Stub for more charts

fields/boolean_fields.py

@@ -0,0 +1,4 @@
+boolean_fields = [
+    '你/妳有沒有抵制過某公司？',
+    '你/妳覺得目前有任何投資嗎？'
+]

page_investing.py

@@ -3,12 +3,15 @@ import pandas as pd
 from matplotlib.font_manager import FontProperties
 import matplotlib.pyplot as plt
 import seaborn as sns
+from fields.likert_fields import likert_fields
 
 @st.cache_data
 def show(df):
     # Load the Chinese font
-    chinese_font = FontProperties(fname='mingliu.ttf', size=12)
+    chinese_font = FontProperties(fname='notosans.ttf', size=12)
     st.title("Investing")
+    st.markdown(
+                f"<h2 style='text-align: center;'>Investing Experience (Overall)</h2>", unsafe_allow_html=True)
     show_investment_count(df, font_prop=chinese_font)
 
 

page_shopping.py

@@ -4,15 +4,18 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import pandas as pd
 import numpy as np
+from fields.likert_flat_fields import likert_flat_fields
+#from fields.boolean_fields import boolean_fields
 
-@st.cache_data
+#@st.cache_data
 def show(df):
     # Load the Chinese font
-    chinese_font = FontProperties(fname='mingliu.ttf', size=12)
+    chinese_font = FontProperties(fname='notosans.ttf', size=12)
     st.title("Shopping")
     st.markdown(
-                f"<h2 style='text-align: center;'>Boycott Count</h2>", unsafe_allow_html=True)
+                f"<h2 style='text-align: center;'>Boycott Count (Overall)</h2>", unsafe_allow_html=True)
     show_boycott_count(df, font_prop=chinese_font)
+    #generate_correlation_chart(df, chinese_font)
 
 def show_boycott_count(df, font_prop):
     # Count the number of people who have invested and who have not
@@ -35,4 +38,44 @@ def show_boycott_count(df, font_prop):
         plt.text(index, value, str(value), ha='center', va='bottom', fontproperties=font_prop)
 
     # Display the chart in Streamlit
+    st.pyplot(plt)
+
+
+def generate_correlation_chart(df, chinese_font):
+
+    boolean_fields = [
+        '你/妳覺得目前有任何投資嗎？'
+    ]
+
+    # Encode boolean fields
+    for field in boolean_fields:
+        df[field + '_encoded'] = df[field].map({'有': 1, '沒有': 0})
+
+    # Combine all fields for correlation
+    all_fields = likert_flat_fields + [f"{field}_encoded" for field in boolean_fields]
+    
+    # Calculate the correlation matrix
+    correlation_data = df[all_fields].corr()
+    
+    # Define a threshold for strong correlations
+    threshold = 0.5
+    
+    # Find all fields that have at least one strong correlation
+    strong_fields = correlation_data.columns[np.abs(correlation_data).max() > threshold]
+
+    # Filter the correlation matrix to only include these fields
+    filtered_correlation_data = correlation_data.loc[strong_fields, strong_fields]
+    
+    # Plot the correlation matrix
+    plt.figure(figsize=(10, 8))
+    ax = sns.heatmap(filtered_correlation_data, annot=True, fmt=".2f", cmap="coolwarm")
+    
+    # Set the labels with the Chinese font
+    ax.set_xticklabels(ax.get_xticklabels(), fontproperties=chinese_font, rotation=45, ha='right')
+    ax.set_yticklabels(ax.get_yticklabels(), fontproperties=chinese_font, rotation=0)
+    
+    # Set the title with the Chinese font
+    plt.title("強相關分析", fontproperties=chinese_font)
+
+    # Show the plot in Streamlit
     st.pyplot(plt)