Update app.py

app.py

@@ -499,6 +499,36 @@ def hierarchical_clustering_with_plotly(df, linkage_method):
 
     return df
 
+
+def upload_and_select_dataframe():
+  st.sidebar.title("File Upload")
+  uploaded_files = st.sidebar.file_uploader("Choose CSV or Excel files", type=["csv", "xlsx", "xls", "xlsb"], accept_multiple_files=True)
+  
+  dataframes = {}
+  for uploaded_file in uploaded_files:
+      try:
+          if uploaded_file.name.endswith(('.csv')):
+              df = pd.read_csv(uploaded_file)
+          elif uploaded_file.name.endswith(('.xls', '.xlsx', '.xlsb')):
+              df = pd.read_excel(uploaded_file)
+          else:
+              st.sidebar.error(f"Unsupported file type: {uploaded_file.name}")
+              continue
+          dataframes[uploaded_file.name] = df
+      except Exception as e:
+          st.sidebar.error(f"Error reading {uploaded_file.name}: {e}")
+  
+  if len(uploaded_files) > 7:
+    st.sidebar.error('Maximum 7 files can be uploaded.')
+    return None
+
+  if dataframes:
+      selected_file = st.sidebar.selectbox("Select a DataFrame", list(dataframes.keys()))
+      return dataframes[selected_file]
+  else:
+      st.sidebar.info("Please upload some files.")
+      return None
+
 empty_col1, main_col, empty_col2 = st.columns([1.6, 2.8, 1.6])
 
 with main_col:
@@ -514,329 +544,319 @@ import streamlit as st
 
 st.markdown('[Click to register a suggestion or comment](https://docs.google.com/forms/d/e/1FAIpQLScLyP7bBbqMfGdspjL7Ij64UZ6v2KjqjKNbm8gwEsgWsFs_Qg/viewform?usp=header)')
 
+st.subheader("Displaying the first few rows of the DataFrame")
+st.dataframe(df.head())
 
-# Main options
+cols = edit_strings(df.columns)
+cols = sorted(list(set(cols)))
 
-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())
+main_option = st.selectbox("Please select an option:", ["Tabulation", "Funnel Analysis", "Segmentation Analysis", "Hypothesis test", "Machine Learning", "Coding"])
 
-        cols = edit_strings(df.columns)
-        cols = sorted(list(set(cols)))
+if main_option == "Tabulation":
+    st.header("Tabulation Analysis")
 
-        main_option = st.selectbox("Please select an option:", ["Tabulation", "Funnel Analysis", "Segmentation Analysis", "Hypothesis test", "Machine Learning", "Coding"])
+    tabulation_option = st.selectbox("Please select the type of analysis:", ["Univariate", "Multivariate", "All"])
+
+    if tabulation_option == "All":
         
-        if main_option == "Tabulation":
-            st.header("Tabulation Analysis")
-    
-            tabulation_option = st.selectbox("Please select the type of analysis:", ["Univariate", "Multivariate", "All"])
+        st.sidebar.header("Settings")
 
-            if tabulation_option == "All":
-                
-                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 = {"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=[]
-                )
-
-                follow_dict = {"single": [], "multi": [], "score": []}
-                
-                st.sidebar.subheader("Follow")
-                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.")
+        main_dict["score"] = st.sidebar.multiselect(
+            'Main: Score answer questions',
+            cols,
+            default=[]
+        )
+
+        follow_dict = {"single": [], "multi": [], "score": []}
+        
+        st.sidebar.subheader("Follow")
+        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)
                         
-            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 Analysis":
-            st.header("Funnel")
-            
-            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))
+                    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.")
 
-            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
-                    )
-                )
+        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)
                     
-            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)
+                else:
+                    st.error("No columns matching the entered pattern were found.")
 
-        elif main_option == "Segmentation Analysis":
-            st.header("Segmentation Analysis")
-            
-            st.sidebar.header("Selection of questions")
-            single_list = st.sidebar.multiselect(
-                'Single answer questions', 
-                cols,
-                default=[]
-            )
+            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))
 
-            multi_list = st.sidebar.multiselect(
-                'Multi answer questions', 
-                cols,
-                default=[]
-            )
+                    st.subheader("Frequency Table")
+                    st.dataframe(frequency_df)
 
-            score_list = st.sidebar.multiselect(
-                'Score answer questions', 
-                cols,
-                default=[]
-            )
+                    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)
 
-            matching_cols1 = []
-            for i in multi_list:
-                matching_cols1 += [col for col in df.columns if is_matching_pattern(col, i)]
+            elif type1 == "Single answer" and type2 == "Score answer":
 
-            df_clean = process_dataframe(df[single_list + matching_cols1])
-            st.subheader("Selected Table")
-            st.dataframe(df_clean)
+                mean_df = two_variable_ssc(df[[var1, var2]], var1, var2)
+                st.subheader("Mean Table")
+                st.write(t_test_data(mean_df))
 
-            linkage_method = st.sidebar.selectbox("Select the Linkage Method of Segmentation Analysis:", ['average', 'single', 'complete', 'weighted', 'centroid', 'median', 'ward'])
-            
-            df_cluster = hierarchical_clustering_with_plotly(df_clean, linkage_method)
+                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)                            
 
-            st.subheader("Cluster Table")
-            st.dataframe(df_clean)
 
-        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.")
+            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:
-                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.info("This section of the program is under development.")
+
+elif main_option == "Funnel Analysis":
+    st.header("Funnel")
+    
+    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.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 == "Segmentation Analysis":
+    st.header("Segmentation Analysis")
+    
+    st.sidebar.header("Selection of questions")
+    single_list = st.sidebar.multiselect(
+        'Single answer questions', 
+        cols,
+        default=[]
+    )
+
+    multi_list = st.sidebar.multiselect(
+        'Multi answer questions', 
+        cols,
+        default=[]
+    )
+
+    score_list = st.sidebar.multiselect(
+        'Score answer questions', 
+        cols,
+        default=[]
+    )
+
+    matching_cols1 = []
+    for i in multi_list:
+        matching_cols1 += [col for col in df.columns if is_matching_pattern(col, i)]
+
+    df_clean = process_dataframe(df[single_list + matching_cols1])
+    st.subheader("Selected Table")
+        st.dataframe(df_clean)
+
+        linkage_method = st.sidebar.selectbox("Select the Linkage Method of Segmentation Analysis:", ['average', 'single', 'complete', 'weighted', 'centroid', 'median', 'ward'])
         
-        elif main_option in ["Machine Learning", "Coding"]:
-            st.info("This section of the program is under development.")
+        df_cluster = hierarchical_clustering_with_plotly(df_clean, linkage_method)
+
+        st.subheader("Cluster Table")
+        st.dataframe(df_clean)
 
-    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.")