diff --git a/external/bart/bart/sched/functions.py b/external/bart/bart/sched/functions.py
index 9185f2ff121be31130e9d0e6dfee4cde19524eb1..ac7c0b2662c3b1222422cc29cd65421d844efbde 100644
--- a/external/bart/bart/sched/functions.py
+++ b/external/bart/bart/sched/functions.py
@@ -139,6 +139,25 @@ def sanitize_asymmetry(series, window=None):
                 if window:
                     series.index.values[-1] = window[1]
 
+            # Remove repeated entries - which could happen if a task switch in
+            # then immediately switches out; ie: time stamp is exactly the same
+            n = 0
+            next = n + 1
+            while next < len(series):
+                if not series.values[n]:
+                    n = next
+                    next = next + 1
+                    continue
+
+                while not series.values[next]:
+                    next = next + 1
+
+                if series.values[n] == series.values[next]:
+                    series = series.drop(series.index[next])
+                else:
+                    n = next
+                    next = next + 1
+
         # No point if the series just has one value and
         # one event. We do not have sufficient data points
         # for any calculation. We should Ideally never reach
diff --git a/lisa/tests/scheduler/eas_behaviour.py b/lisa/tests/scheduler/eas_behaviour.py
index abf0c18da73aa5742e82aa2e159227d4d642f2ee..aea76096b4c74a84dd512812b82515f1b293ecf2 100644
--- a/lisa/tests/scheduler/eas_behaviour.py
+++ b/lisa/tests/scheduler/eas_behaviour.py
@@ -139,6 +139,7 @@ class EASBehaviour(RTATestBundle):
 
         df = self.trace.ftrace.sched_switch.data_frame[['next_comm', '__cpu']]
         df = df[df['next_comm'].isin(tasks)]
+        df = df[~df.index.duplicated()]
         df = df.pivot(index=df.index, columns='next_comm').fillna(method='ffill')
         cpu_df = df['__cpu']
         # Drop consecutive duplicates