diff --git a/vizro-core/examples/visual-vocabulary/custom_charts.py b/vizro-core/examples/visual-vocabulary/custom_charts.py
index 52fb6c226..aee03a461 100644
--- a/vizro-core/examples/visual-vocabulary/custom_charts.py
+++ b/vizro-core/examples/visual-vocabulary/custom_charts.py
@@ -330,33 +330,25 @@ def lollipop(data_frame: pd.DataFrame, **kwargs):
     Returns:
         go.Figure: Lollipop chart.
     """
-    # Should we allow keyword arguments (kwargs) in this context, given the presence of multiple traces?
     # Unlike the column_and_line chart, where all traces hold equal significance, here the traces differ in importance.
     # The primary scatter plot is the main trace, while the additional traces merely serve as connecting lines.
-    # Therefore, should we apply the kwargs solely to the scatter plot, as illustrated below?
+    # Therefore, should we apply the kwargs solely to the main scatter plot, as illustrated below?
     fig = px.scatter(data_frame, **kwargs)
-    x_array = fig.data[0].x
-    y_array = fig.data[0].y
-
-    # Is it necessary to always enable orientation? Unlike bar charts, scatter plots do not have an
-    # orientation argument. The calculation of the trace below needs to adapt based on the orientation.
-    # However, since we can't predict the user's choice for x and y axes, I am not sure how we can dynamically update
-    # the orientation. This chart does not always have a categorical and a numerical column. In can also be
-    # with both numeric columns. Should we introduce this ourselves? Does this have to be part of this example
-    # or can we hard-code for the visual-vocabulary?
+
+    # Enable for both orientations
+    orientation = fig.data[0].orientation
+    x_array = fig.data[0]["x"]
+    y_array = fig.data[0]["y"]
+
     for i in range(len(data_frame)):
         fig.add_trace(
             go.Scatter(
-                x=[0, x_array[i]],
-                y=[y_array[i], y_array[i]],
+                x=[0, x_array[i]] if orientation == "h" else [x_array[i], x_array[i]],
+                y=[y_array[i], y_array[i]] if orientation == "h" else [0, y_array[i]],
                 mode="lines",
             )
         )
 
     fig.update_traces(marker_size=12, line_width=3, line_color=fig.layout.template.layout.colorway[0])
-    fig.update_layout(showlegend=False)
-
-    # These are use-case specific layout updates. Normally not suitable for a chart inside vizro.charts, as these
-    # depend on the data context.
-    fig.update_layout(yaxis_title="", yaxis_showgrid=False)
+    fig.update_layout(showlegend=False, yaxis_title="", yaxis_showgrid=False)
     return fig
diff --git a/vizro-core/examples/visual-vocabulary/pages/examples/lollipop.py b/vizro-core/examples/visual-vocabulary/pages/examples/lollipop.py
index 38c5f6fb6..53129f6e5 100644
--- a/vizro-core/examples/visual-vocabulary/pages/examples/lollipop.py
+++ b/vizro-core/examples/visual-vocabulary/pages/examples/lollipop.py
@@ -10,21 +10,22 @@
 def lollipop(data_frame: pd.DataFrame, **kwargs):
     """Creates a lollipop chart using Plotly."""
     fig = px.scatter(data_frame, **kwargs)
-    x_array = fig.data[0].x
-    y_array = fig.data[0].y
+
+    orientation = fig.data[0].orientation
+    x_array = fig.data[0]["x"]
+    y_array = fig.data[0]["y"]
 
     for i in range(len(data_frame)):
         fig.add_trace(
             go.Scatter(
-                x=[0, x_array[i]],
-                y=[y_array[i], y_array[i]],
+                x=[0, x_array[i]] if orientation == "h" else [x_array[i], x_array[i]],
+                y=[y_array[i], y_array[i]] if orientation == "h" else [0, y_array[i]],
                 mode="lines",
             )
         )
 
     fig.update_traces(marker_size=12, line_width=3, line_color=fig.layout.template.layout.colorway[0])
-    fig.update_layout(showlegend=False)
-    fig.update_layout(yaxis_title="", yaxis_showgrid=False)
+    fig.update_layout(showlegend=False, yaxis_title="", yaxis_showgrid=False)
     return fig