Raise a clear error for unsupported marginal plot types

CHANGELOG.md

@@ -4,6 +4,9 @@ This project adheres to [Semantic Versioning](http://semver.org/).
 
 ## Unreleased
 
+### Fixed
+- Raise a clear `ValueError` when an unsupported marginal plot type is passed to Plotly Express, instead of failing later with a cryptic `'NoneType' object has no attribute 'constructor'` message [[#5625](https://github.com/plotly/plotly.py/pull/5625)]
+
 
 ## [6.8.0] - 2026-06-03
 

plotly/express/_core.py

@@ -971,6 +971,12 @@ def make_trace_spec(args, constructor, attrs, trace_patch):
                     ),
                     marginal=letter,
                 )
+            else:
+                raise ValueError(
+                    "Invalid value '%s' for `marginal_%s`. Supported marginal "
+                    "plot types are: 'rug', 'box', 'violin', 'histogram'."
+                    % (args["marginal_" + letter], letter)
+                )
             if "color" in attrs or "color" not in args:
                 if "marker" not in trace_spec.trace_patch:
                     trace_spec.trace_patch["marker"] = dict()

tests/test_optional/test_px/test_marginals.py

@@ -24,3 +24,15 @@ def test_single_marginals(backend, px_fn, marginal, orientation):
         df, x="total_bill", y="total_bill", marginal=marginal, orientation=orientation
     )
     assert len(fig.data) == 1 + (marginal is not None)
+
+
+def test_unsupported_marginal_raises_clear_error():  # issue 4654
+    # An unsupported marginal type used to fail deep inside make_figure with a
+    # cryptic "'NoneType' object has no attribute 'constructor'". It should
+    # instead raise a clear error naming the supported values.
+    with pytest.raises(ValueError, match="Supported marginal plot types"):
+        px.scatter(x=[1, 2, 3], y=[2, 3, 4], marginal_x="density")
+    with pytest.raises(ValueError, match="Supported marginal plot types"):
+        px.scatter(x=[1, 2, 3], y=[2, 3, 4], marginal_y="density")
+    with pytest.raises(ValueError, match="Supported marginal plot types"):
+        px.histogram(x=[1, 2, 3], marginal="density")