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# -*- coding: utf-8 -*-
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"""
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Created on Wed Jun 5 19:22:56 2024
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@author: 44665
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"""
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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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url = "https://www.bitpush.news/covid19/"
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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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try:
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response = requests.get(url, headers=headers)
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response.raise_for_status() # 检查是否请求成功
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except requests.exceptions.RequestException as e:
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print("请求错误:", e)
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exit()
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html = response.text
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parse = etree.HTMLParser(encoding='utf-8')
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doc = etree.HTML(html)
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# 解析国家、确诊人数和死亡人数
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country = doc.xpath('//div[@class="table_container"]//tbody/tr/td/span/text()')
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person = doc.xpath('//div[@class="table_container"]//tbody/tr/td[2]/text()')
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death = doc.xpath('//div[@class="table_container"]//tbody/tr/td[3]/text()')
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a = country.index('加州')
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state = country[a:]
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state_person = person[a:]
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state_death = death[a:]
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# 去除确诊人数和死亡人数中的逗号，并转换为整数
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person = [int(x.replace(",", "")) for x in person]
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death = [int(x.replace(",", "")) for x in death]
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state_person = [int(x.replace(",", "")) for x in state_person]
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state_death = [int(x.replace(",", "")) for x in state_death]
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# 将数据存储到列表中
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message = list(zip(country, person, death))
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message = sorted(message, key=lambda x: x[-1], reverse=True)
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# 将数据写入 CSV 文件
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with open("content.csv", "w", newline='', encoding='utf-8') as f:
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w = csv.writer(f)
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w.writerows(message)
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# 读取 CSV 文件并创建 DataFrame
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df = pd.read_csv("content.csv", names=["country", "person", "death"])
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df = df.drop(0)
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df = df.head(10)
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# 绘制柱状图
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['figure.figsize'] = (10, 5)
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x = df["country"].values
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y = df["death"].values
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z = df['person'].values
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colors=['red','green','blue','cyan','grey','black','yellow','orange','pink','purple']
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plt.bar(x, y,color=colors)
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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 死亡人数排名前十国家", fontsize=16)
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for i,j in zip(x,y):
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plt.text(i,j,j,ha='center')
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plt.xticks(rotation=45) # x轴标签旋转45度，防止重叠
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plt.show()
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#绘制饼图
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plt.rcParams['figure.figsize'] = (15, 10)
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colors1=['maroon','crimson','yellow','olive','cyan','lavender','purple','teal','pink','magenta']
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explode=[0.1,0,0,0,0,0,0,0,0,0]
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labels=['美国','巴西','印度','俄罗斯','墨西哥','秘鲁','英国','印尼','意大利','伊朗']
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plt.pie(y,explode=explode,labels=labels,colors=colors1,shadow=True,autopct='%.2f%%')
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plt.legend()
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plt.title('COVID-19 死亡人数排名前十国家')
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plt.show()
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#将数据重新存储一个列表
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message = list(zip(country, person, death))
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message = sorted(message, key=lambda x: x[-2], reverse=True)
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# 将数据写入 CSV 文件
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with open("content.csv", "w", newline='', encoding='utf-8') as f:
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w = csv.writer(f)
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w.writerows(message)
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# 读取 CSV 文件并创建 DataFrame
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df = pd.read_csv("content.csv", names=["country", "person", "death"])
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df = df.drop(0)
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df = df.head(20)
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# 绘制两幅折线图
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['figure.figsize'] = (15, 12)
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x = df["country"].values
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y = df["death"].values
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z = df['person'].values
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plt.subplot(211)
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plt.plot(x,z,color='crimson',marker='*',linestyle='--',label='确诊人数')
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plt.legend(loc='upper right')
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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.xticks(rotation=45) # x轴标签旋转45度，防止重叠
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plt.title("COVID-19 确诊人数排名前二十国家确诊人数与死亡人数对比", fontsize=16)
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plt.subplot(212)
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plt.plot(x,y,color='teal',linestyle='-',marker='D',label='死亡人数')
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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.legend(loc='upper right')
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plt.xticks(rotation=45) # x轴标签旋转45度，防止重叠
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plt.show()
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#将州数据再写进列表
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state_message = list(zip(state, state_person, state_death))
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state_message = sorted(state_message, key=lambda x: x[-1], reverse=True)
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# 将数据写入 CSV 文件
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with open("state_content.csv", "w", newline='', encoding='utf-8') as f:
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w = csv.writer(f)
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w.writerows(state_message)
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# 读取 CSV 文件并创建 DataFrame
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df1 = pd.read_csv("state_content.csv", names=["state", "state_person", "state_death"])
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df1 = df1.drop(0)
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df1 = df1.head(10)
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#绘制柱状图
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['figure.figsize'] = (10, 5)
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colors1=['maroon','crimson','yellow','olive','cyan','lavender','purple','teal','pink','magenta']
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x = df1["state"].values
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y = df1["state_death"].values
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plt.barh(x,y,height=0.8,color=colors1)
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plt.xlabel('死亡人数')
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plt.ylabel('州')
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plt.title('美国COVID-19 死亡人数排名前十的州')
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plt.show()
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