pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-countries-pypkg
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-provinces-pypkg
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-cities-pypkg
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-counties-pypkg
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-china-misc-pypkg
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ echarts-united-kingdom-pypkg

from pyecharts.charts import Bar
bar = Bar()
bar.add_xaxis(['小嘉','小琪','大嘉琪','小嘉琪'])
bar.add_yaxis('得票数',[60,60,70,100])
#render会生成本地HTML文件，默认在当前目录生成render.html
# bar.render()
#可以传入路径参数，如 bar.render("mycharts.html")
#可以将图形在jupyter中输出，如 bar.render_notebook()
bar.render_notebook()

from pyecharts.charts import Bar
from pyecharts import options as opts

# 示例数据
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data1 = [123, 153, 89, 107, 98, 23]
data2 = [56, 77, 93, 68, 45, 67]

# 1.x版本支持链式调用
bar = (Bar()
    .add_xaxis(cate)
    .add_yaxis('渠道', data1)
    .add_yaxis('门店', data2)
    .set_global_opts(title_opts=opts.TitleOpts(title="示例", subtitle="副标"))
   )
bar.render_notebook()

from pyecharts.charts import Pie
from pyecharts import options as opts

# 示例数据
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data = [153, 124, 107, 99, 89, 46]

pie = (Pie()
    .add('', [list(z) for z in zip(cate, data)],
      radius=["30%", "75%"],
      rosetype="radius")
    .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例", subtitle="我是副标题"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
   )

pie.render_notebook()

from pyecharts.charts import Line
from pyecharts import options as opts

# 示例数据
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data1 = [123, 153, 89, 107, 98, 23]
data2 = [56, 77, 93, 68, 45, 67]

"""
折线图示例:
1. is_smooth 折线 OR 平滑
2. markline_opts 标记线 OR 标记点
"""
line = (Line()
    .add_xaxis(cate)
    .add_yaxis('电商渠道', data1, 
         markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]))
    .add_yaxis('门店', data2, 
         is_smooth=True, 
         markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="自定义标记点", 
                                       coord=[cate[2], data2[2]], value=data2[2])]))
    .set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例", subtitle="我是副标题"))
   )

line.render_notebook()

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType
import random

province = ['福州市', '莆田市', '泉州市', '厦门市', '漳州市', '龙岩市', '三明市', '南平']
data = [(i, random.randint(200, 550)) for i in province]

geo = (Geo()
    .add_schema(maptype="福建")
    .add("门店数", data,
      type_=ChartType.HEATMAP)
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
      visualmap_opts=opts.VisualMapOpts(),
      legend_opts=opts.LegendOpts(is_show=False),
      title_opts=opts.TitleOpts(title="福建热力地图"))
   )

geo.render_notebook()

# 获取数据
import requests
import json
china_url = 'https://view.inews.qq.com/g2/getOnsInfo"text-align: center">



# 将json数据转存到Excel中
import pandas as pd
#读取文件
with open('./国内疫情.json',encoding='utf-8') as f:
  data = f.read()
  
#将数据转为python数据格式
data = json.loads(data)
type(data)#字典类型
lastUpdateTime = data['lastUpdateTime']
#获取中国所有数据
chinaAreaDict = data['areaTree'][0]
#获取省级数据
provinceList = chinaAreaDict['children']
# 获取的数据有几个省市和地区
print('数据共有：',len(provinceList),'省市和地区')
#将中国数据按城市封装，例如【{湖北，武汉}，{湖北，襄阳}】，为了方便放在dataframe中
china_citylist = []
for x in range(len(provinceList)):
  # 每一个省份的数据
  province =provinceList[x]['name']
  #有多少个市
  province_list = provinceList[x]['children']
  
  for y in range(len(province_list)):
    # 每一个市的数据
    city = province_list[y]['name']
    # 累积所有的数据
    total = province_list[y]['total']
    # 今日的数据
    today = province_list[y]['today']
    china_dict = {'省份':province,
           '城市':city,
           'total':total,
           'today':today
           }
    china_citylist.append(china_dict)


chinaTotaldata = pd.DataFrame(china_citylist)
nowconfirmlist=[]
confirmlist=[]
suspectlist=[]
deadlist=[]
heallist=[]
deadRatelist=[]
healRatelist=[]

# 将整体数据chinaTotaldata的数据添加dataframe
for value in chinaTotaldata['total'] .values.tolist():#转成列表
  confirmlist.append(value['confirm'])
  suspectlist.append(value['suspect'])
  deadlist.append(value['dead'])
  heallist.append(value['heal'])
  deadRatelist.append(value['deadRate'])
  healRatelist.append(value['healRate'])
  nowconfirmlist.append(value['nowConfirm'])
  
chinaTotaldata['现有确诊']=nowconfirmlist  
chinaTotaldata['累计确诊']=confirmlist
chinaTotaldata['疑似']=suspectlist
chinaTotaldata['死亡']=deadlist
chinaTotaldata['治愈']=heallist
chinaTotaldata['死亡率']=deadRatelist
chinaTotaldata['治愈率']=healRatelist

#拆分today列
today_confirmlist=[]
today_confirmCutlist=[]

for value in chinaTotaldata['today'].values.tolist():
  today_confirmlist.append(value['confirm'])
  today_confirmCutlist.append(value['confirmCuts'])
chinaTotaldata['今日确诊']=today_confirmlist
chinaTotaldata['今日死亡']=today_confirmCutlist

#删除total列 在原有的数据基础
chinaTotaldata.drop(['total','today'],axis=1,inplace=True)

# 将其保存到excel中
from openpyxl import load_workbook
book = load_workbook('国内疫情.xlsx')
# 避免了数据覆盖
writer = pd.ExcelWriter('国内疫情.xlsx',engine='openpyxl')
writer.book = book
writer.sheets = dict((ws.title,ws) for ws in book.worksheets)
chinaTotaldata.to_excel(writer,index=False)
writer.save()
writer.close()

chinaTotaldata












作者这边还有国外的，不过没打算分享出来，大家就看看，总的来说我们国内情况还是非常良好的