Update app.py

app.py

@@ -506,294 +506,294 @@ st.markdown('[Click to register a suggestion or comment](https://docs.google.com
 # Main options
 
 uploaded_file = st.file_uploader("Please upload your Excel file", type=["xlsx", "xls"])
-    if uploaded_file:
-        try:
-            df = pd.read_excel(uploaded_file)
-            st.subheader("Displaying the first few rows of the DataFrame")
-            st.dataframe(df.head())
+if uploaded_file:
+    try:
+        df = pd.read_excel(uploaded_file)
+        st.subheader("Displaying the first few rows of the DataFrame")
+        st.dataframe(df.head())
 
-            main_option = st.selectbox("Please select an option:", ["Tabulation", "Funnel", "Hypothesis test", "Machine Learning", "Coding"])
-            
-            if main_option == "Tabulation":
-                st.header("Tabulation Analysis")
-        
-                    tabulation_option = st.selectbox("Please select the type of analysis:", ["Univariate", "Multivariate", "All"])
-        
-                    if tabulation_option == "All":
-        
-                        cols = edit_strings(df.columns)
-                        cols = sorted(list(set(cols)))
-                        
-                        st.sidebar.header("Settings")
-        
-                        main_dict = {"single": [], "multi": [], "score": []}
-                        
-                        st.sidebar.subheader("Main")
-                        main_dict["single"] = st.sidebar.multiselect(
-                            'Main: Single answer questions', 
-                            cols,
-                            default=[]
-                        )
-                        
-                        main_dict["multi"] = st.sidebar.multiselect(
-                            'Main: Multi answer questions',
-                            cols,
-                            default=[]
-                        )
+        main_option = st.selectbox("Please select an option:", ["Tabulation", "Funnel", "Hypothesis test", "Machine Learning", "Coding"])
         
-                        main_dict["score"] = st.sidebar.multiselect(
-                            'Main: Score answer questions',
-                            cols,
-                            default=[]
-                        )
-        
-                        st.sidebar.subheader("Follow")
-        
-                        follow_dict = {"single": [], "multi": [], "score": []}
-                        
-                        st.sidebar.subheader("Main")
-                        follow_dict["single"] = st.sidebar.multiselect(
-                            'Follow: Single answer questions', 
-                            cols,
-                            default=[]
-                        )
-                        
-                        follow_dict["multi"] = st.sidebar.multiselect(
-                            'Follow: Multi answer questions',
-                            cols,
-                            default=[]
-                        )
-
-                        follow_dict["score"] = st.sidebar.multiselect(
-                            'Follow: Score answer questions',
-                            cols,
-                            default=[]
-                        )
-        
-                        all_tabulation(df, main_dict, follow_dict)
-                        
-                    elif tabulation_option == "Univariate":
-                        uni_option = st.selectbox("Select the type of univariate analysis:", ["Multiple answer", "Single answer", "Score answer"])
-        
-                        if uni_option == "Single answer":
-                            var = st.text_input("Please enter the name of the desired column:")
-                            if var:
-                                if var in df.columns:
-                                    result_df = single_answer(df[var])
-                                    st.subheader("Univariate Analysis Results")
-                                    st.dataframe(result_df)
-        
-                                    fig = figo('Bar', result_df["Percentage"][:-1, ], title='Percentage Histogram', xlabel=var, ylabel='Percentage', colorscale='Plotly3')
-                                    st.plotly_chart(fig, use_container_width=True)
-                                else:
-                                    st.error("The entered column was not found.")
-                        elif uni_option == "Multiple answer":
-                            var = st.text_input("Please enter the name of the desired column:")
-                            if var:
-                                matching_cols = [col for col in df.columns if is_matching_pattern(col, var)]
-                                if matching_cols:
-                                    subset_df = df[matching_cols]
-                                    result_df = multi_answer(subset_df)
-                                        
-                                    st.subheader("Multiple Answer Analysis Results")
-                                    st.dataframe(result_df)
+        if main_option == "Tabulation":
+            st.header("Tabulation Analysis")
+    
+                tabulation_option = st.selectbox("Please select the type of analysis:", ["Univariate", "Multivariate", "All"])
+    
+                if tabulation_option == "All":
+    
+                    cols = edit_strings(df.columns)
+                    cols = sorted(list(set(cols)))
+                    
+                    st.sidebar.header("Settings")
+    
+                    main_dict = {"single": [], "multi": [], "score": []}
+                    
+                    st.sidebar.subheader("Main")
+                    main_dict["single"] = st.sidebar.multiselect(
+                        'Main: Single answer questions', 
+                        cols,
+                        default=[]
+                    )
+                    
+                    main_dict["multi"] = st.sidebar.multiselect(
+                        'Main: Multi answer questions',
+                        cols,
+                        default=[]
+                    )
+    
+                    main_dict["score"] = st.sidebar.multiselect(
+                        'Main: Score answer questions',
+                        cols,
+                        default=[]
+                    )
+    
+                    st.sidebar.subheader("Follow")
+    
+                    follow_dict = {"single": [], "multi": [], "score": []}
+                    
+                    st.sidebar.subheader("Main")
+                    follow_dict["single"] = st.sidebar.multiselect(
+                        'Follow: Single answer questions', 
+                        cols,
+                        default=[]
+                    )
+                    
+                    follow_dict["multi"] = st.sidebar.multiselect(
+                        'Follow: Multi answer questions',
+                        cols,
+                        default=[]
+                    )
+
+                    follow_dict["score"] = st.sidebar.multiselect(
+                        'Follow: Score answer questions',
+                        cols,
+                        default=[]
+                    )
+    
+                    all_tabulation(df, main_dict, follow_dict)
+                    
+                elif tabulation_option == "Univariate":
+                    uni_option = st.selectbox("Select the type of univariate analysis:", ["Multiple answer", "Single answer", "Score answer"])
+    
+                    if uni_option == "Single answer":
+                        var = st.text_input("Please enter the name of the desired column:")
+                        if var:
+                            if var in df.columns:
+                                result_df = single_answer(df[var])
+                                st.subheader("Univariate Analysis Results")
+                                st.dataframe(result_df)
+    
+                                fig = figo('Bar', result_df["Percentage"][:-1, ], title='Percentage Histogram', xlabel=var, ylabel='Percentage', colorscale='Plotly3')
+                                st.plotly_chart(fig, use_container_width=True)
+                            else:
+                                st.error("The entered column was not found.")
+                    elif uni_option == "Multiple answer":
+                        var = st.text_input("Please enter the name of the desired column:")
+                        if var:
+                            matching_cols = [col for col in df.columns if is_matching_pattern(col, var)]
+                            if matching_cols:
+                                subset_df = df[matching_cols]
+                                result_df = multi_answer(subset_df)
                                     
-                                    fig = figo('Bar', result_df["Percentage"][:-1], title='Percentage Histogram', xlabel=var, ylabel='Percentage', colorscale='Plotly3')
-                                    st.plotly_chart(fig, use_container_width=True)
-                                else:
-                                    st.error("No columns matching the entered pattern were found.")
-        
-                        elif uni_option == "Score answer":
-                            var = st.text_input("Please enter the name of the desired column:")
-                            if var:
-                                subset_df = df[var]
-                                result_df = score_answer(subset_df)
-        
-                                st.subheader("Score Answer Analysis Results")
+                                st.subheader("Multiple Answer Analysis Results")
                                 st.dataframe(result_df)
                                 
-                                fig = figo('Bar', result_df["Percentage"][:-2], title='Percentage Histogram', xlabel=var, ylabel='Percentage', colorscale='Plotly3')
+                                fig = figo('Bar', result_df["Percentage"][:-1], title='Percentage Histogram', xlabel=var, ylabel='Percentage', colorscale='Plotly3')
                                 st.plotly_chart(fig, use_container_width=True)
                             else:
                                 st.error("No columns matching the entered pattern were found.")
+    
+                    elif uni_option == "Score answer":
+                        var = st.text_input("Please enter the name of the desired column:")
+                        if var:
+                            subset_df = df[var]
+                            result_df = score_answer(subset_df)
+    
+                            st.subheader("Score Answer Analysis Results")
+                            st.dataframe(result_df)
+                            
+                            fig = figo('Bar', result_df["Percentage"][:-2], title='Percentage Histogram', xlabel=var, ylabel='Percentage', colorscale='Plotly3')
+                            st.plotly_chart(fig, use_container_width=True)
+                        else:
+                            st.error("No columns matching the entered pattern were found.")
+                            
+                elif tabulation_option == "Multivariate":
+                    st.subheader("Multivariate Analysis")
+                    var1 = st.text_input("Please enter the name of the first column:")
+                    var2 = st.text_input("Please enter the name of the second column:")
+    
+                    if var1 and var2:
+                        type1 = st.selectbox("Select the type of analysis for the first column:", ["Multiple answer", "Single answer"], key='type1')
+                        type2 = st.selectbox("Select the type of analysis for the second column:", ["Multiple answer", "Single answer", "Score answer"], key='type2')
+    
+                        if type1 == "Single answer" and type2 == "Single answer":
+                            percentile_df, frequency_df = two_variable_ss(df[[var1, var2]], var1, var2)
+                            st.subheader("Percentage Table")
+                            st.write(z_test_data(percentile_df))
+    
+                            st.subheader("Frequency Table")
+                            st.dataframe(frequency_df)
+    
+                            row, col = df.shape
+                            fig = figo('Scatter', percentile_df.iloc[:-1,:], title='Percentage Scatter plot', width=(col*5)+5, height=(row*25) + 10)
+                            st.plotly_chart(fig, use_container_width=True)
+    
+                        elif type1 == "Single answer" and type2 == "Multiple answer":
+                            matching_cols = [col for col in df.columns if is_matching_pattern(col, var2)]
+                            if matching_cols:
+                                percentile_df, frequency_df = two_variable_sm(df[[var1] + matching_cols], var1, matching_cols)
+                                st.subheader("Percentage Table")
+                                st.write(z_test_data(percentile_df))
+    
+                                st.subheader("Frequency Table")
+                                st.dataframe(frequency_df)
+    
+                                row, col = df.shape
+                                fig = figo('Scatter', percentile_df.iloc[:-1,:], title='Percentage Scatter plot', width=(col*5)+5, height=(row*25) + 10)
+                                st.plotly_chart(fig, use_container_width=True)
                                 
-                    elif tabulation_option == "Multivariate":
-                        st.subheader("Multivariate Analysis")
-                        var1 = st.text_input("Please enter the name of the first column:")
-                        var2 = st.text_input("Please enter the name of the second column:")
-        
-                        if var1 and var2:
-                            type1 = st.selectbox("Select the type of analysis for the first column:", ["Multiple answer", "Single answer"], key='type1')
-                            type2 = st.selectbox("Select the type of analysis for the second column:", ["Multiple answer", "Single answer", "Score answer"], key='type2')
-        
-                            if type1 == "Single answer" and type2 == "Single answer":
-                                percentile_df, frequency_df = two_variable_ss(df[[var1, var2]], var1, var2)
+                            else:
+                                st.error("No columns matching the entered pattern were found.")
+    
+                        elif type1 == "Multiple answer" and type2 == "Multiple answer":
+                            matching_cols1 = [col for col in df.columns if is_matching_pattern(col, var1)]
+                            matching_cols2 = [col for col in df.columns if is_matching_pattern(col, var2)]
+                            if matching_cols1 and matching_cols2:
+                                percentile_df, frequency_df = two_variable_mm(df[matching_cols1 + matching_cols2], matching_cols1, matching_cols2)
                                 st.subheader("Percentage Table")
                                 st.write(z_test_data(percentile_df))
-        
+    
                                 st.subheader("Frequency Table")
                                 st.dataframe(frequency_df)
-        
+    
                                 row, col = df.shape
                                 fig = figo('Scatter', percentile_df.iloc[:-1,:], title='Percentage Scatter plot', width=(col*5)+5, height=(row*25) + 10)
                                 st.plotly_chart(fig, use_container_width=True)
-        
-                            elif type1 == "Single answer" and type2 == "Multiple answer":
-                                matching_cols = [col for col in df.columns if is_matching_pattern(col, var2)]
-                                if matching_cols:
-                                    percentile_df, frequency_df = two_variable_sm(df[[var1] + matching_cols], var1, matching_cols)
-                                    st.subheader("Percentage Table")
-                                    st.write(z_test_data(percentile_df))
-        
-                                    st.subheader("Frequency Table")
-                                    st.dataframe(frequency_df)
-        
-                                    row, col = df.shape
-                                    fig = figo('Scatter', percentile_df.iloc[:-1,:], title='Percentage Scatter plot', width=(col*5)+5, height=(row*25) + 10)
-                                    st.plotly_chart(fig, use_container_width=True)
-                                    
-                                else:
-                                    st.error("No columns matching the entered pattern were found.")
-        
-                            elif type1 == "Multiple answer" and type2 == "Multiple answer":
-                                matching_cols1 = [col for col in df.columns if is_matching_pattern(col, var1)]
-                                matching_cols2 = [col for col in df.columns if is_matching_pattern(col, var2)]
-                                if matching_cols1 and matching_cols2:
-                                    percentile_df, frequency_df = two_variable_mm(df[matching_cols1 + matching_cols2], matching_cols1, matching_cols2)
-                                    st.subheader("Percentage Table")
-                                    st.write(z_test_data(percentile_df))
-        
-                                    st.subheader("Frequency Table")
-                                    st.dataframe(frequency_df)
-        
-                                    row, col = df.shape
-                                    fig = figo('Scatter', percentile_df.iloc[:-1,:], title='Percentage Scatter plot', width=(col*5)+5, height=(row*25) + 10)
-                                    st.plotly_chart(fig, use_container_width=True)
-        
-                            elif type1 == "Single answer" and type2 == "Score answer":
-        
-                                mean_df = two_variable_ssc(df[[var1, var2]], var1, var2)
+    
+                        elif type1 == "Single answer" and type2 == "Score answer":
+    
+                            mean_df = two_variable_ssc(df[[var1, var2]], var1, var2)
+                            st.subheader("Mean Table")
+                            st.write(t_test_data(mean_df))
+    
+                            row, col = df.shape
+                            fig = figo('Bar', mean_df["Mean"][:-1], title='Mean Histogram', xlabel=var1, ylabel='Mean', colorscale='Plotly3')
+                            st.plotly_chart(fig, use_container_width=True)                            
+    
+    
+                        elif type1 == "Multiple answer" and type2 == "Score answer":
+                            matching_cols1 = [col for col in df.columns if is_matching_pattern(col, var1)]
+                            if matching_cols1:
+                                mean_df = two_variable_msc(df[matching_cols1 + [var2]], matching_cols1, var2)
                                 st.subheader("Mean Table")
                                 st.write(t_test_data(mean_df))
-        
+    
                                 row, col = df.shape
                                 fig = figo('Bar', mean_df["Mean"][:-1], title='Mean Histogram', xlabel=var1, ylabel='Mean', colorscale='Plotly3')
-                                st.plotly_chart(fig, use_container_width=True)                            
-        
-        
-                            elif type1 == "Multiple answer" and type2 == "Score answer":
-                                matching_cols1 = [col for col in df.columns if is_matching_pattern(col, var1)]
-                                if matching_cols1:
-                                    mean_df = two_variable_msc(df[matching_cols1 + [var2]], matching_cols1, var2)
-                                    st.subheader("Mean Table")
-                                    st.write(t_test_data(mean_df))
+                                st.plotly_chart(fig, use_container_width=True)                    
+                        else:
+                            st.info("This section of the program is under development.")
+
+        elif main_option == "Funnel":
+            st.header("Funnel")
         
-                                    row, col = df.shape
-                                    fig = figo('Bar', mean_df["Mean"][:-1], title='Mean Histogram', xlabel=var1, ylabel='Mean', colorscale='Plotly3')
-                                    st.plotly_chart(fig, use_container_width=True)                    
-                            else:
-                                st.info("This section of the program is under development.")
+            cols = edit_strings(df.columns)
+            cols = sorted(list(set(cols)))
+            
+            st.sidebar.header("Funnel Settings")
+            single_list = st.sidebar.multiselect(
+                'Single answer questions', 
+                cols,
+                default=[]
+            )
+            
+            multi_list = st.sidebar.multiselect(
+                'Multi answer questions',
+                cols,
+                default=[]
+            )
+            selected_dict = {}
+            
+            for option in single_list:
+                selected_dict[option] = "Single"
+            for option in multi_list:
+                selected_dict[option] = "Multi"
+            
+            funnel_frequency, funnel_percentage = funnel(df, selected_dict)
+            st.subheader("Percentage Table")
+            st.dataframe(funnel_percentage)
+            
+            st.subheader("Frequency Table")
+            st.dataframe(funnel_frequency)
+            
+            st.sidebar.header("Chart Settings")
+            bar_columns = st.sidebar.multiselect('Which columns should be displayed as bar charts?', sorted(funnel_percentage.columns))
+            line_columns = st.sidebar.multiselect('Which columns should be displayed as line charts?', sorted(funnel_percentage.columns))
 
-            elif main_option == "Funnel":
-                st.header("Funnel")
+            funnel_percentage_cleaned = funnel_percentage.dropna(axis=0, how='all')
             
-                cols = edit_strings(df.columns)
-                cols = sorted(list(set(cols)))
-                
-                st.sidebar.header("Funnel Settings")
-                single_list = st.sidebar.multiselect(
-                    'Single answer questions', 
-                    cols,
-                    default=[]
-                )
-                
-                multi_list = st.sidebar.multiselect(
-                    'Multi answer questions',
-                    cols,
-                    default=[]
-                )
-                selected_dict = {}
-                
-                for option in single_list:
-                    selected_dict[option] = "Single"
-                for option in multi_list:
-                    selected_dict[option] = "Multi"
-                
-                funnel_frequency, funnel_percentage = funnel(df, selected_dict)
-                st.subheader("Percentage Table")
-                st.dataframe(funnel_percentage)
-                
-                st.subheader("Frequency Table")
-                st.dataframe(funnel_frequency)
-                
-                st.sidebar.header("Chart Settings")
-                bar_columns = st.sidebar.multiselect('Which columns should be displayed as bar charts?', sorted(funnel_percentage.columns))
-                line_columns = st.sidebar.multiselect('Which columns should be displayed as line charts?', sorted(funnel_percentage.columns))
-    
-                funnel_percentage_cleaned = funnel_percentage.dropna(axis=0, how='all')
-                
-                fig = go.Figure()
-                
-                # Define modern and diverse color palette
-                modern_colors = [
-                    "#FF6F61", "#6B5B95", "#88B04B", "#F7CAC9", "#92A8D1", 
-                    "#955251", "#B565A7", "#009B77", "#DD4124", "#45B8AC"
-                ]
-                
-                # Add Bar traces with transparency and custom colors
-                for idx, col in enumerate(bar_columns):
-                    funnel_percentage_col = funnel_percentage_cleaned[col]
-                    fig.add_trace(
-                        go.Bar(
-                            x=funnel_percentage_cleaned.index, 
-                            y=funnel_percentage_col, 
-                            name=col,
-                            marker_color=modern_colors[idx % len(modern_colors)],  # Cycle through colors
-                            opacity=0.8  # Set transparency
-                        )
-                    )
-                
-                # Add Line traces with transparency and custom colors
-                for idx, col in enumerate(line_columns):
-                    funnel_percentage_col = funnel_percentage_cleaned[col]
-                    fig.add_trace(
-                        go.Scatter(
-                            x=funnel_percentage_cleaned.index, 
-                            y=funnel_percentage_col, 
-                            mode='lines', 
-                            name=col,
-                            line=dict(color=modern_colors[(idx + len(bar_columns)) % len(modern_colors)]),  # Cycle through colors
-                            opacity=0.8  # Set transparency
-                        )
+            fig = go.Figure()
+            
+            # Define modern and diverse color palette
+            modern_colors = [
+                "#FF6F61", "#6B5B95", "#88B04B", "#F7CAC9", "#92A8D1", 
+                "#955251", "#B565A7", "#009B77", "#DD4124", "#45B8AC"
+            ]
+            
+            # Add Bar traces with transparency and custom colors
+            for idx, col in enumerate(bar_columns):
+                funnel_percentage_col = funnel_percentage_cleaned[col]
+                fig.add_trace(
+                    go.Bar(
+                        x=funnel_percentage_cleaned.index, 
+                        y=funnel_percentage_col, 
+                        name=col,
+                        marker_color=modern_colors[idx % len(modern_colors)],  # Cycle through colors
+                        opacity=0.8  # Set transparency
                     )
-                        
-                fig.update_layout(
-                    title="Combined Bar and Line Chart",
-                    xaxis_title="Brands",
-                    yaxis_title="Percentage",
-                    template="plotly_dark",
-                    barmode="group",
-                    xaxis=dict(tickmode='linear')
                 )
-                
-                st.plotly_chart(fig)
-    
-
-            elif main_option == "Hypothesis test":
-                st.header("Hypothesis Testing")
-                hypothesis_option = st.selectbox("Please select the type of hypothesis test:", ["Z test", "T test", "Chi-Square test", "ANOVA test"])
             
-                if hypothesis_option != "Z test":
-                    st.info("This section of the program is under development.")
-                else:
-                    uploaded_file = st.file_uploader("Please upload your Excel file for Z-Test", type=["xlsx", "xls"])
-                    if uploaded_file:
-                        result = analyze_z_test(uploaded_file)
-                        if result:
-                            st.success("Z-Test analysis completed successfully.")
+            # Add Line traces with transparency and custom colors
+            for idx, col in enumerate(line_columns):
+                funnel_percentage_col = funnel_percentage_cleaned[col]
+                fig.add_trace(
+                    go.Scatter(
+                        x=funnel_percentage_cleaned.index, 
+                        y=funnel_percentage_col, 
+                        mode='lines', 
+                        name=col,
+                        line=dict(color=modern_colors[(idx + len(bar_columns)) % len(modern_colors)]),  # Cycle through colors
+                        opacity=0.8  # Set transparency
+                    )
+                )
+                    
+            fig.update_layout(
+                title="Combined Bar and Line Chart",
+                xaxis_title="Brands",
+                yaxis_title="Percentage",
+                template="plotly_dark",
+                barmode="group",
+                xaxis=dict(tickmode='linear')
+            )
             
-            elif main_option in ["Machine Learning", "Coding"]:
+            st.plotly_chart(fig)
+
+
+        elif main_option == "Hypothesis test":
+            st.header("Hypothesis Testing")
+            hypothesis_option = st.selectbox("Please select the type of hypothesis test:", ["Z test", "T test", "Chi-Square test", "ANOVA test"])
+        
+            if hypothesis_option != "Z test":
                 st.info("This section of the program is under development.")
+            else:
+                uploaded_file = st.file_uploader("Please upload your Excel file for Z-Test", type=["xlsx", "xls"])
+                if uploaded_file:
+                    result = analyze_z_test(uploaded_file)
+                    if result:
+                        st.success("Z-Test analysis completed successfully.")
+        
+        elif main_option in ["Machine Learning", "Coding"]:
+            st.info("This section of the program is under development.")
 
-        except Exception as e:
-            st.error(f"❌ Error reading the Excel file: {e}")
+    except Exception as e:
+        st.error(f"❌ Error reading the Excel file: {e}")