diff --git a/python/pyspark/pandas/plot/core.py b/python/pyspark/pandas/plot/core.py
index 067c7db664dee..7630ecc398954 100644
--- a/python/pyspark/pandas/plot/core.py
+++ b/python/pyspark/pandas/plot/core.py
@@ -68,19 +68,52 @@ class SampledPlotBase:
     def get_sampled(self, data):
         from pyspark.pandas import DataFrame, Series
 
+        if not isinstance(data, (DataFrame, Series)):
+            raise TypeError("Only DataFrame and Series are supported for plotting.")
+        if isinstance(data, Series):
+            data = data.to_frame()
+
         fraction = get_option("plotting.sample_ratio")
-        if fraction is None:
-            fraction = 1 / (len(data) / get_option("plotting.max_rows"))
-            fraction = min(1.0, fraction)
-        self.fraction = fraction
-
-        if isinstance(data, (DataFrame, Series)):
-            if isinstance(data, Series):
-                data = data.to_frame()
+        if fraction is not None:
+            self.fraction = fraction
             sampled = data._internal.resolved_copy.spark_frame.sample(fraction=self.fraction)
             return DataFrame(data._internal.with_new_sdf(sampled))._to_pandas()
         else:
-            raise TypeError("Only DataFrame and Series are supported for plotting.")
+            from pyspark.sql import Observation
+
+            max_rows = get_option("plotting.max_rows")
+            observation = Observation("ps plotting")
+            sdf = data._internal.resolved_copy.spark_frame.observe(
+                observation, F.count(F.lit(1)).alias("count")
+            )
+
+            rand_col_name = "__ps_plotting_sampled_plot_base_rand__"
+            id_col_name = "__ps_plotting_sampled_plot_base_id__"
+
+            sampled = (
+                sdf.select(
+                    "*",
+                    F.rand().alias(rand_col_name),
+                    F.monotonically_increasing_id().alias(id_col_name),
+                )
+                .sort(rand_col_name)
+                .limit(max_rows + 1)
+                .coalesce(1)
+                .sortWithinPartitions(id_col_name)
+                .drop(rand_col_name, id_col_name)
+            )
+
+            pdf = DataFrame(data._internal.with_new_sdf(sampled))._to_pandas()
+
+            if len(pdf) > max_rows:
+                try:
+                    self.fraction = float(max_rows) / observation.get["count"]
+                except Exception:
+                    pass
+                return pdf[:max_rows]
+            else:
+                self.fraction = 1.0
+                return pdf
 
     def set_result_text(self, ax):
         assert hasattr(self, "fraction")