diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..1f19b2c 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -18,40 +18,55 @@
 
 def green_mean():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素的平均值 (float)"""
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr.mean()
+print(green_mean())
 
 
 def green_double():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素乘以 2 的 ndarray"""
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr * 2
+print(green_double())
 
 
 def green_filter():
     """建立 [10, 20, 30, 40, 50]，回傳大於 25 的元素 (ndarray)"""
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr[arr > 25]
+print(green_filter())
 
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
 # 以下函式會接收從 products.csv 讀出的 prices, stocks 陣列
 # ============================================================
+DATA = 'datasets/ecommerce/products.csv'
+prices = np.genfromtxt(DATA, delimiter=',', skip_header=1 , usecols=3)
+stocks = np.genfromtxt(DATA,delimiter=',', skip_header=1, usecols=4)
 
 def yellow_expensive_count(prices):
     """回傳單價 > 1000 的商品數量 (int)"""
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    return len(prices[prices > 1000])
 
+print(yellow_expensive_count(prices))
 
 def yellow_top3_stock_indices(stocks):
     """
     回傳庫存最多的前 3 個商品的索引位置 (ndarray, 由大到小排)
     提示：np.argsort
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    idx = np.argsort(stocks)
+    top3_stocks = idx [-3:][::-1]
+    return top3_stocks
+
+print(yellow_top3_stock_indices(stocks))
+    
 
 
 def yellow_restock_cost(prices, stocks):
@@ -59,9 +74,13 @@ def yellow_restock_cost(prices, stocks):
     單價 < 500 的商品，每種各進貨 50 個，回傳總花費 (float/int)
     提示：布林遮罩 + .sum()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    mask = prices < 500
+    price_500 = prices[mask]
+    total_500 = (price_500 * 50).sum()
+    return total_500
 
+print(yellow_restock_cost(prices, stocks))
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -76,5 +95,14 @@ def red_double11_prices(prices, stocks):
     回傳每個商品的雙 11 售價 (ndarray)
     提示：np.where 可以巢狀使用
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    prices_07 = prices[stocks >= 100]* 0.7
+    prices_09 = prices[(stocks >= 20) & (stocks <= 99)] * 0.9
+    prices_original = prices[stocks < 20]
+
+    finall_price = np.concatenate([prices_07, prices_09, prices_original])
+    
+    #運用np.where取代if-else的方式
+    finall_price2 = np.where(stocks >= 100, prices * 0.7,#庫存大於100 ,true:價格x0.7
+                             np.where(stocks >= 20,prices * 0.9 ,prices))#庫存 >= 20 ,true:價格x0.9,庫存 < 20,false:價格原價
+    return finall_price2
\ No newline at end of file
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..6d9a375 100644
--- a/homework/m2_pandas_cleaning.py
+++ b/homework/m2_pandas_cleaning.py
@@ -8,8 +8,7 @@
 資料路徑：datasets/ecommerce/orders_raw.csv
 """
 import pandas as pd
-
-
+df = pd.read_csv('datasets/ecommerce/orders_raw.csv')
 # ============================================================
 # 🟢 送分題（每題 10 分，共 30 分）
 # ============================================================
@@ -19,8 +18,10 @@ def green_read_csv():
     讀取 orders_raw.csv，回傳原始 DataFrame（不做任何清理）
     提示：pd.read_csv()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    DATA ='datasets/ecommerce/orders_raw.csv'
+    df = pd.read_csv(DATA)
+    return df
 
 
 def green_shape(df):
@@ -28,8 +29,9 @@ def green_shape(df):
     回傳 DataFrame 的 (列數, 欄數) tuple
     提示：df.shape
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    return df.shape
+
 
 
 def green_dtypes(df):
@@ -37,8 +39,8 @@ def green_dtypes(df):
     回傳 DataFrame 的欄位型別 (Series)
     提示：df.dtypes
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    return df.dtypes
 
 
 # ============================================================
@@ -51,8 +53,11 @@ def yellow_clean_columns(df):
     回傳清理後的 DataFrame（不要修改原始 df）
     提示：df.columns.str.strip().str.lower()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    new_df = df.copy()
+    new_df = new_df.columns.str.strip().str.lower()
+    return new_df
+
 
 
 def yellow_clean_amount(df):
@@ -62,8 +67,17 @@ def yellow_clean_amount(df):
     回傳清理後的 DataFrame（不要修改原始 df）
     提示：.str.replace() + .astype(float)
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    new_df = df.copy()
+    
+    new_df['amount'] = (
+        new_df['amount']
+        .astype(str)
+        .str.replace('$' ,'', regex=False)
+        .str.replace(',' ,'', regex=False)
+        .astype(float)
+    )
+    return new_df
 
 
 def yellow_drop_duplicates(df):
@@ -71,8 +85,8 @@ def yellow_drop_duplicates(df):
     移除完全重複的列，回傳去重後的 DataFrame
     提示：df.drop_duplicates()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    return df.drop_duplicates()
 
 
 # ============================================================
@@ -92,5 +106,26 @@ def red_clean_orders(path):
     回傳：清理後的 DataFrame
     提示：pd.to_datetime(errors='coerce')
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = pd.read_csv(path)
+    
+    df.columns = df.columns.str.strip().str.lower()
+
+    df['amount'] = (
+        df['amount']
+        .astype(str)
+        .str.replace('$' ,'', regex=False)
+        .str.replace(',' ,'', regex=False)
+        .astype(float)
+    )
+
+    df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+    
+    df = df.dropna(subset=['amount','order_date'])
+    
+    df = df.drop_duplicates()
+    
+    return df
+
+my_path = 'datasets/ecommerce/orders_raw.csv'
+print(red_clean_orders(my_path))
\ No newline at end of file
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..e619109 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -10,7 +10,11 @@
   - datasets/ecommerce/products.csv
 """
 import pandas as pd
+import numpy as np
 
+orders = pd.read_csv('datasets/ecommerce/orders_clean.csv')
+customers = pd.read_csv('datasets/ecommerce/customers.csv')
+products = pd.read_csv('datasets/ecommerce/products.csv')
 
 # ============================================================
 # 🟢 送分題（每題 10 分，共 30 分）
@@ -23,20 +27,22 @@ def green_load_and_merge():
     - 再 LEFT JOIN products.csv ON product_id
     提示：pd.merge(how='left')
     """
-    # TODO: 你的程式碼
-    pass
-
-
+    # TODO:
+    df = (orders.merge(customers , on='customer_id', how='left')
+                .merge(products, on='product_id', how='left')
+    )
+    return df
+    
 def green_row_count(df):
     """回傳 DataFrame 的列數 (int)"""
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    return len(df)
 
 
 def green_column_list(df):
     """回傳 DataFrame 的所有欄位名稱 (list)"""
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    return list(df.columns)
 
 
 # ============================================================
@@ -49,8 +55,9 @@ def yellow_top_category(df):
     回傳該類別名稱 (str)
     提示：groupby('category')['amount'].sum()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    category = df.groupby('category')['amount'].sum().sort_values(ascending=False)
+    return category.index[0]
 
 
 def yellow_gold_vip_stats(df):
@@ -59,8 +66,18 @@ def yellow_gold_vip_stats(df):
     回傳 tuple: (訂單數 int, 總金額 float)
     提示：df[df['vip_level'] == 'Gold']
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    # Gold_df = df[df["vip_level"] == "Gold"]
+    Gold_amount = int(df[df["vip_level"] == "Gold"]["amount"].count())
+    Gold_total = float(df[df["vip_level"] == "Gold"]["amount"].sum())
+
+    # df = pd.DataFrame({
+    # "VIP":['Gold'],
+    # "訂單數":[Gold_amount],
+    # "總金額":[Gold_total]
+    # # })
+
+    return (Gold_amount,Gold_total)
 
 
 def yellow_region_avg_amount(df):
@@ -69,9 +86,10 @@ def yellow_region_avg_amount(df):
     回傳 Series（index=region, values=平均金額）
     提示：groupby('region')['amount'].mean()
     """
-    # TODO: 你的程式碼
-    pass
-
+    # TODO:
+    df_mean = df.groupby("region")["amount"].mean().sort_values(ascending=False)
+    print(df_mean)
+    return df_mean
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -93,5 +111,25 @@ def red_rfm_top5(df):
 
     提示：groupby('customer_id').agg(...)
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:  
+    rfm = df.groupby('customer_id').agg(
+              R=('order_date', 'max'),
+              F=('order_id', 'count'),
+              M=('amount', 'sum'),
+         ).reset_index() #計算 RFM過程.
+    
+    name_map = df[['customer_id', 'customer_name']].drop_duplicates() #重複的customer_id/customer_name去除.
+    rfm_named = rfm.merge(name_map, on='customer_id', how='left') #把整理好的customer_id/customer_name合併.
+
+    top5 = (
+    rfm_named
+    .sort_values('M', ascending=False)
+    .head(5)
+    .reset_index(drop=True)
+    [['customer_id', 'customer_name', 'R', 'F', 'M']]
+    )  #按M由大到小排序,取前5筆
+    
+    return top5
+
+
+
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..e48fad6 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -7,6 +7,7 @@
 資料路徑：datasets/ecommerce/orders_enriched.csv
 """
 import pandas as pd
+import numpy as np
 
 
 def _load_data():
@@ -20,48 +21,56 @@ def _load_data():
 # 🟢 送分題（每題 10 分，共 30 分）
 # ============================================================
 
-def green_avg_by_month():
+def green_avg_by_month(df):
     """
     計算每個月份 (1~12) 的平均訂單金額
     回傳 Series（index=月份 1~12, values=平均金額）
     提示：df['order_date'].dt.month
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    # df = _load_data() #用df去替代讀取
+    return df.groupby(df['order_date'].dt.month)['amount'].mean() #使用dt.month抓出1, 2, 3月...
 
 
-def green_top3_dates():
+def green_top3_dates(df):
     """
     找出訂單數最多的前 3 個日期
     回傳 Series（index=日期, values=訂單數, 由多到少排序）
     提示：value_counts().head(3)
     """
-    # TODO: 你的程式碼
-    pass
-
-
-def green_date_range():
+    # TODO: 
+    # ts = df.set_index('order_date').sort_index()
+    # ts['order_id'].resample('D').count().sort_values(ascending=False).head(3)
+    # return ts
+    # df = _load_data() 用df去替代讀取
+    top3_dates =df['order_date'].value_counts().head(3)
+    return top3_dates
+
+def green_date_range(df):
     """
     回傳資料的日期範圍 tuple: (最早日期, 最晚日期)
     格式為 pandas Timestamp
     """
-    # TODO: 你的程式碼
-    pass
-
+    # TODO:
+    # df = _load_data() 用df去替代讀取
+    start_date = df['order_date'].min() #最早日期
+    finally_date = df['order_date'].max() #最晚日期
+    return(start_date, finally_date)
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
 # ============================================================
 
-def yellow_monthly_revenue():
+def yellow_monthly_revenue(df):
     """
     計算每月總營收
     回傳 Series（index=月底日期 period, values=總營收）
     提示：set_index('order_date').resample('ME')['amount'].sum()
     """
-    # TODO: 你的程式碼
-    pass
-
+    # TODO:
+    # df = _load_data() 用df去替代讀取
+    ts = df.set_index('order_date').sort_index()
+    return ts['amount'].resample('ME').sum()
 
 def yellow_rolling_avg(monthly_revenue):
     """
@@ -70,8 +79,10 @@ def yellow_rolling_avg(monthly_revenue):
     回傳 Series（同樣 index，values=移動平均，前 2 筆可為 NaN）
     提示：.rolling(window=3).mean()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    # monthly_revenue = df.set_index('order_date').sort_index()
+    # monthly_revenue['order_id'].resample('ME').count().rolling(window=3).mean().head(15)
+    return monthly_revenue.rolling(window=3).mean()
 
 
 def yellow_category_median(df):
@@ -80,15 +91,21 @@ def yellow_category_median(df):
     回傳 Series（index=category, values=中位數）
     提示：groupby + median + sort_values
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    category_median = (
+        df.groupby('category')['amount']
+          .median()
+          .sort_values(ascending=False) ##.sort_values() Pandas對數值進行排序,ascending=False是數值由大到小排序.
+          .round(1)
+    )
+    return category_median
 
 
 # ============================================================
 # 🔴 挑戰題（25 分）
 # ============================================================
 
-def red_monthly_report():
+def red_monthly_report(df):
     """
     產出月報 DataFrame，每月一列，包含：
     - order_count：當月訂單數
@@ -100,5 +117,16 @@ def red_monthly_report():
     index 為月份 (period 或 datetime)
     提示：resample + agg + pct_change
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    ts = df.set_index('order_date').sort_index()
+
+    report = ts.resample('ME').agg(
+        order_count =('order_id', 'count'),
+        revenue =('amount', 'sum'),
+        active_customers =('customer_id', 'nunique') 
+    )
+
+    report['avg_order_value'] = report['revenue'] / report['order_count']
+    report['revenue_growth'] = ( report['revenue'].pct_change() * 100 ).round(2)
+
+    return report
\ No newline at end of file
diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py
index 7e7335d..8a0b281 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -28,8 +28,15 @@ def green_bar_category():
     回傳 matplotlib Figure 物件
     提示：sns.countplot 或 value_counts().plot.bar()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = _load_data()
+    fig, ax = plt.subplots()
+
+    sns.countplot(data=df, x='category', ax=ax)
+    ax.set_title("Order Count by Category")
+    ax.tick_params(axis='x', rotation=45)
+
+    return fig
 
 
 def green_hist_amount():
@@ -38,8 +45,14 @@ def green_hist_amount():
     回傳 matplotlib Figure 物件
     提示：sns.histplot(bins=20) 或 plt.hist()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = _load_data()
+    fig, ax = plt.subplots()
+
+    sns.histplot(df['amount'], bins=20, ax=ax)
+    ax.set_title("Amount Distribution")
+
+    return fig
 
 
 def green_set_labels():
@@ -50,8 +63,18 @@ def green_set_labels():
     - Y 軸標籤 (ylabel)
     回傳 matplotlib Figure 物件
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    fig, ax = plt.subplots()
+
+    x = ['A', 'B', 'C']
+    y = [10, 20, 15]
+
+    ax.bar(x, y)
+    ax.set_title("Sample Bar Chart")
+    ax.set_xlabel("Category")
+    ax.set_ylabel("Value")
+
+    return fig
 
 
 # ============================================================
@@ -67,8 +90,22 @@ def yellow_line_region_trend():
     回傳 matplotlib Figure 物件
     提示：分別 groupby 再 plot，或用 sns.lineplot(hue='region')
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = df[df['region'].isin(['North', 'South'])]
+
+    df['month'] = df['order_date'].dt.to_period('M').dt.to_timestamp()
+
+    grouped = df.groupby(['month', 'region'])['amount'].sum().reset_index()
+
+    fig, ax = plt.subplots()
+
+    sns.lineplot(data=grouped, x='month', y='amount', hue='region', ax=ax)
+
+    ax.set_title("Monthly Revenue Trend (North vs South)")
+    ax.set_xlabel("Month")
+    ax.set_ylabel("Revenue")
+
+    return fig
 
 
 def yellow_box_vip():
@@ -77,9 +114,15 @@ def yellow_box_vip():
     回傳 matplotlib Figure 物件
     提示：sns.boxplot(x='vip_level', y='amount', data=df)
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = _load_data()
+    fig, ax = plt.subplots()
+
+    sns.boxplot(data=df, x='vip_level', y='amount', ax=ax)
+
+    ax.set_title("Amount Distribution by VIP Level")
 
+    return fig
 
 def yellow_scatter_price_amount():
     """
@@ -87,8 +130,16 @@ def yellow_scatter_price_amount():
     回傳 matplotlib Figure 物件
     提示：plt.scatter() 或 sns.scatterplot()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    df = _load_data()
+    fig, ax = plt.subplots()
+
+    sns.scatterplot(data=df, x='unit_price', y='amount', ax=ax)
+
+    ax.set_title("Unit Price vs Amount")
+
+    return fig
+
 
 
 # ============================================================
@@ -106,5 +157,47 @@ def red_category_dashboard(category="Electronics"):
     回傳 matplotlib Figure 物件
     提示：fig, axes = plt.subplots(2, 2, figsize=(14, 10))
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    df = _load_data()
+
+    df = df[df['category'] == category]
+
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+
+    # 1️⃣ 月營收趨勢
+    df['month'] = df['order_date'].dt.to_period('M').dt.to_timestamp()
+    trend = df.groupby('month')['amount'].sum().reset_index()
+
+    sns.lineplot(data=trend, x='month', y='amount', ax=axes[0, 0])
+    axes[0, 0].set_title("Monthly Revenue Trend")
+
+    # 2️⃣ 各地區營收
+    region_rev = df.groupby('region')['amount'].sum().reset_index()
+
+    sns.barplot(data=region_rev, x='region', y='amount', ax=axes[0, 1])
+    axes[0, 1].set_title("Revenue by Region")
+
+    # 3️⃣ Top 5 商品
+    top_products = (
+        df.groupby('product_name')['amount']
+        .sum()
+        .sort_values(ascending=False)
+        .head(5)
+        .reset_index()
+    )
+
+    sns.barplot(
+        data=top_products,
+        y='product_name',
+        x='amount',
+        ax=axes[1, 0]
+    )
+    axes[1, 0].set_title("Top 5 Products")
+
+    # 4️⃣ 金額分佈
+    sns.histplot(df['amount'], bins=20, ax=axes[1, 1])
+    axes[1, 1].set_title("Amount Distribution")
+
+    plt.tight_layout()
+
+    return fig
\ No newline at end of file
diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py
index 0e2c32a..a612e18 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -25,8 +25,15 @@ def green_plotly_bar():
     回傳 plotly Figure 物件
     提示：px.bar()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv")
+
+    grouped = df.groupby("category")["amount"].sum().reset_index()
+
+    fig = px.bar(grouped, x="category", y="amount", title="Revenue by Category")
+
+    return fig
+
 
 
 def green_plotly_line():
@@ -36,8 +43,17 @@ def green_plotly_line():
     回傳 plotly Figure 物件
     提示：先 groupby 月份算總營收，再 px.line()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv", parse_dates=["order_date"])
+
+    df["month"] = df["order_date"].dt.to_period("M").dt.to_timestamp()
+
+    grouped = df.groupby("month")["amount"].sum().reset_index()
+
+    fig = px.line(grouped, x="month", y="amount",
+                  title="Monthly Revenue Trend")
+
+    return fig
 
 
 def green_plotly_pie():
@@ -47,8 +63,16 @@ def green_plotly_pie():
     回傳 plotly Figure 物件
     提示：px.pie()
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv")
+
+    grouped = df["vip_level"].value_counts().reset_index()
+    grouped.columns = ["vip_level", "count"]
+
+    fig = px.pie(grouped, names="vip_level", values="count",
+                 title="VIP Level Distribution")
+
+    return fig
 
 
 # ============================================================
@@ -62,8 +86,35 @@ def yellow_clean_and_merge(raw_path, customers_path, products_path):
     2. 合併 customers.csv 和 products.csv
     回傳：合併後的 DataFrame
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    df = pd.read_csv(raw_path)
+
+    # 欄位清理
+    df.columns = df.columns.str.strip().str.lower()
+
+    # amount 清理
+    df["amount"] = (
+        df["amount"]
+        .astype(str)
+        .str.replace("$", "", regex=False)
+        .str.replace(",", "", regex=False)
+        .astype(float)
+    )
+    # 日期轉換 (這是 Capstone 的關鍵)
+    df["order_date"] = pd.to_datetime(df["order_date"])
+
+    # 去重與缺值 (假設刪除 amount 缺失的列)
+    df = df.drop_duplicates().dropna(subset=["amount"])
+
+    # 合併三張表
+    customers = pd.read_csv(customers_path)
+    products = pd.read_csv(products_path)
+    
+    # 注意：這裡要確保 id 欄位名稱一致，或使用 left_on/right_on
+    df = df.merge(customers, on="customer_id", how="left")
+    df = df.merge(products, on="product_id", how="left")
+
+    return df
 
 
 def yellow_kpi_summary(df):
@@ -76,9 +127,18 @@ def yellow_kpi_summary(df):
         "avg_order_value": float,     # 平均客單價
     }
     """
-    # TODO: 你的程式碼
-    pass
-
+    # TODO: 
+    total_revenue = df["amount"].sum()
+    order_count = df["order_id"].count()
+    active_customers = df["customer_id"].nunique()
+    avg_order_value = total_revenue / order_count if order_count > 0 else 0
+
+    return {
+        "total_revenue": float(total_revenue),
+        "order_count": int(order_count),
+        "active_customers": int(active_customers),
+        "avg_order_value": float(avg_order_value),
+    }
 
 def yellow_plotly_scatter(df):
     """
@@ -90,8 +150,17 @@ def yellow_plotly_scatter(df):
     回傳 plotly Figure 物件
     提示：px.scatter(hover_data=['product_name'])
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO: 
+    fig = px.scatter(
+        df,
+        x="unit_price",
+        y="amount",
+        color="category",
+        hover_data=["product_name"],
+        title="Unit Price vs Amount"
+    )
+
+    return fig
 
 
 # ============================================================
@@ -114,5 +183,70 @@ def red_dashboard():
     回傳 plotly Figure 物件
     提示：from plotly.subplots import make_subplots
     """
-    # TODO: 你的程式碼
-    pass
+    # TODO:
+    df = yellow_clean_and_merge(
+        "datasets/ecommerce/orders_raw.csv",
+        "datasets/ecommerce/customers.csv",
+        "datasets/ecommerce/products.csv"
+    )
+
+    df["month"] = df["order_date"].dt.to_period("M").dt.to_timestamp()
+
+    # 月營收
+    monthly = df.groupby("month")["amount"].sum().reset_index()
+
+    # Top 商品
+    top_products = (
+        df.groupby("product_name")["amount"]
+        .sum()
+        .sort_values(ascending=False)
+        .head(10)
+        .reset_index()
+    )
+
+    # 地區營收
+    region_rev = df.groupby("region")["amount"].sum().reset_index()
+
+    # 類別營收
+    category_rev = df.groupby("category")["amount"].sum().reset_index()
+
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=(
+            "Monthly Revenue",
+            "Top 10 Products",
+            "Revenue by Region",
+            "Category Share"
+        ),
+        specs=[[{}, {}], [{}, {"type": "domain"}]]
+    )
+
+    # 1️⃣ 月營收
+    fig.add_trace(
+        go.Scatter(x=monthly["month"], y=monthly["amount"], mode="lines"),
+        row=1, col=1
+    )
+
+    # 2️⃣ Top 商品
+    fig.add_trace(
+        go.Bar(x=top_products["product_name"], y=top_products["amount"]),
+        row=1, col=2
+    )
+
+    # 3️⃣ 地區
+    fig.add_trace(
+        go.Bar(x=region_rev["region"], y=region_rev["amount"]),
+        row=2, col=1
+    )
+
+    # 4️⃣ 類別 pie
+    fig.add_trace(
+        go.Pie(labels=category_rev["category"],
+               values=category_rev["amount"],
+               hole=0.4),
+        row=2, col=2
+    )
+
+    fig.update_layout(title="E-commerce Dashboard", height=800)
+
+    return fig
\ No newline at end of file