Update app.py

app.py

@@ -504,304 +504,296 @@ st.markdown('[Click to register a suggestion or comment](https://docs.google.com
 
 
 # Main options
-main_option = st.selectbox("Please select an option:", ["Tabulation", "Funnel", "Hypothesis test", "Machine Learning", "Coding"])
 
-if main_option == "Tabulation":
-    st.header("Tabulation Analysis")
-    uploaded_file = st.file_uploader("Please upload your Excel file", type=["xlsx", "xls"])
+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())
 
-            tabulation_option = st.selectbox("Please select the type of analysis:", ["Univariate", "Multivariate", "All"])
-
-            if tabulation_option == "All":
-
+            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_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)
+                                    
+                                    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)
+                                    
+                                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)
+                                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)                    
+                            else:
+                                st.info("This section of the program is under development.")
+
+            elif main_option == "Funnel":
+                st.header("Funnel")
+            
                 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', 
+                st.sidebar.header("Funnel Settings")
+                single_list = st.sidebar.multiselect(
+                    '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',
+                multi_list = st.sidebar.multiselect(
+                    'Multi answer questions',
                     cols,
                     default=[]
                 )
-
-                st.sidebar.subheader("Follow")
-
-                follow_dict = {"single": [], "multi": [], "score": []}
+                selected_dict = {}
                 
-                st.sidebar.subheader("Main")
-                follow_dict["single"] = st.sidebar.multiselect(
-                    'Follow: Single answer questions', 
-                    cols,
-                    default=[]
-                )
+                for option in single_list:
+                    selected_dict[option] = "Single"
+                for option in multi_list:
+                    selected_dict[option] = "Multi"
                 
-                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)
+                funnel_frequency, funnel_percentage = funnel(df, selected_dict)
+                st.subheader("Percentage Table")
+                st.dataframe(funnel_percentage)
                 
-            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)
-                            
-                            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)
-                            
-                        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)
-                        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)                    
-                    else:
-                        st.info("This section of the program is under development.")
-
-        except Exception as e:
-            st.error(f"❌ Error reading the Excel file: {e}")
-
-elif main_option == "Funnel":
-    st.header("Funnel")
+                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))
     
-    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())
-
-            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
+                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
+                
+                # 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')
                 )
+                
+                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"])
             
-            fig.update_layout(
-                title="Combined Bar and Line Chart",
-                xaxis_title="Brands",
-                yaxis_title="Percentage",
-                template="plotly_dark",
-                barmode="group",
-                xaxis=dict(tickmode='linear')
-            )
+                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.")
             
-            st.plotly_chart(fig)
+            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}")
-    
-
-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.")
+            st.error(f"❌ Error reading the Excel file: {e}")