用Python抓新型冠状病毒肺炎疫情数据，绘制全国疫情分布图

运行结果（2020-2-4日数据）

数据来源

数据来源于腾讯实时疫情动态

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

精度到省级，下载地址

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)

注：绘图代码抄这篇的 https://blog.csdn.net/csdnnews/article/details/104112062)

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