Update app.py

app.py

@@ -104,32 +104,122 @@ class SurveyAnalyzer:
             }
         }
 
-標準差',
-            title='📊 各項滿意度平均分數與標準差',
+    def plot_satisfaction_scores(self, df: pd.DataFrame, venues=None, month=None, age_range=None):
+        """📊 各項滿意度平均分數圖表 - 美化版"""
+        # 過濾數據
+        filtered_df = df.copy()
+        if venues and '全部' not in venues:
+            filtered_df = filtered_df[filtered_df['場域名稱'].isin(venues)]
+        if month and month != '全部':
+            # 假設有一個月份欄位，如果沒有請調整
+            filtered_df = filtered_df[filtered_df['月份'] == month]
+        
+        # 年齡篩選
+        if age_range:
+            ages = self.calculate_age(filtered_df['２．出生年（民國__年）'])
+            age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
+            filtered_df = filtered_df[age_mask]
+            
+        # 計算過濾後數據的平均和標準差
+        satisfaction_means = [filtered_df[col].mean() for col in self.satisfaction_columns]
+        satisfaction_stds = [filtered_df[col].std() for col in self.satisfaction_columns]
+        
+        # 創建數據框
+        satisfaction_df = pd.DataFrame({
+            '滿意度項目': self.satisfaction_short_names,
+            '平均分數': satisfaction_means,
+            '標準差': satisfaction_stds
+        })
+        
+        # 排序結果（可選）
+        satisfaction_df = satisfaction_df.sort_values(by='平均分數', ascending=False)
+        
+        # 建立顏色漸變映射
+        color_scale = [
+            [0, '#90CAF9'],  # 淺藍色
+            [0.5, '#2196F3'],  # 中藍色
+            [1, '#1565C0']  # 深藍色
+        ]
+        
+        # 繪製條形圖
+        fig = px.bar(
+            satisfaction_df, 
+            x='滿意度項目', 
+            y='平均分數',
+            error_y='標準差',
+            title='📊 各項滿意度平均分數與標準差分析',
             color='平均分數',
-            color_continuous_scale='Viridis',
-            text='平均分數'
+            color_continuous_scale=color_scale,
+            text='平均分數',
+            hover_data={
+                '滿意度項目': True, 
+                '平均分數': ':.2f', 
+                '標準差': ':.2f'
+            }
         )
         
         # 調整圖表佈局
         fig.update_layout(
-            font=dict(size=16),
-            title_font=dict(size=24),
+            font=dict(family="Arial", size=16),
+            title_font=dict(family="Arial Black", size=24),
+            title_x=0.5,  # 標題置中
             xaxis_title="滿意度項目",
             yaxis_title="平均分數",
-            yaxis_range=[1, 5],  # 假設評分範圍是 1-5
+            yaxis_range=[0, 5],  # 評分範圍從0開始，視覺上更明顯
+            plot_bgcolor='rgba(240,240,240,0.8)',  # 淺灰色背景
+            paper_bgcolor='white',
+            xaxis_tickangle=-25,  # 斜角標籤，避免重疊
+            margin=dict(l=40, r=40, t=80, b=60),
+            legend_title_text="平均分數",
+            shapes=[
+                # 添加參考線 - 例如4分
+                dict(
+                    type='line',
+                    yref='y', y0=4, y1=4,
+                    xref='paper', x0=0, x1=1,
+                    line=dict(color='rgba(220,20,60,0.5)', width=2, dash='dash')
+                )
+            ],
+            annotations=[
+                # 參考線標籤
+                dict(
+                    x=0.02, y=4.1,
+                    xref='paper', yref='y',
+                    text='優良標準 (4分)',
+                    showarrow=False,
+                    font=dict(size=14, color='rgba(220,20,60,0.8)')
+                )
+            ]
         )
         
         # 調整文字格式
         fig.update_traces(
             texttemplate='%{y:.2f}', 
-            textposition='outside'
+            textposition='outside',
+            marker_line_color='rgb(8,48,107)',
+            marker_line_width=1.5,
+            opacity=0.85
+        )
+        
+        # 添加受訪人數標註
+        num_respondents = len(filtered_df)
+        fig.add_annotation(
+            x=0.5, y=0,
+            xref='paper', yref='paper',
+            text=f'受訪人數: {num_respondents}人',
+            showarrow=False,
+            font=dict(size=16),
+            bgcolor='rgba(255,255,255,0.8)',
+            bordercolor='rgba(0,0,0,0.2)',
+            borderwidth=1,
+            borderpad=4,
+            y=-0.2
         )
         
         st.plotly_chart(fig, use_container_width=True)
 
-    def plot_gender_distribution(self, df: pd.DataFrame, venues=None, month=None):
-        """🟠 性別分佈圓餅圖（使用藍色和紅色）"""
+    def plot_gender_distribution(self, df: pd.DataFrame, venues=None, month=None, age_range=None):
+        """🟠 性別分佈圓餅圖 - 增強精緻版"""
         # 過濾數據
         filtered_df = df.copy()
         if venues and '全部' not in venues:
@@ -138,6 +228,13 @@ class SurveyAnalyzer:
             # 假設有一個月份欄位，如果沒有請調整
             filtered_df = filtered_df[filtered_df['月份'] == month]
             
+        # 年齡篩選
+        if age_range:
+            ages = self.calculate_age(filtered_df['２．出生年（民國__年）'])
+            age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
+            filtered_df = filtered_df[age_mask]
+        
+        # 取得性別資料
         gender_counts = filtered_df['1.   性別'].value_counts().reset_index()
         gender_counts.columns = ['性別', '人數']
         
@@ -146,14 +243,27 @@ class SurveyAnalyzer:
         gender_counts['百分比'] = (gender_counts['人數'] / total * 100).round(1)
         gender_counts['標籤'] = gender_counts.apply(lambda x: f"{x['性別']}: {x['人數']}人 ({x['百分比']}%)", axis=1)
         
-        # 設定顏色映射 - 男性藍色，女性紅色
-        color_map = {'男性': '#2171b5', '女性': '#cb181d'}
+        # 獲取篩選條件說明
+        filter_description = []
+        if venues and '全部' not in venues:
+            filter_description.append(f"場域: {', '.join(venues)}")
+        if month and month != '全部':
+            filter_description.append(f"月份: {month}")
+        if age_range and (age_range[0] != min(self.calculate_age(df['２．出生年（民國__年）'])) or 
+                          age_range[1] != max(self.calculate_age(df['２．出生年（民國__年）']))):
+            filter_description.append(f"年齡: {age_range[0]}-{age_range[1]}歲")
+        
+        filter_text = "（" + ", ".join(filter_description) + "）" if filter_description else ""
+        
+        # 設定顏色映射 - 男性藍色，女性紅色 - 使用更精緻的顏色
+        color_map = {'男性': '#1976D2', '女性': '#D32F2F'}
         
+        # 建立子圖佈局以添加更多自定義元素
         fig = px.pie(
             gender_counts, 
             names='性別', 
             values='人數', 
-            title='🟠 受訪者性別分布',
+            title=f'👥 受訪者性別分布{filter_text}',
             color='性別',
             color_discrete_map=color_map,
             hover_data=['人數', '百分比'],
@@ -161,13 +271,93 @@ class SurveyAnalyzer:
             custom_data=['標籤']
         )
         
-        # 更新悬停信息
+        # 更新圖表佈局
+        fig.update_layout(
+            font=dict(family="Arial", size=16),
+            title_font=dict(family="Arial Black", size=24),
+            title_x=0.5,  # 標題置中
+            legend_title_text="性別",
+            legend=dict(
+                orientation="h",
+                yanchor="bottom",
+                y=-0.2,
+                xanchor="center",
+                x=0.5,
+                font=dict(size=16),
+                bordercolor="#E0E0E0",
+                borderwidth=2
+            ),
+            margin=dict(l=20, r=20, t=80, b=100),
+            paper_bgcolor='white',
+            annotations=[
+                dict(
+                    text=f"總受訪人數: {total}人",
+                    x=0.5, y=-0.3,
+                    xref="paper",
+                    yref="paper",
+                    showarrow=False,
+                    font=dict(size=16, color="#616161")
+                )
+            ]
+        )
+        
+        # 添加男女比例標籤
+        male_count = gender_counts.loc[gender_counts['性別'] == '男性', '人數'].values[0] if '男性' in gender_counts['性別'].values else 0
+        female_count = gender_counts.loc[gender_counts['性別'] == '女性', '人數'].values[0] if '女性' in gender_counts['性別'].values else 0
+        
+        # 計算男女比例
+        if male_count > 0 and female_count > 0:
+            ratio = round(male_count / female_count, 2)
+            ratio_text = f"男女比例 = {ratio}:1"
+        elif male_count > 0 and female_count == 0:
+            ratio_text = "僅有男性"
+        elif female_count > 0 and male_count == 0:
+            ratio_text = "僅有女性"
+        else:
+            ratio_text = "無性別數據"
+            
+        fig.add_annotation(
+            text=ratio_text,
+            x=0.5, y=-0.15,
+            xref="paper",
+            yref="paper",
+            showarrow=False,
+            font=dict(size=16, color="#424242", family="Arial Bold")
+        )
+        
+        # 更新懸停資訊
         fig.update_traces(
             textinfo='percent+label',
-            hovertemplate='%{customdata[0]}'
+            hovertemplate='%{customdata[0]}',
+            textfont_size=16,
+            marker=dict(line=dict(color='#FFFFFF', width=2)),
+            pull=[0.03, 0.03],  # 稍微分離餅圖片段
+            rotation=45  # 旋轉角度
         )
         
         st.plotly_chart(fig, use_container_width=True)
+        
+        # 在圓餅圖下方添加簡單分析
+        st.markdown("""
+        <div style="background-color:#F5F5F5; padding:15px; border-radius:10px; margin-top:10px; border-left:5px solid #1976D2;">
+        <h4 style="color:#1976D2;">📊 性別分佈簡易分析</h4>
+        """, unsafe_allow_html=True)
+        
+        # 生成簡單分析文字
+        if total > 0:
+            majority_gender = '男性' if male_count > female_count else '女性' if female_count > male_count else '男女相等'
+            majority_pct = max(male_count, female_count) / total * 100 if male_count != female_count else 50
+            
+            if male_count != female_count:
+                st.markdown(f"""
+                <p>本次調查中，<strong>{majority_gender}</strong>佔多數，約佔總體的<strong>{majority_pct:.1f}%</strong>。</p>
+                """, unsafe_allow_html=True)
+            else:
+                st.markdown("<p>本次調查中，男女比例相等，各佔50%。</p>", unsafe_allow_html=True)
+        else:
+            st.markdown("<p>目前沒有足夠的性別數據進行分析。</p>", unsafe_allow_html=True)
+            
+        st.markdown("</div>", unsafe_allow_html=True)
 
 # 🎨 Streamlit UI
 def main():
@@ -290,26 +480,73 @@ def main():
         selected_analysis = st.sidebar.radio("選擇要查看的分析", 
                                              ["📋 問卷統計報告", "📊 滿意度統計", "🟠 性別分佈"])
 
+        # 應用所有篩選條件
+        filtered_df = df.copy()
+        if selected_venues and '全部' not in selected_venues:
+            if '場域名稱' in filtered_df.columns:
+                filtered_df = filtered_df[filtered_df['場域名稱'].isin(selected_venues)]
+        if selected_month and selected_month != '全部':
+            if '月份' in filtered_df.columns:
+                filtered_df = filtered_df[filtered_df['月份'] == selected_month]
+        
+        # 年齡篩選
+        if age_range:
+            ages = analyzer.calculate_age(filtered_df['２．出生年（民國__年）'])
+            age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
+            filtered_df = filtered_df[age_mask]
+        
+        # 顯示當前選擇的篩選器
+        filter_status = []
+        if selected_venues and '全部' not in selected_venues:
+            filter_status.append(f"📍 場域: {', '.join(selected_venues)}")
+        if selected_month and selected_month != '全部':
+            filter_status.append(f"📅 月份: {selected_month}")
+        if age_range and (age_range[0] != min(analyzer.calculate_age(df['２．出生年（民國__年）'])) or 
+                          age_range[1] != max(analyzer.calculate_age(df['２．出生年（民國__年）']))):
+            filter_status.append(f"👥 年齡: {age_range[0]}-{age_range[1]}歲")
+            
+        if filter_status:
+            st.markdown("""
+            <div style="background-color:#E3F2FD; padding:10px; border-radius:8px; margin-bottom:20px; border-left:4px solid #1976D2;">
+            <h4 style="margin-bottom:10px; color:#1565C0;">🔍 當前篩選條件</h4>
+            """, unsafe_allow_html=True)
+            
+            for status in filter_status:
+                st.markdown(f"<p style='margin:5px 0;'>{status}</p>", unsafe_allow_html=True)
+                
+            # 顯示篩選後的樣本數
+            st.markdown(f"""
+            <p style='margin-top:10px; font-weight:bold;'>📊 篩選後樣本數: {len(filtered_df)}人</p>
+            </div>
+            """, unsafe_allow_html=True)
+            
+        # 數據分析區塊
         if selected_analysis == "📋 問卷統計報告":
-            st.header("📋 問卷統計報告")
-            report = analyzer.generate_report(df)
-            for category, stats in report.items():
-                with st.expander(f"🔍 {category}", expanded=True):
-                    for key, value in stats.items():
-                        if key == '各項滿意度':
-                            st.write(f"**{key}:**")
-                            for item, item_stats in value.items():
-                                st.write(f"  - **{item}**: {', '.join([f'{k}: {v}' for k, v in item_stats.items()])}")
-                        else:
-                            st.write(f"**{key}**: {value}")
-
-        elif selected_analysis == "📊 滿意度統計":
-            st.header("📊 滿意度統計")
-            analyzer.plot_satisfaction_scores(df)
-
-        elif selected_analysis == "🟠 性別分佈":
-            st.header("🟠 性別分佈")
-            analyzer.plot_gender_distribution(df, selected_venues, selected_month)
+            st.markdown('<h2 style="color:#1976D2;">📋 問卷統計報告</h2>', unsafe_allow_html=True)
+            
+            # 生成目前篩選條件下的報告
+            report = analyzer.generate_report(filtered_df)
+            
+            # 使用卡片樣式顯示統計信息
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.markdown('<div class="card">', unsafe_allow_html=True)
+                st.markdown('<h3 style="color:#1976D2; border-bottom:1px solid #e0e0e0; padding-bottom:10px;">📊 基本統計數據</h3>', unsafe_allow_html=True)
+                
+                for key, value in report['基本統計'].items():
+                    if isinstance(value, dict):
+                        st.markdown(f"<p><strong>{key}:</strong></p>", unsafe_allow_html=True)
+                        for k, v in value.items():
+                            st.markdown(f"<p style='margin-left:20px;'>- {k}: {v}</p>", unsafe_allow_html=True)
+                    else:
+                        st.markdown(f"<p><strong>{key}:</strong> {value}</p>", unsafe_allow_html=True)
+                        
+                st.markdown('</div>', unsafe_allow_html=True)
+            
+            with col2:
+                st.markdown('<div class="card">', unsafe_allow_html=True)
+                
 
 if __name__ == "__main__":
     main()