Python如何绘制全球疫情变化地图

def get_and_save_all_countries():
 """
 获取所有的国家名以及对应的id，保存为文件
 """

 url = 'https://c.m.163.com/ug/api/wuhan/app/data/list-total?t=317452696323'
 list_total_req = requests.get(url, headers=headers)
 if list_total_req.status_code == 200:
 area_tree = list_total_req.json()['data']['areaTree']

 area_dict = {}
 for area in area_tree:
  country_id = area['id']
  name = area['name']
  area_dict[country_id] = name

 area_json = json.dumps(area_dict, ensure_ascii=False) # ensure_ascii=False 防止json编码后中文编程\u开头的字符
 write_file('./config/countries_id2name.json', area_json)

def get_cy_properties():
 # 获取配置文件信息
 countries_id2name = read_file('./config/countries_id2name.json')
 cy_id2name_dict = json.loads(countries_id2name)
 cy_ch3en = {v: k for k, v in countries_dict.items()}

 # 调整国家的名字与配置文件一致
 cy_id2name_dict['879'] = '波斯尼亚和黑塞哥维那'
 cy_id2name_dict['8102'] = '多哥'
 cy_id2name_dict['8143'] = '刚果民主共和国'
 cy_id2name_dict['95983'] = '刚果'
 cy_id2name_dict['8144'] = '中非'
 cy_id2name_dict['95000011'] = '多米尼加'

 cy_props = {}
 for key in cy_id2name_dict:
 cy_name = cy_id2name_dict[key]
 if cy_name in cy_ch3en:
  cy_props[cy_name] = {}
  cy_props[cy_name]['id'] = key
  cy_props[cy_name]['en_name'] = cy_ch3en[cy_name]

 return cy_props

def parse_ncov_data(start_date, end_date, records):
 if not records:
  return

 date_list = get_date_range(start_date, end_date)
 cy_name_list = []
 res = {}
 # 获取各国每天现有确认病例
 for i, record in enumerate(records):
  cy_name = record['cy_en_name']
  cy_name_list.append(cy_name)

  # 解析每天数据并计算现有确认病例
  existing_case_dict = {}
  for ncov_daily in record['data']['list']:
   date_str = ncov_daily['date']
   confirm = ncov_daily['total']['confirm'] # 累计确诊
   heal = ncov_daily['total']['heal'] # 累计确诊
   dead = ncov_daily['total']['dead'] # 累计死亡

   existing_case = confirm - heal - dead
   existing_case_dict[date_str] = existing_case

  last_existing_case = 0
  # 将每天确诊病例数合并到res中
  for date_str in date_list:
   if date_str not in res: # 初始化
    res[date_str] = []

   existing_case = existing_case_dict.get(date_str)
   if existing_case is None:
    existing_case = last_existing_case
   res[date_str].append(existing_case)

   last_existing_case = existing_case

 return date_list, cy_name_list, res

def render_map(date_list, cy_name_list, ncov_data):
 tl = Timeline() # 创建时间线轮播多图，可以让图形按照输入的时间动起来
 # is_auto_play：自动播放
 # play_interval：播放时间间隔，单位：毫秒
 # is_loop_play：是否循环播放
 tl.add_schema(is_auto_play=True, play_interval=50, is_loop_play=False)

 for date_str in date_list: # 遍历时间列表
  map0 = (
   Map() # 创建地图图表
   # 将国家名 cy_name_list 以及各国当天确诊病例 ncov_data[date_str] 加入地图中
   .add("全球疫情趋势", [list(z) for z in zip(cy_name_list, ncov_data[date_str])], 
    "world", is_map_symbol_show=False)
   .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) # 不显示国家名
   .set_global_opts(
    title_opts=opts.TitleOpts(title="%s日" % date_str), # 图表标题
    visualmap_opts=opts.VisualMapOpts(max_=80), # 当确诊病例大于80 ，地图颜色是红色
   )
  )
  tl.add(map0, "%s" % date_str) # 将当天的地图状态加入时间线中

 tl.render() # 生成最终轮播多图，会在当前目录创建 render.html 文件