The new crown epidemic has been going on for several months. At present, the epidemic in China has been basically controlled, and the European and American countries are in the outbreak period. We will see that many websites provide a variety of epidemic statistics. Today we use Python ’s pyecharts framework to Draw some common statistics.
Rose illustration
First, let's draw the Nightingale Rose Chart, which was relatively hot some time ago. The data source is obtained through the interface `https: // lab.isaaclin.cn / nCoV / zh`. We take the countries with more than 2000 deaths in the epidemic. The data, the implementation code is as follows:
url = 'https://lab.isaaclin.cn/nCoV/api/area'
data_json = requests.get(url).json()
country_list = []
count_list = []
ds = {}
for item in data_json['results']:
if item['countryEnglishName']:
if item['deadCount'] is not None and item['countryName'] is not None:
if int(item['deadCount']) > 2000:
d = {item['countryName']:item['deadCount']}
ds.update(d)
ds = dict(sorted(ds.items(), key = lambda k: k[1]))
# 名称有重复的，把国家名作为 key 吧
country_list = ds.keys()
count_list = ds.values()
# 随机颜色生成
def randomcolor(kind):
colors = []
for i in range(kind):
colArr = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F']
color = ""
for i in range(6):
color += colArr[random.randint(0, 14)]
colors.append("#" + color)
return colors
color_series = randomcolor(len(count_list))
# 创建饼图
pie = Pie(init_opts=opts.InitOpts(width='800px', height='900px'))
# 添加数据
pie.add("", [list(z) for z in zip(country_list, count_list)],
radius=['20%', '100%'],
center=['60%', '65%'],
rosetype='area')
# 设置全局配置
# pie.set_global_opts(title_opts=opts.TitleOpts(title='南丁格尔玫瑰图'),
# legend_opts=opts.LegendOpts(is_show=False))
# 设置全局配置项
pie.set_global_opts(title_opts=opts.TitleOpts(title='全球新冠疫情',subtitle='死亡人数超过\n2000 的国家',
title_textstyle_opts=opts.TextStyleOpts(font_size=15,color= '#0085c3'),
subtitle_textstyle_opts= opts.TextStyleOpts(font_size=15,color= '#003399'),
pos_right= 'center',pos_left= '53%',pos_top= '62%',pos_bottom='center'
),
legend_opts=opts.LegendOpts(is_show=False))
# 设置系列配置和颜色
pie.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=12,
formatter='{b}：{c}', font_style='italic',
font_family='Microsoft YaHei'))
pie.set_colors(color_series)
pie.render('南丁格尔玫瑰图.html')
Look at the renderings:
Global Epidemic Map
Next, let's draw a map of the global epidemic situation. We take the data of the cumulative number of deaths in various countries. The code implementation is as follows:
url = 'https://lab.isaaclin.cn/nCoV/api/area'
data = requests.get(url).json()
oversea_confirm = []
for item in data['results']:
if item['countryEnglishName']:
oversea_confirm.append((item['countryEnglishName']
.replace('United States of America', 'United States')
.replace('United Kiongdom', 'United Kingdom'),
item['deadCount']))
world_map = (
Map(init_opts=opts.InitOpts(theme='dark'))
.add('累计死亡人数', oversea_confirm, 'world',is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False, color='#fff'))
.set_global_opts(
title_opts=opts.TitleOpts(title='全球疫情累计死亡人数地图'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=2700,
is_piecewise=True,
pieces=[
{"max": 99999, "min": 10000, "label": "10000人及以上", "color": "#8A0808"},
{"max": 9999, "min": 1000, "label": "1000-9999人", "color": "#B40404"},
{"max": 999, "min": 500, "label": "500-999人", "color": "#DF0101"},
{"max": 499, "min": 100, "label": "100-499人", "color": "#F78181"},
{"max": 99, "min": 10, "label": "10-99人", "color": "#F5A9A9"},
{"max": 9, "min": 0, "label": "1-9人", "color": "#FFFFCC"},
])
)
)
world_map.render(path='全球疫情地图.html')
Look at the renderings:
China epidemic map
We then draw a map of the epidemic situation in China. The data is based on the cumulative number of people diagnosed in each province. The code implementation is as follows:
url = 'https://lab.isaaclin.cn/nCoV/api/area'
data = requests.get(url).json()
province_data = []
for item in data['results']:
if item['countryName'] == '中国':
province_data.append((item['provinceShortName'], item['confirmedCount']))
china_map = (
Map(init_opts=opts.InitOpts(theme='dark'))
.add('确诊人数', province_data, 'china',is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True, color='#ffffff'))
.set_global_opts(
title_opts=opts.TitleOpts(title="中国疫情累计确诊人数地图"),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=2000,
is_piecewise=True,
pieces=[
{"max": 9999, "min": 1000, "label": "1000-9999人", "color": "#B40404"},
{"max": 999, "min": 500, "label": "500-999人", "color": "#DF0101"},
{"max": 499, "min": 100, "label": "100-499人", "color": "#F78181"},
{"max": 99, "min": 10, "label": "10-99人", "color": "#F5A9A9"},
{"max": 9, "min": 0, "label": "1-9人", "color": "#FFFFCC"},
])
)
)
china_map.render(path='中国疫情地图.html')
Look at the renderings:
Heat map
Let's continue to draw the heat map. We still take the data from various provinces in China. The implementation code is as follows:
url = 'https://lab.isaaclin.cn/nCoV/api/area'
data = requests.get(url).json()
cities_data = []
for item in data['results']:
if item['countryName'] == '中国':
if item['cities'] is not None:
cities_data.extend(item['cities'])
hot_geo = (
Geo(init_opts=opts.InitOpts(theme='dark'))
.add_schema(maptype='china')
.add('累计确诊人数',
[(i['cityName'], i['currentConfirmedCount']) for i in cities_data
if i['cityName'] in pyecharts.datasets.COORDINATES.keys()],
type_='heatmap')
.set_global_opts(
title_opts=opts.TitleOpts(title='中国疫情热力图',
pos_left='left'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True,
is_piecewise=False,
range_color=['#0ff', '#0f0', '#ff0', '#f00'])
)
)
hot_geo.render(path='中国疫情热力图.html')
Look at the renderings:
Histogram
Let's next draw a histogram. This time we take data from a province. Because Hubei Province has the largest number of diagnoses, let's use the data from this province. The implementation code is as follows:
url = 'https://lab.isaaclin.cn/nCoV/api/area'
data = requests.get(url).json()
for item in data['results']:
if item['provinceShortName'] == '湖北':
hb_data = item['cities']
hb_bar = (
Bar(init_opts=opts.InitOpts(theme='dark'))
.add_xaxis([hd['cityName'] for hd in hb_data])
.add_yaxis('累计确诊人数', [hd['confirmedCount'] for hd in hb_data])
.add_yaxis('累计治愈人数', [hd['curedCount'] for hd in hb_data])
.reversal_axis()
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="湖北新冠疫情确诊及治愈情况"),
legend_opts=opts.LegendOpts(is_show=True)
)
)
hb_bar.render(path='湖北新冠疫情图.html')
Look at the renderings:
line chart
At present, the above interface does not provide data that returns time series, but a data warehouse is provided on GitHub. There are two formats of JSON and CSV. Due to the poor speed of GitHub, I failed to download the data several times , So let's use the data displayed on WeChat directly. The code implementation is as follows:
x_data = ['2-06', '2-13', '2-20', '2-27', '3-05', '3-12', '3-19', '3-26', '4-02', '4-09', '4-17']
# 现有确诊
y1_data = [20677, 46537, 49156, 36829, 22695, 13171, 6287, 2896, 987, 351, 122]
# 累计治愈
y2_data = [817, 4131, 11788, 26403, 41966, 51533, 58381, 61731, 63612, 64236, 63494]
line = (Line()
.add_xaxis(x_data)
.add_yaxis('现有确诊', y1_data, color='#10aeb5')
.add_yaxis('累计治愈', y2_data, color='#e83132')
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
title_opts=opts.TitleOpts(title='中国疫情随时间变化趋势')
))
line.render(path='中国疫情折线图.html')
Look at the renderings:
If you need the source code, you can scan the QR code below and reply to the background in 200418 .