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