疫情数据分析

1、数据集说明

这是一份来自 Johns Hopkins University 在github 开源的全球新冠肺炎 COVID-19 数据集，每日时间序列汇总，包括确诊、死亡和治愈。所有数据来自每日病例报告。数据持续更新中。

由于数据集中没有美国的治愈数据，所以在统计全球的现有确诊人员和治愈率的时候会有很大误差，代码里面先不做这个处理，期待数据集的完善。

2、数据清洗import pandas as pd confirmed_data = pd.read_csv('time_series_covid19_confirmed_global.csv')deaths_data = pd.read_csv('time_series_covid19_deaths_global.csv')recovered_data = pd.read_csv('time_series_covid19_recovered_global.csv')

In [2]:

# 美国的名称格式化confirmed_data['Country/Region']=confirmed_data['Country/Region'].apply(lambda x: 'United States' if x == 'US' else x)deaths_data['Country/Region']=deaths_data['Country/Region'].apply(lambda x: 'United States' if x == 'US' else x)recovered_data['Country/Region']=recovered_data['Country/Region'].apply(lambda x: 'United States' if x == 'US' else x)# 将台湾的数据归到中国idx = confirmed_data[confirmed_data['Country/Region'] == 'Taiwan*'].index[0]confirmed_data.loc[idx, 'Province/State'] = 'Taiwan'confirmed_data.loc[idx, 'Country/Region'] = 'China'idx = deaths_data[deaths_data['Country/Region'] == 'Taiwan*'].index[0]deaths_data.loc[idx, 'Province/State'] = 'Taiwan'deaths_data.loc[idx, 'Country/Region'] = 'China'idx = recovered_data[recovered_data['Country/Region'] == 'Taiwan*'].index[0]recovered_data.loc[idx, 'Province/State'] = 'Taiwan'recovered_data.loc[idx, 'Country/Region'] = 'China'

In [3]:

# 增加 Country/Region 和 Province/State 的中文冗余列 Country/Region_zh 、Province/State_zhcountry_map = { 'Singapore Rep.': '新加坡', 'Dominican Rep.': '多米尼加', 'Palestine': '巴勒斯坦', 'Bahamas': '巴哈马', 'Timor-Leste': '东帝汶', 'Afghanistan': '阿富汗', 'Guinea-Bissau': '几内亚比绍', "Côte d'Ivoire": '科特迪瓦', 'Siachen Glacier': '锡亚琴冰川', "Br. Indian Ocean Ter.": '英属印度洋领土', 'Angola': '安哥拉', 'Albania': '阿尔巴尼亚', 'United Arab Emirates': '阿联酋', 'Argentina': '阿根廷', 'Armenia': '亚美尼亚', 'French Southern and Antarctic Lands': '法属南半球和南极领地', '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', 'The 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', 'Brunei': '文莱', 'Bhutan': '不丹', 'Botswana': '博茨瓦纳', 'Central African Rep.': '中非', 'Canada': '加拿大', 'Switzerland': '瑞士', 'Chile': '智利', 'China': '中国', 'Ivory Coast': '象牙海岸', 'Cameroon': '喀麦隆', 'Dem. Rep. Congo': '刚果民主共和国', 'Congo': '刚果', 'Colombia': '哥伦比亚', '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', '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', 'Falkland Islands': '福克兰群岛', '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', 'Greenland': '格陵兰', 'Guatemala': '危地马拉', 'French Guiana': '法属圭亚那', 'Guyana': '圭亚那', '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', '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', 'Kazakhstan': '哈萨克斯坦', 'Kenya': '肯尼亚', 'Kyrgyzstan': '吉尔吉斯斯坦', 'Cambodia': '柬埔寨', 'Korea': '韩国', 'Kosovo': '科索沃', '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', 'Morocco': '摩洛哥', 'Moldova': '摩尔多瓦', 'Madagascar': '马达加斯加', 'Mexico': '墨西哥', 'Macedonia': '马其顿', 'Mali': '马里', 'Myanmar': '缅甸', 'Montenegro': '黑山', 'Mongolia': '蒙古', 'Mozambique': '莫桑比克', 'Mauritania': '毛里塔尼亚', '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', 'Qatar': '卡塔尔', 'Romania': '罗马尼亚', 'Russia': '俄罗斯', 'Rwanda': '卢旺达', 'W. Sahara': '西撒哈拉', 'Saudi Arabia': '沙特阿拉伯', 'Sudan': '苏丹', 'S. Sudan': '南苏丹', 'Senegal': '塞内加尔', 'Solomon Is.': '所罗门群岛', 'Sierra Leone': '塞拉利昂', 'El Salvador': '萨尔瓦多', 'Somaliland': '索马里兰', 'Somalia': '索马里', 'Serbia': '塞尔维亚', 'Suriname': '苏里南', 'Slovakia': '斯洛伐克', 'Slovenia': '斯洛文尼亚', 'Sweden': '瑞典', 'Swaziland': '斯威士兰', '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', 'East Timor': '东帝汶', 'Trinidad and Tobago': '特里尼达和多巴哥', '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', 'West Bank': '西岸', 'Yemen': '也门', 'South Africa': '南非', 'Zambia': '赞比亚', 'Zimbabwe': '津巴布韦', 'Comoros': '科摩罗'}province_map = { 'Anhui': '安徽', 'Beijing': '北京', 'Chongqing': '重庆', 'Fujian': '新疆', 'Gansu': '甘肃', 'Guangdong': '广东', 'Guangxi': '广西', 'Guizhou': '贵州', 'Hainan': '海南', 'Hebei': '河北', 'Heilongjiang': '黑龙江', 'Henan': '河南', 'Hong Kong': '香港', 'Hubei': '湖北', 'Hunan': '湖南', 'Inner Mongolia': '内蒙古', 'Jiangsu': '江苏', 'Jiangxi': '江西', 'Jilin': '吉林', 'Liaoning': '辽宁', 'Macau': '澳门', 'Ningxia': '宁夏', 'Qinghai': '青海', 'Shaanxi': '陕西', 'Shandong': '山东', 'Shanghai': '上海', 'Shanxi': '山西', 'Sichuan': '四川', 'Tianjin': '天津', 'Tibet': '西藏', 'Xinjiang': '新疆', 'Yunnan': '云南', 'Zhejiang': '浙江', 'Fujian':'福建', 'Taiwan': '台湾'}confirmed_data['Country/Region_zh'] = confirmed_data['Country/Region'].apply(lambda x: country_map.get(x, x))deaths_data['Country/Region_zh'] = deaths_data['Country/Region'].apply(lambda x: country_map.get(x, x))recovered_data['Country/Region_zh'] = recovered_data['Country/Region'].apply(lambda x: country_map.get(x, x))confirmed_data['Province/State_zh'] = confirmed_data['Province/State'].apply(lambda x: province_map.get(x, x))deaths_data['Province/State_zh'] = deaths_data['Province/State'].apply(lambda x: province_map.get(x, x))recovered_data['Province/State_zh'] = recovered_data['Province/State'].apply(lambda x: province_map.get(x, x))# 调整字段顺序confirmed_data = confirmed_data[['Province/State_zh', 'Country/Region_zh'] + confirmed_data.columns[:-2].to_list()]deaths_data = deaths_data[['Province/State_zh', 'Country/Region_zh'] + deaths_data.columns[:-2].to_list()]recovered_data = recovered_data[['Province/State_zh', 'Country/Region_zh'] + recovered_data.columns[:-2].to_list()]3、数据分析可视化3.1 全球新冠疫情情况3.1.1 全球疫情现状

In [4]:

from pyecharts import options as optsfrom pyecharts.charts import Map, Timeline, Bar, Linefrom pyecharts.components import Tablefrom pyecharts.options import ComponentTitleOptslastdate = confirmed_data.columns[-1]confirmed_total = confirmed_data[lastdate].sum()deaths_total = deaths_data[lastdate].sum()recovered_total = recovered_data[lastdate].sum()deaths_rate = deaths_total / confirmed_totalrecovered_rate = recovered_total / confirmed_totaltable = Table()headers = ['确诊人数', '死亡人数', '治愈人数', '死亡率', '治愈率']rows = [ [confirmed_total, deaths_total, recovered_total, f'{deaths_rate:.2%}', f'{recovered_rate:.2%}'], ]table.add(headers, rows)table.set_global_opts( title_opts=ComponentTitleOpts(title=f'({lastdate})全球疫情情况', subtitle='由于数据集没有美国的治愈数据，所以治愈人数和治愈率都远低于实际，等待数据集完善'))table.render_notebook()

Out[4]:

(3/20/21)全球疫情情况

由于数据集没有美国的治愈数据，所以治愈人数和治愈率都远低于实际，等待数据集完善

确诊人数

死亡人数

治愈人数

死亡率

治愈率

122813796

2709639

69523087

2.21%

56.61%

In [6]:

confirmed = confirmed_data.groupby('Country/Region').agg({lastdate: 'sum'}).to_dict()[lastdate]deaths = deaths_data.groupby('Country/Region').agg({lastdate: 'sum'}).to_dict()[lastdate]recovered = recovered_data.groupby('Country/Region').agg({lastdate: 'sum'}).to_dict()[lastdate]exists_confirmed = {key: value - deaths[key] - recovered[key] for key, value in confirmed.items()}c = ( Map() .add("确诊人数", [*confirmed.items()], "world", is_map_symbol_show=False) .add("治愈人数", [*recovered.items()], "world", is_map_symbol_show=False) .add("死亡人数", [*deaths.items()], "world", is_map_symbol_show=False) .add("现有确诊人数", [*exists_confirmed.items()], "world", is_map_symbol_show=False) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title=f'({lastdate})全球疫情现状', subtitle='由于数据源没有美国的治愈数据,\n所以美国的现有确诊人数并不准确'), visualmap_opts=opts.VisualMapOpts(max_=200000), ))c.render_notebook()

Out[6]:

3.1.2 全球疫情历史发展情况

In [7]:

tl = Timeline()tl.add_schema(# is_auto_play=True, is_loop_play=False, play_interval=200, )target = confirmed_data.columns[6:].to_list()target.reverse()target = target[::7]target.reverse()for dt in target: confirmed = confirmed_data.groupby('Country/Region').agg({dt: 'sum'}).to_dict()[dt] c = ( Map() .add("确诊人数", [*confirmed.items()], "world", is_map_symbol_show=False) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title="全球疫情历史发展情况"), visualmap_opts=opts.VisualMapOpts(max_=200000), ) ) tl.add(c, dt)tl.render_notebook()

Out[7]:

3.1.3 各国确诊人数 TOP20 排行

In [8]:

tl = Timeline()tl.add_schema(# is_auto_play=True, is_loop_play=False, play_interval=100, )for dt in confirmed_data.columns[6:]: confirmed = confirmed_data.groupby('Country/Region_zh').agg({dt: 'sum'}).sort_values(by=dt, ascending=False)[:20].sort_values(by=dt).to_dict()[dt] bar = ( Bar() .add_xaxis([*confirmed.keys()]) .add_yaxis("确诊人数", [*confirmed.values()], label_opts=opts.LabelOpts(position="right")) .reversal_axis() .set_global_opts( title_opts=opts.TitleOpts("各国确诊人数排行 TOP20") ) ) tl.add(bar, dt)tl.render_notebook()

Out[8]:

3.1.4 全球疫情趋势

In [9]:

targets = confirmed_data.columns[6:].to_list()new_confirmed_list = []new_deaths_list = []new_recovered_list = []exists_confirmed_list = []for idx, today in enumerate(targets[1:], 1): yesterday = targets[idx-1] new_confirmed = confirmed_data[today].sum() - confirmed_data[yesterday].sum() new_deaths = deaths_data[today].sum() - deaths_data[yesterday].sum() new_recovered = recovered_data[today].sum() - recovered_data[yesterday].sum() exists_confirmed = confirmed_data[today].sum() - deaths_data[today].sum() - recovered_data[today].sum() new_confirmed_list.append(int(new_confirmed)) new_deaths_list.append(int(new_deaths)) new_recovered_list.append(int(new_recovered)) # 由于数据集中没有美国的治愈数据，所以在统计全球的现有确诊人员和治愈率的时候会有很大误差，代码里面先不做这个处理，期待数据集的完善 exists_confirmed_list.append(int(exists_confirmed))c = ( Line() .add_xaxis(targets[1:]) .add_yaxis('新增确诊人数', new_confirmed_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .add_yaxis('新增治愈人数', new_recovered_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .add_yaxis('新增死亡人数', new_deaths_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .add_yaxis('现有确诊人数', exists_confirmed_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .set_global_opts(title_opts=opts.TitleOpts(title="全球疫情趋势")))c.render_notebook()

Out[9]:

3.2 中国新冠疫情情况3.2.1 中国疫情现状

In [10]:

from pyecharts import options as optsfrom pyecharts.charts import Map, Timeline, Bar, Linefrom pyecharts.components import Tablefrom pyecharts.options import ComponentTitleOptslastdate = confirmed_data.columns[-1]confirmed_data_china = confirmed_data[confirmed_data['Country/Region'] == 'China']deaths_data_china = deaths_data[deaths_data['Country/Region'] == 'China']recovered_data_china = recovered_data[recovered_data['Country/Region'] == 'China']confirmed_total_china = confirmed_data_china[lastdate].sum()deaths_total_china = deaths_data_china[lastdate].sum()recovered_total_china = recovered_data_china[lastdate].sum()exists_confirmed_china = confirmed_total_china - deaths_total_china - recovered_total_chinadeaths_rate_china = deaths_total_china / confirmed_total_chinarecovered_rate_china = recovered_total_china / confirmed_total_chinatable = Table()headers = ['确诊人数', '死亡人数', '治愈人数', '死亡率', '治愈率', '现有确诊人数']rows = [ [confirmed_total_china, deaths_total_china, recovered_total_china, f'{deaths_rate_china:.2%}', f'{recovered_rate_china:.2%}', exists_confirmed_china], ]table.add(headers, rows)table.set_global_opts( title_opts=ComponentTitleOpts(title=f'({lastdate})中国疫情情况'))table.render_notebook()

Out[10]:

(3/20/21)中国疫情情况

确诊人数

死亡人数

治愈人数

死亡率

治愈率

现有确诊人数

102523

4849

97167

4.73%

94.78%

507

In [11]:

confirmed = confirmed_data_china.groupby('Province/State_zh').agg({lastdate: 'sum'}).to_dict()[lastdate]deaths = deaths_data_china.groupby('Province/State_zh').agg({lastdate: 'sum'}).to_dict()[lastdate]recovered = recovered_data_china.groupby('Province/State_zh').agg({lastdate: 'sum'}).to_dict()[lastdate]exists_confirmed = {key: value - deaths[key] - recovered[key] for key, value in confirmed.items()}c = ( Map() .add("确诊人数", [*confirmed.items()], "china", is_map_symbol_show=False) .add("治愈人数", [*recovered.items()], "china", is_map_symbol_show=False) .add("死亡人数", [*deaths.items()], "china", is_map_symbol_show=False) .add("现有确诊人数", [*exists_confirmed.items()], "china", is_map_symbol_show=False) .set_series_opts(label_opts=opts.LabelOpts(is_show=True)) .set_global_opts( title_opts=opts.TitleOpts(title=f'({lastdate})中国疫情现状'), visualmap_opts=opts.VisualMapOpts(max_=1000), ))c.render_notebook()

Out[11]:

3.2.2 中国疫情历史发展情况

In [12]:

tl = Timeline()tl.add_schema(# is_auto_play=True, is_loop_play=False, play_interval=200, )target = confirmed_data_china.columns[6:].to_list()target.reverse()target = target[::7]target.reverse()for dt in target: confirmed = confirmed_data_china.groupby('Province/State_zh').agg({dt: 'sum'}).to_dict()[dt] c = ( Map() .add("确诊人数", [*confirmed.items()], "china", is_map_symbol_show=False) .set_series_opts(label_opts=opts.LabelOpts(is_show=True)) .set_global_opts( title_opts=opts.TitleOpts(title='中国疫情历史发展情况'), visualmap_opts=opts.VisualMapOpts(max_=1000), ) ) tl.add(c, dt)tl.render_notebook()

Out[12]:

3.2.3 各省确诊人数排行 TOP20

In [13]:

tl = Timeline()tl.add_schema(# is_auto_play=True, is_loop_play=False, play_interval=100, )for dt in confirmed_data.columns[6:]: confirmed = confirmed_data_china.groupby('Province/State_zh').agg({dt: 'sum'}).sort_values(by=dt, ascending=False)[:20].sort_values(by=dt).to_dict()[dt] bar = ( Bar() .add_xaxis([*confirmed.keys()]) .add_yaxis("确诊人数", [*confirmed.values()], label_opts=opts.LabelOpts(position="right")) .reversal_axis() .set_global_opts( title_opts=opts.TitleOpts("各省确诊人数排行 TOP20") ) ) tl.add(bar, dt)tl.render_notebook()

Out[13]:

3.2.4 中国疫情趋势

In [14]:

targets = confirmed_data_china.columns[6:].to_list()new_confirmed_list = []new_deaths_list = []new_recovered_list = []exists_confirmed_list = []for idx, today in enumerate(targets[1:], 1): yesterday = targets[idx-1] new_confirmed = confirmed_data_china[today].sum() - confirmed_data_china[yesterday].sum() new_deaths = deaths_data_china[today].sum() - deaths_data_china[yesterday].sum() new_recovered = recovered_data_china[today].sum() - recovered_data_china[yesterday].sum() exists_confirmed = confirmed_data_china[today].sum() - deaths_data_china[today].sum() - recovered_data_china[today].sum() new_confirmed_list.append(int(new_confirmed)) new_deaths_list.append(int(new_deaths)) new_recovered_list.append(int(new_recovered)) exists_confirmed_list.append(int(exists_confirmed))c = ( Line() .add_xaxis(targets[1:]) .add_yaxis('新增确诊人数', new_confirmed_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .add_yaxis('新增治愈人数', new_recovered_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .add_yaxis('新增死亡人数', new_deaths_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .add_yaxis('现有确诊人数', exists_confirmed_list, label_opts=opts.LabelOpts(is_show=False), is_symbol_show=False) .set_global_opts(title_opts=opts.TitleOpts(title="中国疫情趋势")))c.render_notebook()

Out[14]: