如何使用Python爬虫+数据分析对新冠疫情数据实时监控！

【1x00】前言

本来两三个月之前就想搞个疫情数据实时数据展示的，由于各种不可抗拒因素一而再再而三的鸽了，最近终于抽空写了一个，数据是用 Python 爬取的百度疫情实时大数据报告，请求库用的 requests，解析用的 Xpath 语法，词云用的 wordcloud 库，数据可视化用 pyecharts 绘制的地图和折线图，数据储存在 Excel 表格里面，使用 openpyxl 对表格进行处理。

本程序实现了累计确诊地图展示和每日数据变化折线图展示，其他更多数据的获取和展示均可在程序中进行拓展，可以将程序部署在服务器上，设置定时运行，即可实时展示数据，pyecharts 绘图模块也可以整合到 Web 框架（Django、Flask等）中使用。

在获取数据时有全球和境外两个概念，全球包含中国，境外不包含中国，后期绘制的四个图：中国累计确诊地图、全球累计确诊地图（包含中国）、中国每日数据折线图、境外每日数据折线图（不包含中国）。

注意项：直接向该网页发送请求获取的响应中，没有每个国家的每日数据，该数据获取的地址是：https://voice.baidu.com/newpneumonia/get?target=trend&isCaseIn=1&stage=publish

• 预览地址：http://cov.itrhx.com/
• 数据来源：https://voice.baidu.com/act/newpneumonia/newpneumonia/
• pyecharts 文档：https://pyecharts.org/
• openpyxl 文档：https://openpyxl.readthedocs.io/
• wordcloud 文档：http://amueller.github.io/word_cloud/

【2x00】思维导图

【3x00】数据结构分析

通过查看百度的疫情数据页面，可以看到很多整齐的数据，猜测就是疫情相关的数据，保存该页面，对其进行格式化，很容易可以分析出所有的数据都在  里面，其中 title 里面是一些 Unicode 编码，将其转为中文后更容易得到不同的分类数据。

由于数据繁多，可以将数据主体部分提取出来，删除一些重复项和其他杂项，留下数据大体位置并分析数据结构，便于后期的数据提取，经过处理后的数据大致结构如下：

【4x00】主函数 main()

分别将数据获取、词云图绘制、地图绘制写入三个文件：data_get()、data_wordcloud()、data_map()，然后使用一个主函数文件 main.py 来调用这三个文件里面的函数。

import data_get
import data_wordcloud
import data_map

data_dict = data_get.init()
data_get.china_total_data(data_dict)
data_get.global_total_data(data_dict)
data_get.china_daily_data(data_dict)
data_get.foreign_daily_data(data_dict)

data_wordcloud.china_wordcloud()
data_wordcloud.global_wordcloud()

data_map.all_map()

【5x00】数据获取模块 data_get

【5x01】初始化函数 init()

使用 xpath 语法 //script[@id="captain-config"]/text() 提取里面的值，利用 json.loads 方法将其转换为字典对象，以便后续的其他函数调用。

def init():
    headers = {
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.13 Safari/537.36'
    }
    url = 'https://voice.baidu.com/act/newpneumonia/newpneumonia/'
    response = requests.get(url=url, headers=headers)
    tree = etree.HTML(response.text)
    dict1 = tree.xpath('//script[@id="captain-config"]/text()')
    print(type(dict1[0]))
    dict2 = json.loads(dict1[0])
    return dict2

【5x02】中国总数据 china_total_data()

def china_total_data(data):

    """
    1、中国省/直辖市/自治区/行政区疫情数据
    省/直辖市/自治区/行政区：area
    现有确诊：    curConfirm
    累计确诊：    confirmed
    累计治愈：    crued
    累计死亡：    died
    现有确诊增量： curConfirmRelative
    累计确诊增量： confirmedRelative
    累计治愈增量： curedRelative
    累计死亡增量： diedRelative
    """

    wb = openpyxl.Workbook()            # 创建工作簿
    ws_china = wb.active                # 获取工作表
    ws_china.title = "中国省份疫情数据"   # 命名工作表
    ws_china.append(['省/直辖市/自治区/行政区', '现有确诊', '累计确诊', '累计治愈',
                     '累计死亡', '现有确诊增量', '累计确诊增量',
                     '累计治愈增量', '累计死亡增量'])
    china = data['component'][0]['caseList']
    for province in china:
        ws_china.append([province['area'],
                        province['curConfirm'],
                        province['confirmed'],
                        province['crued'],
                        province['died'],
                        province['curConfirmRelative'],
                        province['confirmedRelative'],
                        province['curedRelative'],
                        province['diedRelative']])

    """
    2、中国城市疫情数据
    城市：city
    现有确诊：curConfirm
    累计确诊：confirmed
    累计治愈：crued
    累计死亡：died
    累计确诊增量：confirmedRelative
    """

    ws_city = wb.create_sheet('中国城市疫情数据')
    ws_city.append(['城市', '现有确诊', '累计确诊',
                    '累计治愈', '累计死亡', '累计确诊增量'])
    for province in china:
        for city in province['subList']:
            # 某些城市没有 curConfirm 数据，则将其设置为 0，crued 和 died 为空时，替换成 0
            if 'curConfirm' not in city:
                city['curConfirm'] = '0'
            if city['crued'] == '':
                city['crued'] = '0'
            if city['died'] == '':
                city['died'] = '0'
            ws_city.append([city['city'], '0', city['confirmed'],
                           city['crued'], city['died'], city['confirmedRelative']])

    """
    3、中国疫情数据更新时间：mapLastUpdatedTime
    """

    time_domestic = data['component'][0]['mapLastUpdatedTime']
    ws_time = wb.create_sheet('中国疫情数据更新时间')
    ws_time.column_dimensions['A'].width = 22  # 调整列宽
    ws_time.append(['中国疫情数据更新时间'])
    ws_time.append([time_domestic])

    wb.save('COVID-19-China.xlsx')
    print('中国疫情数据已保存至 COVID-19-China.xlsx！')

【5x03】全球总数据 global_total_data()

全球总数据在提取完成后，进行地图绘制时发现并没有中国的数据，因此在写入全球数据时注意要单独将中国的数据插入 Excel 中。

def global_total_data(data):

    """
    1、全球各国疫情数据
    国家：country
    现有确诊：curConfirm
    累计确诊：confirmed
    累计治愈：crued
    累计死亡：died
    累计确诊增量：confirmedRelative
    """

    wb = openpyxl.Workbook()
    ws_global = wb.active
    ws_global.title = "全球各国疫情数据"

    # 按照国家保存数据
    countries = data['component'][0]['caseOutsideList']
    ws_global.append(['国家', '现有确诊', '累计确诊', '累计治愈', '累计死亡', '累计确诊增量'])
    for country in countries:
        ws_global.append([country['area'],
                          country['curConfirm'],
                          country['confirmed'],
                          country['crued'],
                          country['died'],
                          country['confirmedRelative']])

    # 按照洲保存数据
    continent = data['component'][0]['globalList']
    for area in continent:
        ws_foreign = wb.create_sheet(area['area'] + '疫情数据')
        ws_foreign.append(['国家', '现有确诊', '累计确诊', '累计治愈', '累计死亡', '累计确诊增量'])
        for country in area['subList']:
            ws_foreign.append([country['country'],
                               country['curConfirm'],
                               country['confirmed'],
                               country['crued'],
                               country['died'],
                               country['confirmedRelative']])

    # 在“全球各国疫情数据”和“亚洲疫情数据”两张表中写入中国疫情数据
    ws1, ws2 = wb['全球各国疫情数据'], wb['亚洲疫情数据']
    original_data = data['component'][0]['summaryDataIn']
    add_china_data = ['中国',
                      original_data['curConfirm'],
                      original_data['confirmed'],
                      original_data['cured'],
                      original_data['died'],
                      original_data['confirmedRelative']]
    ws1.append(add_china_data)
    ws2.append(add_china_data)

    """
    2、全球疫情数据更新时间：foreignLastUpdatedTime
    """

    time_foreign = data['component'][0]['foreignLastUpdatedTime']
    ws_time = wb.create_sheet('全球疫情数据更新时间')
    ws_time.column_dimensions['A'].width = 22  # 调整列宽
    ws_time.append(['全球疫情数据更新时间'])
    ws_time.append([time_foreign])

    wb.save('COVID-19-Global.xlsx')
    print('全球疫情数据已保存至 COVID-19-Global.xlsx！')

【5x04】中国每日数据 china_daily_data()

def china_daily_data(data):

    """
    i_dict = data['component'][0]['trend']
    i_dict['updateDate']：日期
    i_dict['list'][0]：确诊
    i_dict['list'][1]：疑似
    i_dict['list'][2]：治愈
    i_dict['list'][3]：死亡
    i_dict['list'][4]：新增确诊
    i_dict['list'][5]：新增疑似
    i_dict['list'][6]：新增治愈
    i_dict['list'][7]：新增死亡
    i_dict['list'][8]：累计境外输入
    i_dict['list'][9]：新增境外输入
    """

    ccd_dict = data['component'][0]['trend']
    update_date = ccd_dict['updateDate']              # 日期
    china_confirmed = ccd_dict['list'][0]['data']     # 每日累计确诊数据
    china_crued = ccd_dict['list'][2]['data']         # 每日累计治愈数据
    china_died = ccd_dict['list'][3]['data']          # 每日累计死亡数据
    wb = openpyxl.load_workbook('COVID-19-China.xlsx')

    # 写入每日累计确诊数据
    ws_china_confirmed = wb.create_sheet('中国每日累计确诊数据')
    ws_china_confirmed.append(['日期', '数据'])
    for data in zip(update_date, china_confirmed):
        ws_china_confirmed.append(data)

    # 写入每日累计治愈数据
    ws_china_crued = wb.create_sheet('中国每日累计治愈数据')
    ws_china_crued.append(['日期', '数据'])
    for data in zip(update_date, china_crued):
        ws_china_crued.append(data)

    # 写入每日累计死亡数据
    ws_china_died = wb.create_sheet('中国每日累计死亡数据')
    ws_china_died.append(['日期', '数据'])
    for data in zip(update_date, china_died):
        ws_china_died.append(data)

    wb.save('COVID-19-China.xlsx')
    print('中国每日累计确诊/治愈/死亡数据已保存至 COVID-19-China.xlsx！')

【5x05】境外每日数据 foreign_daily_data()

def foreign_daily_data(data):

    """
    te_dict = data['component'][0]['allForeignTrend']
    te_dict['updateDate']：日期
    te_dict['list'][0]：累计确诊
    te_dict['list'][1]：治愈
    te_dict['list'][2]：死亡
    te_dict['list'][3]：现有确诊
    te_dict['list'][4]：新增确诊
    """

    te_dict = data['component'][0]['allForeignTrend']
    update_date = te_dict['updateDate']                # 日期
    foreign_confirmed = te_dict['list'][0]['data']     # 每日累计确诊数据
    foreign_crued = te_dict['list'][1]['data']         # 每日累计治愈数据
    foreign_died = te_dict['list'][2]['data']          # 每日累计死亡数据
    wb = openpyxl.load_workbook('COVID-19-Global.xlsx')

    # 写入每日累计确诊数据
    ws_foreign_confirmed = wb.create_sheet('境外每日累计确诊数据')
    ws_foreign_confirmed.append(['日期', '数据'])
    for data in zip(update_date, foreign_confirmed):
        ws_foreign_confirmed.append(data)

    # 写入累计治愈数据
    ws_foreign_crued = wb.create_sheet('境外每日累计治愈数据')
    ws_foreign_crued.append(['日期', '数据'])
    for data in zip(update_date, foreign_crued):
        ws_foreign_crued.append(data)

    # 写入累计死亡数据
    ws_foreign_died = wb.create_sheet('境外每日累计死亡数据')
    ws_foreign_died.append(['日期', '数据'])
    for data in zip(update_date, foreign_died):
        ws_foreign_died.append(data)

    wb.save('COVID-19-Global.xlsx')
    print('境外每日累计确诊/治愈/死亡数据已保存至 COVID-19-Global.xlsx！')

【6x00】词云图绘制模块 data_wordcloud

【6x01】中国累计确诊词云图 foreign_daily_data()

def china_wordcloud():
    wb = openpyxl.load_workbook('COVID-19-China.xlsx')  # 获取已有的xlsx文件
    ws_china = wb['中国省份疫情数据']                     # 获取中国省份疫情数据表
    ws_china.delete_rows(1)                             # 删除第一行
    china_dict = {}                                     # 将省份及其累计确诊按照键值对形式储存在字典中
    for data in ws_china.values:
        china_dict[data[0]] = int(data[2])
    word_cloud = wordcloud.WordCloud(font_path='C:/Windows/Fonts/simsun.ttc',
                                     background_color='#CDC9C9',
                                     min_font_size=15,
                                     width=900, height=500)
    word_cloud.generate_from_frequencies(china_dict)
    word_cloud.to_file('WordCloud-China.png')
    print('中国省份疫情词云图绘制完毕！')

【6x02】全球累计确诊词云图 foreign_daily_data()

def global_wordcloud():
    wb = openpyxl.load_workbook('COVID-19-Global.xlsx')
    ws_global = wb['全球各国疫情数据']
    ws_global.delete_rows(1)
    global_dict = {}
    for data in ws_global.values:
        global_dict[data[0]] = int(data[2])
    word_cloud = wordcloud.WordCloud(font_path='C:/Windows/Fonts/simsun.ttc',
                                     background_color='#CDC9C9',
                                     width=900, height=500)
    word_cloud.generate_from_frequencies(global_dict)
    word_cloud.to_file('WordCloud-Global.png')
    print('全球各国疫情词云图绘制完毕！')

【7x00】地图绘制模块 data_map

【7x01】中国累计确诊地图 china_total_map()

def china_total_map():
    wb = openpyxl.load_workbook('COVID-19-China.xlsx')  # 获取已有的xlsx文件
    ws_time = wb['中国疫情数据更新时间']                   # 获取文件中中国疫情数据更新时间表
    ws_data = wb['中国省份疫情数据']                      # 获取文件中中国省份疫情数据表
    ws_data.delete_rows(1)                              # 删除第一行
    province = []                                       # 省份
    curconfirm = []                                     # 累计确诊
    for data in ws_data.values:
        province.append(data[0])
        curconfirm.append(data[2])
    time_china = ws_time['A2'].value                    # 更新时间

    # 设置分级颜色
    pieces = [
        {'max': 0, 'min': 0, 'label': '0', 'color': '#FFFFFF'},
        {'max': 9, 'min': 1, 'label': '1-9', 'color': '#FFE5DB'},
        {'max': 99, 'min': 10, 'label': '10-99', 'color': '#FF9985'},
        {'max': 999, 'min': 100, 'label': '100-999', 'color': '#F57567'},
        {'max': 9999, 'min': 1000, 'label': '1000-9999', 'color': '#E64546'},
        {'max': 99999, 'min': 10000, 'label': '≧10000', 'color': '#B80909'}
    ]

    # 绘制地图
    ct_map = (
        Map()
        .add(series_name='累计确诊人数', data_pair=[list(z) for z in zip(province, curconfirm)], maptype="china")
        .set_global_opts(
            title_opts=opts.TitleOpts(title="中国疫情数据（累计确诊）",
                                      subtitle='数据更新至：' + time_china + '\n\n来源：百度疫情实时大数据报告'),
            visualmap_opts=opts.VisualMapOpts(max_=300, is_piecewise=True, pieces=pieces)
        )
    )
    return ct_map

【7x02】全球累计确诊地图 global_total_map()

def global_total_map():
    wb = openpyxl.load_workbook('COVID-19-Global.xlsx')
    ws_time = wb['全球疫情数据更新时间']
    ws_data = wb['全球各国疫情数据']
    ws_data.delete_rows(1)
    country = []                        # 国家
    curconfirm = []                     # 累计确诊
    for data in ws_data.values:
        country.append(data[0])
        curconfirm.append(data[2])
    time_global = ws_time['A2'].value   # 更新时间

    # 国家名称中英文映射表
    name_map = {
          "Somalia": "索马里",
          "Liechtenstein": "列支敦士登",
          "Morocco": "摩洛哥",
          "W. Sahara": "西撒哈拉",
          "Serbia": "塞尔维亚",
          "Afghanistan": "阿富汗",
          "Angola": "安哥拉",
          "Albania": "阿尔巴尼亚",
          "Andorra": "安道尔共和国",
          "United Arab Emirates": "阿拉伯联合酋长国",
          "Argentina": "阿根廷",
          "Armenia": "亚美尼亚",
          "Australia": "澳大利亚",
          "Austria": "奥地利",
          "Azerbaijan": "阿塞拜疆",
          "Burundi": "布隆迪",
          "Belgium": "比利时",
          "Benin": "贝宁",
          "Burkina Faso": "布基纳法索",
          "Bangladesh": "孟加拉国",
          "Bulgaria": "保加利亚",
          "Bahrain": "巴林",
          "Bahamas": "巴哈马",
          "Bosnia and Herz.": "波斯尼亚和黑塞哥维那",
          "Belarus": "白俄罗斯",
          "Belize": "伯利兹",
          "Bermuda": "百慕大",
          "Bolivia": "玻利维亚",
          "Brazil": "巴西",
          "Barbados": "巴巴多斯",
          "Brunei": "文莱",
          "Bhutan": "不丹",
          "Botswana": "博茨瓦纳",
          "Central African Rep.": "中非共和国",
          "Canada": "加拿大",
          "Switzerland": "瑞士",
          "Chile": "智利",
          "China": "中国",
          "Côte d'Ivoire": "科特迪瓦",
          "Cameroon": "喀麦隆",
          "Dem. Rep. Congo": "刚果（布）",
          "Congo": "刚果（金）",
          "Colombia": "哥伦比亚",
          "Cape Verde": "佛得角",
          "Costa Rica": "哥斯达黎加",
          "Cuba": "古巴",
          "N. Cyprus": "北塞浦路斯",
          "Cyprus": "塞浦路斯",
          "Czech Rep.": "捷克",
          "Germany": "德国",
          "Djibouti": "吉布提",
          "Denmark": "丹麦",
          "Dominican Rep.": "多米尼加",
          "Algeria": "阿尔及利亚",
          "Ecuador": "厄瓜多尔",
          "Egypt": "埃及",
          "Eritrea": "厄立特里亚",
          "Spain": "西班牙",
          "Estonia": "爱沙尼亚",
          "Ethiopia": "埃塞俄比亚",
          "Finland": "芬兰",
          "Fiji": "斐济",
          "France": "法国",
          "Gabon": "加蓬",
          "United Kingdom": "英国",
          "Georgia": "格鲁吉亚",
          "Ghana": "加纳",
          "Guinea": "几内亚",
          "Gambia": "冈比亚",
          "Guinea-Bissau": "几内亚比绍",
          "Eq. Guinea": "赤道几内亚",
          "Greece": "希腊",
          "Grenada": "格林纳达",
          "Greenland": "格陵兰岛",
          "Guatemala": "危地马拉",
          "Guam": "关岛",
          "Guyana": "圭亚那合作共和国",
          "Honduras": "洪都拉斯",
          "Croatia": "克罗地亚",
          "Haiti": "海地",
          "Hungary": "匈牙利",
          "Indonesia": "印度尼西亚",
          "India": "印度",
          "Br. Indian Ocean Ter.": "英属印度洋领土",
          "Ireland": "爱尔兰",
          "Iran": "伊朗",
          "Iraq": "伊拉克",
          "Iceland": "冰岛",
          "Israel": "以色列",
          "Italy": "意大利",
          "Jamaica": "牙买加",
          "Jordan": "约旦",
          "Japan": "日本",
          "Siachen Glacier": "锡亚琴冰川",
          "Kazakhstan": "哈萨克斯坦",
          "Kenya": "肯尼亚",
          "Kyrgyzstan": "吉尔吉斯斯坦",
          "Cambodia": "柬埔寨",
          "Korea": "韩国",
          "Kuwait": "科威特",
          "Lao PDR": "老挝",
          "Lebanon": "黎巴嫩",
          "Liberia": "利比里亚",
          "Libya": "利比亚",
          "Sri Lanka": "斯里兰卡",
          "Lesotho": "莱索托",
          "Lithuania": "立陶宛",
          "Luxembourg": "卢森堡",
          "Latvia": "拉脱维亚",
          "Moldova": "摩尔多瓦",
          "Madagascar": "马达加斯加",
          "Mexico": "墨西哥",
          "Macedonia": "马其顿",
          "Mali": "马里",
          "Malta": "马耳他",
          "Myanmar": "缅甸",
          "Montenegro": "黑山",
          "Mongolia": "蒙古国",
          "Mozambique": "莫桑比克",
          "Mauritania": "毛里塔尼亚",
          "Mauritius": "毛里求斯",
          "Malawi": "马拉维",
          "Malaysia": "马来西亚",
          "Namibia": "纳米比亚",
          "New Caledonia": "新喀里多尼亚",
          "Niger": "尼日尔",
          "Nigeria": "尼日利亚",
          "Nicaragua": "尼加拉瓜",
          "Netherlands": "荷兰",
          "Norway": "挪威",
          "Nepal": "尼泊尔",
          "New Zealand": "新西兰",
          "Oman": "阿曼",
          "Pakistan": "巴基斯坦",
          "Panama": "巴拿马",
          "Peru": "秘鲁",
          "Philippines": "菲律宾",
          "Papua New Guinea": "巴布亚新几内亚",
          "Poland": "波兰",
          "Puerto Rico": "波多黎各",
          "Dem. Rep. Korea": "朝鲜",
          "Portugal": "葡萄牙",
          "Paraguay": "巴拉圭",
          "Palestine": "巴勒斯坦",
          "Qatar": "卡塔尔",
          "Romania": "罗马尼亚",
          "Russia": "俄罗斯",
          "Rwanda": "卢旺达",
          "Saudi Arabia": "沙特阿拉伯",
          "Sudan": "苏丹",
          "S. Sudan": "南苏丹",
          "Senegal": "塞内加尔",
          "Singapore": "新加坡",
          "Solomon Is.": "所罗门群岛",
          "Sierra Leone": "塞拉利昂",
          "El Salvador": "萨尔瓦多",
          "Suriname": "苏里南",
          "Slovakia": "斯洛伐克",
          "Slovenia": "斯洛文尼亚",
          "Sweden": "瑞典",
          "Swaziland": "斯威士兰",
          "Seychelles": "塞舌尔",
          "Syria": "叙利亚",
          "Chad": "乍得",
          "Togo": "多哥",
          "Thailand": "泰国",
          "Tajikistan": "塔吉克斯坦",
          "Turkmenistan": "土库曼斯坦",
          "Timor-Leste": "东帝汶",
          "Tonga": "汤加",
          "Trinidad and Tobago": "特立尼达和多巴哥",
          "Tunisia": "突尼斯",
          "Turkey": "土耳其",
          "Tanzania": "坦桑尼亚",
          "Uganda": "乌干达",
          "Ukraine": "乌克兰",
          "Uruguay": "乌拉圭",
          "United States": "美国",
          "Uzbekistan": "乌兹别克斯坦",
          "Venezuela": "委内瑞拉",
          "Vietnam": "越南",
          "Vanuatu": "瓦努阿图",
          "Yemen": "也门",
          "South Africa": "南非",
          "Zambia": "赞比亚",
          "Zimbabwe": "津巴布韦",
          "Aland": "奥兰群岛",
          "American Samoa": "美属萨摩亚",
          "Fr. S. Antarctic Lands": "南极洲",
          "Antigua and Barb.": "安提瓜和巴布达",
          "Comoros": "科摩罗",
          "Curaçao": "库拉索岛",
          "Cayman Is.": "开曼群岛",
          "Dominica": "多米尼加",
          "Falkland Is.": "福克兰群岛马尔维纳斯",
          "Faeroe Is.": "法罗群岛",
          "Micronesia": "密克罗尼西亚",
          "Heard I. and McDonald Is.": "赫德岛和麦克唐纳群岛",
          "Isle of Man": "曼岛",
          "Jersey": "泽西岛",
          "Kiribati": "基里巴斯",
          "Saint Lucia": "圣卢西亚",
          "N. Mariana Is.": "北马里亚纳群岛",
          "Montserrat": "蒙特塞拉特",
          "Niue": "纽埃",
          "Palau": "帕劳",
          "Fr. Polynesia": "法属波利尼西亚",
          "S. Geo. and S. Sandw. Is.": "南乔治亚岛和南桑威奇群岛",
          "Saint Helena": "圣赫勒拿",
          "St. Pierre and Miquelon": "圣皮埃尔和密克隆群岛",
          "São Tomé and Principe": "圣多美和普林西比",
          "Turks and Caicos Is.": "特克斯和凯科斯群岛",
          "St. Vin. and Gren.": "圣文森特和格林纳丁斯",
          "U.S. Virgin Is.": "美属维尔京群岛",
          "Samoa": "萨摩亚"
        }

    pieces = [
        {'max': 0, 'min': 0, 'label': '0', 'color': '#FFFFFF'},
        {'max': 49, 'min': 1, 'label': '1-49', 'color': '#FFE5DB'},
        {'max': 99, 'min': 50, 'label': '50-99', 'color': '#FFC4B3'},
        {'max': 999, 'min': 100, 'label': '100-999', 'color': '#FF9985'},
        {'max': 9999, 'min': 1000, 'label': '1000-9999', 'color': '#F57567'},
        {'max': 99999, 'min': 10000, 'label': '10000-99999', 'color': '#E64546'},
        {'max': 999999, 'min': 100000, 'label': '100000-999999', 'color': '#B80909'},
        {'max': 9999999, 'min': 1000000, 'label': '≧1000000', 'color': '#8A0808'}
    ]

    gt_map = (
        Map()
        .add(series_name='累计确诊人数', data_pair=[list(z) for z in zip(country, curconfirm)], maptype="world", name_map=name_map, is_map_symbol_show=False)
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="全球疫情数据（累计确诊）",
                                      subtitle='数据更新至：' + time_global + '\n\n来源：百度疫情实时大数据报告'),
            visualmap_opts=opts.VisualMapOpts(max_=300, is_piecewise=True, pieces=pieces),
        )
    )
    return gt_map

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