疫情分析数据可视化（基于百度api ，四个国家，古老的代码，放出来记录一下）

import requests
import json
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import pandas as pd

startURL = r"https://voice.baidu.com/newpneumonia/getv2?target=trend&isCaseIn=0&from=mola-virus&area="
endURL = r"&stage=publish"

dict1 = ["美国", "俄罗斯", "印度", "以色列"]
# 日期

country_date = []
# 确诊人数
sure_count = []
# 治愈人数
heal_count = []
# 新增人数
add_count = []
# 死亡人数
death_count = []

Res = []

heal_Res = pd.DataFrame()

if __name__ == '__main__':
    plt.figure(1)
    plt.rcParams['savefig.dpi'] = 300
    plt.rcParams['figure.dpi'] = 300
    plt.rcParams['font.sans-serif'] = ['KaiTi', 'SimHei', 'FangSong']
    plt.rcParams['font.size'] = 6
    plt.rcParams['axes.unicode_minus'] = False
    for i in dict1:
        url = startURL + i + endURL
        res = json.loads(requests.get(url).text)
        country_date = res['data'][0]['trend']['updateDate']
        country_date_solve = []
        header = 2019
        for k in country_date:
            if str(k) == "1.1":
                header = header + 1
            k = str(header) + '.' + k
            country_date_solve.append(k)
        sure_count = res['data'][0]['trend']['list'][0]['data']
        heal_count = res['data'][0]['trend']['list'][1]['data']
        add_count = res['data'][0]['trend']['list'][3]['data']
        death_count = res['data'][0]['trend']['list'][2]['data']
        Res.append([country_date_solve, sure_count, heal_count, add_count, death_count])
    for i in range(len(dict1)):
        plt.plot(Res[i][0], Res[i][1], label=dict1[i])
    plt.gca().xaxis.set_major_locator(ticker.MultipleLocator(80))
    plt.legend()
    plt.title('确诊人数')
    plt.show()

    for i in range(len(dict1)):
        plt.plot(Res[i][0], Res[i][3], label=dict1[i])
    plt.gca().xaxis.set_major_locator(ticker.MultipleLocator(80))
    plt.legend()
    plt.title('新增人数')
    plt.show()

    for i in range(len(dict1)):
        plt.plot(Res[i][0], Res[i][4], label=dict1[i])
    plt.gca().xaxis.set_major_locator(ticker.MultipleLocator(80))
    plt.legend()
    plt.title('死亡人数')
    plt.show()

    for i in range(len(dict1)):
        plt.plot(Res[i][0], Res[i][2], label=dict1[i])
        pd.DataFrame(Res[i][0], Res[i][2]).to_csv(str(dict1[i] + '.csv'))

    plt.gca().xaxis.set_major_locator(ticker.MultipleLocator(80))
    plt.legend()
    plt.title('治愈人数')
    plt.show()