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Update app.py
Browse files
app.py
CHANGED
@@ -104,11 +104,25 @@ class SurveyAnalyzer:
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}
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}
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def plot_satisfaction_scores(self, df: pd.DataFrame):
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"""📊 各項滿意度平均分數圖表"""
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#
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# 創建數據框
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satisfaction_df = pd.DataFrame({
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@@ -117,37 +131,96 @@ class SurveyAnalyzer:
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'標準差': satisfaction_stds
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})
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# 繪製條形圖
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fig = px.bar(
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satisfaction_df,
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x='滿意度項目',
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y='平均分數',
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error_y='標準差',
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title='📊
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color='平均分數',
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color_continuous_scale=
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text='平均分數'
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)
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# 調整圖表佈局
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fig.update_layout(
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font=dict(size=16),
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title_font=dict(size=24),
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xaxis_title="滿意度項目",
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yaxis_title="平均分數",
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yaxis_range=[
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)
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# 調整文字格式
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fig.update_traces(
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texttemplate='%{y:.2f}',
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textposition='outside'
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)
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st.plotly_chart(fig, use_container_width=True)
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def plot_gender_distribution(self, df: pd.DataFrame, venues=None, month=None):
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"""🟠
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# 過濾數據
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filtered_df = df.copy()
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if venues and '全部' not in venues:
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@@ -156,6 +229,13 @@ class SurveyAnalyzer:
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# 假設有一個月份欄位,如果沒有請調整
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filtered_df = filtered_df[filtered_df['月份'] == month]
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gender_counts = filtered_df['1. 性別'].value_counts().reset_index()
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gender_counts.columns = ['性別', '人數']
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gender_counts['百分比'] = (gender_counts['人數'] / total * 100).round(1)
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gender_counts['標籤'] = gender_counts.apply(lambda x: f"{x['性別']}: {x['人數']}人 ({x['百分比']}%)", axis=1)
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#
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fig = px.pie(
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gender_counts,
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names='性別',
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values='人數',
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title='
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color='性別',
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color_discrete_map=color_map,
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hover_data=['人數', '百分比'],
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@@ -179,20 +272,144 @@ class SurveyAnalyzer:
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custom_data=['標籤']
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)
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#
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fig.update_traces(
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textinfo='percent+label',
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hovertemplate='%{customdata[0]}'
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)
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st.plotly_chart(fig, use_container_width=True)
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# 🎨 Streamlit UI
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def main():
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st.set_page_config(
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st.
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# 讀取數據
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df = read_google_sheet(sheet_id, gid)
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if df is not None:
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analyzer = SurveyAnalyzer()
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#
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#
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if '場域名稱' in df.columns:
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venues = ['全部'] + sorted(df['場域名稱'].unique().tolist())
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selected_venues = st.sidebar.multiselect("選擇場域", venues, default=['全部'])
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else:
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# 如果沒有場域欄位,創建10個虛擬場域供選擇
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if '月份' in df.columns:
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months = ['全部'] + sorted(df['月份'].unique().tolist())
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selected_month = st.sidebar.selectbox("選擇月份", months)
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else:
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# 如果沒有月份欄位,可以創建虛擬月份選項
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# 📌 基本統計數據
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st.sidebar.header("📌 選擇數據分析")
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selected_analysis = st.sidebar.radio("選擇要查看的分析",
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["📋 問卷統計報告", "📊 滿意度統計", "🟠 性別分佈"])
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if selected_analysis == "📋 問卷統計報告":
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st.
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if __name__ == "__main__":
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main()
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}
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}
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def plot_satisfaction_scores(self, df: pd.DataFrame, venues=None, month=None, age_range=None):
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"""📊 各項滿意度平均分數圖表 - 美化版"""
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# 過濾數據
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filtered_df = df.copy()
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if venues and '全部' not in venues:
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filtered_df = filtered_df[filtered_df['場域名稱'].isin(venues)]
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if month and month != '全部':
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# 假設有一個月份欄位,如果沒有請調整
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filtered_df = filtered_df[filtered_df['月份'] == month]
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# 年齡篩選
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if age_range:
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ages = self.calculate_age(filtered_df['2.出生年(民國__年)'])
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age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
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filtered_df = filtered_df[age_mask]
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# 計算過濾後數據的平均和標準差
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satisfaction_means = [filtered_df[col].mean() for col in self.satisfaction_columns]
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satisfaction_stds = [filtered_df[col].std() for col in self.satisfaction_columns]
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# 創建數據框
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satisfaction_df = pd.DataFrame({
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'標準差': satisfaction_stds
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})
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# 排序結果(可選)
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satisfaction_df = satisfaction_df.sort_values(by='平均分數', ascending=False)
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# 建立顏色漸變映射
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color_scale = [
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[0, '#90CAF9'], # 淺藍色
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[0.5, '#2196F3'], # 中藍色
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[1, '#1565C0'] # 深藍色
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]
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# 繪製條形圖
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fig = px.bar(
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satisfaction_df,
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x='滿意度項目',
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y='平均分數',
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error_y='標準差',
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title='📊 各項滿意度平均分數與標準差分析',
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color='平均分數',
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color_continuous_scale=color_scale,
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text='平均分數',
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hover_data={
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'滿意度項目': True,
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'平均分數': ':.2f',
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'標準差': ':.2f'
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}
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)
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# 調整圖表佈局
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fig.update_layout(
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font=dict(family="Arial", size=16),
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title_font=dict(family="Arial Black", size=24),
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title_x=0.5, # 標題置中
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xaxis_title="滿意度項目",
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yaxis_title="平均分數",
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yaxis_range=[0, 5], # 評分範圍從0開始,視覺上更明顯
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plot_bgcolor='rgba(240,240,240,0.8)', # 淺灰色背景
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paper_bgcolor='white',
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xaxis_tickangle=-25, # 斜角標籤,避免重疊
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margin=dict(l=40, r=40, t=80, b=60),
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legend_title_text="平均分數",
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shapes=[
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# 添加參考線 - 例如4分
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dict(
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type='line',
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yref='y', y0=4, y1=4,
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xref='paper', x0=0, x1=1,
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line=dict(color='rgba(220,20,60,0.5)', width=2, dash='dash')
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)
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],
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annotations=[
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# 參考線標籤
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dict(
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x=0.02, y=4.1,
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xref='paper', yref='y',
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text='優良標準 (4分)',
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showarrow=False,
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font=dict(size=14, color='rgba(220,20,60,0.8)')
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)
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]
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)
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# 調整文字格式
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fig.update_traces(
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texttemplate='%{y:.2f}',
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textposition='outside',
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marker_line_color='rgb(8,48,107)',
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marker_line_width=1.5,
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opacity=0.85
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)
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# 添加受訪人數標註
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num_respondents = len(filtered_df)
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fig.add_annotation(
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x=0.5,
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xref='paper',
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yref='paper',
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text=f'受訪人數: {num_respondents}人',
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showarrow=False,
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font=dict(size=16),
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bgcolor='rgba(255,255,255,0.8)',
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bordercolor='rgba(0,0,0,0.2)',
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borderwidth=1,
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borderpad=4,
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y=-0.2
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)
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st.plotly_chart(fig, use_container_width=True)
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def plot_gender_distribution(self, df: pd.DataFrame, venues=None, month=None, age_range=None):
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"""🟠 性別分佈圓餅圖 - 增強精緻版"""
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# 過濾數據
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filtered_df = df.copy()
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if venues and '全部' not in venues:
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# 假設有一個月份欄位,如果沒有請調整
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filtered_df = filtered_df[filtered_df['月份'] == month]
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# 年齡篩選
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if age_range:
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ages = self.calculate_age(filtered_df['2.出生年(民國__年)'])
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age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
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filtered_df = filtered_df[age_mask]
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# 取得性別資料
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gender_counts = filtered_df['1. 性別'].value_counts().reset_index()
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gender_counts.columns = ['性別', '人數']
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gender_counts['百分比'] = (gender_counts['人數'] / total * 100).round(1)
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gender_counts['標籤'] = gender_counts.apply(lambda x: f"{x['性別']}: {x['人數']}人 ({x['百分比']}%)", axis=1)
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# 獲取篩選條件說明
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filter_description = []
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if venues and '全部' not in venues:
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filter_description.append(f"場域: {', '.join(venues)}")
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if month and month != '全部':
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filter_description.append(f"月份: {month}")
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if age_range and (age_range[0] != min(self.calculate_age(df['2.出生年(民國__年)'])) or
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age_range[1] != max(self.calculate_age(df['2.出生年(民國__年)']))):
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filter_description.append(f"年齡: {age_range[0]}-{age_range[1]}歲")
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filter_text = "(" + ", ".join(filter_description) + ")" if filter_description else ""
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# 設定顏色映射 - 男性藍色,女性紅色 - 使用更精緻的顏色
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color_map = {'男性': '#1976D2', '女性': '#D32F2F'}
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# 建立子圖佈局以添加更多自定義元素
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fig = px.pie(
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gender_counts,
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names='性別',
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values='人數',
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title=f'👥 受訪者性別分布{filter_text}',
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color='性別',
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color_discrete_map=color_map,
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hover_data=['人數', '百分比'],
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custom_data=['標籤']
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)
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# 更新圖表佈局
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fig.update_layout(
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font=dict(family="Arial", size=16),
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title_font=dict(family="Arial Black", size=24),
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title_x=0.5, # 標題置中
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+
legend_title_text="性別",
|
281 |
+
legend=dict(
|
282 |
+
orientation="h",
|
283 |
+
yanchor="bottom",
|
284 |
+
y=-0.2,
|
285 |
+
xanchor="center",
|
286 |
+
x=0.5,
|
287 |
+
font=dict(size=16),
|
288 |
+
bordercolor="#E0E0E0",
|
289 |
+
borderwidth=2
|
290 |
+
),
|
291 |
+
margin=dict(l=20, r=20, t=80, b=100),
|
292 |
+
paper_bgcolor='white',
|
293 |
+
annotations=[
|
294 |
+
dict(
|
295 |
+
text=f"總受訪人數: {total}人",
|
296 |
+
x=0.5, y=-0.3,
|
297 |
+
xref="paper",
|
298 |
+
yref="paper",
|
299 |
+
showarrow=False,
|
300 |
+
font=dict(size=16, color="#616161")
|
301 |
+
)
|
302 |
+
]
|
303 |
+
)
|
304 |
+
|
305 |
+
# 添加男女比例標籤
|
306 |
+
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
|
307 |
+
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
|
308 |
+
|
309 |
+
# 計算男女比例
|
310 |
+
if male_count > 0 and female_count > 0:
|
311 |
+
ratio = round(male_count / female_count, 2)
|
312 |
+
ratio_text = f"男女比例 = {ratio}:1"
|
313 |
+
elif male_count > 0 and female_count == 0:
|
314 |
+
ratio_text = "僅有男性"
|
315 |
+
elif female_count > 0 and male_count == 0:
|
316 |
+
ratio_text = "僅有女性"
|
317 |
+
else:
|
318 |
+
ratio_text = "無性別數據"
|
319 |
+
|
320 |
+
fig.add_annotation(
|
321 |
+
text=ratio_text,
|
322 |
+
x=0.5, y=-0.15,
|
323 |
+
xref="paper",
|
324 |
+
yref="paper",
|
325 |
+
showarrow=False,
|
326 |
+
font=dict(size=16, color="#424242", family="Arial Bold")
|
327 |
+
)
|
328 |
+
|
329 |
+
# 更新懸停資訊
|
330 |
fig.update_traces(
|
331 |
textinfo='percent+label',
|
332 |
+
hovertemplate='%{customdata[0]}',
|
333 |
+
textfont_size=16,
|
334 |
+
marker=dict(line=dict(color='#FFFFFF', width=2)),
|
335 |
+
pull=[0.03, 0.03], # 稍���分離餅圖片段
|
336 |
+
rotation=45 # 旋轉角度
|
337 |
)
|
338 |
|
339 |
st.plotly_chart(fig, use_container_width=True)
|
340 |
+
|
341 |
+
# 在圓餅圖下方添加簡單分析
|
342 |
+
st.markdown("""
|
343 |
+
<div style="background-color:#F5F5F5; padding:15px; border-radius:10px; margin-top:10px; border-left:5px solid #1976D2;">
|
344 |
+
<h4 style="color:#1976D2;">📊 性別分佈簡易分析</h4>
|
345 |
+
""", unsafe_allow_html=True)
|
346 |
+
|
347 |
+
# 生成簡單分析文字
|
348 |
+
if total > 0:
|
349 |
+
majority_gender = '男性' if male_count > female_count else '女性' if female_count > male_count else '男女相等'
|
350 |
+
majority_pct = max(male_count, female_count) / total * 100 if male_count != female_count else 50
|
351 |
+
|
352 |
+
if male_count != female_count:
|
353 |
+
st.markdown(f"""
|
354 |
+
<p>本次調查中,<strong>{majority_gender}</strong>佔多數,約佔總體的<strong>{majority_pct:.1f}%</strong>。</p>
|
355 |
+
""", unsafe_allow_html=True)
|
356 |
+
else:
|
357 |
+
st.markdown("<p>本次調查中,男女比例相等,各佔50%。</p>", unsafe_allow_html=True)
|
358 |
+
else:
|
359 |
+
st.markdown("<p>目前沒有足夠的性別數據進行分析。</p>", unsafe_allow_html=True)
|
360 |
+
|
361 |
+
st.markdown("</div>", unsafe_allow_html=True)
|
362 |
|
363 |
# 🎨 Streamlit UI
|
364 |
def main():
|
365 |
+
st.set_page_config(
|
366 |
+
page_title="數位示範場域問卷調查分析",
|
367 |
+
layout="wide",
|
368 |
+
initial_sidebar_state="expanded"
|
369 |
+
)
|
370 |
|
371 |
+
# 自定義CSS樣式
|
372 |
+
st.markdown("""
|
373 |
+
<style>
|
374 |
+
.main-header {
|
375 |
+
font-size: 42px;
|
376 |
+
font-weight: bold;
|
377 |
+
color: #1E88E5;
|
378 |
+
text-align: center;
|
379 |
+
margin-bottom: 10px;
|
380 |
+
padding-bottom: 15px;
|
381 |
+
border-bottom: 2px solid #e0e0e0;
|
382 |
+
}
|
383 |
+
.sub-header {
|
384 |
+
font-size: 24px;
|
385 |
+
color: #424242;
|
386 |
+
text-align: center;
|
387 |
+
margin-bottom: 30px;
|
388 |
+
}
|
389 |
+
.unit-name {
|
390 |
+
font-size: 28px;
|
391 |
+
font-weight: bold;
|
392 |
+
color: #1565C0;
|
393 |
+
text-align: center;
|
394 |
+
padding: 10px;
|
395 |
+
background-color: #E3F2FD;
|
396 |
+
border-radius: 8px;
|
397 |
+
margin: 20px 0;
|
398 |
+
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
|
399 |
+
}
|
400 |
+
.card {
|
401 |
+
padding: 20px;
|
402 |
+
border-radius: 10px;
|
403 |
+
box-shadow: 0 4px 6px rgba(0,0,0,0.1);
|
404 |
+
margin-bottom: 20px;
|
405 |
+
background-color: white;
|
406 |
+
}
|
407 |
+
</style>
|
408 |
+
""", unsafe_allow_html=True)
|
409 |
+
|
410 |
+
# 主標題與副標題
|
411 |
+
st.markdown('<div class="main-header">📊 數位示範場域問卷調查分析報告</div>', unsafe_allow_html=True)
|
412 |
+
st.markdown('<div class="sub-header">本報告提供全面的問卷調查分析與視覺化圖表,協助了解民眾滿意度與使用者特性</div>', unsafe_allow_html=True)
|
413 |
|
414 |
# 讀取數據
|
415 |
df = read_google_sheet(sheet_id, gid)
|
|
|
417 |
if df is not None:
|
418 |
analyzer = SurveyAnalyzer()
|
419 |
|
420 |
+
# 設置單位名稱(假設有「單位名稱」欄位,若無則顯示預設值)
|
421 |
+
unit_name = "數位示範場域滿意度調查中心"
|
422 |
+
if '單位名稱' in df.columns and not df['單位名稱'].isnull().all():
|
423 |
+
unit_names = df['單位名稱'].unique()
|
424 |
+
if len(unit_names) == 1:
|
425 |
+
unit_name = unit_names[0]
|
426 |
+
|
427 |
+
# 顯示單位名稱
|
428 |
+
st.markdown(f'<div class="unit-name">{unit_name}</div>', unsafe_allow_html=True)
|
429 |
+
|
430 |
+
# 新增場域和月份篩選器(使用更美觀的設計)
|
431 |
+
st.sidebar.markdown("### 🔍 **數據篩選**")
|
432 |
+
st.sidebar.markdown("---")
|
433 |
|
434 |
+
# 場域選擇
|
435 |
if '場域名稱' in df.columns:
|
436 |
venues = ['全部'] + sorted(df['場域名稱'].unique().tolist())
|
|
|
437 |
else:
|
438 |
# 如果沒有場域欄位,創建10個虛擬場域供選擇
|
439 |
+
venue_names = [
|
440 |
+
"臺北數位樂學園", "新北創新學院", "桃園智慧中心",
|
441 |
+
"臺中數位學苑", "臺南創客基地", "高雄創新園區",
|
442 |
+
"宜蘭數位中心", "花蓮創新基地", "臺東學習中心", "金門數位樂園"
|
443 |
+
]
|
444 |
+
venues = ['全部'] + venue_names
|
445 |
|
446 |
+
selected_venues = st.sidebar.multiselect(
|
447 |
+
"📍 **選擇場域**",
|
448 |
+
venues,
|
449 |
+
default=['全部'],
|
450 |
+
help="可選擇多個場域進行數據分析比較"
|
451 |
+
)
|
452 |
+
|
453 |
+
# 月份選擇
|
454 |
if '月份' in df.columns:
|
455 |
months = ['全部'] + sorted(df['月份'].unique().tolist())
|
|
|
456 |
else:
|
457 |
# 如果沒有月份欄位,可以創建虛擬月份選項
|
458 |
+
current_year = datetime.now().year
|
459 |
+
months = ['全部'] + [f'{current_year}年{i+1}月' for i in range(12)]
|
460 |
+
|
461 |
+
selected_month = st.sidebar.selectbox(
|
462 |
+
"📅 **選擇月份**",
|
463 |
+
months,
|
464 |
+
help="選擇特定月份查看數據趨勢"
|
465 |
+
)
|
466 |
+
|
467 |
+
# 年齡區間篩選
|
468 |
+
st.sidebar.markdown("### 📊 **年齡區間篩選**")
|
469 |
+
ages = analyzer.calculate_age(df['2.出生年(民國__年)'])
|
470 |
+
min_age, max_age = int(ages.min()), int(ages.max())
|
471 |
+
age_range = st.sidebar.slider(
|
472 |
+
"選擇年齡範圍",
|
473 |
+
min_age,
|
474 |
+
max_age,
|
475 |
+
(min_age, max_age),
|
476 |
+
help="拖曳調整以篩選特定年齡區間的受訪者"
|
477 |
+
)
|
478 |
|
479 |
# 📌 基本統計數據
|
480 |
st.sidebar.header("📌 選擇數據分析")
|
481 |
selected_analysis = st.sidebar.radio("選擇要查看的分析",
|
482 |
["📋 問卷統計報告", "📊 滿意度統計", "🟠 性別分佈"])
|
483 |
|
484 |
+
# 應用所有篩選條件
|
485 |
+
filtered_df = df.copy()
|
486 |
+
if selected_venues and '全部' not in selected_venues:
|
487 |
+
if '場域名稱' in filtered_df.columns:
|
488 |
+
filtered_df = filtered_df[filtered_df['場域名稱'].isin(selected_venues)]
|
489 |
+
if selected_month and selected_month != '全部':
|
490 |
+
if '月份' in filtered_df.columns:
|
491 |
+
filtered_df = filtered_df[filtered_df['月份'] == selected_month]
|
492 |
+
|
493 |
+
# 年齡篩選
|
494 |
+
if age_range:
|
495 |
+
ages = analyzer.calculate_age(filtered_df['2.出生年(民國__年)'])
|
496 |
+
age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
|
497 |
+
filtered_df = filtered_df[age_mask]
|
498 |
+
|
499 |
+
# 顯示當前選擇的篩選器
|
500 |
+
filter_status = []
|
501 |
+
if selected_venues and '全部' not in selected_venues:
|
502 |
+
filter_status.append(f"📍 場域: {', '.join(selected_venues)}")
|
503 |
+
if selected_month and selected_month != '全部':
|
504 |
+
filter_status.append(f"📅 月份: {selected_month}")
|
505 |
+
if age_range and (age_range[0] != min(analyzer.calculate_age(df['2.出生年(民國__年)'])) or
|
506 |
+
age_range[1] != max(analyzer.calculate_age(df['2.出生年(民國__年)']))):
|
507 |
+
filter_status.append(f"👥 年齡: {age_range[0]}-{age_range[1]}歲")
|
508 |
+
|
509 |
+
if filter_status:
|
510 |
+
st.markdown("""
|
511 |
+
<div style="background-color:#E3F2FD; padding:10px; border-radius:8px; margin-bottom:20px; border-left:4px solid #1976D2;">
|
512 |
+
<h4 style="margin-bottom:10px; color:#1565C0;">🔍 當前篩選條件</h4>
|
513 |
+
""", unsafe_allow_html=True)
|
514 |
+
|
515 |
+
for status in filter_status:
|
516 |
+
st.markdown(f"<p style='margin:5px 0;'>{status}</p>", unsafe_allow_html=True)
|
517 |
+
|
518 |
+
# 顯示篩選後的樣本數
|
519 |
+
st.markdown(f"""
|
520 |
+
<p style='margin-top:10px; font-weight:bold;'>📊 篩選後樣本數: {len(filtered_df)}人</p>
|
521 |
+
</div>
|
522 |
+
""", unsafe_allow_html=True)
|
523 |
+
|
524 |
+
# 數據分析區塊
|
525 |
if selected_analysis == "📋 問卷統計報告":
|
526 |
+
st.markdown('<h2 style="color:#1976D2;">📋 問卷統計報告</h2>', unsafe_allow_html=True)
|
527 |
+
|
528 |
+
# 生成目前篩選條件下的報告
|
529 |
+
report = analyzer.generate_report(filtered_df)
|
530 |
+
|
531 |
+
# 使用卡片樣式顯示統計信息
|
532 |
+
col1, col2 = st.columns(2)
|
533 |
+
|
534 |
+
with col1:
|
535 |
+
st.markdown('<div class="card">', unsafe_allow_html=True)
|
536 |
+
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)
|
537 |
+
|
538 |
+
for key, value in report['基本統計'].items():
|
539 |
+
if isinstance(value, dict):
|
540 |
+
st.markdown(f"<p><strong>{key}:</strong></p>", unsafe_allow_html=True)
|
541 |
+
for k, v in value.items():
|
542 |
+
st.markdown(f"<p style='margin-left:20px;'>- {k}: {v}</p>", unsafe_allow_html=True)
|
543 |
+
else:
|
544 |
+
st.markdown(f"<p><strong>{key}:</strong> {value}</p>", unsafe_allow_html=True)
|
545 |
+
|
546 |
+
st.markdown('</div>', unsafe_allow_html=True)
|
547 |
+
|
548 |
+
with col2:
|
549 |
+
st.markdown('<div class="card">', unsafe_allow_html=True)
|
550 |
+
# Add the rest of the code here
|
551 |
|
552 |
if __name__ == "__main__":
|
553 |
main()
|