Improve notebook and doc

Author: nvitucci

doc/modules/ROOT/pages/tutorials/load-data-via-graph-construction.adoc

@@ -18,25 +18,40 @@ We need an environment where Neo4j and GDS are available, for example
 AuraDS (which comes with GDS preinstalled) or Neo4j Desktop.
 
 Once the credentials to this environment are available, we can install
-the `graphdatascience` package and create the `gds` object.
+the `graphdatascience` package and import the client class.
 
 [source, python, subs=attributes+, role=no-test]
 ----
 !pip install graphdatascience=={docs-version}
 ----
 
-[source, python, role=no-test]
+[source, python, subs=attributes+, role=no-test]
 ----
-# Import the client
 from graphdatascience import GraphDataScience
+----
 
-# Replace with the actual credentials
-AURA_CONNECTION_URI = "neo4j+s://xxxxxxxx.databases.neo4j.io"
-AURA_USERNAME = "neo4j"
-AURA_PASSWORD = ""
+When using a local Neo4j setup, the default connection URI is `bolt://localhost:7687`:
 
-# Configure the client with AuraDS-recommended settings if using AuraDS
-gds = GraphDataScience(AURA_CONNECTION_URI, auth=(AURA_USERNAME, AURA_PASSWORD), aura_ds=True)
+[source, python, subs=attributes+, role=no-test]
+----
+# Replace with the actual connection URI and credentials
+NEO4J_CONNECTION_URI = "bolt://localhost:7687"
+NEO4J_USERNAME = "neo4j"
+NEO4J_PASSWORD = ""
+
+gds = GraphDataScience(NEO4J_CONNECTION_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
+----
+
+When using AuraDS, the connection URI is slightly different as it uses the `neo4j+s` protocol. The client should also include the `aura_ds=True` flag to enable AuraDS-recommended settings.
+
+[source, python, subs=attributes+, role=no-test]
+----
+# Replace with the actual connection URI and credentials
+NEO4J_CONNECTION_URI = "neo4j+s://xxxxxxxx.databases.neo4j.io"
+NEO4J_USERNAME = "neo4j"
+NEO4J_PASSWORD = ""
+
+gds = GraphDataScience(NEO4J_CONNECTION_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD), aura_ds=True)
 ----
 
 We also import `pandas` to create a Pandas `DataFrame` from the original
@@ -110,6 +125,15 @@ Let's check the first 5 rows of the new `DataFrame`:
 nodes.head()
 ----
 
+----
+ nodeId labels subject features
+0 31336 Paper 0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
+1 1061127 Paper 1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...
+2 1106406 Paper 2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
+3 13195 Paper 2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
+4 37879 Paper 3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
+----
+
 Now we create a new `DataFrame` containing the relationships between the nodes.
 To create the equivalent of an undirected graph, we need to add direct
 and inverse relationships explicitly.
@@ -129,6 +153,15 @@ Again, let's check the first 5 rows of the new `DataFrame`:
 relationships.head()
 ----
 
+----
+ sourceNodeId targetNodeId relationshipType
+0 35 1033 CITES
+1 35 103482 CITES
+2 35 103515 CITES
+3 35 1050679 CITES
+4 35 1103960 CITES
+----
+
 Finally, we can create the in-memory graph.
 
 [source, python, role=no-test]
@@ -153,6 +186,21 @@ Let's also count the nodes in the graph:
 G.node_count()
 ----
 
+----
+2708
+----
+
+The count matches with the number of rows in the Pandas dataset:
+
+[source, python, role=no-test]
+----
+len(content)
+----
+
+----
+2708
+----
+
 We can stream the value of the `subject` node property for
 each node in the graph, printing only the first 10.
 
@@ -161,9 +209,25 @@ each node in the graph, printing only the first 10.
 gds.graph.streamNodeProperties(G, ["subject"]).head(10)
 ----
 
+----
+ nodeId nodeProperty propertyValue
+0 31336 subject 0
+1 1061127 subject 1
+2 1106406 subject 2
+3 13195 subject 2
+4 37879 subject 3
+5 1126012 subject 3
+6 1107140 subject 4
+7 1102850 subject 0
+8 31349 subject 0
+9 1106418 subject 4
+----
+
 
 == Cleanup
 
+When the graph is no longer needed, it should be dropped to free up memory:
+
 [source, python, role=no-test]
 ----
 G.drop()