import requests
import json
import re
import pandas as pd
import time
from pyecharts.components import Table
from pyecharts import options as opts
from pyecharts.charts import Bar, Page,Pie,Line,Timeline,Map
from pyecharts.commons.utils import JsCode
from pyecharts.options import DataZoomOpts,ComponentTitleOpts
def Get_HTML():
headers = {'User-Agent': 'Mozilla/5.0'}
url = 'https://m.look.360.cn/events/feiyan?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&location=true&sort=2&_=1649252949072&callback=jsonp2'
try:
r = requests.get(url, headers=headers)
r.raise_for_status()
r.encoding = r.apparent_encoding
re = r.text[7:-2]
response = json.loads(re)
return response
except:
print("error!")
def China_data_Total():
url='http://m.look.360.cn/subject/400?sign=360_6aa05217'
headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:99.0) Gecko/20100101 Firefox/99.0'}
html = requests.get(url, headers=headers).text
pattern=re.compile('"qcmsint1":"0"},"feiyanTrend":(.*),"local":',re.S)
item=re.findall(pattern,html)
response=json.loads(item[0])
data = {
"截止日期":response['total']['modifyTime'],
"现存确诊":response['total']['currentConfirmed'],
"境外输入":response['total']['overseasInput'],
"现存无症状":response['total']['asymptom'],
"累计确诊":response['total']['diagnosed'],
"累计治愈":response['total']['cured'],
"累计死亡":response['total']['died'],
"现存_较昨日":response['newDiff']['currentConfirmed'],
"境外输入_较昨日":response['newDiff']['overseasInput'],
"无症状_较昨日":response['newDiff']['asymptom'],
"累计_较昨日":response['newDiff']['diagnosed'],
"治愈_较昨日":response['newDiff']['cured'],
"死亡_较昨日":response['newDiff']['died']
}
return data
# 国内数据
def China_data(response):
P_name, P_Ljqz, P_Cured, P_Died, P_Xcqz, P_Xzqz = [], [], [], [], [], [] #省数据
C_name, C_Ljqz, C_Cured, C_Died, C_Xcqz, C_Xzqz = [], [], [], [], [], [] #市数据
for i in range(34):
P_name.append(response['data'][i]['cityShortName']) # 城市名称
P_Ljqz.append(response['data'][i]['diagnosed']) # 累计确诊
P_Cured.append(response['data'][i]['cured']) # 治愈
P_Died.append(response['data'][i]['died']) # 死亡
P_Xcqz.append(response['data'][i]['currentConfirmed']) # 现存确诊
P_Xzqz.append(response['data'][i]['diffDiagnosed']) # 新增确诊
data = {
"地区": P_name,
"新增确诊": P_Xzqz,
"现存确诊": P_Xcqz,
"累计确诊": P_Ljqz,
"治愈人数": P_Cured,
"死亡人数": P_Died,
}
return data
#河南省数据
def Henan_data():
url = 'https://m.look.360.cn/events/feiyanCityInfo?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&ename=henan&_=1650426134574&callback=jsonp4'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:99.0) Gecko/20100101 Firefox/99.0'}
html = requests.get(url, headers=headers).text[7:-2]
response=json.loads(html)
# print(response['data']['cityInfo'])
C_name, C_Ljqz, C_Cured, C_Died, C_Xcqz, C_Xzqz = [], [], [], [], [], [] # 市数据
for i in range(len(response['data']['cityInfo'])):
C_name.append(response['data']['cityInfo'][i]['cityName']) #名称
C_Ljqz.append(response['data']['cityInfo'][i]['diagnosed']) # 累计确诊
C_Cured.append(response['data']['cityInfo'][i]['cured']) # 治愈
C_Died.append(response['data']['cityInfo'][i]['died']) # 死亡
C_Xzqz.append(response['data']['cityInfo'][i]['diffDiagnosed']) # 新增确诊
data = {
"地区": C_name,
"新增确诊": C_Xzqz,
"累计确诊": C_Ljqz,
"治愈人数": C_Cured,
"死亡人数": C_Died
}
return data
def Abroad_data(response): # 国外数据
C_name, Ljqz, Cured, Died, Xcqz, Xzqz = [], [], [], [], [], []
for i in range(196):
c_name = response['country'][i]['provinceName'] # 城市名称
ljqz = response['country'][i]['diagnosed'] # 累计确诊
cured = response['country'][i]['cured'] # 治愈
died = response['country'][i]['died'] # 死亡
xzqz = response['country'][i]['diffDiagnosed'] # 新增确诊
C_name.append(c_name)
Ljqz.append(ljqz)
Cured.append(cured)
Died.append(died)
Xzqz.append(xzqz)
data = {
"地区": C_name,
"新增确诊": Xzqz,
"累计确诊": Ljqz,
"治愈人数": Cured,
"死亡人数": Died
}
return data
def TimeLine():
headers = {'User-Agent': 'Mozilla/5.0'}
url = 'https://api.look.360.cn/events/feiyanHomeMulTrend?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&_=1650032782170&callback=jsonp3'
try:
r = requests.get(url, headers=headers)
r.raise_for_status()
r.encoding = r.apparent_encoding
re = r.text[7:-2]
response = json.loads(re)
except:
print("error!")
time,Ljqz, Cured, Died, Xcqz, Xzqz, Xz_Died, Xz_Cured= [], [], [], [], [], [], [], []
for i in range(len(response['data']['timeline_n1'])):
time.append(response['data']['timeline_n1'][i]['time']) #时间
Xzqz.append(response['data']['timeline_n1'][i]['diagnosed']) #新增确诊
Xcqz.append(response['data']['timeline_n2'][i]['total_currentConfirmed']) #现存确诊
Ljqz.append(response['data']['timeline_n2'][i]['total_diagnosed']) #累计确诊
Cured.append(response['data']['timeline_n3'][i]['total_cured']) #累计治愈
Died.append(response['data']['timeline_n3'][i]['total_died']) #累计死亡
Xz_Died.append(response['data']['timeline_n4'][i]['died']) #新增死亡
Xz_Cured.append(response['data']['timeline_n4'][i]['cured']) #新增治愈
data={
"时间":time,
"新增确诊":Xzqz,
"现存确诊":Xcqz,
"累计确诊":Ljqz,
"累计治愈":Cured,
"累计死亡":Died,
"新增死亡":Xz_Died,
"新增治愈":Xz_Cured
}
return data
def View(data_1,data_2,data_3,data_4):
#柱状图
c_1 = Timeline(init_opts=opts.InitOpts(chart_id='1'))
c_1.add_schema(play_interval=1000,
label_opts=opts.series_options.LabelOpts(color='#cfe2f3'),is_auto_play = True)
for i in range(3):
#全国累计详情柱状图
C_1 = (
Bar()
.add_xaxis(data_1['地区'][i*11:(i+1)*11])
.add_yaxis("累计确诊", data_1['累计确诊'][i*11:(i+1)*11],stack='stack1',label_opts=opts.LabelOpts(is_show=False))
.add_yaxis("新增确诊", data_1['新增确诊'][i*11:(i+1)*11],stack='stack1',label_opts=opts.LabelOpts(is_show=False))
.add_yaxis("死亡人数", data_1['死亡人数'][i * 11:(i + 1) * 11],stack='stack1',label_opts=opts.LabelOpts(is_show=False))
.add_yaxis("治愈人数", data_1['治愈人数'][i * 11:(i + 1) * 11],stack='stack1',label_opts=opts.LabelOpts(is_show=False))
.add_yaxis("现存确诊", data_1['现存确诊'][i * 11:(i + 1) * 11],stack='stack1',label_opts=opts.LabelOpts(is_show=False))
.reversal_axis()
# .set_global_opts(legend_opts=opts.LegendOpts(selected_mode='single')) # 单选模式
.set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white')),#调整x轴字体颜色
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white')),
legend_opts=opts.LegendOpts(selected_mode='single'),
title_opts=opts.TitleOpts("国内疫情详情",pos_right=0,pos_top=20,title_textstyle_opts=opts.TextStyleOpts(font_size=13,color="#cfe2f3")))
)
c_1.add(C_1, "第{}页".format(i+1))
# 全国地图
c_2 = (
Map(init_opts=opts.InitOpts(chart_id='2'))
# .add("死亡人数", [list(z) for z in zip(data_1['地区'], data_1['死亡人数'])], "china",is_selected=False) #默认关闭该标签
.add("累计确诊",[list(z) for z in zip(data_1['地区'], data_1['累计确诊'])], "china")
.add("死亡人数", [list(z) for z in zip(data_1['地区'], data_1['死亡人数'])], "china")
.add("治愈人数", [list(z) for z in zip(data_1['地区'], data_1['治愈人数'])], "china")
.add("新增确诊", [list(z) for z in zip(data_1['地区'], data_1['新增确诊'])], "china")
.add("现存确诊", [list(z) for z in zip(data_1['地区'], data_1['现存确诊'])], "china")
.set_global_opts(
title_opts=opts.TitleOpts(""),
visualmap_opts=opts.VisualMapOpts(max_=10000),
legend_opts=opts.LegendOpts(selected_mode='single') #单选模式
)
)
#时间线折线图
c_3 = (
Line(init_opts=opts.InitOpts(chart_id='3'))
.add_xaxis(data_3['时间'])
.add_yaxis("新增确诊",data_3['新增确诊'],label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LabelOpts(color='#DC143C'))
.add_yaxis("现存确诊",data_3['现存确诊'],label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LabelOpts(color='#a98175'))
.add_yaxis("累计确诊",data_3['累计确诊'], label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LabelOpts(color='#60281e'))
.add_yaxis("累计治愈",data_3['累计治愈'], label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LabelOpts(color='#2add9c'))
.add_yaxis("累计死亡",data_3['累计死亡'], label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LabelOpts(color='black'))
.add_yaxis("新增死亡",data_3['新增死亡'], label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LabelOpts(color='#758a99'))
.add_yaxis("新增治愈",data_3['新增治愈'], label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LabelOpts(color='#1bd1a5'))
.set_global_opts(
title_opts=opts.TitleOpts(title="疫情汇总时间线",pos_top=20,title_textstyle_opts=opts.TextStyleOpts(font_size=13,color="#cfe2f3")),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white')), # 调整x轴字体颜色
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white')),
legend_opts=opts.LegendOpts(selected_mode='single')
)
)
#河南省地图
c_4 = (
Map(init_opts=opts.InitOpts(chart_id='4'))
# .add("死亡人数", [list(z) for z in zip(data_1['地区'], data_1['死亡人数'])], "china",is_selected=False) #默认关闭该标签
.add("累计确诊", [list(z) for z in zip(data_4['地区'], data_4['累计确诊'])], "河南")
.add("死亡人数", [list(z) for z in zip(data_4['地区'], data_4['死亡人数'])], "河南")
.add("治愈人数", [list(z) for z in zip(data_4['地区'], data_4['治愈人数'])], "河南")
.add("新增确诊", [list(z) for z in zip(data_4['地区'], data_4['新增确诊'])], "河南")
.set_global_opts(
title_opts=opts.TitleOpts(title='省内详细信息',pos_top=30,title_textstyle_opts=opts.TextStyleOpts(font_size=13, color="#cfe2f3")),
visualmap_opts=opts.VisualMapOpts(max_=500),
legend_opts=opts.LegendOpts(selected_mode='single') # 单选模式
)
)
#省内死亡详情饼图
c_5 = (
Pie(init_opts=opts.InitOpts(chart_id='5'))
.add("死亡人数", [list(z) for z in zip(data_4['地区'], data_4['死亡人数'])])
.set_global_opts(title_opts=opts.TitleOpts('市死亡人数',title_textstyle_opts=opts.TextStyleOpts(font_size=13, color="gray"),),
legend_opts=opts.LegendOpts(is_show=False))
# .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"),)
)
# 汇总数据与昨日相比
data = China_data_Total()
c_7 = (
Pie(init_opts=opts.InitOpts(chart_id='7'))
.set_global_opts(
title_opts=opts.TitleOpts(
title="现存确诊" + " " * 17 + "境外输入" + " " * 17 + "现存无症状", title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="white"), ))
)
c_8 = (
Pie(init_opts=opts.InitOpts(chart_id='8'))
.set_global_opts(
title_opts=opts.TitleOpts(
title="累计确诊"+ " " * 17 + "累计治愈"+ " " * 17 + "累计死亡", title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="white"), ))
)
c_9 = (
Pie(init_opts=opts.InitOpts(chart_id='9'))
.set_global_opts(
title_opts=opts.TitleOpts(
title=str(data['现存_较昨日']) + " " * 25 + str(data['境外输入_较昨日']) + " " * 25 + str(data['无症状_较昨日']), title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="#e9e7ef"), ))
)
c_10 = (
Pie(init_opts=opts.InitOpts(chart_id='10'))
.set_global_opts(
title_opts=opts.TitleOpts(
title=str(data['累计_较昨日']) + " " * 25 + str(data['治愈_较昨日']) + " " * 25+ str(data['死亡_较昨日']), title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="#e9e7ef")))
)
#数字
c_11 = (
Pie(init_opts=opts.InitOpts(chart_id='16'))
.set_global_opts(
title_opts=opts.TitleOpts(
title=str(data['现存确诊']) + " " * 3 + str(data['境外输入']) + " " * 3 + str(data['现存无症状']),
title_textstyle_opts=opts.TextStyleOpts(font_size=23, color="#CD3700")))
)
c_12 = (
Pie(init_opts=opts.InitOpts(chart_id='17'))
.set_global_opts(
title_opts=opts.TitleOpts(
title=str(data['累计确诊']) + " " * 3 + str(data['累计治愈']) + " " * 3 + str(data['累计死亡']),
title_textstyle_opts=opts.TextStyleOpts(font_size=23, color="#7F7F7F")))
)
tu_1 = (
Line(init_opts=opts.InitOpts(width="1500px",
height="850px",
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"},
chart_id='11'))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
title_opts=opts.TitleOpts(),
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_1.add_js_funcs(
"""
var img = new Image(); img.src = './images/bg_body.jpg';
"""
)
tu_2 = (
Line(init_opts=opts.InitOpts(
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"},
chart_id='12'))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_2.add_js_funcs(
"""
var img = new Image(); img.src = './images/bg_box5.png';
"""
)
tu_3 = (
Line(init_opts=opts.InitOpts(chart_id='13',
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"},
))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_3.add_js_funcs(
"""
var img = new Image(); img.src = './images/bg_box5.png';
"""
)
tu_4 = (
Line(init_opts=opts.InitOpts(chart_id='14',
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"},
))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_4.add_js_funcs(
"""
var img = new Image(); img.src = './images/bg_box4.png';
"""
)
tu_5 = (
Line(init_opts=opts.InitOpts(width="1500px",
height="200px",
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"},
chart_id='15'))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
title_opts=opts.TitleOpts(title="全球疫情信息 截至" + data['截止日期'],
pos_left='center',
title_textstyle_opts=opts.TextStyleOpts(font_size=21, color='#51c2d5'),
pos_top='23%'),
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_5.add_js_funcs(
"""
var img = new Image(); img.src = './images/bg_title.png';
"""
)
page = Page(layout=Page.DraggablePageLayout, page_title="可视化大屏")
page.add(tu_1,tu_2,tu_3,tu_4,c_1,c_2,c_3,c_4,c_5,c_7,c_8,c_9,c_10,tu_5,c_11,c_12)
page.render('国内疫情信息.html')
def Style():
# 固定样式
Page.save_resize_html(source="./国内疫情信息.html", # 上面的HTML文件名称
cfg_file="chart_config_3.json", # 保存的json配置文件
dest="new_国内疫情信息可视化大屏.html" # 新HTML文件名称，可以空缺，默认resize_render.html
)
def main():
a=0
while True:
data_1 = China_data(Get_HTML())
data_2 = Abroad_data(Get_HTML())
data_3 = TimeLine()
data_4=Henan_data()
date=China_data_Total()['截止日期']
if date!=a:
View(data_1, data_2, data_3,data_4)
Style()
a=date
main()# -*- coding:utf-8 -*-
import requests
import json
import re
import pandas as pd
import time
from pyecharts import options as opts
from pyecharts.charts import Bar, Page,Pie,Line,Timeline,Grid,Map
from pyecharts.commons.utils import JsCode
def sleeptime(hour, min, sec): # 时间转换
return hour * 3600 + min * 60 + sec
def Get_HTML():
headers = {'User-Agent': 'Mozilla/5.0'}
url = 'https://m.look.360.cn/events/feiyan?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&location=true&sort=2&_=1649252949072&callback=jsonp2'
try:
r = requests.get(url, headers=headers)
r.raise_for_status()
r.encoding = r.apparent_encoding
re = r.text[7:-2]
response = json.loads(re)
return response
except:
print("error!")
def China_data_Total():
url='http://m.look.360.cn/subject/400?sign=360_6aa05217'
headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:99.0) Gecko/20100101 Firefox/99.0'}
html = requests.get(url, headers=headers).text
pattern=re.compile('"qcmsint1":"0"},"feiyanTrend":(.*),"local":',re.S)
item=re.findall(pattern,html)
response=json.loads(item[0])
data = {
"截止日期":response['total']['modifyTime'],
"现存确诊":response['total']['currentConfirmed'],
"境外输入":response['total']['overseasInput'],
"现存无症状":response['total']['asymptom'],
"累计确诊":response['total']['diagnosed'],
"累计治愈":response['total']['cured'],
"累计死亡":response['total']['died'],
"现存_较昨日":response['newDiff']['currentConfirmed'],
"境外输入_较昨日":response['newDiff']['overseasInput'],
"无症状_较昨日":response['newDiff']['asymptom'],
"累计_较昨日":response['newDiff']['diagnosed'],
"治愈_较昨日":response['newDiff']['cured'],
"死亡_较昨日":response['newDiff']['died']
}
return data
def China_data(response): # 国内数据
C_name, Ljqz, Cured, Died, Xcqz, Xzqz = [], [], [], [], [], []
for i in range(34):
c_name = response['data'][i]['city'] # 城市名称
ljqz = response['data'][i]['diagnosed'] # 累计确诊
cured = response['data'][i]['cured'] # 治愈
died = response['data'][i]['died'] # 死亡
xcqz = response['data'][i]['currentConfirmed'] # 现存确诊
xzqz = response['data'][i]['diffDiagnosed'] # 新增确诊
C_name.append(c_name)
Ljqz.append(ljqz)
Cured.append(cured)
Died.append(died)
Xcqz.append(xcqz)
Xzqz.append(xzqz)
data = {
"地区": C_name,
"新增确诊": Xzqz,
"现存确诊": Xcqz,
"累计确诊": Ljqz,
"治愈人数": Cured,
"死亡人数": Died
}
return data
def Abroad_data(response): # 国外数据
C_name, Ljqz, Cured, Died, Xcqz, Xzqz = [], [], [], [], [], []
for i in range(196):
c_name = response['country'][i]['provinceName'] # 城市名称
ljqz = response['country'][i]['diagnosed'] # 累计确诊
cured = response['country'][i]['cured'] # 治愈
died = response['country'][i]['died'] # 死亡
xzqz = response['country'][i]['diffDiagnosed'] # 新增确诊
C_name.append(c_name)
Ljqz.append(ljqz)
Cured.append(cured)
Died.append(died)
Xzqz.append(xzqz)
data = {
"地区": C_name,
"新增确诊": Xzqz,
"累计确诊": Ljqz,
"治愈人数": Cured,
"死亡人数": Died
}
return data
def View(data_1, data_2): # 可视化
# 柱状图
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#7bbfea'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
t_1 = Timeline()
t_1.add_schema(play_interval=1000,
label_opts=opts.series_options.LabelOpts( color='#cfe2f3'))
for i in range(len(data_1['地区'])):
c_1 = (
Bar(init_opts=opts.InitOpts(width="300px",height='300px'))
.add_xaxis(['新增确诊','现存确诊','累计确诊','治愈人数','死亡人数'],)
.add_yaxis("",[data_1['新增确诊'][i],data_1['现存确诊'][i],data_1['累计确诊'][i],data_1['治愈人数'][i],data_1['死亡人数'][i]],
color='#44cef6',)
.reversal_axis()
.set_global_opts(
title_opts=opts.TitleOpts(" (地区: {} )".format(data_1['地区'][i]),title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white')),#调整x轴字体颜色
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white'))
)
)
t_1.add(c_1, "{}".format(data_1['地区'][i]))
# 柱状图
t_2 = Timeline()
t_2.add_schema(play_interval=1000,
label_opts=opts.series_options.LabelOpts(color='#cfe2f3'))
for i in range(len(data_2['地区'])):
c_2 = (
Bar()
.add_xaxis(['新增确诊', '累计确诊', '治愈人数', '死亡人数'], )
.add_yaxis("", [data_2['新增确诊'][i], data_2['累计确诊'][i], data_2['治愈人数'][i],data_2['死亡人数'][i]],
label_opts=opts.LabelOpts(position="right"),
color='#44cef6', )
.reversal_axis()
.set_global_opts(
title_opts=opts.TitleOpts(" (国家: {} )".format(data_2['地区'][i]),pos_right='0',title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white',)), # 调整x轴字体颜色
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white'),position='right')
)
)
t_2.add(c_2, "{}".format(data_2['地区'][i]))
# 饼图
# 转换数据为类型，#转换后数据为['美国', 1010774]
# data_pair = [list(z) for z in zip(x_data, y_data)] # 转换后数据为['美国', 1010774]
c_3 = (
Line()
.add_xaxis(data_2['地区'])
.add_yaxis(
series_name="新增确诊",
y_axis=data_2['新增确诊'],
label_opts=opts.LabelOpts(is_show=False,),
)
.add_yaxis(
series_name="累计确诊",
y_axis=data_2['累计确诊'],
label_opts=opts.LabelOpts(is_show=False,),
)
.add_yaxis(
series_name="治愈人数",
y_axis=data_2['治愈人数'],
label_opts=opts.LabelOpts(is_show=False,),
)
.add_yaxis(
series_name="死亡人数",
y_axis=data_2['死亡人数'],
label_opts=opts.LabelOpts(is_show=False,),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="国外疫情数据",pos_right='0',title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
datazoom_opts=opts.DataZoomOpts(),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white', )), # 调整x轴字体颜色
yaxis_opts=opts.AxisOpts(
position='right',
axistick_opts=opts.AxisTickOpts(is_show=True,),
splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(color='white')
),
)
)
c_5 = ( #地图
Map()
.add("累计确诊", [['河南', 2840], ['香港', 308175], ['吉林', 37712] , ['台湾', 27410], ['上海', 11366], ['浙江', 2819], ['福建', 2905],['山东', 2646], ['黑龙江', 2505], ['广东', 6681], ['四川', 1948], ['辽宁', 1604], ['河北', 1981], ['江苏', 2083], ['广西', 1518], ['北京', 1821], ['海南', 243], ['山西', 350], ['陕西', 3247], ['天津', 1798], ['云南', 2079], ['安徽', 1054], ['江西', 1063], ['湖南', 1335], ['青海', 43], ['内蒙古', 1687],['重庆',2423], ['贵州', 178], ['湖北', 68392], ['甘肃', 681], ['新疆', 999], ['宁夏', 122], ['澳门', 82], ['西藏', 1]], "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="国内现存确诊",title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),visualmap_opts=opts.VisualMapOpts(max_=10000))
)
data = China_data_Total()
c_6 = (
Pie(init_opts=opts.InitOpts())
.set_global_opts(
title_opts=opts.TitleOpts(
title="现存确诊:" + str(data['现存确诊']) + " " * 5 + "境外输入:" + str(data['境外输入']) + " " * 5 + "现存无症状:" + str(
data['现存无症状']), title_textstyle_opts=opts.TextStyleOpts(font_size=15, color="#ffb3a7"), ))
)
c_7 = (
Pie(init_opts=opts.InitOpts())
.set_global_opts(
title_opts=opts.TitleOpts(
title="累计确诊:" + str(data['累计确诊']) + " " * 5 + "累计治愈:" + str(data['累计治愈']) + " " * 5 + "累计死亡:" + str(
data['累计死亡']), title_textstyle_opts=opts.TextStyleOpts(font_size=15, color="#ffb3a7"), ))
)
c_8 = (
Pie(init_opts=opts.InitOpts())
.set_global_opts(
title_opts=opts.TitleOpts(
title="较昨日:" + str(data['现存_较昨日']) + " " * 25 + "较昨日:" + str(data['境外输入_较昨日']) + " " * 25 + "较昨日:" + str(
data['无症状_较昨日']), title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="#e9e7ef"), ))
)
c_9 = (
Pie(init_opts=opts.InitOpts())
.set_global_opts(
title_opts=opts.TitleOpts(
title="较昨日:" + str(data['累计_较昨日']) + " " * 25 + "较昨日:" + str(data['治愈_较昨日']) + " " * 25 + "较昨日:" + str(
data['死亡_较昨日']), title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="#e9e7ef"), ))
)
c_10 = ( # 地图
Map()
.add("死亡人数",[['河南', 22], ['香港', 8827], ['吉林', 5], ['台湾', 854], ['上海', 7], ['浙江', 1], ['福建', 1], ['山东', 7], ['广东', 8], ['黑龙江', 13], ['四川', 3], ['江苏', 0], ['河北', 7], ['辽宁', 2], ['广西', 2], ['北京', 9], ['海南', 6], ['山西', 0], ['陕西', 3], ['云南', 2], ['安徽', 6], ['湖南', 4], ['江西', 1], ['天津', 3], ['青海', 0], ['内蒙古', 1], ['重庆', 6], ['贵州', 2], ['湖北', 4512], ['甘肃', 2], ['新疆', 3], ['宁夏', 0], ['澳门', 0], ['西藏', 0]], "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="国内各地死亡人数", title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
visualmap_opts=opts.VisualMapOpts(max_=10000))
)
tu_1 = (
Line(init_opts=opts.InitOpts(width="1500px",
height="850px",
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
title_opts=opts.TitleOpts(),
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_1.add_js_funcs(
"""
var img = new Image(); img.src = './images/bg_body.jpg';
"""
)
tu_2 = (
Line(init_opts=opts.InitOpts(width="600px",
height="700px",
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
title_opts=opts.TitleOpts(
pos_left='center',
title_textstyle_opts=opts.TextStyleOpts(font_size=25, color='#51c2d5'),
pos_top='5%'),
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_2.add_js_funcs(
"""
var img = new Image(); img.src = './images/data_icon.png';
"""
)
tu_3 = (
Line(init_opts=opts.InitOpts(width="1500px",
height="200px",
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}))
.add_xaxis([None])
.add_yaxis("", [None])
.set_global_opts(
title_opts=opts.TitleOpts(title="全球疫情信息 截至"+data['截止日期'],
pos_left='center',
title_textstyle_opts=opts.TextStyleOpts(font_size=21, color='#51c2d5'),
pos_top='5%'),
yaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False))
)
tu_3.add_js_funcs(
"""
var img = new Image(); img.src = './images/bg_title.png';
"""
)
# page = Page(layout=Page.DraggablePageLayout, page_title="模拟")
# page.add(tu_1,t_1, t_2, c_3,c_5,c_6,c_7,c_8,c_9,tu_2,tu_3,c_10)
# page.render('全球疫情信息.html')
# 固定样式
Page.save_resize_html(source="全球疫情信息.html", # 上面的HTML文件名称
cfg_file="chart_config.json", # 保存的json配置文件
dest="new_全球疫情信息.html" # 新HTML文件名称，可以空缺，默认resize_render.html
)
def main():
second = sleeptime(5, 0, 0) # 设置时间
data_1 = China_data(Get_HTML()) # 国内数据
data_2 = Abroad_data(Get_HTML()) # 国外数据
data_11 = pd.DataFrame(data_1)
data_22 = pd.DataFrame(data_2)
while True:
data_11.to_csv('./国内疫情数据.csv')
data_22.to_csv('./国外疫情数据.csv')
View(data_1, data_2)
time.sleep(second)
# main()
data_1 = China_data(Get_HTML()) # 国内数据
data_2 = Abroad_data(Get_HTML()) # 国外数据
data_11 = pd.DataFrame(data_1)
data_22 = pd.DataFrame(data_2)
# data_11
# data_22
View(data_1,data_2)
# print(data_1)
from pyecharts.commons.utils import JsCode
value=[7.0,11.0,50.0,112.0]
color_function = """
function (params) {
if (params.value == 7.0)
return '#66CCCC';
else if (params.value ==11.0)
return '#CCCCFF';
else if (params.value ==50.0)
return '#FF6666';
else return '#CCFFCC';
}
"""
c_3 = (
Bar(init_opts=opts.InitOpts(width="800px",height="400px",))
.add_xaxis(['睡觉','说话','离开','学习'],)
.add_yaxis("",value,itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
.set_global_opts(
title_opts=opts.TitleOpts(title="国内疫情数据"),
legend_opts=opts.LegendOpts(pos_left="20%"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
)
)from pyecharts.charts import Bar
from pyecharts import options as opts
from snapshot_phantomjs import snapshot
# 导入输出图片工具
from pyecharts.render import make_snapshot
# 使用snapshot-selenium 渲染图片
from snapshot_selenium import snapshot
# 创建一个柱状图Bar实例
bar = (
Bar()
# 添加X轴数据
.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
# 添加Y轴数据,系列的名称
.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
.add_yaxis("商家B", [8, 15, 60, 20, 25, 30])
# 添加标题
.set_global_opts(title_opts=opts.TitleOpts(title="主标题: 双十一销量", subtitle="副标题:服饰类"))
)
# 输出保存为图片
make_snapshot(snapshot, bar.render("./图片.html"), "./003_Options配置项_自定义样式_保存图片.jpeg")
# make_snapshot(snapshot, bar.render(), "./图片.png")import requests
import json
from lxml import etree
import pandas as pd
import time
from pyecharts import options as opts
from pyecharts.charts import Bar
def sleeptime(hour, min, sec): #时间转换
return hour * 3600 + min * 60 + sec
def Get_HTML(url):
headers={'User-Agent':'Mozilla/5.0'}
try:
r=requests.get(url,headers=headers)
r.raise_for_status()
r.encoding=r.apparent_encoding
return r.text
except:
print("error!")
def Parse_page():
C_name,Ljqz,Cured,Died,Xcqz,Xzqz=[],[],[],[],[],[]
url='https://m.look.360.cn/events/feiyan?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&location=true&sort=2&_=1649252949072&callback=jsonp2'
re=Get_HTML(url)
re=re[7:-2]
response=json.loads(re)
for i in range(34):
c_name=response['data'][i]['data']['provinceName'] #城市名称
ljqz=response['data'][i]['diagnosed'] #累计确诊
cured=response['data'][i]['cured'] #治愈
died=response['data'][i]['died'] #死亡
xcqz=response['data'][i]['currentConfirmed'] #现存确诊
xzqz=response['data'][i]['diffDiagnosed'] #新增确诊
C_name.append(c_name)
Ljqz.append(ljqz)
Cured.append(cured)
Died.append(died)
Xcqz.append(xcqz)
Xzqz.append(xzqz)
# columns=['新增确诊','现存确诊','累计确诊','治愈人数','死亡人数']
# index=C_name
data={
"地区":C_name,
"新增确诊":Xzqz,
"现存确诊":Xcqz,
"累计确诊":Ljqz,
"治愈人数":Cured,
"死亡人数":Died
}
# print(data)
return data
def View():
data=Parse_page()
c = (
Bar()
.add_xaxis(data['地区'])
.add_yaxis("新增确诊",data['新增确诊'], stack="stack1")
.add_yaxis("现存确诊",data['现存确诊'] , stack="stack1")
.add_yaxis("累计确诊",data['累计确诊'] , stack="stack1")
.add_yaxis("治愈人数",data['治愈人数'] , stack="stack1")
.add_yaxis("死亡人数",data['死亡人数'] , stack="stack1")
.set_global_opts(
title_opts=opts.TitleOpts(title="国内疫情数据数据（全部）"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
)
.render("国内疫情数据数据.html")
)
def main():
data=Parse_page()
data=pd.DataFrame(data)
second = sleeptime(24,0,0)
while True:
# print(data)
data.to_csv('./国内疫情数据.csv')
View()
time.sleep(second)
# main()
data=Parse_page()
data=pd.DataFrame(data)
data