diff --git a/optuna/visualization/_rank.py b/optuna/visualization/_rank.py
index 15f74ea6ae..d7e4597af4 100644
--- a/optuna/visualization/_rank.py
+++ b/optuna/visualization/_rank.py
@@ -208,17 +208,49 @@ def _get_rank_subplot_info(
 
 
 def _get_axis_info(trials: list[FrozenTrial], param_name: str) -> _AxisInfo:
-    values: list[str | float | None]
+    # Single-pass extraction of required statistics
+    values: list[str | float | None] = []
+    min_value = None
+    max_value = None
+    unique_values = set()
+    has_none = False
+
     is_numerical = _is_numerical(trials, param_name)
     if is_numerical:
-        values = [t.params.get(param_name) for t in trials]
+        for t in trials:
+            v = t.params.get(param_name)
+            values.append(v)
+            if v is not None:
+                if min_value is None or v < min_value:
+                    min_value = v
+                if max_value is None or v > max_value:
+                    max_value = v
     else:
-        values = [
-            str(t.params.get(param_name)) if param_name in t.params else None for t in trials
-        ]
+        for t in trials:
+            if param_name in t.params:
+                v = str(t.params[param_name])
+            else:
+                v = None
+            values.append(v)
+            unique_values.add(v)
+            if v is None:
+                has_none = True
+
+        # For categorical, compute min/max indices after
+        # (keeping values as category labels)
 
-    min_value = min([v for v in values if v is not None])
-    max_value = max([v for v in values if v is not None])
+    if is_numerical:
+        # At this point, min_value and max_value are computed in previous loop
+        # Ensure filtered for None
+        if min_value is None or max_value is None:
+            # This raises ValueError like min/max with an empty sequence
+            raise ValueError("No non-None values in values for numerical parameter.")
+    else:
+        # unique_values already collected, has_none flags if None is present
+        unique_values_count = len(unique_values)
+        span = unique_values_count - 1
+        if has_none:
+            span -= 1
 
     if _is_log_scale(trials, param_name):
         min_value = float(min_value)
@@ -239,13 +271,11 @@ def _get_axis_info(trials: list[FrozenTrial], param_name: str) -> _AxisInfo:
         is_cat = False
 
     else:
-        unique_values = set(values)
-        span = len(unique_values) - 1
-        if None in unique_values:
-            span -= 1
         padding = span * PADDING_RATIO
-        min_value = -padding
-        max_value = span + padding
+        min_axis = -padding
+        max_axis = span + padding
+        min_value = min_axis
+        max_value = max_axis
         is_log = False
         is_cat = True