diff --git a/lisa/analysis/idle.py b/lisa/analysis/idle.py
index 8a08b5a65c..e674cb1efe 100644
--- a/lisa/analysis/idle.py
+++ b/lisa/analysis/idle.py
@@ -64,7 +64,7 @@ def df_cpus_idle(self, cpus=None):
         # The event uses an unsigned int even though the kernel uses -1, so use
         # -1 to avoid being tied to the event field type size
         non_idle = (2 ** 32) -1
-        df['state'].replace(non_idle, -1, inplace=True)
+        df['state'] = df['state'].replace(non_idle, -1)
         return df
 
     @TraceAnalysisBase.cache
diff --git a/lisa/analysis/tasks.py b/lisa/analysis/tasks.py
index a0daaeab02..a94fab9d17 100644
--- a/lisa/analysis/tasks.py
+++ b/lisa/analysis/tasks.py
@@ -730,7 +730,7 @@ def f(state):
         df_add_delta(df, col='duration', inplace=True)
 
         if not np.isnan(preempted_value):
-            df['active'].fillna(preempted_value, inplace=True)
+            df['active'] = df['active'].fillna(preempted_value)
 
         # Merge consecutive activations' duration. They could have been
         # split in two by a bit of preemption, and we don't want that to
@@ -747,8 +747,7 @@ def f(state):
         sleep = sleep.reindex(active.index, method='bfill')
         duty_cycle = active / (active + sleep)
 
-        df['duty_cycle'] = duty_cycle
-        df['duty_cycle'].ffill(inplace=True)
+        df['duty_cycle'] = duty_cycle.ffill()
 
         return df
 
diff --git a/lisa/datautils.py b/lisa/datautils.py
index 029196c9b2..fdd471fc8e 100644
--- a/lisa/datautils.py
+++ b/lisa/datautils.py
@@ -1446,7 +1446,7 @@ def df_combine_duplicates(df, func, output_col, cols=None, all_col=True, prune=T
         except KeyError:
             pass
         else:
-            init_df[output_col].fillna(fill, inplace=True)
+            init_df[output_col] = df[output_col].fillna(fill)
 
     if prune:
         # Only keep the first row of each duplicate run
diff --git a/lisa/pelt.py b/lisa/pelt.py
index 915329003e..dc87356710 100644
--- a/lisa/pelt.py
+++ b/lisa/pelt.py
@@ -93,7 +93,7 @@ def simulate_pelt(activations, init=0, index=None, clock=None, capacity=None, wi
     df = pd.DataFrame({'activations': activations})
     if capacity is not None:
         df['rel_capacity'] = capacity.reindex(df.index, method='ffill') / scale
-        df['rel_capacity'].bfill(inplace=True)
+        df['rel_capacity'] = df['rel_capacity'].bfill()
 
     if clock is None:
         # If we have the CPU capacity at hand, we can make a fake PELT clock
diff --git a/lisa/stats.py b/lisa/stats.py
index 18644863e1..b36d1a4291 100644
--- a/lisa/stats.py
+++ b/lisa/stats.py
@@ -795,7 +795,7 @@ def _df_stats(self):
         unit_col = self._unit_col
         default_unit = ''
         if unit_col in df:
-            df[unit_col].fillna(default_unit, inplace=True)
+            df[unit_col] = df[unit_col].fillna(default_unit)
         else:
             df[unit_col] = default_unit