page_investing.py

import streamlit as st
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='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, chinese_font)
    st.markdown(
                f"<h2 style='text-align: center;'>Choice Experiment</h2>", unsafe_allow_html=True)
    visualize_investment_data(df, chinese_font)


def show_investment_count(df, chinese_font):
    # 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().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=chinese_font)

    # Add labels and title
    plt.xlabel('Do you currently have any investment?', fontsize=12, fontproperties=chinese_font)
    plt.ylabel('Count', fontsize=12, fontproperties=chinese_font)
    plt.title("Number of People Who Have/Haven't Invested", fontsize=16, fontproperties=chinese_font)

    # Display values on the bars
    for index, value in enumerate(investment_count['Count']):
        plt.text(index, value, str(value), ha='center', va='bottom', fontproperties=chinese_font)

    # Display the chart in Streamlit
    st.pyplot(plt)

def visualize_investment_data(df, chinese_font):

    # Field related to investment choices
    investment_field = "你/妳選哪個投資？"
    title = "Investment Choices"

    # Summarize the data
    investment_data = df[investment_field].value_counts().head(100)  # Adjust the number as needed
    
    # Plot the data
    plt.figure(figsize=(10, 6))
    investment_data.plot(kind='bar', color='skyblue')
    plt.title(title, fontproperties=chinese_font)
    plt.xlabel('Investment Options', fontproperties=chinese_font)
    plt.ylabel('Number of Responses', fontproperties=chinese_font)
    plt.xticks(rotation=45, ha='right', fontproperties=chinese_font)
    plt.tight_layout()

    # Display plot in Streamlit
    st.pyplot(plt)