We collect the real-time data released by Tencent through the requests library; because the data we get is in json format, we also need the json library to analyze the data; the linked list data can be standardized easily using the pandas Library
import requests import json import pandas as pd # Tencent data interface obtains json format epidemic data def get_ncp_data(): url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5' data = requests.get(url).json()['data'] return data # Flattening China's epidemic data def flatten_ncp_data(): all = json.loads(get_ncp_data()) # Initialization result list cities = [] # Latest data update time date = all['lastUpdateTime'] # Level 1: Country china = all['areaTree'][0]['children'] # Access to China data # Second level: Province for province in china: province_ncp = province['children'] # Third floor: City for city in province_ncp: # Output format city_ncp = { 'date': date, 'Province': province['name'], 'city': city['name'], 'New confirmation': city['today']['confirm'], 'New cure': city['today']['heal'], 'New death': city['today']['dead'], 'Cumulative acknowledgement': city['total']['confirm'], 'Cumulative cure': city['total']['heal'], 'Cumulative death': city['total']['dead'] } cities.append(city_ncp) return cities
Call the function - you can get the data - and the result is as follows:
data =get_ncp_data() cities = flatten_ncp_data()
[{'date': '2020-02-13 16:35:45', 'Province': 'Hubei', 'city': 'Wuhan', 'New confirmation': 13436, 'New cure': 0, 'New death': 0, 'Cumulative acknowledgement': 32994, 'Cumulative cure': 1915, 'Cumulative death': 1036}, {'date': '2020-02-13 16:35:45', 'Province': 'Hubei', 'city': 'Xiaogan', 'New confirmation': 123, 'New cure': 0, 'New death': 0, 'Cumulative acknowledgement': 2874, 'Cumulative cure': 207, 'Cumulative death': 49}, .......
Here, use the pyecharts library to draw a real-time epidemic map:
import datetime from pyecharts import options as opts from pyecharts.charts import Map # Rendering visualization map def render_map_chart(): cities = flatten_ncp_data() df = pd.DataFrame(cities) map_chart = Map() map_chart.add( "Whole country NCP Distribution of confirmed cases", [list(z) for z in zip(list(df["Province"]), list(df['Cumulative acknowledgement']))], "china", is_map_symbol_show=False ) map_chart.set_global_opts( title_opts=opts.TitleOpts( title="NCP Epidemic map(" + str(datetime.date.today()) + ")" ), visualmap_opts=opts.VisualMapOpts( is_piecewise=True, pieces=[ {"min": 1, "max": 9, "label": "1-9 people", "color": "#FFE6BE"}, {"min": 10, "max": 99, "label": "10-99 people", "color": "#FFB769"}, {"min": 100, "max": 499, "label": "100-499 people", "color": "#FF8F66"}, {"min": 500, "max": 999, "label": "500-999 people", "color": "#ED514E"}, {"min": 1000, "max": 9999, "label": "1000-9999 people", "color": "#CA0D11"}, {"min": 10000, "max": 100000, "label": "10000 Above human", "color": "#A52A2A"} ])) map_chart.render('ncp_map_chart_{}.html'.format(datetime.date.today())) render_map_chart()
After calling the function, you can get the following webpage, as shown in the screenshot:
I hope the Chinese war "epidemic" can come to a successful end. I wish everyone good health! The country is prosperous and the people are at peace
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