# 如何将json数据解析成我们所熟悉的Python数据类型？
import json
# 将json格式的str转化成python的数据格式：字典
dic = json.loads('{"name":"Tom","age":23}')
res = json.loads('["name","age","gender"]')
print(f'利用loads将json字符串转化成Python数据类型{dic}',type(dic))
print(f'利用loads将json字符串转化成Python数据类型{res}',type(res))

dics = {"name":"Tom","age":23}
result = json.dumps(dics)
print(type(result))
result

import requests
import json
# 国内疫情数据
China_url = 'https://view.inews.qq.com/g2/getOnsInfo"text-align: center">

2.将json格式的数据保存到Excel


        无论是json数据存储的，还是Python的基本数据类型存储的，对于数据分析都不是很友好，所以我们可以将其数据存储类型转化为pandas的DataFrame类型，因为DataFrame和Excel可以更好的相互转换。
生成的数据模式如下：

将以上的数据进行处理，获得Excel表一样规范的数据格式。


import pandas as pd
chinaTotalData = pd.DataFrame(china_citylist)

# 将整体数据chinaTotalData中的today和total数据添加到DataFrame中
# 处理total字典里面的各个数据项
# ======================================================================
confirmlist = []
suspectlist = []
deadlist = []
heallist = []
deadRatelist = []
healRatelist = []
# print(chinaTotalData['total'].values.tolist()[0])
for value in chinaTotalData['total'].values.tolist():
 confirmlist.append(value['confirm'])
 suspectlist.append(value['suspect'])
 deadlist.append(value['dead'])
 heallist.append(value['heal'])
 deadRatelist.append(value['deadRate'])
 healRatelist.append(value['healRate'])

chinaTotalData['confirm'] = confirmlist
chinaTotalData['suspect'] = suspectlist
chinaTotalData['dead'] = deadlist
chinaTotalData['heal'] = heallist
chinaTotalData['deadRate'] = deadRatelist
chinaTotalData['healRate'] = healRatelist
# ===================================================================
# 创建全国today数据
today_confirmlist = []
today_confirmCutslist = []
for value in chinaTotalData['today'].values.tolist():
 today_confirmlist.append(value['confirm'])
 today_confirmCutslist.append(value['confirmCuts'])

chinaTotalData['today_confirm'] = today_confirmlist
chinaTotalData['today_confirmCuts'] = today_confirmCutslist
# ==================================================================
# 删除total、today两列
chinaTotalData.drop(['total','today'],axis=1,inplace=True)
chinaTotalData.head()
# 将其保存到Excel中
chinaTotalData.to_excel('2021-02-03国内疫情.xlsx',index=False)


处理好的数据结构如下表：

3.应用pyecharts进行数据可视化


        pyecharts是一款将python与echarts结合的强大的数据可视化工具。绘制出来的图比Python的Matplotlib简单美观。使用之前需要在Python环境中按照pycharts。在终端中输入命令：pip install pyecharts
利用pyecharts绘制疫情地图

        根据上面的疫情数据，我们可以利用其画出全国的疫情地图

在绘制前，我们需要安装echarts的地图包（可根据不同的地图需求进行安装）


pip install echarts-countries-pypkg
pip install echarts-china-provinces-pypkg
pip install echarts-china-cities-pypkg
pip install echarts-china-misc-pypkg
pip install echarts-china-countries-pypkg
pip install echarts-united-kingdom-pypkg





# 导入对应的绘图工具包
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Map

df = pd.read_excel('./2021-02-03国内疫情.xlsx')
# 1.根据绘制国内总疫情图（确诊）
data = df.groupby(by='province',as_index=False).sum()
data_list = list(zip(data['province'].values.tolist(),data['confirm'].values.tolist()))
# 数据格式[(黑龙江,200),(吉林,300),...]

def map_china() -> Map:
 c = (
  Map()
  .add(series_name="确诊病例",data_pair=data_list,maptype='china')
  .set_global_opts(
   title_opts = opts.TitleOpts(title='疫情地图'),
   visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
     pieces = [{"max":9, "min":0, "label":"0-9","color":"#FFE4E1"},
       {"max":99, "min":10, "label":"10-99","color":"#FF7F50"},
       {"max":499, "min":100, "label":"100-4999","color":"#F08080"},
       {"max":999, "min":500, "label":"500-999","color":"#CD5C5C"},
       {"max":9999, "min":1000, "label":"1000-9999","color":"#990000"},
       {"max":99999, "min":10000, "label":"10000-99999","color":"#660000"},]
   )
  )
 )
 return c

d_map = map_china()
d_map.render("mapEchrts.html")


最终的运行效果如下：

注：以上的运行环境是Python3.7版本，IDE是基于浏览器端的Jupter Notebook。
以上就是Python实现疫情地图可视化的详细内容，更多关于python 疫情地图可视化的资料请关注其它相关文章！