完成作業繳交 - 吳榮軒 - AIPE03

homework/m1_numpy.py

@@ -20,20 +20,22 @@ 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
-
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr.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
-
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr * 2
 
 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
-
-
+    arr = np.array([10, 20, 30, 40, 50])
+    return arr[arr > 25]
 # ============================================================
 # 🟡 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
@@ -43,6 +45,7 @@ def yellow_expensive_count(prices):
     """回傳單價 > 1000 的商品數量 (int)"""
     # TODO: 你的程式碼
     pass
+    return int((prices > 1000).sum())
 
 
 def yellow_top3_stock_indices(stocks):
@@ -52,7 +55,7 @@ def yellow_top3_stock_indices(stocks):
     """
     # TODO: 你的程式碼
     pass
-
+    return np.argsort(stocks)[::-1][:3]
 
 def yellow_restock_cost(prices, stocks):
     """
@@ -61,20 +64,15 @@ def yellow_restock_cost(prices, stocks):
     """
     # TODO: 你的程式碼
     pass
-
-
+    return (prices[prices < 500] * 50).sum()
 # ============================================================
 # 🔴 挑戰題（25 分）
 # ============================================================
 
 def red_double11_prices(prices, stocks):
-    """
-    雙 11 定價規則（必須向量化，不能用 for-loop）：
-    - 庫存 >= 100：打 7 折
-    - 庫存 20~99：打 9 折
-    - 庫存 < 20：原價
-    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
+    return np.where(
+        stocks >= 100,
+        prices * 0.7,
+        np.where(stocks >= 20, prices * 0.9, prices)
+    )

homework/m2_pandas_cleaning.py

@@ -20,7 +20,9 @@ def green_read_csv():
     提示：pd.read_csv()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv('../datasets/ecommerce/orders_raw.csv')
+    return df
+
 
 
 def green_shape(df):
@@ -29,7 +31,7 @@ def green_shape(df):
     提示：df.shape
     """
     # TODO: 你的程式碼
-    pass
+    return df.shape
 
 
 def green_dtypes(df):
@@ -38,7 +40,8 @@ def green_dtypes(df):
     提示：df.dtypes
     """
     # TODO: 你的程式碼
-    pass
+    return df.dtypes
+
 
 
 # ============================================================
@@ -52,7 +55,9 @@ def yellow_clean_columns(df):
     提示：df.columns.str.strip().str.lower()
     """
     # TODO: 你的程式碼
-    pass
+    df_new = df.copy()
+    df_new.columns = df_new.columns.str.strip().str.lower()
+    return df_new
 
 
 def yellow_clean_amount(df):
@@ -63,7 +68,16 @@ def yellow_clean_amount(df):
     提示：.str.replace() + .astype(float)
     """
     # TODO: 你的程式碼
-    pass
+    df_new = df.copy()
+    df_new['amount'] = (
+        df_new['amount']
+        .astype(str)
+        .str.replace('$', '')
+        .str.replace(',', '')
+        .astype(float)
+    )
+
+    return df_new
 
 
 def yellow_drop_duplicates(df):
@@ -72,7 +86,9 @@ def yellow_drop_duplicates(df):
     提示：df.drop_duplicates()
     """
     # TODO: 你的程式碼
-    pass
+    df_new = df.copy()
+    df_new = df_new.drop_duplicates()
+    return df_new
 
 
 # ============================================================
@@ -93,4 +109,34 @@ def red_clean_orders(path):
     提示：pd.to_datetime(errors='coerce')
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv(path)
+
+    df.columns = df.columns.str.strip().str.lower()
+
+    df['amount'] = (
+        df['amount']
+        .astype(str)
+        .str.replace('$', '')
+        .str.replace(',', '')
+        .astype(float)
+    )
+
+    df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+
+    df = df.dropna(subset=['amount'])
+
+    df = df.dropna(subset=['order_date'])
+
+    df = df.drop_duplicates()
+
+    return df
+
+
+
+
+
+
+
+
+
+

homework/m3_pandas_advanced.py

@@ -24,19 +24,30 @@ def green_load_and_merge():
     提示：pd.merge(how='left')
     """
     # TODO: 你的程式碼
-    pass
+    DATA = '../datasets/ecommerce'
+    orders    = pd.read_csv(f'{DATA}/orders_clean.csv', parse_dates=['order_date'])
+    customers = pd.read_csv(f'{DATA}/customers.csv')
+    products  = pd.read_csv(f'{DATA}/products.csv')
+
+    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
+    return df.shape[0]
 
 
 def green_column_list(df):
     """回傳 DataFrame 的所有欄位名稱 (list)"""
     # TODO: 你的程式碼
-    pass
+    return list(df.columns)
 
 
 # ============================================================
@@ -50,7 +61,7 @@ def yellow_top_category(df):
     提示：groupby('category')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    return df.groupby('category')['amount'].sum().idxmax()
 
 
 def yellow_gold_vip_stats(df):
@@ -60,7 +71,12 @@ def yellow_gold_vip_stats(df):
     提示：df[df['vip_level'] == 'Gold']
     """
     # TODO: 你的程式碼
-    pass
+    df_gold = df.groupby('vip_level').agg(
+    總訂單數 = ('order_id', 'count'),
+    總金額 = ('amount', 'sum')
+    ).loc['Gold']
+
+    return f'(訂單數 {df_gold.values[0].astype(int)}, 總金額 {df_gold.values[1]})'
 
 
 def yellow_region_avg_amount(df):
@@ -70,7 +86,8 @@ def yellow_region_avg_amount(df):
     提示：groupby('region')['amount'].mean()
     """
     # TODO: 你的程式碼
-    pass
+    region_info = df.groupby('region')['amount'].mean()
+    return region_info
 
 
 # ============================================================
@@ -94,4 +111,10 @@ def red_rfm_top5(df):
     提示：groupby('customer_id').agg(...)
     """
     # TODO: 你的程式碼
-    pass
+    rfm = df.groupby('customer_id').agg(
+        R = ('order_date', 'max'),
+        F = ('order_id', 'count'),
+        M = ('amount', 'sum')
+    ).reset_index().sort_value('M', ascending = False).head()
+
+    return rfm

homework/m4_timeseries.py

@@ -27,7 +27,11 @@ def green_avg_by_month():
     提示：df['order_date'].dt.month
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    df['month'] = df['order_date'].dt.month
+    df_mon = df.groupby('month')['amount'].mean()
+    return df_mon
 
 
 def green_top3_dates():
@@ -37,7 +41,11 @@ def green_top3_dates():
     提示：value_counts().head(3)
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    ts = df.set_index('order_date').sort_index()
+    top3 = ts['order_id'].resample('D').count().head(3)
+    return top3
 
 
 def green_date_range():
@@ -46,7 +54,10 @@ def green_date_range():
     格式為 pandas Timestamp
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+
+    return f'({list(df['order_date'].sort_values())[0]}, {list(df['order_date'].sort_values())[-1]})'
 
 
 # ============================================================
@@ -60,7 +71,14 @@ def yellow_monthly_revenue():
     提示：set_index('order_date').resample('ME')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    ts = df.set_index('order_date').sort_index()
+
+    mon_rev = ts.resample('ME')['amount'].sum()
+
+    return mon_rev
+
 
 
 def yellow_rolling_avg(monthly_revenue):
@@ -71,7 +89,17 @@ def yellow_rolling_avg(monthly_revenue):
     提示：.rolling(window=3).mean()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    ts = df.set_index('order_date').sort_index()
+
+    mon_rev = ts.resample('ME')['amount'].sum()
+
+    mon_roll = mon_rev.rolling(window = monthly_revenue).mean()
+
+    return mon_roll
+
+
 
 
 def yellow_category_median(df):
@@ -81,7 +109,8 @@ def yellow_category_median(df):
     提示：groupby + median + sort_values
     """
     # TODO: 你的程式碼
-    pass
+    cate_med = df.groupby('category')['amount'].median().sort_values(ascending = False)
+    return cate_med
 
 
 # ============================================================
@@ -101,4 +130,27 @@ def red_monthly_report():
     提示：resample + agg + pct_change
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    ts = df.set_index('order_date').sort_index()
+
+    mon_count = ts['order_id'].resample('ME').count()
+
+    mon_rev = ts['amount'].resample('ME').sum()
+
+    mon_act = ts['customer_id'].resample('ME').nunique()
+
+    mon_avg = mon_rev / mon_count
+
+    mon_rev_grow = mon_rev.pct_change()
+
+    mon_info = pd.DataFrame({
+        'order_count': mon_count,
+        'revenue': mon_rev,
+        'active_customers': mon_act,
+        'avg_order_value': mon_avg,
+        'revenue_growth': mon_rev_grow,
+    })
+
+    return mon_info
+