diff --git a/CHANGELOG.md b/CHANGELOG.md
index d02f0609..b95523e2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,8 @@
 # Latest
 
+# v0.10.1
+## What's Changed
+After resampling the figure, the line goes beyond figure frontier indicating presence of data. https://github.com/predict-idlab/plotly-resampler/pull/308
 
 # v0.10.0
 ## New Features
diff --git a/plotly_resampler/aggregation/plotly_aggregator_parser.py b/plotly_resampler/aggregation/plotly_aggregator_parser.py
index c359aa29..d9049363 100644
--- a/plotly_resampler/aggregation/plotly_aggregator_parser.py
+++ b/plotly_resampler/aggregation/plotly_aggregator_parser.py
@@ -70,6 +70,8 @@ def get_start_end_indices(hf_trace_data, axis_type, start, end) -> Tuple[int, in
             x_step = hf_trace_data["x"].step
             start_idx = int(max((start - x_start) // x_step, 0))
             end_idx = int((end - x_start) // x_step)
+            start_idx = max(0, start_idx - 1)
+            end_idx = min(end_idx + 1, len(hf_trace_data["x"]))
             return start_idx, end_idx
         # TODO: this can be performed as-well for a fixed frequency range-index w/ freq
 
@@ -85,6 +87,8 @@ def get_start_end_indices(hf_trace_data, axis_type, start, end) -> Tuple[int, in
         # Search the index-positions
         start_idx = bisect.bisect_left(hf_trace_data["x"], start)
         end_idx = bisect.bisect_right(hf_trace_data["x"], end)
+        start_idx = max(0, start_idx - 1)
+        end_idx = min(end_idx + 1, len(hf_trace_data["x"]))
         return start_idx, end_idx
 
     @staticmethod
diff --git a/pyproject.toml b/pyproject.toml
index 115523ad..0b251bf0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "plotly-resampler"  # Do not forget to update the __init__.py __version__ variable
-version = "0.10.0"
+version = "0.10.1"
 description = "Visualizing large time series with plotly"
 authors = ["Jonas Van Der Donckt", "Jeroen Van Der Donckt", "Emiel Deprost"]
 readme = "README.md"
diff --git a/tests/test_figure_resampler.py b/tests/test_figure_resampler.py
index 640914ec..dab9795f 100644
--- a/tests/test_figure_resampler.py
+++ b/tests/test_figure_resampler.py
@@ -296,8 +296,8 @@ def test_log_axis():
         assert len(out) == 2
         assert (x1 - x0) < 10
         assert len(out[1]["x"]) == 1000
-        assert out[-1]["x"][0] >= 100
-        assert out[-1]["x"][-1] <= 50_000
+        assert out[-1]["x"][0] >= 99
+        assert out[-1]["x"][-1] <= 50_001
 
 
 def test_add_traces_from_other_figure():
@@ -997,8 +997,8 @@ def test_time_tz_slicing():
         start_idx, end_idx = PlotlyAggregatorParser.get_start_end_indices(
             hf_data_dict, hf_data_dict["axis_type"], t_start, t_stop
         )
-        assert (s.index[start_idx] - t_start) <= pd.Timedelta(seconds=1)
-        assert (s.index[min(end_idx, n - 1)] - t_stop) <= pd.Timedelta(seconds=1)
+        assert (s.index[start_idx] - t_start) <= pd.Timedelta(seconds=2)
+        assert (s.index[min(end_idx, n - 1)] - t_stop) <= pd.Timedelta(seconds=2)
 
 
 def test_time_tz_slicing_different_timestamp():
@@ -1064,10 +1064,10 @@ def test_different_tz_no_tz_series_slicing():
         )
         assert (
             s.tz_localize(None).index[start_idx].tz_localize(t_start.tz) - t_start
-        ) <= pd.Timedelta(seconds=1)
+        ) <= pd.Timedelta(seconds=2)
         assert (
             s.tz_localize(None).index[end_idx].tz_localize(t_stop.tz) - t_stop
-        ) <= pd.Timedelta(seconds=1)
+        ) <= pd.Timedelta(seconds=2)
 
 
 def test_multiple_tz_no_tz_series_slicing():
diff --git a/tests/test_figurewidget_resampler.py b/tests/test_figurewidget_resampler.py
index 03f73bd4..06fb61d2 100644
--- a/tests/test_figurewidget_resampler.py
+++ b/tests/test_figurewidget_resampler.py
@@ -794,7 +794,7 @@ def test_hf_data_property_reload_data():
     assert np.all(fwr.hf_data[0]["y"] == new_y)
 
     fwr.reload_data()
-    assert (fwr.data[0]["x"][0] >= 10_000) & (fwr.data[0]["x"][-1] <= 20_000)
+    assert (fwr.data[0]["x"][0] >= 9_999) & (fwr.data[0]["x"][-1] <= 20_001)
     assert np.all(fwr.data[0]["y"] == new_y[fwr.data[0]["x"]])
     assert (fwr.layout["yaxis"].range[0] == -20) & (fwr.layout["yaxis"].range[-1] == 3)
 
@@ -869,8 +869,8 @@ def test_hf_data_property_subplots_reload_data():
     assert np.all(fwr.hf_data[0]["y"] == new_y)
 
     fwr.reload_data()
-    assert (fwr.data[0]["x"][0] >= 10_000) & (fwr.data[0]["x"][-1] <= 20_000)
-    assert (fwr.data[1]["x"][0] >= 40_000) & (fwr.data[1]["x"][-1] <= 60_000)
+    assert (fwr.data[0]["x"][0] >= 9_999) & (fwr.data[0]["x"][-1] <= 20_001)
+    assert (fwr.data[1]["x"][0] >= 39_999) & (fwr.data[1]["x"][-1] <= 60_001)
     assert np.all(fwr.data[0]["y"] == new_y[fwr.data[0]["x"]])
     assert np.all(fwr.data[1]["y"] == new_y[fwr.data[1]["x"]])
     assert (fwr.layout["yaxis"].range[0] == -20) & (fwr.layout["yaxis"].range[-1] == 3)
@@ -900,8 +900,8 @@ def test_hf_data_subplots_non_shared_xaxes():
     assert 0 <= x_0[0] <= (n / 1000)
     assert (n - 1000) <= x_0[-1] <= n - 1
     x_1 = fwr.data[1]["x"]
-    assert 40_000 <= x_1[0] <= 40_000 + (20_000 / 1000)
-    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_000
+    assert 39_999 <= x_1[0] <= 40_001 + (20_000 / 1000)
+    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_001
 
 
 def test_hf_data_subplots_non_shared_xaxes_row_col_none():
@@ -925,8 +925,8 @@ def test_hf_data_subplots_non_shared_xaxes_row_col_none():
     assert 0 <= x_0[0] <= (n / 1000)
     assert (n - 1000) <= x_0[-1] <= n - 1
     x_1 = fwr.data[1]["x"]
-    assert 40_000 <= x_1[0] <= 40_000 + (20_000 / 1000)
-    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_000
+    assert 39_999 <= x_1[0] <= 40_001 + (20_000 / 1000)
+    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_001
 
 
 def test_updates_two_traces():