']}]->(_64)
 Lana Wachowski  | DIRECTED        | (Lana Wachowski)-[:DIRECTED {}]->(_64)
 David Mitchell  | WROTE           | (David Mitchell)-[:WROTE {}]->(_64)

Other possible completions are:

# pandas
results.to_data_frame()
results.to_series()

# sympy
results.to_matrix()

# numpy
results.to_ndarray()

magic.gif

---

Solve

You've heard of the classic "Six Degrees of Kevin Bacon"? That is simply a shortest path query called the "Bacon Path".

  1. Variable length patterns
  2. Built-in shortestPath() algorithm

Movies and actors up to 4 "hops" away from Kevin Bacon

cypher:

MATCH (bacon:Person {name:"Kevin Bacon"})-[*1..4]-(hollywood)
RETURN DISTINCT hollywood

python:

results = graph.run('MATCH (bacon:Person {name:"Kevin Bacon"})-[*1..4]-(hollywood) RETURN DISTINCT hollywood')
results.data()

Out[]: [{'hollywood': Node('Person', born=1971, name='Paul Bettany')},
        {'hollywood': Node('Person', born=1956, name='Tom Hanks')},
        {'hollywood': Node('Person', born=1976, name='Audrey Tautou')},
        {'hollywood': Node('Person', born=1939, name='Ian McKellen')},
        ...
        {'hollywood': Node('Movie', released=2006, tagline='Break The Codes', title='The Da Vinci Code')},
        {'hollywood': Node('Person', born=1977, name='Liv Tyler')},
        {'hollywood': Node('Movie', released=1996, tagline='In every life there comes a time when that thing you dream becomes that thing you do', title='That Thing You Do')}]

Note that this return a lot of results:

results = graph.run('MATCH (bacon:Person {name:"Kevin Bacon"})-[*1..4]-(hollywood) RETURN DISTINCT hollywood')
len(results.data())
Out[]: 133

Bacon path, the shortest path of any relationships to Meg Ryan

cypher:

MATCH p=shortestPath(
  (bacon:Person {name:"Kevin Bacon"})-[*]-(meg:Person {name:"Meg Ryan"})
)
RETURN p

python:

results = graph.run('MATCH p=shortestPath((bacon:Person {name:"Kevin Bacon"})-[*]-(meg:Person {name:"Meg Ryan"})) RETURN p')
results.data()

Out[]: [{'p': Path(subgraph=Subgraph({Node('Person', born=1958, name='Kevin Bacon'), Node('Movie', released=1992, tagline="In the heart of the
       nation's capital, in a courthouse of the U.S. government, one man will stop at nothing to keep his honor, and one will stop at nothing
       to find the truth.", title='A Few Good Men'), Node('Person', born=1947, name='Rob Reiner'), Node('Movie', released=1998, tagline='At odds
       in life... in love on-line.', title='When Harry Met Sally'), Node('Person', born=1961, name='Meg Ryan')}, {ACTED_IN(Node('Person', born=1958,
       name='Kevin Bacon'), Node('Movie', released=1992, tagline="In the heart of the nation's capital, in a courthouse of the U.S. government, one
       man will stop at nothing to keep his honor, and one will stop at nothing to find the truth.", title='A Few Good Men'), roles=['Capt. Jack
       Ross']), DIRECTED(Node('Person', born=1947, name='Rob Reiner'), Node('Movie', released=1992, tagline="In the heart of the nation's capital,
       in a courthouse of the U.S. government, one man will stop at nothing to keep his honor, and one will stop at nothing to find the truth.",
       title='A Few Good Men')), PRODUCED(Node('Person', born=1947, name='Rob Reiner'), Node('Movie', released=1998, tagline='At odds in life...
       in love on-line.', title='When Harry Met Sally')), ACTED_IN(Node('Person', born=1961, name='Meg Ryan'), Node('Movie', released=1998,
       tagline='At odds in life... in love on-line.', title='When Harry Met Sally'), roles=['Sally Albright'])}), sequence=(Node('Person',born=1958,
       name='Kevin Bacon'), ACTED_IN(Node('Person', born=1958, name='Kevin Bacon'), Node('Movie', released=1992, tagline="In the heart of the
       nation's capital, in a courthouse of the U.S. government, one man will stop at nothing to keep his honor, and one will stop at nothing to
       find the truth.", title='A Few Good Men'), roles=['Capt. Jack Ross']), Node('Movie', released=1992, tagline="In the heart of the nation's
       capital, in a courthouse of the U.S. government, one man will stop at nothing to keep his honor, and one will stop at nothing to find the
       truth.", title='A Few Good Men'), DIRECTED(Node('Person', born=1947, name='Rob Reiner'), Node('Movie', released=1992, tagline="In the heart
       of the nation's capital, in a courthouse of the U.S. government, one man will stop at nothing to keep his honor, and one will stop at
       nothing to find the truth.", title='A Few Good Men')), Node('Person', born=1947, name='Rob Reiner'), PRODUCED(Node('Person', born=1947,
       name='Rob Reiner'), Node('Movie', released=1998, tagline='At odds in life... in love on-line.', title='When Harry Met Sally')), Node('Movie',
       released=1998, tagline='At odds in life... in love on-line.', title='When Harry Met Sally'), ACTED_IN(Node('Person', born=1961, name='Meg
       Ryan'), Node('Movie', released=1998, tagline='At odds in life... in love on-line.', title='When Harry Met Sally'), roles=['Sally Albright']),
       Node('Person', born=1961, name='Meg Ryan')))}]

For more about shortest path:

https://neo4j.com/docs/developer-manual/current/cypher/clauses/match/#query-shortest-path

https://neo4j.com/docs/graph-algorithms/current/algorithms/shortest-path/

---

Recommend

Let's recommend new co-actors for Tom Hanks. A basic recommendation approach is to find connections past an immediate neighborhood which are themselves well connected.

For Tom Hanks, that means:

  1. Find actors that Tom Hanks hasn't yet worked with, but his co-actors have.
  2. Find someone who can introduce Tom to his potential co-actor.

Extend Tom Hanks co-actors, to find co-co-actors who haven't worked with Tom Hanks...

cypher:

MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors),
      (coActors)-[:ACTED_IN]->(m2)<-[:ACTED_IN]-(cocoActors)
WHERE NOT (tom)-[:ACTED_IN]->()<-[:ACTED_IN]-(cocoActors) AND tom <> cocoActors
RETURN cocoActors.name AS Recommended, count(*) AS Strength ORDER BY Strength DESC

python:

results = graph.run('MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors), (coActors)-[:ACTED_IN]->(m2)<-[:ACTED_IN]-(cocoActors) WHERE NOT (tom)-[:ACTED_IN]->()<-[:ACTED_IN]-(cocoActors) AND tom <> cocoActors RETURN cocoActors.name AS Recommended, count(*) AS Strength ORDER BY Strength DESC')
results.data()

Out[]: [{'Recommended': 'Tom Cruise', 'Strength': 5},
        {'Recommended': 'Cuba Gooding Jr.', 'Strength': 4},
        {'Recommended': 'Keanu Reeves', 'Strength': 4},
        {'Recommended': 'Carrie Fisher', 'Strength': 3},
        {'Recommended': 'Carrie-Anne Moss', 'Strength': 3},
        {'Recommended': 'Kelly McGillis', 'Strength': 3},
        {'Recommended': 'Val Kilmer', 'Strength': 3},
        {'Recommended': 'Laurence Fishburne', 'Strength': 3},
        {'Recommended': 'Jack Nicholson', 'Strength': 3},
        ...
        {'Recommended': 'Emil Eifrem', 'Strength': 1},
        {'Recommended': 'Christian Bale', 'Strength': 1},
        {'Recommended': 'Robin Williams', 'Strength': 1},
        {'Recommended': 'Demi Moore', 'Strength': 1},
        {'Recommended': 'Aaron Sorkin', 'Strength': 1},
        {'Recommended': 'Natalie Portman', 'Strength': 1}]

Find someone to introduce Tom Hanks to Tom Cruise

cypher:

MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors),
  (coActors)-[:ACTED_IN]->(m2)<-[:ACTED_IN]-(cruise:Person {name:"Tom Cruise"})
RETURN tom, m, coActors, m2, cruise

python:

results = graph.run('MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors), (coActors)-[:ACTED_IN]->(m2)<-[:ACTED_IN]-(cruise:Person {name:"Tom Cruise"}) RETURN tom, m, coActors, m2, cruise')
results.data()

Out[]: [{'tom': Node('Person', born=1956, name='Tom Hanks'),
         'm': Node('Movie', released=1990, tagline='A story of love, lava and burning desire.', title='Joe Versus the Volcano'),
         'coActors': Node('Person', born=1961, name='Meg Ryan'),
         'm2': Node('Movie', released=1986, tagline='I feel the need, the need for speed.', title='Top Gun'),
         'cruise': Node('Person', born=1962, name='Tom Cruise')},
        {'tom': Node('Person', born=1956, name='Tom Hanks'),
         'm': Node('Movie', released=1999, tagline="Walk a mile you'll never forget.", title='The Green Mile'),
         'coActors': Node('Person', born=1961, name='Bonnie Hunt'),
         'm2': Node('Movie', released=2000, tagline='The rest of his life begins now.', title='Jerry Maguire'),
         'cruise': Node('Person', born=1962, name='Tom Cruise')},
        {'tom': Node('Person', born=1956, name='Tom Hanks'),
         'm': Node('Movie', released=1998, tagline='At odds in life... in love on-line.', title="You've Got Mail"),
         'coActors': Node('Person', born=1961, name='Meg Ryan'),
         'm2': Node('Movie', released=1986, tagline='I feel the need, the need for speed.', title='Top Gun'),
         'cruise': Node('Person', born=1962, name='Tom Cruise')},
        {'tom': Node('Person', born=1956, name='Tom Hanks'),
         'm': Node('Movie', released=1995, tagline='Houston, we have a problem.', title='Apollo 13'),
         'coActors': Node('Person', born=1958, name='Kevin Bacon'),
         'm2': Node('Movie', released=1992, tagline="In the heart of the nation's capital, in a courthouse of the U.S. government, one man will stop at nothing to keep his honor, and one will stop at nothing to find the truth.", title='A Few Good Men'),
         'cruise': Node('Person', born=1962, name='Tom Cruise')},
        {'tom': Node('Person', born=1956, name='Tom Hanks'),
         'm': Node('Movie', released=1993, tagline='What if someone you never met, someone you never saw, someone you never knew was the only someone for you?', title='Sleepless in Seattle'),
         'coActors': Node('Person', born=1961, name='Meg Ryan'),
         'm2': Node('Movie', released=1986, tagline='I feel the need, the need for speed.', title='Top Gun'),
         'cruise': Node('Person', born=1962, name='Tom Cruise')}]

This allows you to use the full force of python on the results. That's pretty great.

---

Clean up

When you're done experimenting, you can remove the movie data set.

Note:

  1. Nodes can't be deleted if relationships exist
  2. Delete both nodes and relationships together

WARNING: This will remove all Person and Movie nodes!

Delete all Movie and Person nodes, and their relationships

cypher:

MATCH (n) DETACH DELETE n

python:

graph = Graph(password='[yoursekretpasswordhere]')
len(graph.match())

Out[]: 253

https://py2neo.org/v5/database.html#py2neo.database.Transaction.delete

# !! WARNING: This will remove all Person and Movie nodes !!

graph.delete_all()

---

Confirm that the Movie Graph is gone

cypher:

MATCH (n) RETURN n

python:

len(graph.match())

Out[]: 0
graph.match().all()

Out[]: []

---

This guide does not cover many interesting features of neo4j and py2neo such as the ability to update and merge:

https://py2neo.org/v5/data.html

https://py2neo.org/v5/database.html

Importantly the ogm ("Object Graph Mapper", analogous to the orm "Object Relational Mapper" used by many frameworks for traditional relational databases) feature is not covered here: https://py2neo.org/v5/ogm.html

~Fin

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Working through "The Movie Graph" as Py2Neo

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