Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+
+# Load data function
+def load_data(uploaded_file):
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        df.fillna(0, inplace=True)
+        if '出表日期' in df.columns:
+            df['出表日期'] = df['出表日期'].astype(str)
+        if '公司代號' in df.columns:
+            df['公司代號'] = df['公司代號'].astype(str)
+        return df
+    else:
+        st.warning("請上傳檔案。")
+        return None
+
+# Merge dataframes
+def merge_dataframes(df1, df2, on_columns):
+    if df1 is None or df2 is None:
+        return None
+    for col in on_columns:
+        if col in df1.columns and col in df2.columns:
+            df1[col] = df1[col].astype(str)
+            df2[col] = df2[col].astype(str)
+    return pd.merge(df1, df2, on=on_columns, how="outer")
+
+# Filter dataframe
+def filter_dataframe(df, prefix):
+    return df[df['公司代號'].astype(str).str.startswith(prefix)]
+
+# Get specific company data
+def get_specific_company(df, company_code):
+    return df[df['公司代號'] == company_code]
+
+# Plot radar chart
+def plot_radar_chart(avg_values, specific_company_values, categories, prefix, specific_company_name):
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatterpolar(
+        r=avg_values,
+        theta=categories,
+        fill='toself',
+        name=f"股號前兩位『{prefix}』的族群"
+    ))
+
+    fig.add_trace(go.Scatterpolar(
+        r=specific_company_values,
+        theta=categories,
+        fill='toself',
+        name=f'{specific_company_name}'
+    ))
+
+    fig.update_layout(
+        polar=dict(radialaxis=dict(visible=True, range=[0, 100])),
+        showlegend=True,
+        title="董事會和投資人溝通指標比較"
+    )
+
+    st.plotly_chart(fig)
+
+# Plot emission chart
+def plot_emission_chart(filtered_df, avg_emissions, prefix):
+    emission_columns = ['範疇一排放量(噸CO2e)', '範疇二排放量(噸CO2e)', '範疇三排放量(噸CO2e)']
+    fig = go.Figure()
+
+    for scope, color in zip(emission_columns, ['blue', 'green', 'red']):
+        fig.add_trace(go.Bar(
+            x=filtered_df['公司名稱'],
+            y=filtered_df[scope],
+            name=scope,
+            marker_color=color
+        ))
+
+        fig.add_trace(go.Scatter(
+            x=filtered_df['公司名稱'],
+            y=[avg_emissions[scope]] * len(filtered_df),
+            mode='lines',
+            line=dict(color=color, dash='dash'),
+            name=f'{scope}平均值'
+        ))
+
+    fig.update_layout(
+        title=f"代號前兩位『{prefix}』的族群 - 各範疇排放量",
+        barmode='group',
+        xaxis_title="公司名稱",
+        yaxis_title="排放量(噸CO2e)"
+    )
+
+    st.plotly_chart(fig)
+
+# Plot energy usage
+def plot_energy_usage(filtered_df, avg_energy_usage):
+    fig_energy = px.bar(filtered_df, x='公司名稱', y='使用率(再生能源/總能源)', title="再生能源使用率")
+
+    fig_energy.add_trace(go.Scatter(
+        x=filtered_df['公司名稱'],
+        y=[avg_energy_usage] * len(filtered_df),
+        mode='lines',
+        line=dict(color='red', dash='dash'),
+        name='群體平均值'
+    ))
+
+    fig_energy.update_layout(
+        yaxis_title="再生能源使用率 (%)",
+        xaxis_title="公司名稱"
+    )
+
+    st.plotly_chart(fig_energy)
+# Plot waste management box plots
+def plot_waste_management(df_group, df_specific, company_name):
+    columns_to_analyze = ['有害廢棄物量-數據(公噸)', '非有害廢棄物量-數據(公噸)', '總重量(有害+非有害)-數據(公噸)', '廢棄物密集度-密集度(公噸/單位)']
+
+    fig = go.Figure()
+
+    # Loop through the columns and plot box plot for each column
+    for col in columns_to_analyze:
+        fig.add_trace(go.Box(y=df_group[col], name=f'母群體-{col}', boxmean=True, boxpoints='outliers'))
+
+        if not df_specific.empty:
+            specific_value = df_specific[col].values[0]
+            
+            # Highlight specific company's value
+            fig.add_trace(go.Scatter(
+                y=[specific_value],
+                x=[f'母群體-{col}'],
+                mode='markers',
+                name=f'{company_name}-{col}',
+                marker=dict(color='red', size=11, symbol='star'),
+                showlegend=True,
+                hovertext=f'公司名稱: {company_name}, 值: {specific_value}'
+            ))
+
+    # Update layout
+    fig.update_layout(
+        title=f"廢棄物統計數據箱型圖 (包含指定公司名稱 {company_name} 數據)",
+        yaxis_title="數值 (公噸)",
+        xaxis_title="廢棄物項目",
+        boxmode='group'
+    )
+
+    # Display the plot in Streamlit
+    st.plotly_chart(fig)
+
+    
+# Main function update
+def main():
+    st.title("公司數據分析儀表板")
+    
+    # File upload
+    st.sidebar.header("上傳 CSV 檔案")
+    investor_file = st.sidebar.file_uploader("上傳 投資人溝通.csv", type=["csv"])
+    board_file = st.sidebar.file_uploader("上傳 董事會.csv", type=["csv"])
+    emission_file = st.sidebar.file_uploader("上傳 溫室氣體排放.csv", type=["csv"])
+    energy_file = st.sidebar.file_uploader("上傳 能源管理.csv", type=["csv"])
+    waste_file = st.sidebar.file_uploader("上傳 廢棄物管理.csv", type=["csv"])
+
+    # Load data
+    investor_df = load_data(investor_file)
+    board_df = load_data(board_file)
+    emission_df = load_data(emission_file)
+    energy_df = load_data(energy_file)
+    waste_df = load_data(waste_file)
+
+    # Merge data
+    merged_df1 = merge_dataframes(investor_df, board_df, ["公司代號", "公司名稱", "出表日期", "報告年度"])
+    merged_df2 = merge_dataframes(emission_df, energy_df, ["公司代號", "公司名稱", "出表日期", "報告年度"])
+
+    # User input
+    prefix = st.sidebar.text_input("輸入公司代號前兩位")
+    specific_company_code = st.sidebar.text_input("輸入四位數字公司代號")
+
+    # Waste management analysis
+    if waste_df is not None and prefix:
+        waste_df['公司代號前兩位'] = waste_df['公司代號'].astype(str).str[:2]
+        df_group = waste_df[waste_df['公司代號前兩位'] == prefix]
+        df_specific = waste_df[waste_df['公司代號'] == specific_company_code]
+
+        if not df_specific.empty:
+            company_name = df_specific['公司名稱'].values[0]
+            plot_waste_management(df_group, df_specific, company_name)
+        else:
+            st.warning(f"找不到公司代號為 {specific_company_code} 的廢棄物管理數據")
+
+
+
+    # Handle 投資人溝通和董事會資料
+    if merged_df1 is not None and prefix and specific_company_code:
+        columns_of_interest = ['董事出席董事會出席率', '董事進修時數符合進修要點比率', '公司年度召開法說會次數(次)']
+        for col in ['董事出席董事會出席率', '董事進修時數符合進修要點比率']:
+            merged_df1[col] = merged_df1[col].replace({'%': ''}, regex=True).astype(float)
+
+        filtered_df1 = filter_dataframe(merged_df1, prefix)
+        avg_values = filtered_df1[columns_of_interest].mean()
+        specific_company_df1 = get_specific_company(merged_df1, specific_company_code)
+
+        if not specific_company_df1.empty:
+            specific_company_name = specific_company_df1['公司名稱'].iloc[0]
+            specific_company_values = specific_company_df1[columns_of_interest].iloc[0]
+            plot_radar_chart(avg_values, specific_company_values, ['董事出席率', '董事進修時數符合比率', '年度法說會次數'], prefix, specific_company_name)
+        else:
+            st.warning(f"找不到公司代號 {specific_company_code} 的資料")
+
+    # Handle 溫室氣體排放和能源管理資料
+    if merged_df2 is not None and prefix:
+        emission_columns = ['範疇一排放量(噸CO2e)', '範疇二排放量(噸CO2e)', '範疇三排放量(噸CO2e)']
+        energy_column = '使用率(再生能源/總能源)'
+        merged_df2[energy_column] = merged_df2[energy_column].replace({'%': ''}, regex=True).astype(float)
+
+        filtered_df2 = filter_dataframe(merged_df2, prefix)
+        specific_company_df2 = get_specific_company(merged_df2, specific_company_code)
+
+        if not filtered_df2.empty:
+            avg_emissions = filtered_df2[emission_columns].mean()
+            plot_emission_chart(filtered_df2, avg_emissions, prefix)
+
+            avg_energy_usage = filtered_df2[energy_column].mean()
+            plot_energy_usage(filtered_df2, avg_energy_usage)
+
+            if not specific_company_df2.empty:
+                specific_energy_usage = specific_company_df2[energy_column].iloc[0]
+                comparison_data = {
+                    '公司名稱': [specific_company_df2['公司名稱'].iloc[0], f"{prefix} 母群體平均"],
+                    '再生能源使用率 (%)': [specific_energy_usage, avg_energy_usage]
+                }
+                comparison_df = pd.DataFrame(comparison_data)
+                st.write("\n再生能源使用率比較表格：")
+                st.write(comparison_df)
+            else:
+                st.warning(f"找不到公司代號 {specific_company_code} 的能源管理數據")
+        else:
+            st.warning(f"找不到前兩碼為 {prefix} 的公司數據")
+
+if __name__ == "__main__":
+    main()