diff --git a/bigframes/operations/_matplotlib/core.py b/bigframes/operations/_matplotlib/core.py
index 7cbeb3df4f..b4beea75fd 100644
--- a/bigframes/operations/_matplotlib/core.py
+++ b/bigframes/operations/_matplotlib/core.py
@@ -17,6 +17,9 @@
 
 import matplotlib.pyplot as plt
 
+DEFAULT_SAMPLING_N = 1000
+DEFAULT_SAMPLING_STATE = 0
+
 
 class MPLPlot(abc.ABC):
     @abc.abstractmethod
@@ -45,8 +48,10 @@ def generate(self) -> None:
 
     def _compute_plot_data(self, data):
         # TODO: Cache the sampling data in the PlotAccessor.
-        sampling_n = self.kwargs.pop("sampling_n", 100)
-        sampling_random_state = self.kwargs.pop("sampling_random_state", 0)
+        sampling_n = self.kwargs.pop("sampling_n", DEFAULT_SAMPLING_N)
+        sampling_random_state = self.kwargs.pop(
+            "sampling_random_state", DEFAULT_SAMPLING_STATE
+        )
         return data.sample(
             n=sampling_n,
             random_state=sampling_random_state,
diff --git a/tests/system/small/operations/test_plotting.py b/tests/system/small/operations/test_plotting.py
index 47491cdada..5ca3382e2a 100644
--- a/tests/system/small/operations/test_plotting.py
+++ b/tests/system/small/operations/test_plotting.py
@@ -17,6 +17,7 @@
 import pandas._testing as tm
 import pytest
 
+import bigframes.operations._matplotlib.core as bf_mpl
 import bigframes.pandas as bpd
 
 
@@ -209,11 +210,10 @@ def test_scatter(scalars_dfs):
 
 
 def test_sampling_plot_args_n():
-    df = bpd.DataFrame(np.arange(1000), columns=["one"])
+    df = bpd.DataFrame(np.arange(bf_mpl.DEFAULT_SAMPLING_N * 10), columns=["one"])
     ax = df.plot.line()
     assert len(ax.lines) == 1
-    # Default sampling_n is 100
-    assert len(ax.lines[0].get_data()[1]) == 100
+    assert len(ax.lines[0].get_data()[1]) == bf_mpl.DEFAULT_SAMPLING_N
 
     ax = df.plot.line(sampling_n=2)
     assert len(ax.lines) == 1
@@ -221,7 +221,7 @@ def test_sampling_plot_args_n():
 
 
 def test_sampling_plot_args_random_state():
-    df = bpd.DataFrame(np.arange(1000), columns=["one"])
+    df = bpd.DataFrame(np.arange(bf_mpl.DEFAULT_SAMPLING_N * 10), columns=["one"])
     ax_0 = df.plot.line()
     ax_1 = df.plot.line()
     ax_2 = df.plot.line(sampling_random_state=100)