diff --git a/python/test/t_FunctionalChaos_ishigami.py b/python/test/t_FunctionalChaos_ishigami.py
index 9b3b6a20500..6cab98782b6 100755
--- a/python/test/t_FunctionalChaos_ishigami.py
+++ b/python/test/t_FunctionalChaos_ishigami.py
@@ -1,7 +1,6 @@
 #! /usr/bin/env python
 
 import openturns as ot
-import math as m
 from openturns.usecases import ishigami_function
 
 ot.TESTPREAMBLE()
diff --git a/python/test/t_LinearModelValidation_std.py b/python/test/t_LinearModelValidation_std.py
index 10b56ee59b5..124dcc0907c 100644
--- a/python/test/t_LinearModelValidation_std.py
+++ b/python/test/t_LinearModelValidation_std.py
@@ -1,8 +1,6 @@
 #! /usr/bin/env python
 
 import openturns as ot
-import math as m
-from openturns.usecases import ishigami_function
 from openturns.testing import assert_almost_equal
 
 ot.TESTPREAMBLE()
@@ -17,8 +15,8 @@
 print("samplingSize = ", samplingSize)
 aCollection = []
 marginal1 = ot.Uniform(-1.0, 1.0)
-aCollection.append( marginal1 )
-aCollection.append( marginal1 )
+aCollection.append(marginal1)
+aCollection.append(marginal1)
 distribution = ot.ComposedDistribution(aCollection)
 inputSample = distribution.getSample(samplingSize)
 print("inputSample=", inputSample)
@@ -26,13 +24,13 @@
 noise = ot.Normal(0.0, 0.5)
 outputSample = g(inputSample) + noise.getSample(samplingSize)
 print("outputSample=", outputSample)
-lmAlgo = ot.LinearModelAlgorithm (inputSample, outputSample)
+lmAlgo = ot.LinearModelAlgorithm(inputSample, outputSample)
 result = lmAlgo.getResult()
 
 # Create LOO validation
 validationLOO = ot.LinearModelValidation(result, ot.LinearModelValidation.LEAVEONEOUT)
 print(validationLOO)
-assert(validationLOO.getMethod() == ot.LinearModelValidation.LEAVEONEOUT)
+assert validationLOO.getMethod() == ot.LinearModelValidation.LEAVEONEOUT
 
 # Compute analytical LOO MSE
 print("Compute Analytical LOO MSE")
@@ -59,7 +57,7 @@
 print("Naive LOO MSE = ", mseLOOnaive)
 
 # Test
-rtolLOO = 1.e-12
+rtolLOO = 1.0e-12
 atolLOO = 0.0
 assert_almost_equal(mseLOOAnalytical[0], mseLOOnaive, rtolLOO, atolLOO)
 
@@ -70,15 +68,17 @@
 print("sampleVariance = ", sampleVariance)
 r2ScoreReference = 1.0 - mseLOOAnalytical[0] / sampleVariance[0]
 print("Computed R2 score = ", r2ScoreReference)
-rtolLOO = 1.e-12
+rtolLOO = 1.0e-12
 atolLOO = 0.0
 assert_almost_equal(r2ScoreReference, r2ScoreLOO[0], rtolLOO, atolLOO)
 
 # Create KFold validation
-validationKFold = ot.LinearModelValidation(result, ot.LinearModelValidation.KFOLD, kFoldParameter)
+validationKFold = ot.LinearModelValidation(
+    result, ot.LinearModelValidation.KFOLD, kFoldParameter
+)
 print(validationKFold)
-assert(validationKFold.getKParameter() == kFoldParameter)
-assert(validationKFold.getMethod() == ot.LinearModelValidation.KFOLD)
+assert validationKFold.getKParameter() == kFoldParameter
+assert validationKFold.getMethod() == ot.LinearModelValidation.KFOLD
 
 # Compute analytical KFold MSE
 mseKFoldAnalytical = validationKFold.computeMeanSquaredError()
@@ -106,7 +106,7 @@
 print("Naive KFold MSE = ", mseKFoldnaive)
 
 # Test
-rtolKFold = 1.e-7
+rtolKFold = 1.0e-7
 atolKFold = 0.0
 assert_almost_equal(mseKFoldAnalytical, mseKFoldnaive, rtolKFold, atolKFold)
 
@@ -115,6 +115,6 @@
 print("Analytical K-Fold R2 score = ", r2ScoreKFold)
 r2ScoreReference = 1.0 - mseKFoldAnalytical[0] / sampleVariance[0]
 print("Computed R2 score = ", r2ScoreReference)
-rtolKFold = 1.e-12
+rtolKFold = 1.0e-12
 atolKFold = 0.0
 assert_almost_equal(r2ScoreReference, r2ScoreKFold[0], rtolLOO, atolLOO)