Visualized by pyEcharts

Introducing respective packet

from pyecharts import options as opts
from pyecharts.charts import Map
import requests, json

Obtain the appropriate information epidemic

How to explain the meaning of crawling information and corresponding information can refer to my other article "Data crawling pneumonia epidemic" , the definition of variables remain the same, not repeat them here.

url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
area = requests.get(url).json()
data = json.loads(area['data'])

# 全球的疫情数量
all_counties = data['areaTree']

Data packets

list = []
all_provinces = all_counties[0]['children']
for i in range(len(all_provinces)):
    city_name = all_provinces[i]
    list.append((city_name['name'],city_name['total']['confirm']))

Visualization

pyecharts library is used to generate a graph Echarts. Echarts Baidu is a open source data visualization JS library. Personally recommended pyechats for visualization.

pyecharts Quick Start can refer to this website

c = (
    Map()
    .add(" ",list,"china")
    .set_global_opts(title_opts = opts.TitleOpts(title = "中国肺炎确诊分布图"),
    visualmap_opts=opts.VisualMapOpts(  
        is_piecewise=True,  # 设置为分段
        pieces=[
        {"max":9, "min":1, "label": "1-9人"},
        {"max":99, "min":10, "label": "10-99人"},
        {"max":499, "min":100, "label": "100-499人"},
        {"max":999, "min":500, "label": "500-999人"},
        {"max":9999, "min":1000, "label": "1000-9999人"},
        {"max":99999, "min":10000, "label": "10000人以上"},
        ])
        )
    )

# c.render('map.html')
c.render_notebook() # 随时随地渲染图表

The results show

The complete code

from pyecharts import options as opts
from pyecharts.charts import Map
import requests, json


def get_data():
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
    area = requests.get(url).json()
    data = json.loads(area['data'])
    all_counties = data['areaTree']
    list = []
    all_provinces = all_counties[0]['children']
    for i in range(len(all_provinces)):
        city_name = all_provinces[i]
        list.append((city_name['name'],city_name['total']['confirm']))
    

def visualize():    
    c = (
        Map()
        .add(" ",list,"china")
        .set_global_opts(title_opts = opts.TitleOpts(title = "中国肺炎确诊分布图"),
        visualmap_opts=opts.VisualMapOpts(  
            is_piecewise=True,  # 设置为分段
            pieces=[
            {"max":9, "min":1, "label": "1-9人"},
            {"max":99, "min":10, "label": "10-99人"},
            {"max":499, "min":100, "label": "100-499人"},
            {"max":999, "min":500, "label": "500-999人"},
            {"max":9999, "min":1000, "label": "1000-9999人"},
            {"max":99999, "min":10000, "label": "10000人以上"},
            ])
            )
        )
    c.render('map.html')
    
def main():
    get_data()
    visualize()
    
    
if __name__ == '__main__':
    main()