Python:学习实现肺炎疫情实时数据

学习链接：
Python实战：抓肺炎疫情实时数据，画2019-nCoV疫情地图
Python实战：抓取肺炎疫情实时数据，画2019-nCoV疫情地图

参照以上两个链接实现了从腾讯新冠状病毒肺炎疫情实时追踪

学习链接内容中包含了如何爬取数据和如何实现数据爬取，不再赘述，现从实现过程中遇到的几个问题进行总结：

安装各个模块

• requests模块
  requests模块是支持HTTP连接保持和连接池，支持使用cookie保持回话，支持文件上传，支持自动响应内容的编码，支持国际化的URL和POST数据自动编码
  安装方式：cmd → pip install requests
• numpy模块
  numpy支持大量的维度数组与矩阵运算
  安装方式：cmd → pip install numpy
• matplotlib模块
  matplotlib是python的绘图库，可与numpy一起使用，提供了一种有效的MatLab开源替代方案
  安装方式：cmd → pip install matplotlib
• basemap模块
  首先，因为basemap是基于geos，故需要首先安装geos
  安装方式：cmd→ pip install geos
  然后从https://www.lfd.uci.edu/~gohlke/pythonlibs/下载对应Python版本和系统版本的basemap和pyproj文件
  例如：我的系统是64位，python版本是3.5，下载的文件为：
  
  现在之后将文件置于pip安装目录下，使用pip install即可安装

总的来说，pip是个好东西。

代码

import time
import json
import requests
from datetime import datetime
import numpy as np
import matplotlib
import matplotlib.figure
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.dates as mdates


plt.rcParams['font.sans-serif'] = ['FangSong']  # 设置默认字体
plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像时'-'显示为方块的问题

def catch_daily():
    """抓取每日确诊和死亡数据"""
    
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_cn_day_counts&callback=&_=%d'%int(time.time()*1000)
    data = json.loads(requests.get(url=url).json()['data'])
    data.sort(key=lambda x:x['date'])
    
    date_list = list() # 日期
    confirm_list = list() # 确诊
    suspect_list = list() # 疑似
    dead_list = list() # 死亡
    heal_list = list() # 治愈
    for item in data:
        month, day = item['date'].split('/')
        date_list.append(datetime.strptime('2020-%s-%s'%(month, day), '%Y-%m-%d'))
        confirm_list.append(int(item['confirm']))
        suspect_list.append(int(item['suspect']))
        dead_list.append(int(item['dead']))
        heal_list.append(int(item['heal']))
    
    return date_list, confirm_list, suspect_list, dead_list, heal_list



def catch_distribution():
    """抓取行政区域确诊分布数据"""
    
    data1 = {}
    url='https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5&&callback=&_=%d'%int(time.time()*1000)
    data = json.loads(requests.get(url=url).json()['data'])
    lis=[]

    for m in range(len(data['areaTree'][0]['children'])): 
        for n in range(len(data['areaTree'][0]['children'][m]['children'])):
            info={}
            info['country']=data['areaTree'][0]['name']#国家
            info['pronvice']=data['areaTree'][0]['children'][m]['name']#省份  
            info['city']=data['areaTree'][0]['children'][m]['children'][n]['name']#城市   len(data['areaTree'][0]['children'][0]['children'])
            info['total_confirm']=data['areaTree'][0]['children'][m]['children'][n]['total']['confirm']
            info['total_suspect']=data['areaTree'][0]['children'][m]['children'][n]['total']['suspect']
            info['total_dead']=data['areaTree'][0]['children'][m]['children'][n]['total']['dead']
            info['total_heal']=data['areaTree'][0]['children'][m]['children'][n]['total']['heal']
            info['today_confirm']=data['areaTree'][0]['children'][m]['children'][n]['today']['confirm']
            info['today_suspect']=data['areaTree'][0]['children'][m]['children'][n]['today']['suspect']
            info['today_dead']=data['areaTree'][0]['children'][m]['children'][n]['today']['dead']
            info['today_heal']=data['areaTree'][0]['children'][m]['children'][n]['today']['heal']
            lis.append(info)


    for item in lis:
        
        if item['pronvice'] not in data1:
            data1.update({item['pronvice']:0})
        data1[item['pronvice']] += int(item['total_confirm'])
    
    return data1


def plot_daily():
    """绘制每日确诊和死亡数据"""
    
    date_list, confirm_list, suspect_list, dead_list, heal_list = catch_daily() # 获取数据
    
    plt.figure('2019-nCoV疫情统计图表', facecolor='#f4f4f4', figsize=(10, 8))
    plt.title('2019-nCoV疫情曲线', fontsize=20)
    
    plt.plot(date_list, confirm_list, label='确诊')
    plt.plot(date_list, suspect_list, label='疑似')
    plt.plot(date_list, dead_list, label='死亡')
    plt.plot(date_list, heal_list, label='治愈')
    
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d')) # 格式化时间轴标注
    plt.gcf().autofmt_xdate() # 优化标注（自动倾斜）
    plt.grid(linestyle=':') # 显示网格
    plt.legend(loc='best') # 显示图例
    #plt.savefig('2019-nCoV疫情曲线.png') # 保存为文件
    plt.show()

    
def plot_distribution():
    """绘制行政区域确诊分布数据"""
    
    data = catch_distribution()
    
    font = FontProperties(fname='china-shapefiles/simsun.ttf', size=14)
    font_11 = FontProperties(fname='china-shapefiles/simsun.ttf', size=11)
    lat_min = 0
    lat_max = 60
    lon_min = 70
    lon_max = 140
    
    handles = [
            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),
]
    labels = [ '1-9人', '10-99人', '100-999人', '>1000人']

    provincePos = {
        "辽宁省":[121.7,40.9],
        "吉林省":[124.5,43.5],
        "黑龙江省":[125.6,46.5],
        "北京市":[116.0,39.9],
        "天津市":[117.0,38.7],
        "内蒙古自治区":[110.0,41.5],
        "宁夏回族自治区":[105.2,37.0],
        "山西省":[111.0,37.0],
        "河北省":[114.0,37.8],
        "山东省":[116.5,36.0],
        "河南省":[111.8,33.5],
        "陕西省":[107.5,33.5],
        "湖北省":[111.0,30.5],
        "江苏省":[119.2,32.5],
        "安徽省":[115.5,31.8],
        "上海市":[121.0,31.0],
        "湖南省":[110.3,27.0],
        "江西省":[114.0,27.0],
        "浙江省":[118.8,28.5],
        "福建省":[116.2,25.5],
        "广东省":[113.2,23.1],
        "*省":[120.5,23.5],
        "海南省":[108.0,19.0],
        "广西壮族自治区":[107.3,23.0],
        "重庆市":[106.5,29.5],
        "云南省":[101.0,24.0],
        "贵州省":[106.0,26.5],
        "四川省":[102.0,30.5],
        "甘肃省":[103.0,35.0],
        "青海省":[95.0,35.0],
        "**自治区":[85.5,42.5],
        "*自治区":[85.0,31.5],
        "香港特别行政区":[115.1,21.2],
        "澳门特别行政区":[112.5,21.2]
    }

    
    fig = matplotlib.figure.Figure()
    fig.set_size_inches(10, 8) # 设置绘图板尺寸
    axes = fig.add_axes((0.1, 0.12, 0.8, 0.8)) # rect = l,b,w,h

    #圆柱投影
    #m = Basemap(llcrnrlon=lon_min, urcrnrlon=lon_max, llcrnrlat=lat_min, urcrnrlat=lat_max, resolution='l', ax=axes)


    #兰勃脱等角投影
    #m = Basemap(projection='lcc', width=5000000, height=5000000, lat_0=36, lon_0=102, resolution='l', ax=axes)


    #正射投影
    m = Basemap(projection='ortho', lat_0=30, lon_0=105, resolution='l', ax=axes)


    m.readshapefile('./china-shapefiles/china', 'province', drawbounds=True)
    m.readshapefile('./china-shapefiles/china_nine_dotted_line', 'section', drawbounds=True)
    m.drawcoastlines(color='black') # 洲际线
    m.drawcountries(color='black')  # 国界线
    m.drawparallels(np.arange(lat_min,lat_max,10), labels=[1,0,0,0]) #画经度线
    m.drawmeridians(np.arange(lon_min,lon_max,10), labels=[0,0,0,1]) #画纬度线

    pset=set()
    for info, shape in zip(m.province_info, m.province):
        pname = info['OWNER'].strip('\x00')
        fcname = info['FCNAME'].strip('\x00')
        if pname != fcname: # 不绘制海岛
            continue
        
        for key in data.keys():
            if key in pname:
                if data[key] == 0:
                    color = '#f0f0f0'
                    poly = Polygon(shape, facecolor=color, edgecolor=color)
                    axes.add_patch(poly)
                elif data[key] < 10:
                    color = '#ffaa85'
                    poly = Polygon(shape, facecolor=color, edgecolor=color)
                    axes.add_patch(poly)
                elif data[key] <100:
                    color = '#ff7b69'
                    poly = Polygon(shape, facecolor=color, edgecolor=color)
                    axes.add_patch(poly)
                elif  data[key] < 1000:
                    color = '#bf2121'
                    poly = Polygon(shape, facecolor=color, edgecolor=color)
                    axes.add_patch(poly)
                else:
                    color = '#7f1818'
                    poly = Polygon(shape, facecolor=color, edgecolor=color)
                    axes.add_patch(poly)
                break

        pos=provincePos[pname]
        text=pname.replace('自治区','').replace('特别行政区','').replace('壮族','').replace('*','').replace('回族','').replace("省", "").replace("市", "")
        if text not in pset:
            x,y=m(pos[0],pos[1])
            axes.text(x,y,text,fontproperties=font_11,color='#00FFFF')
            pset.add(text)

    
    axes.legend(handles, labels, bbox_to_anchor=(0.5, -0.11), loc='lower center', ncol=4, prop=font)
    axes.set_title("2019-nCoV疫情地图", fontproperties=font)
    FigureCanvasAgg(fig)
    fig.savefig('2019-nCoV疫情地图.png')
    fig.set_visible(b=True)


if __name__ == '__main__':
    plot_daily()
    plot_distribution()

代码内容源自于以上两个学习连接中代码的整合，以上代码段实现了折线图的显示和地图的下载。