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Python抓新型冠状病毒肺炎疫情数据并绘制全国疫情分布的代码实例

100 次  2020-10-21  分类 : python爬虫

运行结果(2020-2-4日数据)

数据来源

news.qq.com/zt2020/page/feiyan.htm

抓包分析

日报数据格式

"chinaDayList": [{
"date": "01.13",
"confirm": "41",
"suspect": "0",
"dead": "1",
"heal": "0"
}, {
"date": "01.14",
"confirm": "41",
"suspect": "0",
"dead": "1",
"heal": "0"
}, {
"date": "01.15",
"confirm": "41",
"suspect": "0",
"dead": "2",
"heal": "5"
}, {
。。。。。。

全国各地疫情数据格式

"lastUpdateTime": "2020-02-04 12:43:19",
"areaTree": [{
"name": "中国",
"children": [{
"name": "湖北",
"children": [{
"name": "武汉",
"total": {
"confirm": 6384,
"suspect": 0,
"dead": 313,
"heal": 303
},
"today": {
"confirm": 1242,
"suspect": 0,
"dead": 48,
"heal": 79
}
}, {
"name": "黄冈",
"total": {
"confirm": 1422,
"suspect": 0,
"dead": 19,
"heal": 36
},
"today": {
"confirm": 176,
"suspect": 0,
"dead": 2,
"heal": 9
}
}, {
。。。。。。

地图数据

github.com/dongli/china-shapefiles

代码实现

#%%

import time, json, requests
from datetime import datetime
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.font_manager import FontProperties
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
import numpy as np
import jsonpath

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号

#%%

# 全国疫情地区分布(省级确诊病例)
def catch_cn_disease_dis():
 timestamp = '%d'%int(time.time()*1000)
 url_area = ('https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
    '&callback=&_=') + timestamp
 world_data = json.loads(requests.get(url=url_area).json()['data'])
 china_data = jsonpath.jsonpath(world_data, 
         expr='$.areaTree[0].children[*]')
 list_province = jsonpath.jsonpath(china_data, expr='$[*].name')
 list_province_confirm = jsonpath.jsonpath(china_data, expr='$[*].total.confirm')
 dic_province_confirm = dict(zip(list_province, list_province_confirm)) 
 return dic_province_confirm

area_data = catch_cn_disease_dis()
print(area_data)

#%%

# 抓取全国疫情按日期分布
'''
数据源:
"chinaDayList": [{
"date": "01.13",
"confirm": "41",
"suspect": "0",
"dead": "1",
"heal": "0"
}, {
"date": "01.14",
"confirm": "41",
"suspect": "0",
"dead": "1",
"heal": "0"
}
'''
def catch_cn_daily_dis():
 timestamp = '%d'%int(time.time()*1000)
 url_area = ('https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
    '&callback=&_=') + timestamp
 world_data = json.loads(requests.get(url=url_area).json()['data'])
 china_daily_data = jsonpath.jsonpath(world_data, 
         expr='$.chinaDayList[*]')

 # 其实没必要单独用list存储,json可读性已经很好了;这里这样写仅是为了少该点老版本的代码  
 list_dates = list() # 日期
 list_confirms = list() # 确诊
 list_suspects = list() # 疑似
 list_deads = list() # 死亡
 list_heals = list() # 治愈  
 for item in china_daily_data:
  month, day = item['date'].split('.')
  list_dates.append(datetime.strptime('2020-%s-%s'%(month, day), '%Y-%m-%d'))
  list_confirms.append(int(item['confirm']))
  list_suspects.append(int(item['suspect']))
  list_deads.append(int(item['dead']))
  list_heals.append(int(item['heal']))  
 return list_dates, list_confirms, list_suspects, list_deads, list_heals  

list_date, list_confirm, list_suspect, list_dead, list_heal = catch_cn_daily_dis() 
print(list_date)


#%%

# 绘制每日确诊和死亡数据
def plot_cn_daily():
 # list_date, list_confirm, list_suspect, list_dead, list_heal = catch_cn_daily_dis() 

 plt.figure('novel coronavirus', facecolor='#f4f4f4', figsize=(10, 8))
 plt.title('全国新型冠状病毒疫情曲线', fontsize=20)
 print('日期元素数:', len(list_date), "\n确诊元素数:", len(list_confirm))
 plt.plot(list_date, list_confirm, label='确诊')
 plt.plot(list_date, list_suspect, label='疑似')
 plt.plot(list_date, list_dead, label='死亡')
 plt.plot(list_date, list_heal, label='治愈')
 xaxis = plt.gca().xaxis 
 # x轴刻度为1天
 xaxis.set_major_locator(matplotlib.dates.DayLocator(bymonthday=None, interval=1, tz=None))
 xaxis.set_major_formatter(mdates.DateFormatter('%m月%d日'))
 plt.gcf().autofmt_xdate() # 优化标注(自动倾斜)
 plt.grid(linestyle=':') # 显示网格
 plt.xlabel('日期',fontsize=16)
 plt.ylabel('人数',fontsize=16)
 plt.legend(loc='best')

plot_cn_daily()

#%%

# 绘制全国省级行政区域确诊分布图
count_iter = 0
def plot_cn_disease_dis():
 # area_data = catch_area_distribution()
 font = FontProperties(fname='res/coure.fon', size=14)

 # 经纬度范围
 lat_min = 10 # 纬度
 lat_max = 60
 lon_min = 70 # 经度
 lon_max = 140

 # 标签颜色和文本 
 legend_handles = [
    matplotlib.patches.Patch(color='#7FFFAA', alpha=1, linewidth=0),
    matplotlib.patches.Patch(color='#ffaa85', alpha=1, linewidth=0),
    matplotlib.patches.Patch(color='#ff7b69', alpha=1, linewidth=0),
    matplotlib.patches.Patch(color='#bf2121', alpha=1, linewidth=0),
    matplotlib.patches.Patch(color='#7f1818', alpha=1, linewidth=0),
 ]
 legend_labels = ['0人', '1-10人', '11-100人', '101-1000人', '>1000人']

 fig = plt.figure(facecolor='#f4f4f4', figsize=(10, 8)) 
 # 新建区域
 axes = fig.add_axes((0.1, 0.1, 0.8, 0.8)) # left, bottom, width, height, figure的百分比,从figure 10%的位置开始绘制, 宽高是figure的80%
 axes.set_title('全国新型冠状病毒疫情地图(确诊)', fontsize=20) # fontproperties=font 设置失败 
 # bbox_to_anchor(num1, num2), num1用于控制legend的左右移动,值越大越向右边移动,num2用于控制legend的上下移动,值越大,越向上移动。
 axes.legend(legend_handles, legend_labels, bbox_to_anchor=(0.5, -0.11), loc='lower center', ncol=5) # prop=font

 china_map = Basemap(llcrnrlon=lon_min, urcrnrlon=lon_max, llcrnrlat=lat_min, urcrnrlat=lat_max, resolution='l', ax=axes)
 # labels=[True,False,False,False] 分别代表 [left,right,top,bottom]
 china_map.drawparallels(np.arange(lat_min,lat_max,10), labels=[1,0,0,0]) # 画经度线
 china_map.drawmeridians(np.arange(lon_min,lon_max,10), labels=[0,0,0,1]) # 画纬度线
 china_map.drawcoastlines(color='black') # 洲际线
 china_map.drawcountries(color='red') # 国界线
 china_map.drawmapboundary(fill_color = 'aqua')
 # 画中国国内省界和九段线
 china_map.readshapefile('res/china-shapefiles-master/china', 'province', drawbounds=True)
 china_map.readshapefile('res/china-shapefiles-master/china_nine_dotted_line', 'section', drawbounds=True)

 global count_iter
 count_iter = 0

 # 内外循环不能对调,地图中每个省的数据有多条(绘制每一个shape,可以去查一下第一条“台湾省”的数据)
 for info, shape in zip(china_map.province_info, china_map.province):
  pname = info['OWNER'].strip('\x00')
  fcname = info['FCNAME'].strip('\x00')
  if pname != fcname: # 不绘制海岛
   continue
  is_reported = False # 西藏没有疫情,数据源就不取不到其数据 
  for prov_name in area_data.keys():    
   count_iter += 1
   if prov_name in pname:
    is_reported = True
    if area_data[prov_name] == 0:
     color = '#f0f0f0'
    elif area_data[prov_name] <= 10:
     color = '#ffaa85'
    elif area_data[prov_name] <= 100:
     color = '#ff7b69'
    elif area_data[prov_name] <= 1000:
     color = '#bf2121'
    else:
     color = '#7f1818'
    break

  if not is_reported:
   color = '#7FFFAA'

  poly = Polygon(shape, facecolor=color, edgecolor=color)
  axes.add_patch(poly)


plot_cn_disease_dis()
print('迭代次数', count_iter)

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