diff --git a/libpysal/graph/base.py b/libpysal/graph/base.py
index fe4ee038b..6c13bda48 100644
--- a/libpysal/graph/base.py
+++ b/libpysal/graph/base.py
@@ -189,6 +189,45 @@ def to_W(self):  # noqa: N802
         -------
         libpysal.weights.W
             representation of graph as a weights.W object
+
+        Examples
+        --------
+        >>> import geopandas as gpd
+        >>> from geodatasets import get_path
+        >>> nybb = gpd.read_file(get_path('nybb')).set_index("BoroName")
+        >>> nybb
+                       BoroCode  ...                                           geometry
+        BoroName                 ...
+        Staten Island         5  ...  MULTIPOLYGON (((970217.022 145643.332, 970227....
+        Queens                4  ...  MULTIPOLYGON (((1029606.077 156073.814, 102957...
+        Brooklyn              3  ...  MULTIPOLYGON (((1021176.479 151374.797, 102100...
+        Manhattan             1  ...  MULTIPOLYGON (((981219.056 188655.316, 980940....
+        Bronx                 2  ...  MULTIPOLYGON (((1012821.806 229228.265, 101278...
+        [5 rows x 4 columns]
+
+        >>> contiguity = graph.Graph.build_contiguity(nybb)
+        >>> contiguity.adjacency
+        focal          neighbor
+        Staten Island  Staten Island    0
+        Queens         Brooklyn         1
+                       Manhattan        1
+                       Bronx            1
+        Brooklyn       Queens           1
+                       Manhattan        1
+        Manhattan      Queens           1
+                       Brooklyn         1
+                       Bronx            1
+        Bronx          Queens           1
+                       Manhattan        1
+        Name: weight, dtype: int64
+
+        >>> w = contiguity.to_W()
+        >>> w.neighbors
+        {'Bronx': ['Queens', 'Manhattan'],
+         'Brooklyn': ['Queens', 'Manhattan'],
+         'Manhattan': ['Queens', 'Brooklyn', 'Bronx'],
+         'Queens': ['Brooklyn', 'Manhattan', 'Bronx'],
+         'Staten Island': []}
         """
         grouper = self._adjacency.groupby(level=0)
         neighbors = {}