The nodes and relationships in the graph are summarized below.

* There is a uniqueness constraint on this property for the given node label.

None of the relationships in the graph have any properties.

The already-created database is located in fleets.graphdb.zip.

Alternatively, to create the database from scratch, do the following in the given order:

• Execute scrape_airlines.py. This will create a 180KB file airlines_raw.csv in the current directory.
• Execute scrape_aircraft.py. This will create a 2,181KB file aircraft_raw.csv in the current directory.
• Execute clean.py. This will create a 98KB file airlines_clean.csv and a 2,140KB file aircraft_clean.csv in the current directory. The raw datasets airlines_raw.csv and aircraft_raw.csv can be deleted by uncommenting os.remove() (lines 41 and 42) in clean.py. Or you can just delete them. :)

Before going on to the below steps, make sure Neo4j is up and running at http://localhost:7474/db/data. If the URL is something else, change the value of this string at line 5 of the following files.

• Execute load_airlines.py.
  
• Execute load_aircraft.py.

After these scripts complete, the database should be fully populated.

MATCH (c:Country)<-[:BASED_IN]-(a:Airline)<-[:OWNED_BY]-(:Aircraft {status:'Active'})-[:SERIES]->()-[:MODEL]->(m:Model {name:'Boeing 747'})
WITH a, COUNT(m) AS count, c
ORDER BY count DESC
RETURN a.name AS Airline, c.name AS Country, count AS `Number of Boeing 747 Owned`
LIMIT 10

MATCH p = (:Airline {name:'Lufthansa'})<-[:OWNED_BY]-(:Aircraft)-[:SERIES]->(:Series)-[:MODEL]->(m:Model)
WHERE substring(m.name, 0 , 6) = 'Airbus'
RETURN p