python 绘制新冠肺炎疫情地图

参考链接：
（1）实时更新|新冠肺炎疫情地图
https://news.sina.cn/zt_d/yiqing0121
（2）实时的可视化疫情地图
https://blog.csdn.net/weixin_43139613/article/details/104123136

from pyecharts import options as opts
from pyecharts.charts import Map
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
browser = webdriver.Chrome()
wait = WebDriverWait(browser,10)
browser.get('https://news.sina.cn/zt_d/yiqing0121')
provinces = []
values = []
for i in range(2,69,2):
    province = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,'#mylist > div.mapCont.mapMoreCont > div.m_list > div:nth-child({}) > span.c1'.format(i))))
    provinces.append(province.text)
    value = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,'#mylist > div.mapCont.mapMoreCont > div.m_list > div:nth-child({}) > span.c2'.format(i))))
    values.append(value.text)


def map_visualmap() -> Map:
    c = (
        Map()
        .add("", [list(z) for z in zip(provinces, values)], "china")
        .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图"),
            visualmap_opts=opts.VisualMapOpts(max_=600),
        )
    )
    return c.render()
map_visualmap()

运行结果：

#[补充]
#selenium爬虫丁香医生实时播报
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
import pandas as pd
import re
browser = webdriver.Chrome()
wait = WebDriverWait(browser,10)
browser.get('http://3g.dxy.cn/newh5/view/pneumonia_timeline?whichFrom=dxy')
date = []
title = []
try:
    for i in range(1,800):
        d = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,'#root > div > div.tab2___PhOZ6 > div:nth-child({}) > div.tabLeft2___SbuNE > span.leftTime___2zf53'.format(i))))
        date.append(d.text)
        t = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,'#root > div > div.tab2___PhOZ6 > div:nth-child({}) > div.tabRight___3Z0eJ > p.topicTitle___2ovVO'.format(i))))
        title.append(t.text)
except:
    pass
date2 = []
for j in range(len(date)):
    if '\n' in date[j]:
        date2.append(date[j].split('\n')[1])
    else:
        date2.append(date[j])
data = {'时间':date2,'标题':title}
df = pd.DataFrame(data)
df.to_excel('实时播报--.xlsx')

（3）新型冠状病毒 - 实时数据采集 | 爬虫篇
https://blog.csdn.net/qq_34409973/article/details/104133205
源代码 来源：https://github.com/Programming-With-Love/2019-nCoV

import requests
import re
from bs4 import BeautifulSoup
from time import sleep
import json
from prettytable import ALL
from prettytable import PrettyTable
hubei = {}
guangdong = {}
zhejiang = {}
beijing = {}
shanghai = {}
hunan = {}
anhui = {}
chongqing = {}
sichuan = {}
shandong = {}
guangxi = {}
fujian = {}
jiangsu = {}
henan = {}
hainan = {}
tianjin = {}
jiangxi = {}
shanxi1 = {} # 陕西
guizhou = {}
liaoning = {}
xianggang = {}
heilongjiang = {}
aomen = {}
xinjiang = {}
gansu = {}
yunnan = {}
taiwan = {}
shanxi2 = {} # 山西
jilin = {}
hebei = {}
ningxia = {}
neimenggu = {}
qinghai = {} # none
xizang = {} # none
provinces_idx = [hubei, guangdong, zhejiang, chongqing, hunan, anhui, beijing,
                 shanghai, henan, guangxi, shandong, jiangxi, jiangsu, sichuan,
                 liaoning, fujian, heilongjiang, hainan, tianjin, hebei, shanxi2,
                 yunnan, xianggang, shanxi1, guizhou, jilin, gansu, taiwan,
                 xinjiang, ningxia, aomen, neimenggu, qinghai, xizang]
map = {
    '湖北':0, '广东':1, '浙江':2, '北京':3, '上海':4, '湖南':5, '安徽':6, '重庆':7,
    '四川':8, '山东':9, '广西':10, '福建':11, '江苏':12, '河南':13, '海南':14,
    '天津':15, '江西':16, '陕西':17, '贵州':18, '辽宁':19, '香港':20, '黑龙江':21,
    '澳门':22, '新疆':23, '甘肃':24, '云南':25, '台湾':26, '山西':27, '吉林':28,
    '河北':29, '宁夏':30, '内蒙古':31, '青海':32, '西藏':33
}

def query(province):
    table = PrettyTable(['地区', '确诊', '死亡', '治愈'])
    for (k, v) in province.items():
        name = k
        table.add_row([name, v[0] if v[0] != 0 else '-', v[1] if v[1] != 0 else '-', v[2] if v[2] != 0 else '-'])
    if len(province.keys()) != 0:
        print(table)
    else:
        print("暂无") 
        
def is_json(json_str):
    try:
        json.loads(json_str)
    except ValueError:
        return False
    return True
    
def main():
    url = "https://3g.dxy.cn/newh5/view/pneumonia"
    try:
        headers = {}
        headers['user-agent'] = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36' #http头大小写不敏感
        headers['accept'] = 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8'
        headers['Connection'] = 'keep-alive'
        headers['Upgrade-Insecure-Requests'] = '1'
        r = requests.get(url, headers=headers)
        r.raise_for_status()
        r.encoding = r.apparent_encoding
        soup = BeautifulSoup(r.text,'lxml')
        table = PrettyTable(['地区', '确诊', '死亡', '治愈'])
        table.hrules = ALL
        while True:
            r = requests.get("https://service-f9fjwngp-1252021671.bj.apigw.tencentcs.com/release/pneumonia")
            json_str = json.loads(r.text)
            if json_str['error'] == 0:
                break
        print("==================================全国数据==================================")
        print()
        print("     确诊 " + str(json_str['data']['statistics']['confirmedCount']) + " 例"
            + "       " + "疑似 " + str(json_str['data']['statistics']['suspectedCount']) + " 例"
            + "       " + "死亡" + str(json_str['data']['statistics']['deadCount']) + " 例"
            + "       " + "治愈" + str(json_str['data']['statistics']['curedCount']) + " 例\n")
        print("==================================相关情况==================================")
        print()
        print("传染源：" + json_str['data']['statistics']['infectSource'])
        print("病毒：" + json_str['data']['statistics']['virus'])
        print("传播途径：" + json_str['data']['statistics']['passWay'])
        print(json_str['data']['statistics']['remark1'])
        print(json_str['data']['statistics']['remark2'] + "\n")         
        print("==================================国内情况==================================")
        print()
        json_provinces = re.findall("{\"provinceName\":(.*?)]}", str(soup))
        idx = 0
        for province in json_provinces:
            if is_json(province):
                pass
            else:
                province = "{\"provinceName\":" + province + "]}"
                province = json.loads(province)
            province_name = province['provinceShortName']
            if province_name == '待明确地区':
                    break
            confirmed = province['confirmedCount'] if province['confirmedCount'] != 0 else '-'
            # suspected = province['suspectedCount'] if province['suspectedCount'] != 0 else '-'
            cured = province['curedCount'] if province['curedCount'] != 0 else '-'
            dead = province['deadCount'] if province['deadCount'] != 0 else '-'
            table.add_row([province_name, confirmed, dead, cured])
            map[province_name] = idx
            idx = idx + 1
            for city in province['cities']:
                provinces_idx[map[province_name]][city['cityName']] = [city['confirmedCount'], city['deadCount'], city['curedCount']]
        print(table)
        print()
        print("==================================国外情况==================================")
        print()
        json_provinces = str(re.findall("\"id\":949(.*?)]}", str(soup)))
        json_provinces = json_provinces[:1] + "{\"id\":949" + json_provinces[2:]
        json_provinces = json_provinces[:len(json_provinces) - 2] + json_provinces[len(json_provinces) - 1:]
        provinces = json.loads(json_provinces)
        table = PrettyTable(['地区', '确诊', '死亡', '治愈'])
        for province in provinces:
            confirmed = province['confirmedCount'] if province['confirmedCount'] != 0 else '-'
            dead = province['deadCount'] if province['deadCount'] != 0 else '-'
            cured = province['curedCount'] if province['curedCount'] != 0 else '-'
            table.add_row([province['provinceName'], confirmed, dead, cured])
        print(table)
        print()
        print("==================================最新消息==================================")
        print()
        idx = 0
        for news in json_str['data']['timeline']:
            if idx == 5:
                break
            print(news['pubDateStr'] + "  " + news['title'])
            idx = idx + 1
        print()
        key = input("请输入您想查询详细信息的省份，例如 湖北\n")
        print()
        if key in map.keys():
            query(provinces_idx[map[key]])
        else:
            print("暂无相关信息")
            
        print("\n欢迎提出各种意见")
    except:
        print("连接失败")
if __name__ == '__main__':
    main()
    sleep(30)

（4）Python实战：抓肺炎疫情实时数据，画2019-nCoV疫情地图
https://blog.csdn.net/xufive/article/details/104093197

以下为全部代码，除了疫情地图，还包括了全国每日武汉肺炎确诊数据的下载和可视化。

# -*- coding: utf-8 -*-

import time
import json
import requests
from datetime import datetime
import numpy as np
import matplotlib
import matplotlib.figure
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

plt.rcParams['font.sans-serif'] = ['FangSong']  # 设置默认字体
plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像时'-'显示为方块的问题

def catch_daily():
    """抓取每日确诊和死亡数据"""
    
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_cn_day_counts&callback=&_=%d'%int(time.time()*1000)
    data = json.loads(requests.get(url=url).json()['data'])
    data.sort(key=lambda x:x['date'])
    
    date_list = list() # 日期
    confirm_list = list() # 确诊
    suspect_list = list() # 疑似
    dead_list = list() # 死亡
    heal_list = list() # 治愈
    for item in data:
        month, day = item['date'].split('/')
        date_list.append(datetime.strptime('2020-%s-%s'%(month, day), '%Y-%m-%d'))
        confirm_list.append(int(item['confirm']))
        suspect_list.append(int(item['suspect']))
        dead_list.append(int(item['dead']))
        heal_list.append(int(item['heal']))
    
    return date_list, confirm_list, suspect_list, dead_list, heal_list

def catch_distribution():
    """抓取行政区域确诊分布数据"""
    
    data = {}
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5&callback=&_=%d'%int(time.time()*1000)
    for item in json.loads(requests.get(url=url).json()['data'])['areaTree'][0]['children']:
        if item['name'] not in data:
            data.update({item['name']:0})
        for city_data in item['children']:
            data[item['name']] += int(city_data['total']['confirm'])
    
    return data

def plot_daily():
    """绘制每日确诊和死亡数据"""
    
    date_list, confirm_list, suspect_list, dead_list, heal_list = catch_daily() # 获取数据
    
    plt.figure('2019-nCoV疫情统计图表', facecolor='#f4f4f4', figsize=(10, 8))
    plt.title('2019-nCoV疫情曲线', fontsize=20)
    
    plt.plot(date_list, confirm_list, label='确诊')
    plt.plot(date_list, suspect_list, label='疑似')
    plt.plot(date_list, dead_list, label='死亡')
    plt.plot(date_list, heal_list, label='治愈')
    
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d')) # 格式化时间轴标注
    plt.gcf().autofmt_xdate() # 优化标注（自动倾斜）
    plt.grid(linestyle=':') # 显示网格
    plt.legend(loc='best') # 显示图例
    plt.savefig('2019-nCoV疫情曲线.png') # 保存为文件
    #plt.show()

def plot_distribution():
    """绘制行政区域确诊分布数据"""
    
    data = catch_distribution()
    
    font_14 = FontProperties(fname='res/simsun.ttf', size=14)
    font_11 = FontProperties(fname='res/simsun.ttf', size=11)
    
    width = 1600
    height = 800
    rect = [0.1, 0.12, 0.8, 0.8]
    lat_min = 0
    lat_max = 60
    lon_min = 77
    lon_max = 140
    
    '''全球等经纬投影模式使用以下设置，否则使用上面的对应设置
    width = 3000
    height = 1500
    rect = [0, 0, 1, 1]
    lat_min = -90
    lat_max = 90
    lon_min = 0
    lon_max = 360
    '''
    
    handles = [
            matplotlib.patches.Patch(color='#ffaa85', alpha=1, linewidth=0),
            matplotlib.patches.Patch(color='#ff7b69', alpha=1, linewidth=0),
            matplotlib.patches.Patch(color='#bf2121', alpha=1, linewidth=0),
            matplotlib.patches.Patch(color='#7f1818', alpha=1, linewidth=0),
]
    labels = [ '1-9人', '10-99人', '100-999人', '>1000人']
    
    provincePos = {
        "辽宁省":[121.7,40.9],
        "吉林省":[124.5,43.5],
        "黑龙江省":[125.6,46.5],
        "北京市":[116.0,39.9],
        "天津市":[117.0,38.7],
        "内蒙古自治区":[110.0,41.5],
        "宁夏回族自治区":[105.2,37.0],
        "山西省":[111.0,37.0],
        "河北省":[114.0,37.8],
        "山东省":[116.5,36.0],
        "河南省":[111.8,33.5],
        "陕西省":[107.5,33.5],
        "湖北省":[111.0,30.5],
        "江苏省":[119.2,32.5],
        "安徽省":[115.5,31.8],
        "上海市":[121.0,31.0],
        "湖南省":[110.3,27.0],
        "江西省":[114.0,27.0],
        "浙江省":[118.8,28.5],
        "福建省":[116.2,25.5],
        "广东省":[113.2,23.1],
        "台湾省":[120.5,23.5],
        "海南省":[108.0,19.0],
        "广西壮族自治区":[107.3,23.0],
        "重庆市":[106.5,29.5],
        "云南省":[101.0,24.0],
        "贵州省":[106.0,26.5],
        "四川省":[102.0,30.5],
        "甘肃省":[103.0,35.0],
        "青海省":[95.0,35.0],
        "新疆维吾尔自治区":[85.5,42.5],
        "西藏自治区":[85.0,31.5],
        "香港特别行政区":[115.1,21.2],
        "澳门特别行政区":[112.5,21.2]
    }
    
    fig = matplotlib.figure.Figure()
    fig.set_size_inches(width/100, height/100) # 设置绘图板尺寸
    axes = fig.add_axes(rect)
    
    # 兰博托投影模式，局部
    m = Basemap(projection='lcc', llcrnrlon=77, llcrnrlat=14, urcrnrlon=140, urcrnrlat=51, lat_1=33, lat_2=45, lon_0=100, ax=axes)
    
    # 兰博托投影模式，全图
    #m = Basemap(projection='lcc', llcrnrlon=80, llcrnrlat=0, urcrnrlon=140, urcrnrlat=51, lat_1=33, lat_2=45, lon_0=100, ax=axes)
    
    # 圆柱投影模式，局部
    #m = Basemap(llcrnrlon=lon_min, urcrnrlon=lon_max, llcrnrlat=lat_min, urcrnrlat=lat_max, resolution='l', ax=axes)
    
    # 正射投影模式
    #m = Basemap(projection='ortho', lat_0=36, lon_0=102, resolution='l', ax=axes)
	
	# 全球等经纬投影模式，
    #m = Basemap(llcrnrlon=lon_min, urcrnrlon=lon_max, llcrnrlat=lat_min, urcrnrlat=lat_max, resolution='l', ax=axes)
    #m.etopo()
    
    m.readshapefile('res/china-shapefiles-master/china', 'province', drawbounds=True)
    m.readshapefile('res/china-shapefiles-master/china_nine_dotted_line', 'section', drawbounds=True)
    m.drawcoastlines(color='black') # 洲际线
    m.drawcountries(color='black')  # 国界线
    m.drawparallels(np.arange(lat_min,lat_max,10), labels=[1,0,0,0]) #画经度线
    m.drawmeridians(np.arange(lon_min,lon_max,10), labels=[0,0,0,1]) #画纬度线
    
    pset = set()
    for info, shape in zip(m.province_info, m.province):
        pname = info['OWNER'].strip('\x00')
        fcname = info['FCNAME'].strip('\x00')
        if pname != fcname: # 不绘制海岛
            continue
        
        for key in data.keys():
            if key in pname:
                if data[key] == 0:
                    color = '#f0f0f0'
                elif data[key] < 10:
                    color = '#ffaa85'
                elif data[key] <100:
                    color = '#ff7b69'
                elif  data[key] < 1000:
                    color = '#bf2121'
                else:
                    color = '#7f1818'
                break
        
        poly = Polygon(shape, facecolor=color, edgecolor=color)
        axes.add_patch(poly)
        
        pos = provincePos[pname]
        text = pname.replace("自治区", "").replace("特别行政区", "").replace("壮族", "").replace("维吾尔", "").replace("回族", "").replace("省", "").replace("市", "")
        if text not in pset:
            x,  y = m(pos[0], pos[1])
            axes.text(x,  y, text, fontproperties=font_11, color='#00FFFF')
            pset.add(text)
    
    axes.legend(handles, labels, bbox_to_anchor=(0.5, -0.11), loc='lower center', ncol=4, prop=font_14)
    axes.set_title("2019-nCoV疫情地图", fontproperties=font_14)
    FigureCanvasAgg(fig)
    fig.savefig('2019-nCoV疫情地图.png')

if __name__ == '__main__':
    plot_daily()
    plot_distribution()

（5）利用Python爬取新冠肺炎疫情实时数据，Pyecharts画2019-nCoV疫情地图
https://blog.csdn.net/weixin_43130164/article/details/104113559

import time 
import json
import requests
from datetime import datetime
import pandas as pd 
import numpy as np 
def catch_data():
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
    reponse = requests.get(url=url).json()
    #返回数据字典
    data = json.loads(reponse['data'])
    return data
data = catch_data()
data.keys()
dict_keys(['chinaTotal', 'chinaAdd', 'lastUpdateTime', 'areaTree', 'chinaDayList', 'chinaDayAddList'])

# 数据集包括["国内总量","国内新增","更新时间","数据明细","每日数据","每日新增"]
lastUpdateTime = data['lastUpdateTime']
chinaTotal = data['chinaTotal']
chinaAdd = data['chinaAdd']
print(chinaTotal)
print(chinaAdd)

{'confirm': 17238, 'suspect': 21558, 'dead': 361, 'heal': 475}
{'confirm': 2858, 'suspect': 2014, 'dead': 57, 'heal': 147}

#国内数据处理1
# 数据明细，数据结构比较复杂，一步一步打印出来看，先明白数据结构
areaTree = data['areaTree']
# 国内数据
china_data = areaTree[0]['children']
china_list = []
for a in range(len(china_data)):
    province = china_data[a]['name']
    province_list = china_data[a]['children']
    for b in range(len(province_list)):
        city = province_list[b]['name']
        total = province_list[b]['total']
        today = province_list[b]['today']
        china_dict = {}
        china_dict['province'] = province
        china_dict['city'] = city
        china_dict['total'] = total
        china_dict['today'] = today
        china_list.append(china_dict)
        
china_data = pd.DataFrame(china_list)
china_data.head()

#国内数据处理2
# 定义数据处理函数
def confirm(x):
    confirm = eval(str(x))['confirm']
    return confirm
def suspect(x):
    suspect = eval(str(x))['suspect']
    return suspect
def dead(x):
    dead = eval(str(x))['dead']
    return dead
def heal(x):
    heal =  eval(str(x))['heal']
    return heal
# 函数映射
china_data['confirm'] = china_data['total'].map(confirm)
china_data['suspect'] = china_data['total'].map(suspect)
china_data['dead'] = china_data['total'].map(dead)
china_data['heal'] = china_data['total'].map(heal)
china_data['addconfirm'] = china_data['today'].map(confirm)
china_data['addsuspect'] = china_data['today'].map(suspect)
china_data['adddead'] = china_data['today'].map(dead)
china_data['addheal'] = china_data['today'].map(heal)
china_data = china_data[["province","city","confirm","suspect","dead","heal","addconfirm","addsuspect","adddead","addheal"]]
china_data.head()

#国际数据处理
global_data = pd.DataFrame(data['areaTree'])
global_data['confirm'] = global_data['total'].map(confirm)
global_data['suspect'] = global_data['total'].map(suspect)
global_data['dead'] = global_data['total'].map(dead)
global_data['heal'] = global_data['total'].map(heal)
global_data['addconfirm'] = global_data['today'].map(confirm)
global_data['addsuspect'] = global_data['today'].map(suspect)
global_data['adddead'] = global_data['today'].map(dead)
global_data['addheal'] = global_data['today'].map(heal)
world_name = pd.read_excel("世界各国中英文对照.xlsx")
global_data = pd.merge(global_data,world_name,left_on ="name",right_on = "中文",how="inner")
global_data = global_data[["name","英文","confirm","suspect","dead","heal","addconfirm","addsuspect","adddead","addheal"]]
global_data.head()

#日数据处理
chinaDayList = pd.DataFrame(data['chinaDayList'])
chinaDayList = chinaDayList[['date','confirm','suspect','dead','heal']]
chinaDayList.head()

#日新增数据处理
chinaDayAddList = pd.DataFrame(data['chinaDayAddList'])
chinaDayAddList = chinaDayAddList[['date','confirm','suspect','dead','heal']]
chinaDayAddList.head()

#数据可视化
#总数据明细
from pyecharts.charts import * #导入所有图表
from pyecharts import options as opts
#导入pyecharts的主题（如果不使用可以跳过）
from pyecharts.globals import ThemeType
total_pie = Pie(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS,width = '500px',height ='350px'))  #设置主题，和画布大小
total_pie.add("",[list(z) for z in zip(chinaTotal.keys(), chinaTotal.values())],
            center=["50%", "50%"], #图的位置
            radius=[50, 80])   #内外径大小
total_pie.set_global_opts(
            title_opts=opts.TitleOpts(title="全国总量",subtitle=("截止"+lastUpdateTime)))
total_pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{c}"))  #标签格式
total_pie.render_notebook()
totaladd_pie = Pie(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS,width = '500px',height ='350px'))  #设置主题，和画布大小
totaladd_pie.add("",[list(z) for z in zip(chinaAdd.keys(), chinaAdd.values())],
            center=["50%", "50%"],
            radius=[50, 80])
totaladd_pie.set_global_opts(
            title_opts=opts.TitleOpts(title="昨日新增"))
totaladd_pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{c}"))  #标签格式
totaladd_pie.render_notebook()

#全球疫情热图
world_map = Map(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS))
world_map.add("",[list(z) for z in zip(list(global_data["英文"]), list(global_data["confirm"]))], "world",is_map_symbol_show=False)
world_map.set_global_opts(title_opts=opts.TitleOpts(title="2019_nCoV-世界疫情地图"),
                          visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
                          pieces = [
                        {"min": 101 , "label": '>100'}, #不指定 max，表示 max 为无限大
                        {"min": 10, "max": 100, "label": '10-100'},
                        {"min": 0, "max": 9, "label": '0-9' }]))
world_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
world_map.render_notebook()

#中国疫情地图热图绘制
#数据处理
area_data = china_data.groupby("province")["confirm"].sum().reset_index()
area_data.columns = ["province","confirm"]
area_map = Map(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS))
area_map.add("",[list(z) for z in zip(list(area_data["province"]), list(area_data["confirm"]))], "china",is_map_symbol_show=False)
area_map.set_global_opts(title_opts=opts.TitleOpts(title="2019_nCoV中国疫情地图"),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
                pieces = [
                        {"min": 1001 , "label": '>1000',"color": "#893448"}, #不指定 max，表示 max 为无限大
                        {"min": 500, "max": 1000, "label": '500-1000',"color": "#ff585e"},
                        {"min": 101, "max": 499, "label": '101-499',"color": "#fb8146"},
                        {"min": 10, "max": 100, "label": '10-100',"color": "#ffb248"},
                        {"min": 0, "max": 9, "label": '0-9',"color" : "#fff2d1" }]))
area_map.render_notebook()

#绘制每日数据趋势
#每日累计数据趋势
line1 = Line(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS))
line1.add_xaxis(list(chinaDayList["date"]))
line1.add_yaxis("治愈",list(chinaDayList["heal"]),is_smooth=True)
line1.add_yaxis("死亡", list(chinaDayList["dead"]),is_smooth=True)
line1.set_global_opts(title_opts=opts.TitleOpts(title="Line1-治愈与死亡趋势"))
line1.render_notebook()
#每日累计确诊趋势
line2 = Line(init_opts=opts.InitOpts(theme=ThemeType.SHINE))
line2.add_xaxis(list(chinaDayList["date"]))
line2.add_yaxis("确诊",list(chinaDayList["confirm"]))
line2.add_yaxis("疑似", list(chinaDayList["suspect"]))
line2.set_global_opts(title_opts=opts.TitleOpts(title="Line2-确诊与疑似趋势"))
line2.render_notebook()
#每日新增
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.WESTEROS,width = '900px',height ='400px'))
bar .add_xaxis(list(chinaDayAddList["date"]))
bar .add_yaxis("确诊", list(chinaDayAddList["confirm"]))
bar .add_yaxis("疑似", list(chinaDayAddList["suspect"]))
bar .add_yaxis("死亡", list(chinaDayAddList["dead"]))
bar .add_yaxis("治愈", list(chinaDayAddList["heal"]))
bar .set_global_opts(title_opts=opts.TitleOpts(title="每日新增数据趋势"))
bar.render_notebook()

#图片汇总
page = Page()
page.add(total_pie)
page.add(totaladd_pie)
page.add(world_map)
page.add(area_map)
page.add(line1)
page.add(line2)
page.add(bar)
page.render("2019_nCoV 可视化.html")