Merge pull request #171 from neo4j/rels-only-df

Make node_dfs optional in from_dfs integration

Author: FlorentinD

changelog.md

@@ -6,6 +6,7 @@
 
 ## New features
 
+* Allow visualization based only on relationship DataFrames, without specifying node DataFrames in `from_dfs`
 
 ## Bug fixes
 

docs/source/integration.rst

@@ -38,6 +38,7 @@ The ``from_dfs`` method takes two mandatory positional parameters:
   on corresponding nodes under that field name.
   Otherwise, the column name will be a key in each node's `properties` dictionary, that maps to the node's corresponding
   value in the column.
+  If the graph has no node properties, the nodes can be derived from the relationships DataFrame alone.
 * A Pandas ``DataFrame``, or iterable (eg. list) of DataFrames representing the relationships of the graph.
   The rows of the DataFrame(s) should represent the individual relationships, and the columns should represent the
   relationship IDs and attributes.

python-wrapper/src/neo4j_viz/pandas.py

@@ -13,18 +13,40 @@
 
 
 def _from_dfs(
-    node_dfs: DFS_TYPE,
+    node_dfs: Optional[DFS_TYPE],
     rel_dfs: DFS_TYPE,
     node_radius_min_max: Optional[tuple[float, float]] = (3, 60),
     rename_properties: Optional[dict[str, str]] = None,
 ) -> VisualizationGraph:
+    relationships = _parse_relationships(rel_dfs, rename_properties=rename_properties)
+
+    if node_dfs is None:
+        has_size = False
+        node_ids = set()
+        for rel in relationships:
+            node_ids.add(rel.source)
+            node_ids.add(rel.target)
+        nodes = [Node(id=id) for id in node_ids]
+    else:
+        nodes, has_size = _parse_nodes(node_dfs, rename_properties=rename_properties)
+
+    VG = VisualizationGraph(nodes=nodes, relationships=relationships)
+
+    if node_radius_min_max is not None and has_size:
+        VG.resize_nodes(node_radius_min_max=node_radius_min_max)
+
+    return VG
+
+
+def _parse_nodes(node_dfs: DFS_TYPE, rename_properties: Optional[dict[str, str]]) -> tuple[list[Node], bool]:
     if isinstance(node_dfs, DataFrame):
         node_dfs_iter: Iterable[DataFrame] = [node_dfs]
+    elif node_dfs is None:
+        node_dfs_iter = []
     else:
         node_dfs_iter = node_dfs
 
     all_node_field_aliases = Node.all_validation_aliases()
-    all_rel_field_aliases = Relationship.all_validation_aliases()
 
     has_size = True
     nodes = []
@@ -42,13 +64,18 @@ def _from_dfs(
                     properties[key] = value
 
             nodes.append(Node(**top_level, properties=properties))
+    return nodes, has_size
+
+
+def _parse_relationships(rel_dfs: DFS_TYPE, rename_properties: Optional[dict[str, str]]) -> list[Relationship]:
+    all_rel_field_aliases = Relationship.all_validation_aliases()
 
     if isinstance(rel_dfs, DataFrame):
         rel_dfs_iter: Iterable[DataFrame] = [rel_dfs]
     else:
         rel_dfs_iter = rel_dfs
+    relationships: list[Relationship] = []
 
-    relationships = []
     for rel_df in rel_dfs_iter:
         for _, row in rel_df.iterrows():
             top_level = {}
@@ -62,17 +89,11 @@ def _from_dfs(
                     properties[key] = value
 
             relationships.append(Relationship(**top_level, properties=properties))
-
-    VG = VisualizationGraph(nodes=nodes, relationships=relationships)
-
-    if node_radius_min_max is not None and has_size:
-        VG.resize_nodes(node_radius_min_max=node_radius_min_max)
-
-    return VG
+    return relationships
 
 
 def from_dfs(
-    node_dfs: DFS_TYPE,
+    node_dfs: Optional[DFS_TYPE],
     rel_dfs: DFS_TYPE,
     node_radius_min_max: Optional[tuple[float, float]] = (3, 60),
 ) -> VisualizationGraph:
@@ -85,8 +106,9 @@ def from_dfs(
 
     Parameters
     ----------
-    node_dfs: Union[DataFrame, Iterable[DataFrame]]
+    node_dfs: Optional[Union[DataFrame, Iterable[DataFrame]]]
         DataFrame or iterable of DataFrames containing node data.
+        If None, the nodes will be created from the source and target node ids in the rel_dfs.
     rel_dfs: Union[DataFrame, Iterable[DataFrame]]
         DataFrame or iterable of DataFrames containing relationship data.
     node_radius_min_max : tuple[float, float], optional

python-wrapper/tests/test_pandas.py

@@ -1,6 +1,7 @@
 from pandas import DataFrame
 from pydantic_extra_types.color import Color
 
+from neo4j_viz.node import Node
 from neo4j_viz.pandas import from_dfs
 
 
@@ -45,6 +46,31 @@ def test_from_df() -> None:
     assert VG.relationships[1].properties == {"weight": 2.0}
 
 
+def test_from_rel_dfs() -> None:
+    relationships = [
+        DataFrame(
+            {
+                "source": [0, 1],
+                "target": [1, 0],
+                "caption": ["REL", "REL2"],
+                "weight": [1.0, 2.0],
+            }
+        ),
+        DataFrame(
+            {
+                "source": [2, 3],
+                "target": [1, 0],
+                "caption": ["REL", "REL2"],
+                "weight": [1.0, 2.0],
+            }
+        ),
+    ]
+    VG = from_dfs(None, relationships)
+
+    assert len(VG.relationships) == 4
+    assert VG.nodes == [Node(id=id) for id in [0, 1, 2, 3]]
+
+
 def test_from_dfs() -> None:
     nodes = [
         DataFrame(