前言
博客是2年前写的，中间有好多网友私信我获取源码及指出部分bug，感谢支持！
取不到数据的原因是数据接口发生较大变化，最近刚好有时间，所以重新整理了一下。
第一部分 网页分析
- 数据源 腾讯疫情实时追踪
今天重新整理了一下，发现数据结构和之前的有所变化，把具体的操作步骤也说一下吧！打开网址推荐使用火狐浏览器，F12 进入开发者工具（刷新一下页面），如下，所有数据都可以通过接口获取：
- 国内数据接口：
https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules= - 各省历史数据接口：
https://api.inews.qq.com/newsqa/v1/query/pubished/daily/list?adCode= - 国外数据接口：
https://api.inews.qq.com/newsqa/v1/automation/modules/list?modules=
第二部分 数据获取
导入模块
import time
import json
import requests
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False
plt.style.use('ggplot')
抓取数据
获取步骤：
- 先定义接口调用函数
- 通过接口名获取数据，然后用pandas处理成Dataframe格式
def catch_data(api_name):url = 'https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules=' + api_namereponse = requests.get(url=url).json()return reponse
国内数据明细接口-chinaDayList
# 近60天国内现有数据汇总
chinadaylist = catch_data('chinaDayList')
chinadaylist = pd.DataFrame(chinadaylist['data']['chinaDayList'])
chinadaylist['date'] = pd.to_datetime(chinadaylist['y'].astype('str') + '.' + chinadaylist['date'])
chinadaylist = chinadaylist[['date','confirm','heal','dead','importedCase','nowConfirm','nowSevere','localConfirm']]
chinadaylist.columns = ['日期','累计确诊','累计治愈','累计死亡','累计境外输入','现有确诊','现有重症','本土现有确诊']
chinadaylist.tail()
国内每日新增数据接口-chinaDayAddListNew
#国内每日新增数据
chinanewadd = catch_data('chinaDayAddListNew')
chinanewadd = pd.DataFrame(chinanewadd['data']['chinaDayAddListNew'])
chinanewadd['date'] = pd.to_datetime(chinanewadd['y'].astype('str') + '.' + chinanewadd['date'])
chinanewadd = chinanewadd[['date','confirm','dead','heal','infect','importedCase','localConfirmadd','localinfectionadd']]
chinanewadd.columns = ['日期','新增确诊','新增死亡','新增治愈','新增无症状','新增境外','本土新增确诊','本土新增无症状']
chinanewadd.tail()
国内城市数据接口-diseaseh5Shelf
省份数据和城市数据处理方法：
- 观察网页框架，省份数据在diseaseh5Shelf这个接口
- diseaseh5Shelf返回一个字典，数据在areaTree，areaTree是一个list，list中第一个元素的children是一个存储省份数据的list
- children共有34个元素，每一个元素是一个省份数，dict格式，包含name,adcode,total,today,children,前四个是省份总数据，children是省份城市明细
- 城市数据和省份数据结构一样，但每一个children包含的城市数量不同用province_catch_data[i][‘children’]判断数量
#省份数据明细处理
province_data = pd.DataFrame()
#获取所有城市数据，第一步先处理省数据
province_catch_data = catch_data('diseaseh5Shelf')['data']['diseaseh5Shelf']['areaTree'][0]['children']
for i in range(len(province_catch_data)):province_total = province_catch_data[i]['total'] #省总数据province_total['name'] = province_catch_data[i]['name'] #省名province_total['adcode'] = province_catch_data[i]['adcode'] #省代码province_total['date'] = province_catch_data[i]['date'] #更新日期province_today = province_catch_data[i]['today'] #省当日数据province_today['name'] = province_catch_data[i]['name'] #省名province_total = pd.DataFrame(province_total,index=[i])province_today = pd.DataFrame(province_today,index=[i])province_today.rename({'confirm':'confirm_add'},inplace=True,axis=1) #today里面的confirm实际是每日新增merge_data = province_total.merge(province_today,how='left',on='name') #合并省总数据和当日数据province_data = pd.concat([province_data,merge_data]) #拼接省份数据
province_data = province_data[['name','adcode','date','confirm','provinceLocalConfirm','heal','dead','nowConfirm','confirm_add','local_confirm_add','wzz_add','abroad_confirm_add','dead_add','mediumRiskAreaNum','highRiskAreaNum','isUpdated']]
province_data.columns = ['省份','代码','日期','累计确诊','本土累计','累计治愈','累计死亡','现有确诊','当日新增','新增本土','新增无症状','新增境外','新增死亡','中风险数量','高风险数量','是否更新']
province_data = province_data.sort_values(by='累计确诊',ascending=False,ignore_index=True)
province_data.head()
df_city_data_total = pd.DataFrame()
for x in range(len(province_catch_data)):province_dict = province_catch_data[x]['children']province_name = province_catch_data[x]['name']df_city_data = pd.DataFrame()for i in range(len(province_dict)):city_total = province_dict[i]['total']city_total['province_name'] = province_name #省名city_total['name'] = province_dict[i]['name'] #市区名city_total['adcode'] = province_dict[i]['adcode'] #市区代码city_total['date'] = province_dict[i]['date'] #更新日期city_today = province_dict[i]['today'] #当日数据city_today['province_name'] = province_name #省名city_today['name'] = province_dict[i]['name'] #市区名city_total = pd.DataFrame(city_total,index=[i])city_today = pd.DataFrame(city_today,index=[i])city_today.rename({'confirm':'confirm_add'},inplace=True,axis=1) #today里面的confirm实际是每日新增merge_city = city_total.merge(city_today,how='left',on=['province_name','name'])df_city_data = pd.concat([df_city_data,merge_city])df_city_data_total = pd.concat([df_city_data_total,df_city_data])
df_city_data_total = df_city_data_total[['province_name','name','adcode','date','confirm','provinceLocalConfirm','heal','dead','nowConfirm','confirm_add','local_confirm_add','wzz_add','mediumRiskAreaNum','highRiskAreaNum']]
df_city_data_total.columns = ['省份','城市','代码','日期','累计确诊','本土累计','累计治愈','累计死亡','现有确诊','当日新增','新增本土','新增无症状','中风险数量','高风险数量']
df_city_data_total =df_city_data_total.sort_values(by='累计确诊',ascending=False,ignore_index=True)
df_city_data_total.head()
省份历史数据明细
#各省份历史数据明细,缺失台湾香港澳门,城市历史数据更换城市code即可
province_history_data = pd.DataFrame()
for code in province_data['代码']:if code != '':history_data = requests.get('https://api.inews.qq.com/newsqa/v1/query/pubished/daily/list?adCode=' + str(code)).json()['data']history_df = pd.DataFrame(history_data)history_df['date'] = pd.to_datetime(history_df['year'].astype('str') + '.' + history_df['date'])history_df_use = history_df[['date','province','confirm','dead','heal','wzz','newConfirm','newHeal','newDead','wzz_add']]history_df_use.columns = ['日期','省份','累计确诊','累计死亡','累计治愈','无症状','新增确诊','新增治愈','新增死亡','新增无症状']province_history_data = pd.concat([province_history_data,history_df_use])province_history_data.shape
国外累计最新数据
#海外最新数据
aboard_data = requests.get('https://api.inews.qq.com/newsqa/v1/automation/modules/list?modules=WomAboard').json()['data']['WomAboard']
aboard_data = pd.DataFrame(aboard_data)
aboard_data_use = aboard_data[['pub_date','continent','name','confirm','dead','heal','nowConfirm','confirmAdd']]
aboard_data_use.columns = ['日期','大洲','国家','累计确诊','累计死亡','累计治愈','现有确诊','新增确诊']
aboard_data_use.head()
第三部分 数据可视化
导入pyecharts绘图相关包
from pyecharts.charts import * #导入所有图表
from pyecharts import options as opts
#导入pyecharts的主题（如果不使用可以跳过）
from pyecharts.globals import ThemeType
from pyecharts.commons.utils import JsCode
from pyecharts.globals import CurrentConfig, NotebookType
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_NOTEBOOK
数据详情-Table
from pyecharts.components import Table
from pyecharts.options import ComponentTitleOpts
table = Table()
headers = list(chinadaylist.columns)
rows = chinadaylist.sort_values(by='日期',ascending=False).head(1).values
table.add(headers=headers,rows=rows)
table.set_global_opts(title_opts=ComponentTitleOpts(title="国内最新数据", subtitle="更新日期：" + chinadaylist['日期'].astype('str').max()))
table.render_notebook()
组合图（bar/line）
bar = Bar()
bar.add_xaxis(list(chinadaylist["日期"].astype('str')))
bar.add_yaxis(series_name ='累计确诊',y_axis=list(chinadaylist["累计确诊"]))
bar.add_yaxis(series_name ="现有确诊",y_axis=list(chinadaylist['现有确诊']))
bar.extend_axis(yaxis=opts.AxisOpts(name='治愈率',axislabel_opts=opts.LabelOpts(formatter="{value}%")))
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #不显示数据标签
bar.set_global_opts(title_opts=opts.TitleOpts(title="国内累计确诊趋势",subtitle="数据来自腾讯疫情数据（含港澳台）", #添加副标题pos_left="center", #标题位置pos_top="top"),legend_opts=opts.LegendOpts(pos_left="left"), #图例位置-左侧xaxis_opts=opts.AxisOpts(type_="category",axislabel_opts=opts.AxisTickOpts()),yaxis_opts=opts.AxisOpts(name="人数"))
line = Line()
line.add_xaxis(list(chinadaylist["日期"].astype('str')))
line.add_yaxis(series_name="治愈率（%）",y_axis=(chinadaylist['累计治愈']/chinadaylist['累计确诊']).round(decimals=3)*100,yaxis_index=1,symbol_size=3,is_smooth=True,label_opts=opts.LabelOpts(is_show=False),tooltip_opts=opts.TooltipOpts(formatter=JsCode("function (params) {return params.value+ '%'}"),is_show_content = True))bar.overlap(line) ##图形叠加
bar.render_notebook()
折线图美化
background_color_js = ("new echarts.graphic.LinearGradient(0, 0, 0,1, ""[{offset: 0, color: '#99cccc'}, {offset: 1, color: '#00bfff'}], false)")line1 = Line(init_opts=opts.InitOpts(theme=ThemeType.ROMA,bg_color=JsCode(background_color_js))) #设置主题&背景颜色line1.add_xaxis(list(chinanewadd["日期"].astype('str'))) #添加x轴line1.add_yaxis(series_name = "新增确诊",y_axis = list(chinanewadd["新增确诊"]), #增加Y轴数据is_smooth=True,#添加Y轴，平滑曲线areastyle_opts=opts.AreaStyleOpts(opacity=0.3), #区域阴影透明度is_symbol_show = True,label_opts=opts.LabelOpts(is_show=False),yaxis_index = 0 #指定y轴顺序) #不显示标签line1.add_yaxis(series_name = "新增本土",y_axis = list(chinanewadd["本土新增确诊"]),is_smooth=True,areastyle_opts=opts.AreaStyleOpts(opacity=0.3),is_symbol_show = True,#是否显示标记
# symbol = 'circle' #标记类型 'circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow', 'none'label_opts=opts.LabelOpts(is_show=False),yaxis_index = 1)
#增加副轴
line1.extend_axis(yaxis=opts.AxisOpts(name="新增本土(人)",name_location="end", #轴标题位置type_="value",#轴类型is_inverse=False, #逆序刻度值axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True)))
#设置图表格式
line1.set_global_opts(title_opts=opts.TitleOpts(title="国内每日新增趋势", #添加主标题subtitle="数据来自腾讯疫情数据（含港澳台）", #添加副标题subtitle_textstyle_opts = opts.TextStyleOpts(color='#000000'),pos_left="center", #标题位置pos_top="top"),legend_opts=opts.LegendOpts(pos_left="40%",pos_top='10%'), #图例位置-左侧xaxis_opts=opts.AxisOpts(type_="category",axislabel_opts=opts.AxisTickOpts()),yaxis_opts=opts.AxisOpts(name="新增确诊（人）", type_="value",
# max_=100000),datazoom_opts=opts.DataZoomOpts(type_= 'slider',range_start=80 ,#横轴开始百分百range_end=100) , #横轴结束百分比toolbox_opts=opts.ToolboxOpts(is_show=True, #显示工具窗口orient='vertical', #垂直排列工具窗口pos_left='95%',pos_top='middle'))line1.render_notebook()
多tab轮播
map1= Map(init_opts=opts.InitOpts(width="900px",height="500px",bg_color=None))
map1.add(series_name = "累计确诊",data_pair = [list(z) for z in zip(province_data['省份'],province_data['累计确诊'])],maptype = "china",is_map_symbol_show=False)map1.set_global_opts(title_opts=opts.TitleOpts(title="全国疫情地图-累计确诊",subtitle="更新日期：" + province_data['日期'].astype('str').max(),subtitle_textstyle_opts = opts.TextStyleOpts(color='#ffffff'),pos_left="center"),legend_opts=opts.LegendOpts(is_show=True, pos_top="40px", pos_left="30px"),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,range_text=['高', '低'],pieces=[{"min": 50000, "color": "#751d0d"},{"min": 10000, "max": 49999, "color": "#ae2a23"},{"min": 5000, "max": 9999, "color": "#d6564c"},{"min": 1000, "max": 4999, "color": "#f19178"},{"min": 500, "max": 999, "color": "#f7d3a6"},{"min": 100, "max": 499, "color": "#fdf2d3"},{"min": 0, "max": 99, "color": "#FFFFFF"}]),toolbox_opts=opts.ToolboxOpts(is_show=True, #显示工具窗口orient='vertical', #垂直排列工具窗口pos_left='95%',pos_top='middle'),)map2= Map(init_opts=opts.InitOpts(width="900px",height="500px",bg_color=None))
map2.add(series_name = "现有确诊",data_pair = [list(z) for z in zip(province_data['省份'],province_data['现有确诊'])],maptype = "china",is_map_symbol_show=False)map2.set_global_opts(title_opts=opts.TitleOpts(title="全国疫情地图-现有确诊",subtitle="更新日期：" + province_data['日期'].astype('str').max(),subtitle_textstyle_opts = opts.TextStyleOpts(color='#ffffff'),pos_left="center"),legend_opts=opts.LegendOpts(is_show=True, pos_top="40px", pos_left="30px"),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,range_text=['高', '低'],pieces=[{"min": 10000, "color": "#751d0d"},{"min": 1000, "max": 9999, "color": "#ae2a23"},{"min": 500, "max": 999, "color": "#d6564c"},{"min": 100, "max": 499, "color": "#f19178"},{"min": 10, "max": 99, "color": "#f7d3a6"},{"min": 1, "max": 9, "color": "#fdf2d3"},{"min": 0, "max": 0, "color": "#FFFFFF"}]),toolbox_opts=opts.ToolboxOpts(is_show=True, #显示工具窗口orient='vertical', #垂直排列工具窗口pos_left='95%',pos_top='middle'),)
##i添加选项卡tab
tab = Tab()
tab.add(map1, "累计确诊地图")
tab.add(map2, "现有确诊地图")tab.render_notebook()
时间轴轮播-map
```python
##给日期排序
province_history_data['date_rank'] = province_history_data['日期'].rank(method='dense',ascending=True)
df_list = []
#取前15日数据，可任意变更
for i in range(1,15):df_list.append(province_history_data.loc[province_history_data['date_rank']==i])tl = Timeline(init_opts=opts.InitOpts(theme=ThemeType.CHALK,width="900px", height="600px")) #时间轴for idx in range(len(df_list)):#循环给时间轴增加图形provinces = []confirm_value = []date = df_list[idx]['日期'].astype('str').unique()[0]for item_pv in df_list[idx]['省份']:provinces.append(item_pv)for item_pc in df_list[idx]['累计确诊']:confirm_value.append(item_pc)zipped = zip(provinces, confirm_value)f_map = Map(init_opts=opts.InitOpts(width="800",height="500px"))f_map.add(series_name="确诊数量",data_pair=[list(z) for z in zipped],maptype="china",is_map_symbol_show=False)f_map.set_global_opts(title_opts=opts.TitleOpts(title="全国疫情地图-累计确诊",subtitle="更新日期：" + date,subtitle_textstyle_opts = opts.TextStyleOpts(color='#ffffff'),pos_left="center"),legend_opts=opts.LegendOpts(is_show=False, pos_top="40px", pos_left="30px"),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,range_text=['高', '低'],pieces=[{"min": 1000, "color": "#CC0033"},{"min": 200, "max": 999, "color": "#FF4500"},{"min": 50, "max": 199, "color": "#FF8C00"},{"min": 1, "max": 49, "color": "#FFDAB9"},{"min": 0, "max": 0, "color": "#F5F5F5"}],textstyle_opts = opts.TextStyleOpts(color='#ffffff'),pos_bottom='15%',pos_left='5%'))tl.add(f_map, "{}".format(date)) #添加图形
tl.add_schema(is_timeline_show=True, # 是否显示play_interval=1200, # 播放间symbol=None, # 图标is_loop_play=True , # 循环播放is_auto_play = True)
tl.render_notebook()
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