python爬取疫情数据并存入excel中（包括国内各省份，全球，国内外历史疫情数据）代码可以直接运行

流程

1.进入获取疫情的url

例如：
腾讯新闻的疫情网站 https://news.qq.com/zt2020/page/feiyan.htm#/
网易新闻：https://wp.m.163.com/163/page/news/virus_report/index.html_nw_=1&anw=1

只需要找到网站的url以及user-agent后，进入url查看json数据格式，按照步骤即可访问。
2.为了避免反爬，伪装成浏览器：
找到headers = {‘user-agent’ : ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36 Edg/97.0.1072.55’} ，进行浏览器访问。
3.分析url，找到数据存放的规律
4.进行数据读取和存储

爬取全球最新疫情数据

import requests #爬取网页
import json #json文件可以通过角标索引读取内容 爬取json文件
import xlwings as xw #导入excel
url = 'https://c.m.163.com/ug/api/wuhan/app/data/list-total?t=329822670771' #请求URL
headers = {'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36 Edg/97.0.1072.55'} #浏览器访问 
response = requests.get(url , headers = headers)
#print(response.status_code) #200表示访问成功
#print(response.json()) # 打印内容


wb = xw.Book() #相当于打开excel操作
sht = wb.sheets('sheet1') #相当于在excel里加了一个工作表
sht.range('A1').values = '地区'
sht.range('B1').values = '新增确诊'
sht.range('C1').values = '累计确诊'
sht.range('D1').values = '死亡'
sht.range('E1').values = '治愈'
sht.range('F1').values = '日期'

在进入url分析数据格式后，将数据取出放入excel中。

json_data = response.json()['data']['areaTree']
#print(json_data)
for i in range(206):
    earth_data = json_data[i]
    #print(earth_data)
    name = earth_data['name']
    sht.range(f'A{i+2}').value = name
    today_confirm = json.dumps(earth_data['today']['confirm'])
    sht.range(f'B{i+2}').value = today_confirm
    total_confirm = json.dumps(earth_data['total']['confirm'])
    sht.range(f'C{i+2}').value = total_confirm
    total_dead = json.dumps(earth_data['total']['dead'])
    sht.range(f'D{i+2}').value = total_dead
    total_heal = json.dumps(earth_data['total']['heal'])
    sht.range(f'E{i+2}').value = total_heal
    date = earth_data['lastUpdateTime']
    sht.range(f'F{i+2}').value = date
    #print("地区:"+name, "新增确诊:"+today_confirm, "累计确诊:"+total_confirm , "死亡"+total_dead,"治愈"+total_heal)

运行结果：

同理，爬取中国疫情历史数据

import requests #爬取网页
import json #爬取数据
import xlwings as xw #导入excel
url = 'https://c.m.163.com/ug/api/wuhan/app/data/list-total?t=329822670771'
headers = {'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36 Edg/97.0.1072.55'}
response = requests.get(url , headers = headers)
#print(response.status_code) #200表示访问成功
#print(response.json()) # 打印内容

wb = xw.Book() #相当于打开excel操作
sht = wb.sheets('sheet1') #相当于在excel里加了一个工作表
sht.range('A1').values = '地区'
sht.range('B1').values = '新增确诊'
sht.range('C1').values = '累计确诊'
sht.range('D1').values = '死亡'
sht.range('E1').values = '治愈'
sht.range('F1').values = '日期'
json_data = response.json()['data']['chinaDayList']
#print(json_data)
for i in range(59):
    earth_data = json_data[i]
    #print(earth_data)
    #name = earth_data['name']
    #sht.range(f'A{i+2}').value = name
    today_confirm = json.dumps(earth_data['today']['confirm'])
    sht.range(f'B{i+2}').value = today_confirm
    total_confirm = json.dumps(earth_data['total']['confirm'])
    sht.range(f'C{i+2}').value = total_confirm
    total_dead = json.dumps(earth_data['total']['dead'])
    sht.range(f'D{i+2}').value = total_dead
    total_heal = json.dumps(earth_data['total']['heal'])
    sht.range(f'E{i+2}').value = total_heal
    date = earth_data['date']
    sht.range(f'F{i+2}').value = date
    #print("地区:"+name, "新增确诊:"+today_confirm, "累计确诊:"+total_confirm , "死亡"+total_dead,"治愈"+total_heal)

运行结果：

同理，爬取美国2020-2022年疫情历史数据

import requests #爬取网页
import json #爬取数据
import xlwings as xw #导入excel
url = 'https://c.m.163.com/ug/api/wuhan/app/data/list-by-area-code?areaCode=7&t=1649117007316'
headers = {'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36 Edg/97.0.1072.55'}
response = requests.get(url , headers = headers)
wb = xw.Book() #相当于打开excel操作
sht = wb.sheets('sheet1') #相当于在excel里加了一个工作表
sht.range('A1').values = '地区'
sht.range('B1').values = '新增确诊'
sht.range('C1').values = '累计确诊'
sht.range('D1').values = '死亡'
sht.range('E1').values = '治愈'
sht.range('F1').values = '日期'
json_data = response.json()['data']['list']
#print(json_data)
for i in range(772):
    earth_data = json_data[i]
    #print(earth_data)
    #name = earth_data['name']
    #sht.range(f'A{i+2}').value = name
    today_confirm = json.dumps(earth_data['today']['confirm'])
    sht.range(f'B{i+2}').value = today_confirm
    total_confirm = json.dumps(earth_data['total']['confirm'])
    sht.range(f'C{i+2}').value = total_confirm
    total_dead = json.dumps(earth_data['total']['dead'])
    sht.range(f'D{i+2}').value = total_dead
    total_heal = json.dumps(earth_data['total']['heal'])
    sht.range(f'E{i+2}').value = total_heal
    date = earth_data['date']
    sht.range(f'F{i+2}').value = date

运行结果：

爬取国内各省份疫情最新数据

import pandas as pd
import requests
import json
def get_data():
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
    area = requests.get(url).json()
    data = json.loads(area['data'])
    update_time = data['lastUpdateTime']
    all_counties = data['areaTree']
    all_list = []
    for country_data in all_counties:
        if country_data['name'] != '中国':
            continue
        else:
            all_provinces = country_data['children']
            for province_data in all_provinces:
                province_name = province_data['name']
                all_cities = province_data['children']
                for city_data in all_cities:
                    city_name = city_data['name']
                    city_total = city_data['total']
                    province_result = {'province': province_name, 'city': city_name,'update_time': update_time}
                    province_result.update(city_total)
                    all_list.append(province_result)

    df = pd.DataFrame(all_list)
    
    df.to_csv('data.csv', index=False,encoding="utf_8_sig")
get_data()

运行结果：

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