In [1]:

    

%load_ext cypher


    

In [2]:

    

%config CypherMagic


    

    




CypherMagic options
-----------------
CypherMagic.auto_html=<Bool>
    Current: False
    Return a D3 representation of the graph instead of regular result sets
CypherMagic.auto_limit=<Int>
    Current: 0
    Automatically limit the size of the returned result sets
CypherMagic.auto_networkx=<Bool>
    Current: False
    Return Networkx MultiDiGraph instead of regular result sets
CypherMagic.auto_pandas=<Bool>
    Current: False
    Return Pandas DataFrame instead of regular result sets
CypherMagic.data_contents=<Bool>
    Current: True
    Bring extra data to render the results as a graph
CypherMagic.display_limit=<Int>
    Current: 0
    Automatically limit the number of rows displayed (full result set is still
    stored)
CypherMagic.feedback=<Bool>
    Current: True
    Print number of rows affected
CypherMagic.rest=<Bool>
    Current: False
    Return full REST representations of objects inside the result sets
CypherMagic.short_errors=<Bool>
    Current: True
    Don't display the full traceback on Neo4j errors
CypherMagic.style=<Unicode>
    Current: u'DEFAULT'
    Set the table printing style to any of prettytable's defined styles
    (currently DEFAULT, MSWORD_FRIENDLY, PLAIN_COLUMNS, RANDOM)

In [3]:

    

%cypher match (n)-[r]-() delete n, r


    

    




6 relationship deleted.
6 nodes deleted.






    
Out[3]:





[]

In [4]:

    

%%cypher
create
    // Nodes
    (Neo:Crew {name:'Neo'}),
    (Morpheus:Crew {name: 'Morpheus'}),
    (Trinity:Crew {name: 'Trinity'}),
    (Cypher:Crew:Matrix {name: 'Cypher'}),
    (Smith:Matrix {name: 'Agent Smith'}),
    (Architect:Matrix {name:'The Architect'}),
    // Relationships
    (Neo)-[:KNOWS]->(Morpheus),
    (Neo)-[:LOVES]->(Trinity),
    (Morpheus)-[:KNOWS]->(Trinity),
    (Morpheus)-[:KNOWS]->(Cypher),
    (Cypher)-[:KNOWS]->(Smith),
    (Smith)-[:CODED_BY]->(Architect);


    

    




7 labels added.
6 nodes created.
6 properties set.
6 relationships created.






    
Out[4]:





[]

In [5]:

    

%cypher match (n)-[r]-() return n, count(r) as degree order by degree desc


    

    




6 rows affected.






    
Out[5]:





    

        
n
        
degree
    
    

        
{u'name': u'Morpheus'}
        
3
    
    

        
{u'name': u'Cypher'}
        
2
    
    

        
{u'name': u'Neo'}
        
2
    
    

        
{u'name': u'Trinity'}
        
2
    
    

        
{u'name': u'Agent Smith'}
        
2
    
    

        
{u'name': u'The Architect'}
        
1
    

In [6]:

    

results = %cypher match (n)-[r]-() return n.name as name, type(r) as rel, count(r) as degree order by degree desc


    

    




9 rows affected.

In [7]:

    

%matplotlib inline


    

In [8]:

    

results.get_dataframe()


    

    
Out[8]:






  

    

      

      
name
      
rel
      
degree
    
  
  

    

      
0
      
      Morpheus
      
    KNOWS
      
 3
    
    

      
1
      
        Cypher
      
    KNOWS
      
 2
    
    

      
2
      
   Agent Smith
      
    KNOWS
      
 1
    
    

      
3
      
           Neo
      
    LOVES
      
 1
    
    

      
4
      
       Trinity
      
    LOVES
      
 1
    
    

      
5
      
           Neo
      
    KNOWS
      
 1
    
    

      
6
      
       Trinity
      
    KNOWS
      
 1
    
    

      
7
      
 The Architect
      
 CODED_BY
      
 1
    
    

      
8
      
   Agent Smith
      
 CODED_BY
      
 1
    
  

In [9]:

    

results.plot()


    

    
Out[9]:





[<matplotlib.lines.Line2D at 0x7f707f29b3d0>]






    

In [10]:

    

results.bar()


    

    
Out[10]:





<Container object of 9 artists>






    

In [11]:

    

results.pie()


    

    
Out[11]:





([<matplotlib.patches.Wedge at 0x7f03e1ec9f90>,
  <matplotlib.patches.Wedge at 0x7f03e1e58a50>,
  <matplotlib.patches.Wedge at 0x7f03e1e63410>,
  <matplotlib.patches.Wedge at 0x7f03e1e63d90>,
  <matplotlib.patches.Wedge at 0x7f03e1e70750>,
  <matplotlib.patches.Wedge at 0x7f03e1e7d110>,
  <matplotlib.patches.Wedge at 0x7f03e1e7da90>,
  <matplotlib.patches.Wedge at 0x7f03e1e8a450>,
  <matplotlib.patches.Wedge at 0x7f03e1e8add0>],
 [<matplotlib.text.Text at 0x7f03e1e584d0>,
  <matplotlib.text.Text at 0x7f03e1e58fd0>,
  <matplotlib.text.Text at 0x7f03e1e63990>,
  <matplotlib.text.Text at 0x7f03e1e70350>,
  <matplotlib.text.Text at 0x7f03e1e70cd0>,
  <matplotlib.text.Text at 0x7f03e1e7d690>,
  <matplotlib.text.Text at 0x7f03e1e7df50>,
  <matplotlib.text.Text at 0x7f03e1e8a9d0>,
  <matplotlib.text.Text at 0x7f03e1e16390>])






    

In [11]:

    

for i in range(1, 5):
    %cypher match (n) return n, n.name limit {i}


    

    




1 rows affected.
2 rows affected.
3 rows affected.
4 rows affected.

In [12]:

    

results.draw()


    

    
Out[12]:





(<networkx.classes.multidigraph.MultiDiGraph at 0x7f707f154790>,
 <matplotlib.axes._subplots.AxesSubplot at 0x7f707f1bf2d0>,
 None)






    

In [13]:

    

results.graph


    

    
Out[13]:





<networkx.classes.multidigraph.MultiDiGraph at 0x7f707f09d2d0>

In [14]:

    

results.dataframe


    

    
Out[14]:






  

    

      

      
name
      
rel
      
degree
    
  
  

    

      
0
      
      Morpheus
      
    KNOWS
      
 3
    
    

      
1
      
        Cypher
      
    KNOWS
      
 2
    
    

      
2
      
   Agent Smith
      
    KNOWS
      
 1
    
    

      
3
      
           Neo
      
    LOVES
      
 1
    
    

      
4
      
       Trinity
      
    LOVES
      
 1
    
    

      
5
      
           Neo
      
    KNOWS
      
 1
    
    

      
6
      
       Trinity
      
    KNOWS
      
 1
    
    

      
7
      
 The Architect
      
 CODED_BY
      
 1
    
    

      
8
      
   Agent Smith
      
 CODED_BY
      
 1
    
  

In [15]:

    

print(results.csv())


    

    




name,rel,degree
Morpheus,KNOWS,3
Cypher,KNOWS,2
Agent Smith,KNOWS,1
Neo,LOVES,1
Trinity,LOVES,1
Neo,KNOWS,1
Trinity,KNOWS,1
The Architect,CODED_BY,1
Agent Smith,CODED_BY,1

In [16]:

    

from cypher import run
run("match (n)-[r]-() return n.name as name, type(r) as rel, count(r) as degree order by degree desc")


    

    




9 rows affected.






    
Out[16]:





    

        
name
        
rel
        
degree
    
    

        
Morpheus
        
KNOWS
        
3
    
    

        
Cypher
        
KNOWS
        
2
    
    

        
Agent Smith
        
KNOWS
        
1
    
    

        
Neo
        
LOVES
        
1
    
    

        
Trinity
        
LOVES
        
1
    
    

        
Neo
        
KNOWS
        
1
    
    

        
Trinity
        
KNOWS
        
1
    
    

        
The Architect
        
CODED_BY
        
1
    
    

        
Agent Smith
        
CODED_BY
        
1