Fix labels and change chart type

page_shopping.py

@@ -2,6 +2,7 @@ import streamlit as st
 from kmodes.kmodes import KModes
 from matplotlib.font_manager import FontProperties
 import matplotlib.pyplot as plt
+import seaborn as sns
 import pandas as pd
 import numpy as np
 from fields.prod_feat_flat_fields import prod_feat_flat_fields
@@ -13,21 +14,37 @@ def show(df):
     chinese_font = FontProperties(fname='mingliu.ttf', size=12)
     st.title("Shopping")
     st.write("Clustering Students based on Product Feature choices")
-    show_boycott_count(df)
+    st.title("Boycott Count")
+    show_boycott_count(df, font_prop=chinese_font)
     clusters = perform_kmodes_clustering(df, prod_feat_flat_fields)
-    plot_feature_preferences(clusters, font_prop=chinese_font)
+    st.title("Feature Preferences")
     show_radar_chart(clusters, font_prop=chinese_font)
+    st.title("Feature Preferences")
+    plot_feature_preferences(clusters, font_prop=chinese_font)
 
-def show_boycott_count(df):
+def show_boycott_count(df, font_prop):
     # Count the number of people who have invested and who have not
-    investment_count = df["你/妳有沒有抵制過某公司？"].value_counts()
+    investment_count = df["你/妳有沒有抵制過某公司？"].value_counts().reset_index()
+    investment_count.columns = ['Boycott', 'Count']
+
+    # Create a bar chart using seaborn
+    plt.figure(figsize=(10, 6))
+    barplot = sns.barplot(x='Boycott', y='Count', data=investment_count, palette='viridis')
+    ax = plt.gca()  # Get the current Axes instance on the current figure matching the given keyword args, or create one.
+    ax.set_xticklabels(ax.get_xticklabels(), fontproperties=font_prop)
 
-    # Create a table to display the count
-    investment_table = pd.DataFrame(
-        {'Have you ever boycotted a company?': investment_count.index, 'Count': investment_count.values})
+    # Add labels and title
+    plt.xlabel('Have you ever boycotted a company?', fontsize=12, fontproperties=font_prop)
+    plt.ylabel('Count', fontsize=12, fontproperties=font_prop)
+    plt.title("Number of People Who Have/Haven't Boycotted a Company", fontsize=16, fontproperties=font_prop)
+
+    # Display values on the bars
+    for index, value in enumerate(investment_count['Count']):
+        plt.text(index, value, str(value), ha='center', va='bottom', fontproperties=font_prop)
+
+    # Display the chart in Streamlit
+    st.pyplot(plt)
 
-    # Display the DataFrame as a table in Streamlit
-    st.table(investment_table)
 
 
 def perform_kmodes_clustering(df, feature_columns, n_clusters=3):
@@ -58,9 +75,9 @@ def perform_kmodes_clustering(df, feature_columns, n_clusters=3):
 def show_radar_chart(clusters, font_prop):
 
     df_dict={
-        'Persona 1 (Cluster 0)': clusters[0],
-        'Persona 2 (Cluster 1)': clusters[1],
-        'Persona 3 (Cluster 2)': clusters[2]
+        'Eco-conscious Shopper (n=340)': clusters[0],
+        'Casual Eco-Interested)': clusters[1],
+        'Eco Advocate (n=126)': clusters[2]
     }
 
     feature_translations_dict = dict(zip(prod_feat_flat_fields, feature_translations))
@@ -100,6 +117,7 @@ def show_radar_chart(clusters, font_prop):
         ax.fill(angles, data, alpha=0.25)
     
     # Add legend
+    plt.legend(title='Personas')
     plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))
     
     # Add a title