diff --git a/examples/scripts/approximation_methods.py b/examples/scripts/approximation_methods.py
index a17fd51..732711f 100644
--- a/examples/scripts/approximation_methods.py
+++ b/examples/scripts/approximation_methods.py
@@ -88,31 +88,11 @@ def run_FORM_simple(
     iter_number = optimResult.getIterationNumber()
 
     dfResult = pd.DataFrame()
-    dfResult = dfResult.append(
-        pd.DataFrame([result.getEventProbability()], index=["Probability of failure"])
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getGeneralisedReliabilityIndex()],
-            index=["Generalised reliability index"],
-        )
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame([iter_number], index=["Number of iterations"])
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getStandardSpaceDesignPoint()],
-            index=["Standard space design point"],
-        )
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getPhysicalSpaceDesignPoint()],
-            index=["Physical space design point"],
-        )
-    )
-
+    dfResult = pd.DataFrame([result.getEventProbability()], index=["Probability of failure"])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getGeneralisedReliabilityIndex()], index=["Generalised reliability index"])])
+    dfResult = pd.concat([dfResult, pd.DataFrame([iter_number()], index=["Number of iterations"])])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getStandardSpaceDesignPoint()], index=["Standard space design point"])])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getPhysicalSpaceDesignPoint()], index=["Physical space design point"])])
     dfResult = dfResult.reset_index()
     dfResult.columns = ["", "Results - FORM (" + nearestPointAlgo + ")"]
     pd.options.display.float_format = "{:,.2E}".format
diff --git a/examples/scripts/simulation_methods.py b/examples/scripts/simulation_methods.py
index 5873a69..d647b80 100644
--- a/examples/scripts/simulation_methods.py
+++ b/examples/scripts/simulation_methods.py
@@ -139,26 +139,10 @@ def function_intersection(X):
 
     result = simulation.getResult()
 
-    dfResult = pd.DataFrame()
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getProbabilityEstimate()], index=["Probability of failure"]
-        )
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getCoefficientOfVariation()], index=["Coefficient of varation"],
-        )
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame([result.getConfidenceLength()], index=["95 % Confidence length"])
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getOuterSampling() * result.getBlockSize()],
-            index=["Number of calls"],
-        )
-    )
+    dfResult = pd.DataFrame([result.getProbabilityEstimate()], index=["Probability of failure"])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getCoefficientOfVariation()], index=["Coefficient of varation"])])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getConfidenceLength()], index=["95 % Confidence length"])])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getOuterSampling() * result.getBlockSize()], index=["Number of calls"])])
     dfResult = dfResult.reset_index()
     dfResult.columns = ["", "Results - Monte Carlo"]
 
@@ -322,26 +306,10 @@ def function_intersection(X):
 
     result = simulation.getResult()
 
-    dfResult = pd.DataFrame()
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getProbabilityEstimate()], index=["Probability of failure"]
-        )
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getCoefficientOfVariation()], index=["Coefficient of varation"],
-        )
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame([result.getConfidenceLength()], index=["95 % Confidence length"])
-    )
-    dfResult = dfResult.append(
-        pd.DataFrame(
-            [result.getOuterSampling() * result.getBlockSize()],
-            index=["Number of calls"],
-        )
-    )
+    dfResult = pd.DataFrame([result.getProbabilityEstimate()], index=["Probability of failure"])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getCoefficientOfVariation()], index=["Coefficient of variation"])])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getConfidenceLength()], index=["95 % Confidence length"])])
+    dfResult = pd.concat([dfResult, pd.DataFrame([result.getOuterSampling() * result.getBlockSize()], index=["Number of calls"])])
     dfResult = dfResult.reset_index()
     dfResult.columns = ["", "Results - Importance Sampling"]
 
diff --git a/requirements.txt b/requirements.txt
index d65ee89..80ab225 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,7 +9,7 @@ pre-commit
 flake8
 sphinx-gallery
 numpydoc
-pandas<2.0
+pandas
 tqdm
 shapely
 lxml-html-clean
diff --git a/requirements.yml b/requirements.yml
deleted file mode 100644
index 405e886..0000000
--- a/requirements.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-# $ conda env create --file requirements.yml
-# $ conda env update --file requirements.yml
-name: otbenchmark
-dependencies:
-- python==3.9.1
-- numpy==1.21.*
-- matplotlib==3.4.2
-- scipy==1.7.*
-- openturns>=1.16
-- jupyterlab==3.0.16
-- nbconvert==6.1.0
-- black==21.12b0
-- pre-commit
-- flake8
-- pandas==1.3.*
-- tqdm==4.61.2
-- shapely==1.7.1
-- pip:
-  - black-nb==0.5.0
-