from pyquery import PyQuery as pq from bs4 import BeautifulSoup import datetime import numpy as np import re import requests import time, json, requests import jsonpath from pyecharts.charts import Map import pyecharts.options as opts #%% shenname="浙江" # 全国疫情地区分布(各省确诊病例) def catch_cn_disease_dis(): timestamp = ‘%d‘%int(time.time()*1000) url_area = (‘https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5‘ ‘&callback=&_=‘) + timestamp world_data = json.loads(requests.get(url=url_area).json()[‘data‘]) china_data = jsonpath.jsonpath(world_data, expr=‘$.areaTree[0].children[*]‘) ls_province_names = jsonpath.jsonpath(china_data, expr=‘$[*].name‘) ls_confirm_vals = jsonpath.jsonpath(china_data, expr=‘$[*].total.confirm‘) ls_province_confirm = list(zip(ls_province_names, ls_confirm_vals,)) return ls_province_confirm, world_data #%% ls_province_cfm, dic_world_data = catch_cn_disease_dis() print(ls_province_cfm) #%% # 绘制全国疫情地图 def map_cn_disease_dis() -> Map: c = ( Map() .add(‘中国‘, ls_province_cfm, ‘china‘) .set_global_opts( title_opts=opts.TitleOpts(title=‘全国新型冠状病毒疫情地图（确诊数）‘), visualmap_opts=opts.VisualMapOpts(is_show=True, split_number=7, is_piecewise=True, # 是否为分段型 pos_top=‘center‘, pieces=[ {‘min‘: 50000, ‘color‘: ‘#7f1818‘}, #不指定 max {‘min‘: 2000, ‘max‘: 4999}, {‘min‘: 1000, ‘max‘: 1999}, {‘min‘: 500, ‘max‘: 999}, {‘min‘: 100, ‘max‘: 499}, {‘min‘: 10, ‘max‘: 99}, {‘min‘: 0, ‘max‘: 9} ], ), ) ) return c # 获取河南省确诊分布数据 def catch_fj_disease_dis(): dic_world_data = catch_cn_disease_dis()[1] dic_fj_cfm = dict() # 解析河南各个城市的数据 dic_fj = jsonpath.jsonpath(dic_world_data, expr=‘$.areaTree[0].children[?(@.name=="‘+shenname+‘")].children[*]‘) for item in dic_fj: if item[‘name‘] not in dic_fj_cfm: dic_fj_cfm.update({item[‘name‘]: 0}) dic_fj_cfm[item[‘name‘]] += int(item[‘total‘][‘confirm‘]) return dic_fj_cfm dic_fj_cfm = catch_fj_disease_dis() # %% # 绘制河南省疫情地图 def map_fj_disease_dis() -> Map: # dic_fj_cfm = catch_fj_disease_dis ls_fj_cities = [name + ‘市‘ for name in dic_fj_cfm.keys()] c = ( Map() .add(shenname, [list(z) for z in zip(ls_fj_cities, dic_fj_cfm.values())], shenname) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, formatter=‘{b}\n{c}例‘)) .set_global_opts( title_opts=opts.TitleOpts(title=shenname+‘新型冠状病毒疫情地图（确诊数）‘), visualmap_opts=opts.VisualMapOpts(is_show=True, split_number=5, is_piecewise=True, # 是否为分段型 pos_top=‘center‘, pieces=[ {‘min‘: 100}, {‘min‘: 50, ‘max‘: 100}, {‘min‘: 20, ‘max‘: 49}, {‘min‘: 10, ‘max‘: 19}, {‘min‘: 1, ‘max‘: 9}, {‘value‘: 0, "label": ‘无确诊病例‘, "color": ‘green‘}], ), ) ) return c map_fj_disease_dis().render(shenname+‘疫情地图.html‘) map_cn_disease_dis().render("全国疫情地图.html")
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from pyquery import PyQuery as pq from bs4 import BeautifulSoup import datetime import numpy as np import re import requests import time, json, requests import jsonpath from pyecharts.charts import Map import pyecharts.options as opts #%% shenname="湖北" # 全国疫情地区分布(各省确诊病例) def catch_cn_disease_dis(): timestamp = ‘%d‘%int(time.time()*1000) url_area = (‘https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5‘ ‘&callback=&_=‘) + timestamp world_data = json.loads(requests.get(url=url_area).json()[‘data‘]) china_data = jsonpath.jsonpath(world_data, expr=‘$.areaTree[0].children[*]‘) ls_province_names = jsonpath.jsonpath(china_data, expr=‘$[*].name‘) ls_confirm_vals = jsonpath.jsonpath(china_data, expr=‘$[*].total.confirm‘) ls_province_confirm = list(zip(ls_province_names, ls_confirm_vals,)) return ls_province_confirm, world_data #%% ls_province_cfm, dic_world_data = catch_cn_disease_dis() print(ls_province_cfm) #%% # 绘制全国疫情地图 def map_cn_disease_dis() -> Map: c = ( Map() .add(‘中国‘, ls_province_cfm, ‘china‘) .set_global_opts( title_opts=opts.TitleOpts(title=‘全国新型冠状病毒疫情地图（确诊数）‘), visualmap_opts=opts.VisualMapOpts(is_show=True, split_number=7, is_piecewise=True, # 是否为分段型 pos_top=‘center‘, pieces=[ {‘min‘: 50000, ‘color‘: ‘#7f1818‘}, #不指定 max {‘min‘: 2000, ‘max‘: 4999}, {‘min‘: 1000, ‘max‘: 1999}, {‘min‘: 500, ‘max‘: 999}, {‘min‘: 100, ‘max‘: 499}, {‘min‘: 10, ‘max‘: 99}, {‘min‘: 0, ‘max‘: 9} ], ), ) ) return c # 获取河南省确诊分布数据 def catch_fj_disease_dis(): dic_world_data = catch_cn_disease_dis()[1] dic_fj_cfm = dict() # 解析河南各个城市的数据 dic_fj = jsonpath.jsonpath(dic_world_data, expr=‘$.areaTree[0].children[?(@.name=="‘+shenname+‘")].children[*]‘) for item in dic_fj: if item[‘name‘] not in dic_fj_cfm: dic_fj_cfm.update({item[‘name‘]: 0}) dic_fj_cfm[item[‘name‘]] += int(item[‘total‘][‘confirm‘]) return dic_fj_cfm dic_fj_cfm = catch_fj_disease_dis() # %% # 绘制河南省疫情地图 def map_fj_disease_dis() -> Map: # dic_fj_cfm = catch_fj_disease_dis ls_fj_cities = [name + ‘市‘ for name in dic_fj_cfm.keys()] c = ( Map() .add(shenname, [list(z) for z in zip(ls_fj_cities, dic_fj_cfm.values())], shenname) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, formatter=‘{b}\n{c}例‘)) .set_global_opts( title_opts=opts.TitleOpts(title=shenname+‘新型冠状病毒疫情地图（确诊数）‘), visualmap_opts=opts.VisualMapOpts(is_show=True, split_number=7, is_piecewise=True, # 是否为分段型 pos_top=‘center‘, pieces=[ {‘min‘: 5000}, {‘min‘: 1000, ‘max‘: 4999}, {‘min‘: 100, ‘max‘: 999}, {‘min‘: 50, ‘max‘: 99}, {‘min‘: 20, ‘max‘: 49}, {‘min‘: 10, ‘max‘: 19}, {‘min‘: 1, ‘max‘: 9}, {‘value‘: 0, "label": ‘无确诊病例‘, "color": ‘green‘}], ), ) ) return c map_fj_disease_dis().render(shenname+‘疫情地图.html‘) map_cn_disease_dis().render("全国疫情地图.html")
python pyecharts 用Python制作疫情的实时数据地图（PS：全国以及每个省（转)
原文：https://www.cnblogs.com/gisoracle/p/12293122.html