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, requestsfrom datetime import datetimeimport matplotlibimport matplotlib.pyplot as pltimport matplotlib.dates as mdatesfrom matplotlib.font_manager import FontPropertiesfrom mpl_toolkits.basemap import Basemapfrom matplotlib.patches import Polygonimport numpy as npimport jsonpathplt.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='$[*].total.confirm') dic_province_confirm = dict(zip(list_province, list_province_confirm)) return dic_province_confirmarea_data = catch_cn_disease_dis()print(area_data)#%%# 抓取全国疫情按日期分布'''数据源："chinaDayList": [{ "date": "01.13", "confirm": "41", "suspect": "0", "dead": "1", "heal": "0" }, { "date": "01.14", "confirm": "41", "suspect": "0", "dead": "1", "heal": "0" }'''def catch_cn_daily_dis(): timestamp = '%d'%int(time.time()*1000) url_area = ('https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5' '&callback=&_=') + timestamp world_data = json.loads(requests.get(url=url_area).json()['data']) china_daily_data = jsonpath.jsonpath(world_data, expr='$.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 = 0def 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)

以上就是小编整理的全部知识点内容，感谢大家的学习和对的支持。