Update app.py

app.py

@@ -7,7 +7,6 @@ from google.oauth2.service_account import Credentials
 import gspread
 import plotly.express as px
 import plotly.graph_objects as go
-from plotly.subplots import make_subplots
 
 # Google Sheets credentials
 SCOPE = ['https://www.googleapis.com/auth/spreadsheets']
@@ -89,12 +88,16 @@ def clean_rating(x):
     return float(str(x).replace('分數', '').replace('分', ''))
 
 def create_price_rating_scatter(df):
+    if df.empty:
+        st.warning("沒有可視化的有效數據。請檢查輸入資料。")
+        return None  # 無數據時返回空
+
     fig = px.scatter(
         df,
         x='價格',
         y='評分',
         text='飯店名稱',
-        size='價格',
+        size='價格',  # 點的大小根據價格變化
         color='評分',
         title='台南飯店價格與評分關係圖',
         labels={'價格': '房價 (TWD)', '評分': '評分 (0-10)'}
@@ -142,8 +145,17 @@ elif mode == "資料視覺化":
     st.header("分析與視覺化")
     try:
         df = pd.read_csv('booking_hotels_tainan.csv', encoding='utf-8-sig')
-        df['價格'] = pd.to_numeric(df['價格'], errors='coerce')
-        df['評分'] = df['評分'].apply(clean_rating)
+        
+        # 清理數據
+        df['價格'] = pd.to_numeric(df['價格'], errors='coerce')  # 無效值轉為 NaN
+        df['評分'] = df['評分'].apply(clean_rating)  # 清理評分數據
+        df = df.dropna(subset=['價格'])  # 移除價格為 NaN 的行
+
+        # 顯示數據摘要
+        st.write(f"有效數據行數：{len(df)}")
+        st.write(f"價格缺失值數量：{df['價格'].isna().sum()}")
+
+        # 繪製圖表
         st.plotly_chart(create_price_rating_scatter(df))
         st.plotly_chart(create_price_distribution(df))
     except Exception as e: