diff --git a/doc/tutorial/single_cell_model.rst b/doc/tutorial/single_cell_model.rst
index f0f8c56b1bad5062705492662fe4d76697f64984..388c1254599efc7c271cb24f3e3a4fa390a41b69 100644
--- a/doc/tutorial/single_cell_model.rst
+++ b/doc/tutorial/single_cell_model.rst
@@ -142,7 +142,7 @@ results! Let's take a look at what the spike detector and a voltage probes from
     import pandas, seaborn # You may have to pip install these.
     seaborn.set_theme() # Apply some styling to the plot
     df = pandas.DataFrame({'t/ms': m.traces[0].time, 'U/mV': m.traces[0].value})
-    seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV").savefig('single_cell_model_result.svg')
+    seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",ci=None).savefig('single_cell_model_result.svg')
 
 Step **(7)** accesses :meth:`arbor.single_cell_model.spikes<arbor.single_cell_model.spikes>`
 to print the spike times. A single spike should be generated at around the same time the stimulus
diff --git a/python/example/network_ring.py b/python/example/network_ring.py
index e486c624a71b39f235f4f293743cb4d63d8f274d..49fe5a6932f7a393251cbebcd2c5303da041a4fc 100644
--- a/python/example/network_ring.py
+++ b/python/example/network_ring.py
@@ -152,4 +152,4 @@ for gid in range(ncells):
     df_list.append(pandas.DataFrame({'t/ms': times, 'U/mV': volts, 'Cell': f"cell {gid}"}))
 
 df = pandas.concat(df_list)
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Cell").savefig('network_ring_result.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Cell",ci=None).savefig('network_ring_result.svg')
diff --git a/python/example/single_cell_model.py b/python/example/single_cell_model.py
index 7634541773075f7f0b12cc38c043ebf4e4f184a6..d3f6bf9c3c04fbb063392b7aa95f72d2f4d38286 100644
--- a/python/example/single_cell_model.py
+++ b/python/example/single_cell_model.py
@@ -37,7 +37,7 @@ else:
 print("Plotting results ...")
 seaborn.set_theme() # Apply some styling to the plot
 df = pandas.DataFrame({'t/ms': m.traces[0].time, 'U/mV': m.traces[0].value})
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV").savefig('single_cell_model_result.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",ci=None).savefig('single_cell_model_result.svg')
 
 # (9) Optionally, you can store your results for later processing.
 df.to_csv('single_cell_model_result.csv', float_format='%g')
diff --git a/python/example/single_cell_multi_branch.py b/python/example/single_cell_multi_branch.py
index 6bc6c013fe2e98dcd56f23023a6927a52d197864..ce14a6bb573af75fdcc8ae61474b61995228294c 100644
--- a/python/example/single_cell_multi_branch.py
+++ b/python/example/single_cell_multi_branch.py
@@ -107,4 +107,4 @@ df = pandas.DataFrame()
 for t in m.traces:
     df=df.append(pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': t.location, "Variable": t.variable}) )
 
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable").savefig('single_cell_multi_branch_result.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_multi_branch_result.svg')
diff --git a/python/example/single_cell_swc.py b/python/example/single_cell_swc.py
index 5136218b6f4982ff872bd92ef2c97d4a081af0fb..fe72c3fa9b75d108fa78da65594ef4019196c428 100644
--- a/python/example/single_cell_swc.py
+++ b/python/example/single_cell_swc.py
@@ -87,4 +87,4 @@ for t in m.traces:
 
 df = pandas.concat(df_list)
 
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable").savefig('single_cell_swc.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_swc.svg')