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完成作業繳交-王宥鈞-AIPE03 #16

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382 changes: 382 additions & 0 deletions homework/m1_numpy.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "dedf1249",
"metadata": {},
"source": [
"\"\"\"\n",
"M1 NumPy 向量化思維 — 課後作業\n",
"================================\n",
"請完成以下每個函式，用 NumPy 向量化寫法（不要 for-loop）。\n",
"完成後 git push，GitHub Actions 會自動批改並顯示成績與解答。\n",
"\n",
"提示：\n",
"- np.array, np.where, np.argsort\n",
"- 布林遮罩: arr[arr > 10]\n",
"- 統計: .sum(), .mean(), .max(), .min()\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"id": "60c0496d",
"metadata": {},
"source": [
"# ============================================================\n",
"# 🟢 送分題（每題 10 分，共 30 分）\n",
"# ============================================================"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5567ab7e",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "de47a63f",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import numpy as np\n",
"\n",
"\n",
"\n",
"def green_mean():\n",
" \"\"\"建立 [10, 20, 30, 40, 50]，回傳所有元素的平均值 (float)\"\"\"\n",
" # TODO: 你的程式碼\n",
" pass\n",
"\n",
"\n",
"def green_double():\n",
" \"\"\"建立 [10, 20, 30, 40, 50]，回傳所有元素乘以 2 的 ndarray\"\"\"\n",
" # TODO: 你的程式碼\n",
" pass\n",
"\n",
"\n",
"def green_filter():\n",
" \"\"\"建立 [10, 20, 30, 40, 50]，回傳大於 25 的元素 (ndarray)\"\"\"\n",
" # TODO: 你的程式碼\n",
" pass\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "378063f7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30.0\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"#def green_mean():\n",
" #\"\"\"建立 [10, 20, 30, 40, 50]，回傳所有元素的平均值 (float)\"\"\"\n",
" # TODO: 你的程式碼\n",
" \n",
"arr = np.array([10, 20, 30, 40, 50])\n",
"avg_arr = np.mean(arr)\n",
"print(avg_arr)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d62c7972",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 20 40 60 80 100]\n"
]
}
],
"source": [
"#def green_double():\n",
"#\"\"\"建立 [10, 20, 30, 40, 50]，回傳所有元素乘以 2 的 ndarray\"\"\"\n",
"# TODO: 你的程式碼\n",
"\n",
"arr = np.array([10, 20, 30, 40, 50])\n",
"twotimes_arr = np.multiply(arr, 2)\n",
"print(twotimes_arr)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d70bddec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[30 40 50]\n"
]
}
],
"source": [
"#def green_filter():\n",
" #\"\"\"建立 [10, 20, 30, 40, 50]，回傳大於 25 的元素 (ndarray)\"\"\"\n",
" # TODO: 你的程式碼\n",
"arr = np.array([10, 20, 30, 40, 50])\n",
"biggerthan25_arr = arr[arr > 25]\n",
"print(biggerthan25_arr)"
]
},
{
"cell_type": "markdown",
"id": "df18d551",
"metadata": {},
"source": [
"# ============================================================\n",
"# 🟡 核心題（每題 15 分，共 45 分）\n",
"# 以下函式會接收從 products.csv 讀出的 prices, stocks 陣列\n",
"# ============================================================"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "02a97124",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def yellow_expensive_count(prices):\n",
" \"\"\"回傳單價 > 1000 的商品數量 (int)\"\"\"\n",
" # TODO: 你的程式碼\n",
" pass\n",
"\n",
"\n",
"def yellow_top3_stock_indices(stocks):\n",
" \"\"\"\n",
" 回傳庫存最多的前 3 個商品的索引位置 (ndarray, 由大到小排)\n",
" 提示：np.argsort\n",
" \"\"\"\n",
" # TODO: 你的程式碼\n",
" pass\n",
"\n",
"\n",
"def yellow_restock_cost(prices, stocks):\n",
" \"\"\"\n",
" 單價 < 500 的商品，每種各進貨 50 個，回傳總花費 (float/int)\n",
" 提示：布林遮罩 + .sum()\n",
" \"\"\"\n",
" # TODO: 你的程式碼\n",
" pass\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "24269aa8",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(r\"D:\\python_workstation\\iSpan_python-DA-cookbooks\\python-da-homework-2026-main\\python-da-homework-2026-main\\datasets\\ecommerce\\products.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e328bf6b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"DATA = pd.read_csv(r\"D:\\python_workstation\\iSpan_python-DA-cookbooks\\python-da-homework-2026-main\\python-da-homework-2026-main\\datasets\\ecommerce\\products.csv\")\n",
"prices = DATA.iloc[:, 3].to_numpy()\n",
"stocks = DATA.iloc[:, 4].to_numpy()\n",
"#def yellow_expensive_count(prices):\n",
"#\"\"\"回傳單價 > 1000 的商品數量 (int)\"\"\"\n",
"# TODO: 你的程式碼\n",
"\n",
"prices \n",
"prices[prices > 1000]\n",
"len(prices[prices > 1000])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8f9d602b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 0, 17, 22, 24, 5, 25, 2, 11, 13, 23, 1, 29, 20, 14, 12, 16,\n",
" 28, 21, 27, 19, 15, 4, 9, 26, 6, 18, 3, 8, 7])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"\n",
"DATA = pd.read_csv(r\"D:\\python_workstation\\iSpan_python-DA-cookbooks\\python-da-homework-2026-main\\python-da-homework-2026-main\\datasets\\ecommerce\\products.csv\")\n",
"prices = DATA.iloc[:, 3].to_numpy()\n",
"stocks = DATA.iloc[:, 4].to_numpy()\n",
"\n",
"#def yellow_top3_stock_indices(stocks):\n",
" #\"\"\"\n",
" #回傳庫存最多的前 3 個商品的索引位置 (ndarray, 由大到小排)\n",
" #提示：np.argsort\n",
" #\"\"\"\n",
"\n",
"\n",
"stocks\n",
"np.argsort(stocks)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "62a610a4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.int64(120300)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#def yellow_restock_cost(prices, stocks):\n",
"# \"\"\"\n",
"#單價 < 500 的商品，每種各進貨 50 個，回傳總花費 (float/int)\n",
"#提示：布林遮罩 + .sum()\n",
"#\"\"\"\n",
"# TODO: 你的程式碼\n",
" \n",
"prices[prices<500] #找出單價小於500的價格\n",
"prices[prices<500] * 50 #每個商品進貨50個\n",
"sum(prices[prices<500] * 50) #總價格"
]
},
{
"cell_type": "markdown",
"id": "3fa5a64a",
"metadata": {},
"source": [
"# ============================================================\n",
"# 🔴 挑戰題（25 分）\n",
"# ============================================================"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b62c2097",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1312.5, 1202.6, 203. , 426.3, 1075.9, 392.7, 766.5, 1238.3,\n",
" 526.4, 1677.2, 228.9, 219.8, 1292.2, 267.4, 189.7, 1460.9,\n",
" 133.7, 700. , 1132.6, 331.8, 410.9, 558. , 141.3, 1724.4,\n",
" 1386.9, 1329.3, 1755.9, 1935. , 1627. , 1506. ])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"#def red_double11_prices(prices, stocks):\n",
"#\"\"\"\n",
"#雙 11 定價規則（必須向量化，不能用 for-loop):\n",
"#- 庫存 >= 100：打 7 折\n",
"#- 庫存 20~99：打 9 折\n",
"#- 庫存 < 20：原價\n",
"#回傳每個商品的雙 11 售價 (ndarray)\n",
"#提示：np.where 可以巢狀使用\n",
"#\"\"\"\n",
"\n",
"\n",
"prices_07 = prices[stocks >= 100] *0.7\n",
"prices_09 = prices[(stocks >= 20) & (stocks <= 99)] *0.9\n",
"prices_original = prices[stocks < 20]\n",
"\n",
"# np.concatenate 沿指定軸連接多個陣列\n",
"total_price = np.concatenate([prices_07, prices_09, prices_original])\n",
"total_price\n",
"\n",
" \n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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