说明: 本文是Python可视化技术结合时下热点进行进行开发、讲解的案例，也课程思政的一个形式。文章遵守CSDN平台规定和国家法规（非新闻资质的网站不允许发布疫情数据），对运行结果（2月8日）中涉及数据部分进行屏蔽，同时删除提供数据的网站链接。
文章目录
运行结果（-2-8当天）基本方案数据格式全国疫情地图实现福建省疫情地图实现福州市疫情地图实现其他运行结果（-2-8当天）
基本方案
web请求用requests网页内容解析用pyquery、beautifulsoup和正则表达式地图用pyecharts v1.6.2数据来源 全国数据来源于“腾讯实时疫情动态网页”，直接get到json福州市数据来源于“福建省疾病预防控制中心官网”疫情通报页面，需要解析页面数据格式
请参考本人其他两篇中的抓包分析过程
用Python抓新型冠状病毒肺炎实时数据，绘制市内疫情地图
用Python抓新型冠状病毒肺炎疫情数据，绘制全国疫情分布图
全国疫情地图实现
#%%import time, json, requestsimport jsonpathfrom pyecharts.charts import Mapimport pyecharts.options as opts#%%# 全国疫情地区分布(各省确诊病例)def catch_cn_disease_dis():timestamp = '%d'%int(time.time()*1000)url_area = ('https://view./g2/getOnsInfo?name=disease_h5''&callback=&_=') + timestampworld_data = json.loads(requests.get(url=url_area).json()['data'])china_data = jsonpath.jsonpath(world_data, expr='$.areaTree[0].children[*]')ls_province_names = jsonpath.jsonpath(china_data, expr='$[*].name')ls_confirm_vals = jsonpath.jsonpath(china_data, expr='$[*].total.confirm')ls_province_confirm = list(zip(ls_province_names, ls_confirm_vals,)) return ls_province_confirm, world_data#%%ls_province_cfm, dic_world_data = catch_cn_disease_dis()print(ls_province_cfm)#%%# 绘制全国疫情地图def map_cn_disease_dis() -> Map:c = (Map().add('中国', ls_province_cfm, 'china').set_global_opts(title_opts=opts.TitleOpts(title='全国新型冠状病毒疫情地图（确诊数）'),visualmap_opts=opts.VisualMapOpts(is_show=True,split_number=6,is_piecewise=True, # 是否为分段型pos_top='center',pieces=[{'min': 10000, 'color': '#7f1818'}, #不指定 max{'min': 1000, 'max': 10000},{'min': 500, 'max': 999},{'min': 100, 'max': 499},{'min': 10, 'max': 99},{'min': 0, 'max': 5} ],),))return cmap_cn_disease_dis().render('全国疫情地图.html')
福建省疫情地图实现
# 获取福建省确诊分布数据def catch_fj_disease_dis():dic_world_data = catch_cn_disease_dis()[1]dic_fj_cfm = dict() # 解析福建各个城市的数据dic_fj = jsonpath.jsonpath(dic_world_data, expr='$.areaTree[0].children[?(@.name=="福建")].children[*]') for item in dic_fj:if item['name'] not in dic_fj_cfm: dic_fj_cfm.update({item['name']:0}) dic_fj_cfm[item['name']] += int(item['total']['confirm']) return dic_fj_cfm dic_fj_cfm = catch_fj_disease_dis()#%%# 绘制福建省疫情地图def map_fj_disease_dis() -> Map:# dic_fj_cfm = catch_fj_disease_disls_fj_cities = [name + '市' for name in dic_fj_cfm.keys()]c = (Map().add('福建省', [list(z) for z in zip(ls_fj_cities, dic_fj_cfm.values())], '福建').set_series_opts(label_opts=opts.LabelOpts(is_show=True, formatter='{b}\n{c}例')).set_global_opts(title_opts=opts.TitleOpts(title='福建新型冠状病毒疫情地图（确诊数）'),visualmap_opts=opts.VisualMapOpts(is_show=True,split_number=6,is_piecewise=True, # 是否为分段型pos_top='center',pieces=[{'min': 50}, {'min': 30, 'max': 49},{'min': 20, 'max': 29},{'min': 10, 'max': 19},{'min': 1, 'max': 9},{'value': 0, "label": '无确诊病例', "color": 'green'} ],),))return cmap_fj_disease_dis().render('福建疫情地图.html')
福州市疫情地图实现
from pyquery import PyQuery as pqfrom bs4 import BeautifulSoupimport datetimeimport numpy as npimport re# 获取福建省疾病预防控制中心官网疫情通告列表def catch_fz_disease_rpt_list() -> str: session = requests.session()crawl_timestamp = int(datetime.datetime.timestamp(datetime.datetime.now()) * 1000)keyword = {'txtkeyword':'福建省新增新型冠状病毒感染的肺炎疫情情况'}html = '' while True:try:rsp = session.get('/search', params=keyword)except requests.exceptions.ChunkedEncodingError:continuersp.raise_for_status() # 非200则抛出异常（rsp.status_code != 200）html = rsp.contentbreak return html#%%html = catch_fz_disease_rpt_list()#%%# 获取最新一期的疫情通告链接地址def catch_fz_disease_latest_rpt(): # html = catch_fz_disease_rpt_list()doc = pq(html)# 方法一：第一条数据，doc('.list li a').attr.href即可得到所要链接# 方法二：指定日期, doc('.list li:contains("-02-02") a').attr.href# 但是这里咱们多写点，练习嘛,乱写news = doc('.list li').items()dates = []for item in news:date_str = item('span').text().strip() date = datetime.datetime.strptime(date_str,'%Y-%m-%d')dates.append(date)temp = np.array(dates)latest_date = temp.max()latest_date_str = latest_date.strftime('%Y-%m-%d')latest_date_url = doc('.list li:contains("{0}") a'.format(latest_date_str)).attr.hreflatest_date_url = '' + latest_date_urlreturn latest_date_url#%%print(catch_fz_disease_latest_rpt())#%%# 解析网页，获取确诊和疑似病例数据文本def catch_fz_disease_dis():latest_date_url = catch_fz_disease_latest_rpt()soup = ''while True:try:rsp = requests.session().get(latest_date_url)except requests.exceptions.ChunkedEncodingError:continuersp.raise_for_status() # 非200则抛出异常（rsp.status_code != 200）soup = BeautifulSoup(rsp.content, 'lxml')# print(soup)break reg = pile('.*福州市.*')soup = soup.find('div', class_='showCon')tag = soup.find_all(text=reg)if len(tag) != 4:raise Exception('查找到值的次数必须等于 4. 实际值为: {}'.format(len(tag))) area_data = {}# area_data.update({'confirm_added':tag[0]})# area_data.update({'suspend_added':tag[1]})area_data.update({'confirm':tag[2]})area_data.update({'suspend':tag[3]})return area_data#%%fz_data = catch_fz_disease_dis()print(fz_data)#%%import re# 解析各区县数据def exact_towns_dis(): # fz_data = catch_fz_disease_dis()pattern = pile('(?<=、|（)\D+[市|县|区]\d+例')town_list = pattern.findall(fz_data['confirm'])# town_list = fz_data['confirm'].split('（|(')[1].split('）)')[0].split('、')# 平潭单列，不处理 town_data = {'福州市区':0} for town in town_list:match_num = re.search(r'\d+(?=例)', town)match_town_name = re.search(r'\D+[市|县|区]', town)if match_num and town:match_num = int(match_num.group())match_town_name = match_town_name.group()else:continueif match_town_name == '长乐区': # 地图中长乐为市match_town_name = '长乐市' town_data.update({match_town_name: match_num}) # 晋安、鼓楼、马尾、仓山、台江if match_town_name[-1] == '区' : town_data['福州市区'] += match_numreturn town_data#%%fz_town_data = exact_towns_dis()print(fz_town_data)#%%from mons.utils import JsCodedef map_fz_disease_dis() -> Map:# fz_town_data = exact_towns_dis()# ls_fz_towns = [name + '市' for name in fz_town_data.keys()]c = (Map().add('福州市（不含平潭）', [list(z) for z in zip(fz_town_data.keys(), fz_town_data.values())], '福州').set_series_opts(label_opts=opts.LabelOpts(is_show=True,# return params.value[2]; // 不存在这个值,写个错误的让地图绘制默认值 formatter=JsCode("""function(params){if (typeof(params.data) == 'undefined') {return params.value[2]; } else {return params.data.name + params.data.value + '例';} }"""))) .set_global_opts(title_opts=opts.TitleOpts(title='福州市新型冠状病毒疫情地图',subtitle='其中,福州主城区(晋安、马尾、鼓楼、仓山、台江)\n共确诊{}例'.format(fz_town_data['福州市区'])),visualmap_opts=opts.VisualMapOpts(is_show=True,split_number=6,is_piecewise=True, # 是否为分段型pos_top='center',pieces=[ {'min': 20},{'min': 10, 'max': 19},{'min': 5, 'max': 9},{'min': 1, 'max': 4}])))return c#%%map_fz_disease_dis().render('福州疫情地图.html')
其他
请参考本人其他篇绘制抓取实时数据，使用Basemap绘制分布图，使用plt绘制走势图的博文。
另外，pyecharts使用案例：
用Python pyecharts v1.x 绘制图形（一）:柱状图、柱状堆叠图、条形图、直方图、帕累托图、饼图、圆环图、玫瑰图
用Python pyecharts v1.x 绘制图形（二）:折线图、折线面积图、散点图、雷达图、箱线图、词云图
