diff --git a/python/xy/pyplot/_axes.py b/python/xy/pyplot/_axes.py
index 6710fd18..2d4b45f0 100644
--- a/python/xy/pyplot/_axes.py
+++ b/python/xy/pyplot/_axes.py
@@ -186,7 +186,11 @@ def convert(values: Any) -> Any:
     indexes = {
         "segments": (0, 2) if axis == "x" else (1, 3),
         "triangle_mesh": (0, 2, 4) if axis == "x" else (1, 3, 5),
+        "area": (0,) if axis == "x" else (1,),
         "step": (0,) if axis == "x" else (1,),
+        # Compact stairs store (values, edges), the reverse of ordinary
+        # Cartesian (x, y) argument order.
+        "stairs": (1,) if axis == "x" else (0,),
         "stem": (0,) if axis == "x" else (1,),
         "errorbar": (0,) if axis == "x" else (1,),
         "hexbin": (0,) if axis == "x" else (1,),
@@ -195,6 +199,8 @@ def convert(values: Any) -> Any:
     for index in indexes:
         args[index] = convert(args[index])
     entry["args"] = tuple(args)
+    if factory == "area" and axis == "y" and "base" in entry.get("kwargs", {}):
+        entry["kwargs"]["base"] = convert(entry["kwargs"]["base"])
 
 
 def _nonlinear_ticks(domain: tuple[float, float], spec: dict[str, Any]) -> np.ndarray:
@@ -2697,9 +2703,17 @@ def _iter_entry_arrays(self, axis: str) -> Iterator[tuple[np.ndarray, bool]]:
                 indexes = {
                     "segments": (0, 2) if axis == "x" else (1, 3),
                     "triangle_mesh": (0, 2, 4) if axis == "x" else (1, 3, 5),
+                    "area": (0,) if axis == "x" else (1,),
+                    # xy.stairs(values, edges) is compact and deliberately
+                    # reverses the ordinary x/y argument order.
+                    "stairs": (1,) if axis == "x" else (0,),
                 }.get(factory, ())
                 for index in indexes:
                     yield np.asarray(entry["args"][index], dtype=np.float64).reshape(-1), True
+                if factory == "area" and axis == "y":
+                    base = entry.get("kwargs", {}).get("base")
+                    if base is not None:
+                        yield np.asarray(base, dtype=np.float64).reshape(-1), True
                 if factory == "contour":
                     z = np.asarray(entry["args"][0])
                     coordinates = entry.get("kwargs", {}).get(key)
diff --git a/tests/pyplot/test_gallery_hist_errorbar_compat.py b/tests/pyplot/test_gallery_hist_errorbar_compat.py
index 87579ec1..86384705 100644
--- a/tests/pyplot/test_gallery_hist_errorbar_compat.py
+++ b/tests/pyplot/test_gallery_hist_errorbar_compat.py
@@ -62,6 +62,51 @@ def test_hist_dataset_style_lengths_must_match_dataset_count() -> None:
         ax.hist([[0, 1], [2, 3]], bins=2, linewidth=[1, 2, 3])
 
 
+@pytest.mark.parametrize("histtype", ["step", "stepfilled"])
+def test_hist_step_geometry_contributes_to_autoscale(histtype: str) -> None:
+    _fig, ax = plt.subplots()
+
+    counts, edges, _container = ax.hist(
+        [-3.0, -1.0, -0.5, 0.0, 0.5, 1.0, 3.0],
+        bins=np.arange(-4.0, 4.1, 0.5),
+        histtype=histtype,
+        weights=np.full(7, 1 / 7),
+    )
+
+    assert not ax._axis_is_dataless("x")
+    assert not ax._axis_is_dataless("y")
+    assert ax._entry_extent("x") == pytest.approx((edges[0], edges[-1]))
+    assert ax._entry_extent("y") == pytest.approx((0.0, counts.max()))
+
+    core = ax._build_chart(350, 300).figure()
+    assert core.x_range() == pytest.approx((-4.24, 4.24))
+    assert core.y_range()[1] > counts.max()
+
+
+def test_hist_density_and_probability_weights_match_numpy() -> None:
+    values = np.array([-2.2, -1.8, -0.4, -0.1, 0.2, 0.7, 1.1, 2.4])
+    edges = np.array([-3.0, -1.0, 0.0, 0.5, 1.5, 3.0])
+    _fig, (density_ax, weights_ax) = plt.subplots(1, 2)
+
+    density, returned_edges, _ = density_ax.hist(values, bins=edges, density=True, histtype="step")
+    weighted, _, _ = weights_ax.hist(
+        values,
+        bins=edges,
+        weights=np.full(len(values), 1 / len(values)),
+        histtype="step",
+    )
+
+    expected_density, expected_edges = np.histogram(values, bins=edges, density=True)
+    expected_weighted, _ = np.histogram(
+        values, bins=edges, weights=np.full(len(values), 1 / len(values))
+    )
+    np.testing.assert_allclose(density, expected_density)
+    np.testing.assert_array_equal(returned_edges, expected_edges)
+    np.testing.assert_allclose(weighted, expected_weighted)
+    assert np.sum(density * np.diff(edges)) == pytest.approx(1.0)
+    assert weighted.sum() == pytest.approx(1.0)
+
+
 def test_errorbar_forwards_marker_size_and_linestyle_to_data_line_only() -> None:
     _fig, ax = plt.subplots()
     container = ax.errorbar(