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()