用Matplotlib跟踪疫情实时监控2019nCoV

本文环境 windows10+pycharm+python3.6+matplotlib

项目已经上传到github 和 百度网盘 欢迎大家自取
链接https://lixiang007.top/
百度网盘地址：

链接：https://pan.baidu.com/s/1I-qvUn3SIDyLiVEa5q9KxQ 
提取码：xaup

项目代码位置：


1.数据下载

首先找到数据源。
https://news.qq.com/zt2020/page/feiyan.htm?from=timeline&isappinstalled=0

有了网址怎么抓数据呢？这里以firefox为例（其他浏览器基本类似）。


刷新页面，我们很快就能发现，应答类型为json格式的这个请求，最有可能包含我们需要的数据了：




深入分析，我们就得到了url地址、请求方法、参数、应答格式等信息。查询参数中，callback是回调函数名，我们可以尝试置空，_应该是以毫秒为单位的当前时间戳。有了这些信息，分分钟就可以抓到数据了。我们先在IDLE中以交互方式抓一下看看效果：

import time, json, requests
url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_area_counts&callback=&_=%d'%int(time.time()*1000)
data = json.loads(requests.get(url=url).json()['data'])
print(len(data))
print(data[0])
print(data[-1])

只要两行代码，就可以抓到数据了。

2.数据处理

以省为单位画疫情图，我们只需要统计同属一个省的所有地市的确诊数据即可。最终的数据抓取代码如下：

import time, json, requests

def catch_distribution():
    """抓取行政区域确诊分布数据"""
    
    data = dict()
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_area_counts&callback=&_=%d'%int(time.time()*1000)
    for item in json.loads(requests.get(url=url).json()['data']):
        if item['area'] not in data:
            data.update({item['area']:0})
        data[item['area']] += int(item['confirm'])
    
    return data

3.数据可视化

首先先做一些准备
需要pyproj-1.9.5.1-cp36-cp36m-win_amd64
basemap-1.2.1-cp36-cp36m-win_amd64
已经上传到我csdn的资源 windows用户需要自取 需要1积分哦 嘻嘻嘻

C:\Windows\system32>cd C:\Users\88304\Desktop

C:\Users\88304\Desktop>pip install pyproj-1.9.5.1-cp36-cp36m-win_amd64.whl
Processing c:\users\88304\desktop\pyproj-1.9.5.1-cp36-cp36m-win_amd64.whl
Installing collected packages: pyproj
Successfully installed pyproj-1.9.5.1

C:\Users\88304\Desktop>pip install basemap-1.2.1-cp36-cp36m-win_amd64.whl
Processing c:\users\88304\desktop\basemap-1.2.1-cp36-cp36m-win_amd64.whl
Collecting pyshp>=1.2.0
  Using cached pyshp-2.1.0.tar.gz (215 kB)
Requirement already satisfied: six in c:\program files\python36\lib\site-packages (from basemap==1.2.1) (1.13.0)
Requirement already satisfied: pyproj>=1.9.3 in c:\program files\python36\lib\site-packages (from basemap==1.2.1) (1.9.5.1)
Requirement already satisfied: matplotlib!=3.0.1,>=1.0.0 in c:\program files\python36\lib\site-packages (from basemap==1.2.1) (3.1.2)
Requirement already satisfied: numpy>=1.2.1 in c:\program files\python36\lib\site-packages (from basemap==1.2.1) (1.17.4)
Requirement already satisfied: python-dateutil>=2.1 in c:\program files\python36\lib\site-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (2.8.1)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\program files\python36\lib\site-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (2.4.5)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\program files\python36\lib\site-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (1.1.0)
Requirement already satisfied: cycler>=0.10 in c:\program files\python36\lib\site-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (0.10.0)
Requirement already satisfied: setuptools in c:\program files\python36\lib\site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (42.0.2)
Building wheels for collected packages: pyshp
  Building wheel for pyshp (setup.py) ... done
  Created wheel for pyshp: filename=pyshp-2.1.0-py3-none-any.whl size=32613 sha256=a96678bf30c567522cbe27504cae084c3148332669b7ad222dac4eeaa690fb25
  Stored in directory: c:\users\88304\appdata\local\pip\cache\wheels\1b\ab\b5\333342ac6838b5ab6977d3b5d29140cf3e60b01b220f8178c0
Successfully built pyshp
Installing collected packages: pyshp, basemap
Successfully installed basemap-1.2.1 pyshp-2.1.0

数据可视化，我习惯使用matplotlib模块。matplotlib有很多扩展工具包（toolkits），比如，画3D需要mplot3d工具包，画地图的话，则需要basemap工具包，以及处理地图投影的pyproj模块。另外画海陆分界线、国界线、行政分界线等还需要shape数据。所需模块请自行安装，shape文件可以从这里下载，绘图用到的矢量字库可以从自己的电脑上随便找一个（我用的是simsun.ttf）。我的主程序是2019nCoV.py，shape文件下载下来之后，是这样保存的：
文件夹结构如下：


以下为全部代码，除了疫情地图，还包括了全国每日武汉肺炎确诊数据的下载和可视化。

# -*- coding: utf-8 -*-

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"""
    
    data = {'西藏':0}
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_area_counts&callback=&_=%d'%int(time.time()*1000)
    for item in json.loads(requests.get(url=url).json()['data']):
        if item['area'] not in data:
            data.update({item['area']:0})
        data[item['area']] += int(item['confirm'])
    
    return data

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='res/simsun.ttf', size=14)
    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']
    
    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
    m = Basemap(llcrnrlon=lon_min, urcrnrlon=lon_max, llcrnrlat=lat_min, urcrnrlat=lat_max, resolution='l', ax=axes)
    m.readshapefile('res/china-shapefiles-master/china', 'province', drawbounds=True)
    m.readshapefile('res/china-shapefiles-master/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
    
    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'
                elif data[key] < 10:
                    color = '#ffaa85'
                elif data[key] <100:
                    color = '#ff7b69'
                elif  data[key] < 1000:
                    color = '#bf2121'
                else:
                    color = '#7f1818'
                break
        
        poly = Polygon(shape, facecolor=color, edgecolor=color)
        axes.add_patch(poly)
    
    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')

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

运行结果

"C:\Program Files\Python36\pythonw.exe" C:/Users/88304/Desktop/lixiang/2019nCoV_lx.py

进程已结束,退出代码0

还可以使用正射投影：
m = Basemap(projection=‘ortho’, lat_0=30, lon_0=105, resolution=‘l’, ax=axes)
效果如下哈：

"C:\Program Files\Python36\pythonw.exe" C:/Users/88304/Desktop/lixiang/2019nCoV_lx.py
Warning: Cannot label parallels on Orthographic basemap'Warning: Cannot label meridians on full-disk
Geostationary, Orthographic or Azimuthal equidistant basemap
进程已结束,退出代码0

这样没关系的