# 計算 BMI 指數並判斷體重狀態

height_cm = float(input("請輸入身高（公分）："))
weight_kg = float(input("請輸入體重（公斤）："))

# 身高從公分轉換成公尺
height_m = height_cm / 100
bmi = weight_kg / (height_m ** 2)

# 依 BMI 範圍判斷體重狀態
if bmi < 18.5:
    status = "過輕"
elif bmi < 24:
    status = "正常"
elif bmi < 27:
    status = "過重"
else:
    status = "肥胖"

print(f"你的 BMI 是 {bmi:.2f}，體重狀態：{status}")

# 印出九九乘法表，每一行對齊

# 外層迴圈：被乘數 1~9
for i in range(1, 10):
    # 內層迴圈：乘數 1~9
    for j in range(1, 10):
        # 用 :2 讓數字靠右對齊寬度 2，維持每欄整齊
        print(f"{i}×{j}={i * j:<2}", end=" ")
    # 每跑完一行（內層迴圈結束）才換行
    print()

# 簡易計算機：支援加減乘除，可重複輸入直到輸入 q 退出

def calculator(a, op, b):
    """依運算符號 op 計算 a 與 b，回傳結果字串"""
    try:
        if op == "+":
            return a + b
        elif op == "-":
            return a - b
        elif op == "*":
            return a * b
        elif op == "/":
            return a / b
        else:
            return "不支援的運算符號（僅支援 + - * /）"
    except ZeroDivisionError:
        return "錯誤：除數不可為 0"


while True:
    raw = input("請輸入運算式（格式：數字 運算符 數字，輸入 q 離開）：").strip()
    if raw.lower() == "q":
        print("再見！")
        break

    try:
        a_str, op, b_str = raw.split()
        a, b = float(a_str), float(b_str)
    except ValueError:
        print("輸入格式錯誤，請重新輸入，例如：3 + 5")
        continue

    result = calculator(a, op, b)
    print(f"結果：{result}")

# 秒數轉換成「時 分 秒」

total_seconds = int(input("請輸入秒數："))

hours = total_seconds // 3600
remaining = total_seconds % 3600

minutes = remaining // 60
seconds = remaining % 60

print(f"{hours} 時 {minutes} 分 {seconds} 秒")

import numpy as np

# 固定亂數種子，方便重現結果（可移除）
np.random.seed(42)

# 建立 5x5、範圍 0~100 的隨機整數矩陣
matrix = np.random.randint(0, 101, size=(5, 5))
print("原始矩陣：")
print(matrix)

# 每「列」平均：axis=1 表示沿著欄方向計算（每一列得到一個值）
row_means = matrix.mean(axis=1)
print("\n每列平均：")
print(row_means)

# 每「欄」最大值：axis=0 表示沿著列方向計算（每一欄得到一個值）
col_max = matrix.max(axis=0)
print("\n每欄最大值：")
print(col_max)

# 標準化成 0~1 之間：(x - 最小值) / (最大值 - 最小值)
normalized = (matrix - matrix.min()) / (matrix.max() - matrix.min())
print("\n標準化結果：")
print(normalized)

import requests


def check_urls(urls):
    """接受網址列表，回傳 {網址: 狀態碼或錯誤訊息} 字典"""
    results = {}
    for url in urls:
        try:
            # timeout=5 秒，避免網站沒回應導致程式卡住
            response = requests.get(url, timeout=5)
            results[url] = response.status_code
        except requests.exceptions.Timeout:
            results[url] = "逾時"
        except requests.exceptions.RequestException as e:
            # 涵蓋連線錯誤、DNS 解析失敗等其他網路例外
            results[url] = f"錯誤：{e}"
    return results


if __name__ == "__main__":
    urls = [
        "https://www.python.org",
        "https://www.google.com",
        "https://this-domain-does-not-exist-abc123.com",
    ]
    status_map = check_urls(urls)
    for url, status in status_map.items():
        print(f"{url} -> {status}")

import csv
import requests
from bs4 import BeautifulSoup

BOARD_URL = "https://www.ptt.cc/bbs/Python/index.html"
# PTT 會檢查是否已通過 18 歲同意頁，設定 cookie 避免被導去同意頁
COOKIES = {"over18": "1"}


def fetch_articles(url):
    """爬取單一頁面的文章標題與作者，回傳 list of dict"""
    resp = requests.get(url, cookies=COOKIES, timeout=5)
    resp.raise_for_status()
    soup = BeautifulSoup(resp.text, "html.parser")

    articles = []
    for entry in soup.select("div.r-ent"):
        title_tag = entry.select_one("div.title a")
        # 已刪除的文章沒有 <a> 標籤，title 會顯示「本文已被刪除」
        if title_tag is None:
            title = "（已刪除）"
            author = "（未知）"
        else:
            title = title_tag.text.strip()
            author_tag = entry.select_one("div.meta div.author")
            author = author_tag.text.strip() if author_tag else "（未知）"
        articles.append({"title": title, "author": author})
    return articles


def main():
    articles = fetch_articles(BOARD_URL)[:20]

    with open("ptt_python_board.csv", "w", newline="", encoding="utf-8-sig") as f:
        writer = csv.DictWriter(f, fieldnames=["title", "author"])
        writer.writeheader()
        writer.writerows(articles)

    print(f"共爬取 {len(articles)} 篇文章，已存成 ptt_python_board.csv")


if __name__ == "__main__":
    main()

import time
import random
import requests
import pandas as pd
from bs4 import BeautifulSoup

# 假設商品列表頁的換頁規律是 ?page=N（依實際網站調整）
BASE_URL = "https://example-shop.com/products?page={}"
HEADERS = {"User-Agent": "Mozilla/5.0"}


def fetch_page(page: int):
    """爬取單一頁的商品資料，回傳 list of dict"""
    url = BASE_URL.format(page)
    resp = requests.get(url, headers=HEADERS, timeout=5)
    resp.raise_for_status()
    soup = BeautifulSoup(resp.text, "html.parser")

    products = []
    for item in soup.select(".product-item"):
        name_tag = item.select_one(".product-name")
        price_tag = item.select_one(".product-price")
        rating_tag = item.select_one(".product-rating")
        if not (name_tag and price_tag):
            continue
        # 價格文字可能含「$」「,」等符號，需清理後轉數字
        price_text = price_tag.text.strip().replace("$", "").replace(",", "")
        products.append({
            "name": name_tag.text.strip(),
            "price": float(price_text),
            "rating": float(rating_tag.text.strip()) if rating_tag else None,
        })
    return products


def main():
    all_products = []
    for page in range(1, 6):  # 前 5 頁
        all_products.extend(fetch_page(page))
        # 加入隨機延遲，避免對伺服器造成負擔或被判定為機器人
        time.sleep(random.uniform(1, 2))

    df = pd.DataFrame(all_products)

    print("=== 商品統計報告 ===")
    print(f"總商品數：{len(df)}")
    print(f"最高價格：{df['price'].max():.0f}")
    print(f"最低價格：{df['price'].min():.0f}")
    print(f"平均價格：{df['price'].mean():.2f}")

    df.to_csv("products_report.csv", index=False, encoding="utf-8-sig")
    print("已輸出 products_report.csv")


if __name__ == "__main__":
    main()

import pandas as pd

# 讀取原始資料（假設欄位有 name, class, score，score 可能是字串或缺失值 NaN）
df = pd.read_csv("students_raw.csv")

# 1. 將分數欄位統一轉換成數字型態（無法轉換的值會變成 NaN，方便後續補值）
df["score"] = pd.to_numeric(df["score"], errors="coerce")

# 2. 移除完全重複的資料列
df = df.drop_duplicates()

# 3. 用「班級平均分數」填補缺失分數
#    先計算每個班級的平均分數，再對應填回缺失值
class_avg = df.groupby("class")["score"].transform("mean")
df["score"] = df["score"].fillna(class_avg)

# 4. 四捨五入到小數點後 1 位，讓資料乾淨
df["score"] = df["score"].round(1)

df.to_csv("students_cleaned.csv", index=False, encoding="utf-8-sig")
print("清洗完成，已輸出 students_cleaned.csv")
print(df)

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

# 讓中文字型正常顯示（Windows 可用「Microsoft JhengHei」，Mac 可用「PingFang TC」）
plt.rcParams["font.sans-serif"] = ["Microsoft JhengHei"]
plt.rcParams["axes.unicode_minus"] = False

# 讀取範例銷售資料，欄位假設為：month, region, category, amount
df = pd.read_csv("sales_data.csv")

with PdfPages("sales_report.pdf") as pdf:
    # (1) 各月份銷售額折線圖
    monthly = df.groupby("month")["amount"].sum().sort_index()
    fig1, ax1 = plt.subplots(figsize=(8, 5))
    ax1.plot(monthly.index, monthly.values, marker="o")
    ax1.set_title("各月份銷售額趨勢")
    ax1.set_xlabel("月份")
    ax1.set_ylabel("銷售額")
    pdf.savefig(fig1)
    plt.close(fig1)

    # (2) 各地區銷售佔比圓餅圖
    region_sales = df.groupby("region")["amount"].sum()
    fig2, ax2 = plt.subplots(figsize=(6, 6))
    ax2.pie(region_sales.values, labels=region_sales.index, autopct="%1.1f%%")
    ax2.set_title("各地區銷售佔比")
    pdf.savefig(fig2)
    plt.close(fig2)

    # (3) 各商品類別長條圖
    category_sales = df.groupby("category")["amount"].sum().sort_values(ascending=False)
    fig3, ax3 = plt.subplots(figsize=(8, 5))
    ax3.bar(category_sales.index, category_sales.values)
    ax3.set_title("各商品類別銷售額")
    ax3.set_xlabel("商品類別")
    ax3.set_ylabel("銷售額")
    pdf.savefig(fig3)
    plt.close(fig3)

print("報告已產出：sales_report.pdf")