Delete '疫情数据爬取.py'

疫情数据爬取.py

@@ -1,142 +0,0 @@
-import requests
-from lxml import etree
-import csv
-import pandas as pd
-import matplotlib.pyplot as plt
-# 步骤一（替换sans-serif字体）
-plt.rcParams['font.sans-serif'] = ['SimHei'] 
-# 步骤二（解决坐标轴负数的负号显示问题）
-plt.rcParams['axes.unicode_minus'] = False
-# 设置请求头信息
-headers = {
-    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.69 Safari/537.36"
-}
-
-url = "https://www.bitpush.news/covid19/"
-
-try:
-    # 发起 GET 请求
-    response = requests.get(url, headers=headers)
-    response.raise_for_status()
-    response.encoding = response.apparent_encoding  # 确保编码正确
-    html = response.text
-except requests.RequestException as e:
-    print(f"请求出错: {e}")
-    html = ""
-
-if html:
-    # 解析 HTML
-    doc = etree.HTML(html)
-
-    # 获取国家名称
-    country= doc.xpath('//div[@class="table_container"]//tbody/tr/td/span/text()')
-    # 获取确诊人数
-    person = doc.xpath('//div[@class="table_container"]//tbody/tr/td[2]/text()')
-    person = [x.replace(",", "") for x in person]
-    # 获取死亡人数
-    death = doc.xpath('//div[@class="table_container"]//tbody/tr/td[3]/text()')
-    death = [x.replace(",", "") for x in death]
-
-    # 组合数据
-    message = list(zip(country, person, death))
-    # 按死亡人数排序
-    message1 = sorted(message, key=lambda x: int(x[-1]), reverse=True)
-
-    # 保存数据到CSV文件
-    with open("content1.csv", "w", newline='', encoding='utf-8') as f:
-        w = csv.writer(f)
-        w.writerow(["country", "person", "death"])  # 添加表头
-        w.writerows(message1)
-
-    # 读取数据
-    df = pd.read_csv("content1.csv")
-    df1 = df.head(10)
-
-    # 绘制柱状图
-    plt.figure(figsize=(12, 6))
-    plt.bar(df1["country"], df1["death"].astype(int))
-    plt.xlabel("国家", fontsize=14)
-    plt.ylabel("死亡人数", fontsize=14)
-    plt.title("COVID-19死亡人数前10的国家", fontsize=16)
-
-    # 显示每个柱的数值
-    for i, v in enumerate(df1["death"].astype(int)):
-        plt.text(i, v + 0.02 * max(df1["death"].astype(int)), str(v), ha='center', fontsize=12)
-
-    plt.xticks(rotation=45)
-    plt.tight_layout()
-    plt.show()
-    # 按确诊人数排序
-    message2 = sorted(message, key=lambda x: int(x[-2]), reverse=True)
-
-    # 保存数据到CSV文件
-    with open("content2.csv", "w", newline='', encoding='utf-8') as f:
-        w = csv.writer(f)
-        w.writerow(["country", "person", "death"])  # 添加表头
-        w.writerows(message2)
-
-    # 读取数据
-    df = pd.read_csv("content2.csv")
-    df2 = df.head(10)
-
-    # 绘制柱状图
-    plt.figure(figsize=(12, 6))
-    plt.bar(df2["country"], df2["person"].astype(int))
-    plt.xlabel("国家", fontsize=14)
-    plt.ylabel("确诊人数", fontsize=14)
-    plt.title("COVID-19确诊人数前10的国家", fontsize=16)
-
-    # 显示每个柱的数值
-    for i, v in enumerate(df2["person"].astype(int)):
-        plt.text(i, v + 0.02 * max(df2["death"].astype(int)), str(v), ha='center', fontsize=12)
-
-    plt.xticks(rotation=45)
-    plt.tight_layout()
-    plt.show()
-    # 获取州的名称
-    a=country.index('加州')
-    zhou=country[a:]
-    # 获取确诊人数
-    person1=person[a:]
-     # 获取死亡人数
-    death1=death[a:]
-    #组合数据
-    message0=list(zip(zhou,person1,death1))
-    # 按死亡人数排序
-    message3=sorted(message0, key=lambda x: int(x[-1]), reverse=True)
-    # 保存数据到CSV文件
-    with open("content3.csv", "w", newline='', encoding='utf-8') as f:
-        w = csv.writer(f)
-        w.writerow(["zhou", "person1", "death1"])  # 添加表头
-        w.writerows(message3)
-    # 读取数据
-    df = pd.read_csv("content3.csv")
-    df3 = df.head(10)
-    # 绘制折线图
-    plt.figure(figsize=(12, 6))
-    plt.plot(df3["zhou"], df3["death1"].astype(int), marker='o')
-    plt.xlabel("州", fontsize=14)
-    plt.ylabel("死亡人数", fontsize=14)
-    plt.title("美国COVID9死亡人数前十的州", fontsize=16)
-    # 显示每个点的数值
-    for i, v in enumerate(df3["death1"].astype(int)):
-        plt.text(i, v + 0.02 * max(df3["death1"].astype(int)), str(v), ha='center', fontsize=12)
-    # 按确诊人数排序
-    message4=sorted(message0, key=lambda x: int(x[-2]), reverse=True)
-    # 保存数据到CSV文件
-    with open("content4.csv", "w", newline='', encoding='utf-8') as f:
-        w = csv.writer(f)
-        w.writerow(["zhou", "person1", "death1"])  # 添加表头
-        w.writerows(message4)
-    # 读取数据
-    df = pd.read_csv("content4.csv")
-    df4 = df.head(10)
-    # 绘制折线图
-    plt.figure(figsize=(12, 6))
-    plt.plot(df3["zhou"], df3["person1"].astype(int), marker='o')
-    plt.xlabel("州", fontsize=14)
-    plt.ylabel("确诊人数", fontsize=14)
-    plt.title("美国COVID9确诊人数前十的州", fontsize=16)
-     # 显示每个点的数值
-    for i, v in enumerate(df3["person1"].astype(int)):
-        plt.text(i, v + 0.02 * max(df3["death1"].astype(int)), str(v), ha='center', fontsize=12)