Update app.py

app.py

@@ -135,7 +135,7 @@ def multi_answer(df):
             friquency[i] = 0
             
 
-    friquency_dataframe = pd.DataFrame({"Value": friquency.keys(), 'Frequency': friquency.values(), "Percentage": np.array(list(friquency.values()))/len(df.dropna(how='all'))*100}).sort_values(by='Value')
+    friquency_dataframe = pd.DataFrame({"Value": friquency.keys(), 'Frequency': friquency.values(), "Percentage": np.array(list(friquency.values()))/len(df.dropna(how='all'))}).sort_values(by='Value')
     friquency_dataframe.loc[len(friquency_dataframe)] = ['Sample_size', len(df.dropna(how='all')), 1]
     return friquency_dataframe
     
@@ -144,7 +144,7 @@ def single_answer(df):
     friquency_dataframe = pd.DataFrame({
         'Value': counter.index, 
         'Frequency': counter.values, 
-        'Percentage': (counter.values / counter.sum()) * 100}).sort_values(by='Value')
+        'Percentage': (counter.values / counter.sum())}).sort_values(by='Value')
     friquency_dataframe.loc[len(friquency_dataframe)] = ['Sample_size', len(df.dropna()), 1]
     return friquency_dataframe
 
@@ -154,7 +154,7 @@ def score_answer(df):
     friquency_dataframe = pd.DataFrame({
         'Value': list(counter.index)+["Meen", "Variance"],
         'Frequency': list(counter.values)+[df.mean(), df.var()], 
-        'Percentage': list((counter.values / counter.sum()) * 100)+["", ""]})
+        'Percentage': list((counter.values / counter.sum()))+["", ""]})
     
     return friquency_dataframe
 
@@ -166,7 +166,7 @@ def two_variable_ss(df, var1, var2):
     #friquency_dataframe = sorting(friquency_dataframe)
 
     column_sums = friquency_dataframe.sum(axis=0)
-    percentage_dataframe = friquency_dataframe.div(column_sums, axis=1) * 100
+    percentage_dataframe = friquency_dataframe.div(column_sums, axis=1)
 
     friquency_dataframe['Total'] = list(single_answer(df[var1]).iloc[:,1])[:-1]
     friquency_dataframe.loc['Sample_size'] = list(single_answer(df[var2]).iloc[:,1])
@@ -307,8 +307,8 @@ def z_test_data(df):
         for j in range(1, num_cols -1):
             n1 = df.iloc[-1, -1]
             n2 = df.iloc[-1, j]
-            p1 = df.iloc[i, -1]/100
-            p2 = df.iloc[i, j]/100
+            p1 = df.iloc[i, -1]
+            p2 = df.iloc[i, j]
             p_value = z_testes(n1, n2, p1, p2)
             if pd.notnull(p_value) and p_value <= 0.05:
                 styles.iloc[i, j] = 'background-color: lightgreen'