normality Tests

.gitignore

@@ -158,3 +158,4 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+./Examples/df_trans_merged_textflow.csv
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textflow/Test.py

@@ -7,7 +7,8 @@ from statsmodels.stats.diagnostic import lilliefors
 
 class Test():
     #https://towardsdatascience.com/normality-tests-in-python-31e04aa4f411
-    def __init__(self,parametricTest=["Shapiro","D'Agostino","Anderson-Darling","Chi-Square","Lilliefors","Jarque–Bera","Kolmogorov-Smirnov"],nonParametricTest=["mannwhitneyu","wilcoxon","kruskal"]):
+    def __init__(self,normalityTest=["Shapiro","D'Agostino","Anderson-Darling","Chi-Square","Lilliefors","Jarque–Bera","Kolmogorov-Smirnov"], parametricTest=["mannwhitneyu","wilcoxon","kruskal"], nonParametricTest=["mannwhitneyu","wilcoxon","kruskal"]):
+        self.normalityTest = normalityTest
         self.parametricTest = parametricTest
         self.nonParametricTest = nonParametricTest
         
@@ -19,52 +20,67 @@ class Test():
         #   Histograma
         pass
 
-    def applyParametric(self,df):
+    def applyNormalTest(self,df):
         testFinal = pd.DataFrame()
-        #Numeric Cols, hay que filtrar el df
-        for i in self.parametricTest:
+        testFinal.index = list(df.columns)
+        dicResult={}
+        for i in self.normalityTest:
             if i == "Shapiro":
                 test = df.apply(lambda x: shapiro(x), axis=0)
                 test.index = ['Shapiro stat', 'Shapiro p-value']
                 test = test.transpose()
+                testFinal['Shapiro stat'] = list(test['Shapiro stat'])
+                testFinal['Shapiro p-value'] = list(test['Shapiro p-value'])
             elif i == "D'Agostino":
                 test = df.apply(lambda x: normaltest(x), axis=0)
                 test.index = ["D'Agostino stat", "D'Agostino p-value"]
                 test = test.transpose()
+                testFinal["D'Agostino stat"] = list(test["D'Agostino stat"])
+                testFinal["D'Agostino p-value"] = list(test["D'Agostino p-value"])
             elif i == "Anderson-Darling": 
                 test = df.apply(lambda x: anderson(x), axis=0)
                 test.index = ['Anderson-Darling stat', 'Anderson-Darling crit_val', 'Anderson-Darling sig_level']
                 test = test.transpose()
-                pass
+                testFinal['Anderson-Darling stat'] = list(test['Anderson-Darling stat'])
+                testFinal['Anderson-Darling crit_val'] = list(test['Anderson-Darling crit_val'])
+                testFinal['Anderson-Darling sig_level'] = list(test['Anderson-Darling sig_level'])
             elif i == "Chi-Square":
                 test = df.apply(lambda x: chisquare(x), axis=0)
                 test.index = ['Chi-Square stat', 'Chi-Square p-value']
                 test = test.transpose()
-                pass
+                testFinal['Chi-Square stat'] = list(test['Chi-Square stat'])
+                testFinal['Chi-Square p-value'] = list(test['Chi-Square p-value'])
             elif i == "Lilliefors": 
                 test = df.apply(lambda x: lilliefors(x), axis=0)
                 test.index = ['Lilliefors stat', 'Lilliefors p-value']
                 test = test.transpose()
-                pass
+                testFinal['Lilliefors stat'] = list(test['Lilliefors stat'])
+                testFinal['Lilliefors p-value'] = list(test['Lilliefors p-value'])
             elif i == "Jarque–Bera": 
                 test = df.apply(lambda x: jarque_bera(x), axis=0)
-                test.index = ['Shapiro stat', 'Shapiro p-value']
+                test.index = ['Jarque–Bera stat', 'Jarque–Bera p-value']
                 test = test.transpose()
-                pass
+                testFinal['Jarque–Bera stat'] = list(test['Jarque–Bera stat'])
+                testFinal['Jarque–Bera p-value'] = list(test['Jarque–Bera p-value'])
             elif i == "Kolmogorov-Smirnov":
                 test = df.apply(lambda x: kstest(x, 'norm'), axis=0)
                 test.index = ["Kolmogorov-Smirnov stat", "Kolmogorov-Smirnov p-value"]
                 test = test.transpose()
+                testFinal['Kolmogorov-Smirnov stat'] = list(test['Kolmogorov-Smirnov stat'])
+                testFinal['Kolmogorov-Smirnov p-value'] = list(test['Kolmogorov-Smirnov p-value'])
         
-        for t in self.parametricTest:
+        for t in self.normalityTest:
             if t != "Anderson-Darling":
-                print("Pass the test of"+t)
-                print(list(test[test[t+' p-value'] > 0.05].index))
+                print("Pass the test of "+t)
+                print(list(testFinal[testFinal[t+' p-value'] > 0.05].index))
+                dicResult[t] = list(testFinal[testFinal[t+' p-value'] > 0.05].index)
             else:
-                for i in range(len(list(test[t+' crit_val'].index))):
-                    sig_level, crit_val = test[t+' sig_level'][i], test[t+' crit_val'][i]
-                    print("Pass the test of"+t)
-                    print(list(test[test[t+' stat'] < crit_val].index),"at {sig_level} level of significance")
-                
+                sig_level, crit_val = list(testFinal[t+' sig_level'])[0], list(testFinal[t+' crit_val'])[0]
+                for i in range(len(crit_val)):
+                    print("Pass the test of "+t)
+                    print(list(testFinal[testFinal[t+' stat'] < crit_val[i]].index),"at "+str(sig_level[i])+" level of significance")
+                    dicResult[t+' '+sig_level[i]+' sig_lev'] = list(testFinal[testFinal[t+' p-value'] > 0.05].index)
+        
+        return testFinal, dicResult        
                 
                 
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