Add manual docs for from_dfs importer

Author: adamnsch

docs/source/importing.rst

@@ -7,6 +7,65 @@ In this section we will cover how to import data from `Pandas DataFrames <https:
 `Neo4j Graph Data Science <https://neo4j.com/docs/graph-data-science/current/>`_.
 
 
+Pandas DataFrames
+-----------------
+
+The ``neo4j-viz`` library provides a convenience method for importing data from Pandas ``DataFrame``s.
+It requires and additional dependency to be installed, which you can do by running:
+
+.. code-block:: bash
+
+    pip install neo4j-viz[pandas]
+
+Once you have installed the additional dependency, you can use the :doc:`from_gds <./api-reference/from_pandas>` method
+to import pandas ``DataFrame``s.
+
+The ``from_dfs`` method takes two mandatory positional parameters:
+
+* A Pandas ``DataFrame``, or iterable (eg. list) of ``DataFrame``s representing the nodes of the graph.
+  The rows of the ``DataFrame``(s) should represent the individual nodes, and the columns should represent the node
+  IDs and properties. The columns map directly to fields on :doc:`Nodes <./api-reference/node>`, and as such
+  should follow the same naming conventions.
+* A Pandas ``DataFrame``, or iterable (eg. list) of ``DataFrame``s 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 properties. The columns map directly to fields on
+  :doc:`Relationships <./api-reference/relationship>`, and as such should follow the same naming conventions.
+
+``from_dfs`` also takes an optional property, ``node_radius_min_max``, can be used (and is used by default) to scale
+the node sizes for the visualization.
+It is a tuple of two floats, representing the radii (sizes) in pixels of the smallest and largest nodes respectively in
+the visualization.
+This can be useful if node sizes vary a lot, or are all very small or very big.
+
+
+Example
+~~~~~~~
+
+In this small example, we import a tiny toy graph representing a social network from two Pandas ``DataFrame``s.
+As we can see the column names of the ``DataFrame``s map directly to the fields of :doc:`Nodes <./api-reference/node>`
+and :d
+
+.. code-block:: python
+    from pandas import DataFrame
+    from neo4j_viz.pandas import from_dfs
+
+    nodes = DataFrame({
+        "id": [1, 2, 3],
+        "caption": ["Alice", "Bob", "Charlie"],
+        "size": [20, 10, 10],
+    })
+    relationships = DataFrame({
+        "source": [1, 2],
+        "target": [2, 3],
+        "caption": ["LIKES", "KNOWS"],
+    })
+
+    VG = from_dfs(nodes, relationships)
+
+For another example of the ``from_dfs`` importer in action, see the
+:doc:`Visualizing Snowflake Tables tutorial <./tutorials/snowpark-nvl-example>`.
+
+
 Neo4j Graph Data Science (GDS) library
 --------------------------------------
 
@@ -22,6 +81,7 @@ Once you have installed the additional dependency, you can use the :doc:`from_gd
 to import projections from the GDS library.
 
 The ``from_dfs`` method takes two mandatory positional parameters:
+
 * An initialized ``GraphDataScience`` object for the connection to the GDS instance, and
 * A ``Graph`` representing the projection that one wants to import.
 
@@ -74,8 +134,3 @@ We use the "pagerank" property to determine the size of the nodes, and the "comp
 
 Please see the :doc:`Visualizing Neo4j Graph Data Science (GDS) Graphs tutorial <./tutorials/gds-nvl-example>` for a
 more extensive example.
-
-
-Pandas DataFrames
------------------
-

docs/source/index.rst

@@ -6,7 +6,7 @@
 Graph Visualization for Python by Neo4j documentation
 =====================================================
 
-This is the documentation for the `neo4j-viz` Python library by Neo4j.
+This is the documentation for the ``neo4j-viz`` Python library by Neo4j.
 The library allows you to visualize graph data interactively in Python using a simple API.
 
 The library wraps the `Neo4j Visualization JavaScript library (NVL) <https://neo4j.com/docs/nvl/current/>`_, and provides additional features for working with graph data in Python.