Consider necessary columns from complex arguments when interchanging dataframes

Author: guyrosin

packages/python/plotly/plotly/express/_core.py

@@ -1419,9 +1419,17 @@ def build_dataframe(args, constructor):
             else:
                 # Save precious resources by only interchanging columns that are
                 # actually going to be plotted.
-                columns = [
+                necessary_columns = [
                     i for i in args.values() if isinstance(i, str) and i in columns
                 ]
+                for field in args:
+                    if field in array_attrables and isinstance(
+                        args[field], (list, dict)
+                    ):
+                        necessary_columns.extend(
+                            [i for i in args[field] if i in columns]
+                        )
+                columns = list(dict.fromkeys(necessary_columns))
                 args["data_frame"] = pd.api.interchange.from_dataframe(
                     args["data_frame"].select_columns_by_name(columns)
                 )

packages/python/plotly/plotly/tests/test_optional/test_px/test_px_input.py

@@ -327,6 +327,32 @@ def test_build_df_from_vaex_and_polars(test_lib):
     )
 
 
+@pytest.mark.skipif(
+    version.parse(pd.__version__) < version.parse("2.0.2"),
+    reason="plotly doesn't use a dataframe interchange protocol for pandas < 2.0.2",
+)
+@pytest.mark.parametrize("test_lib", ["vaex", "polars"])
+def test_build_df_with_hover_data_from_vaex_and_polars(test_lib):
+    if test_lib == "vaex":
+        import vaex as lib
+    else:
+        import polars as lib
+
+    # take out the 'species' columns since the vaex implementation does not cover strings yet
+    iris_pandas = px.data.iris()[["petal_width", "sepal_length", "sepal_width"]]
+    iris_vaex = lib.from_pandas(iris_pandas)
+    args = dict(
+        data_frame=iris_vaex,
+        x="petal_width",
+        y="sepal_length",
+        hover_data=["sepal_width"],
+    )
+    out = build_dataframe(args, go.Scatter)
+    assert_frame_equal(
+        iris_pandas.reset_index()[out["data_frame"].columns], out["data_frame"]
+    )
+
+
 def test_timezones():
     df = pd.DataFrame({"date": ["2015-04-04 19:31:30+1:00"], "value": [3]})
     df["date"] = pd.to_datetime(df["date"])