Show investing in a chart

page_investing.py

@@ -1,20 +1,37 @@
 import streamlit as st
 import pandas as pd
-
+from matplotlib.font_manager import FontProperties
+import matplotlib.pyplot as plt
+import seaborn as sns
 
 @st.cache_data
 def show(df):
+    # Load the Chinese font
+    chinese_font = FontProperties(fname='mingliu.ttf', size=12)
     st.title("Investing")
-    show_investment_count(df)
+    show_investment_count(df, font_prop=chinese_font)
 
 
-def show_investment_count(df):
+def show_investment_count(df, font_prop):
     # Count the number of people who have invested and who have not
-    investment_count = df['你/妳覺得目前有任何投資嗎？'].value_counts()
+    investment_count = df["你/妳覺得目前有任何投資嗎？"].value_counts().reset_index()
+    investment_count.columns = ['Investment', 'Count']
+
+    # Create a bar chart using seaborn
+    plt.figure(figsize=(10, 6))
+    barplot = sns.barplot(x='Investment', 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)
+
+    # Add labels and title
+    plt.xlabel('Do you currently have any investment?', fontsize=12, fontproperties=font_prop)
+    plt.ylabel('Count', fontsize=12, fontproperties=font_prop)
+    plt.title("Number of People Who Have/Haven't Invested", 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)
 
-    # Create a table to display the count
-    investment_table = pd.DataFrame(
-        {'Do you have any investment currently?': investment_count.index, 'Count': investment_count.values})
+    # Display the chart in Streamlit
+    st.pyplot(plt)
 
-    # Display the DataFrame as a table in Streamlit
-    st.table(investment_table)

page_shopping.py

@@ -8,7 +8,7 @@ import numpy as np
 from fields.prod_feat_flat_fields import prod_feat_flat_fields
 from fields.feature_translations import feature_translations
 
-#@st.cache_data
+@st.cache_data
 def show(df):
     # Load the Chinese font
     chinese_font = FontProperties(fname='mingliu.ttf', size=12)
@@ -24,12 +24,12 @@ def show(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().reset_index()
-    investment_count.columns = ['Boycott', 'Count']
+    boycott_count = df["你/妳有沒有抵制過某公司？"].value_counts().reset_index()
+    boycott_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')
+    barplot = sns.barplot(x='Boycott', y='Count', data=boycott_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)
 
@@ -39,7 +39,7 @@ def show_boycott_count(df, 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']):
+    for index, value in enumerate(boycott_count['Count']):
         plt.text(index, value, str(value), ha='center', va='bottom', fontproperties=font_prop)
 
     # Display the chart in Streamlit