1.本章利用python爬取-nCoV确诊数据并制作pyecharts可视化地图；
2.主要内容为绘制出中国各省疫情数据，疫情数据从四个维度进行可视化展示：累积确诊人数、现存确诊人数、治愈人数、死亡人数。鼠标移动到省份上方，则展示出相关数据；地图上添加的图例，用颜色深浅和界定确诊人数的数量范围直观反应各省份疫情现状况，点击图例的颜色框可以让图上的对应区域颜色显现或消失。
3.上图为丁香园数据疫情图，我们可以从腾讯新闻网站搜索相关疫情数据，附上疫情数据接口:
https://view./g2/getOnsInfo?name=disease_h5
打开疫情数据接口链接如下图所示：**
4.密密麻麻的数据我们用Jupyter Notebook分析下疫情数据
请求疫情数据，代码如下，结果如图所示：
from fake_useragent import UserAgentimport requestsimport jsonua = UserAgent()headers = {'User-Agent':ua.chrome}url = 'https://view./g2/getOnsInfo?name=disease_h5'response = requests.get(url,headers)list_json = json.loads(response.text)print(type(list_json))list_json
5.获取疫情数据里的’data‘数据，
代码如下，结果如图所示：
global false,null,truefalse = null = true = ""jo = list_json['data']type(jo)
6.将获取到的’data‘字符串数据转换成字典类型，方便数据操作；
代码如下，结果如图所示：
data = eval(jo)data
7.获取数据如下图所示
8.获取各个省份确诊人数的信息
9.疫情数据从四个维度进行可视化展示：累积确诊人数、现存确诊人数、治愈人数、死亡人数。
图中为北京疫情数据：
累积确诊人数（confirm）、
现存确诊人数（nowConfirm）、
治愈人数（heal）、
死亡人数（dead）；
10.获取并打印数据
代码如下，结果如图所示：
print(data['areaTree'][0]['children'][0]['name'])print(data['areaTree'][0]['children'][0]['total']['nowConfirm'])print(data['areaTree'][0]['children'][0]['total']['confirm'])print(data ['areaTree'][0]['children'][0]['total']['dead'])print(data['areaTree'][0]['children'][0]['total']['heal'])
11.整理合并各个省份确诊人数的信息：
代码如下，结果如图所示：
# 地区area = []# 现存确诊nowConfirm = []# 累积确诊confirm = []# 死亡人数dead = []# 治愈人数heal = []for i in range(34):# 获取添加中国各个省份名称area.append(data['areaTree'][0]['children'][i]['name'])# 获取添加中国各个省份现存确诊人数nowConfirm.append(data['areaTree'][0]['children'][i]['total']['nowConfirm'])# 获取添加中国各个省份累积确诊人数confirm.append(data['areaTree'][0]['children'][i]['total']['confirm'])# 获取添加中国各个省份死亡人数dead.append(data['areaTree'][0]['children'][i]['total']['dead'])# 获取添加中国各个省份治愈人数heal.append(data['areaTree'][0]['children'][i]['total']['heal'])# 将数据封装成[‘北京’，[325,923,9,589]]这样的形式方便于数据可视化data_pair = []for i in range(34):x = []# 添加该省的累积确诊人数x.append(confirm[i])# 添加该省的死亡人数x.append(dead[i])# 添加该省的治愈人数x.append(heal[i])# 添加该省的现存确诊人数x.append(nowConfirm[i])# 将中国各省疫情数据添加到data_pair数组里data_pair.append(x)testv = []for i in range(34):# 向中国各省与该省疫情数据匹配testMap = [area[i],data_pair[i]]#获取整理各个省份确诊人数的信息testv.append(testMap)lastUpdateTime = data['lastUpdateTime']print(lastUpdateTime)testv
12（桥黑板！！！重点！）
画图：获取各个省份确诊人数的信息以及最后更新的时间并制作pyecharts可视化地图
代码如下，结果如图所示：
from pyecharts import options as optsfrom pyecharts.charts import Mapfrom mons.utils import JsCodeimport osc=(Map()#主要方法，用于添加图表的数据和设置各种配置项.add(#这个参数必须有，必须写，哪怕你传递一个空字符串，也要写，因为不写这个参数，会报错series_name = "",## 数据项data_pair = testv,# mapType显示地图范围,为china,显示中国地图maptype = "china",# 去掉地图标识小红点is_map_symbol_show=False,# 是否显示标签label_opts =opts.LabelOpts(is_show = True),)#设置全局配置项.set_global_opts(title_opts = opts.TitleOpts(title="中国疫情地图",subtitle = "XinXinM"+"\n\n\n全国各省份确诊人数分布\n\n截至: "+lastUpdateTime,),visualmap_opts = opts.VisualMapOpts(# 是否为分段型is_piecewise=True,# 自定义的每一段的范围，以及每一段的文字，以及每一段的特别的样式pieces= [# 不指定 max，表示 max 为无限大（Infinity）。{"max": 0,"label":"0","color":"#ffffff"},{"min": 1, "max": 10,"color":"#ebb4a8"},{"min": 10, "max": 100,"color":"#e09694"},{"min": 100, "max": 500,"color":"#cb8382"},{"min": 500, "max": 1000,"color":"#b27372"},{"min": 1000, "color":"#976461"},],# 是否反转 visualMap 组件is_inverse=True,# visualMap 组件离容器右侧的距离pos_right='right',),tooltip_opts=opts.TooltipOpts(# 提示框浮层的背景颜色。background_color='white',# 提示框浮层的边框宽。border_width=1,# 文字样式配置项，参考 `series_options.TextStyleOpts`textstyle_opts=opts.TextStyleOpts(color='#00C791'),# 回调函数，回调函数格式：# (params: Object|Array) => string# 参数 params 是 formatter 需要的单个数据集。formatter=(JsCode("""function(params){return params.name + ' : ' + '<br/>'+ '现存确诊：'+params.data.value[3] + '<br/>'+ '累积确诊：'+params.data.value[0] + '<br/>'+ '死亡人数：'+params.data.value[1] + '<br/>'+ '治愈人数：'+params.data.value[2];}"""))))# 默认将会在根目录下生成一个 render.html 的文件，支持 path 参数，设置文件保存位置.render("China_-nCov_map.html"),)#system函数可以将字符串转化成命令在服务器上运行，其会创建一个子进程在系统上执行命令行，子进程的执行结果无法影响主进程；os.system("China_-nCov_map.html")
完整代码：
from fake_useragent import UserAgentimport requestsimport jsonfrom pyecharts import options as optsfrom pyecharts.charts import Mapfrom mons.utils import JsCodeimport osclass nCov_:# 伪造随机的User-Agentua = UserAgent()def __init__(self):# 伪造随机的User-Agentua = UserAgent()# 设置了一个headers，在构建request时传入，在请求时，就加入了headers传送，服务器若识别了是浏览器发来的请求，就会得到响应。self.headers = {# 伪造Chrome浏览器用户代理'User-Agent':ua.chrome}# 腾讯新闻网站疫情数据接口self.url = 'https://view./g2/getOnsInfo?name=disease_h5'# 解析接口数据def parse_url(self):response = requests.get(self.url,headers=self.headers)# 将请求到的数据转化为字典类型list_json = json.loads(response.text)# 返回接口数据信息return list_jsondef getDateList(self,list_json):#将data数据类型str，转成dict类型 方便操作数据# 当字典中，有键值为True或则False这种bool类型时，使用eval会报错NameError: name 'false' is not definedglobal false,null,truefalse = null = true = ""jo = list_json['data']# 将jo字符串数据转换为dict类型方便数据操作data = eval(jo)return datadef main(self):list_json = self.parse_url()data = self.getDateList(list_json)return datanCov_ = nCov_()data = nCov_.main()# 数据最新更新时间lastUpdateTime = data['lastUpdateTime']# 地区area = []# 现存确诊nowConfirm = []# 累积确诊confirm = []# 死亡人数dead = []# 治愈人数heal = []for i in range(34):# 获取添加中国各个省份名称area.append(data['areaTree'][0]['children'][i]['name'])# 获取添加中国各个省份现存确诊人数nowConfirm.append(data['areaTree'][0]['children'][i]['total']['nowConfirm'])# 获取添加中国各个省份累积确诊人数confirm.append(data['areaTree'][0]['children'][i]['total']['confirm'])# 获取添加中国各个省份死亡人数dead.append(data['areaTree'][0]['children'][i]['total']['dead'])# 获取添加中国各个省份治愈人数heal.append(data['areaTree'][0]['children'][i]['total']['heal'])# 将数据封装成[‘北京’，[325,923,9,589]]这样的形式方便于数据可视化data_pair = []for i in range(34):x = []# 添加该省的累积确诊人数x.append(confirm[i])# 添加该省的死亡人数x.append(dead[i])# 添加该省的治愈人数x.append(heal[i])# 添加该省的现存确诊人数x.append(nowConfirm[i])# 将中国各省疫情数据添加到data_pair数组里data_pair.append(x)testv = []for i in range(34):# 向中国各省与该省疫情数据匹配testMap = [area[i],data_pair[i]]#获取整理各个省份确诊人数的信息testv.append(testMap)c=(Map()#主要方法，用于添加图表的数据和设置各种配置项.add(#这个参数必须有，必须写，哪怕你传递一个空字符串，也要写，因为不写这个参数，会报错series_name = "",## 数据项data_pair = testv,# mapType显示地图范围,为china,显示中国地图maptype = "china",# 去掉地图标识小红点is_map_symbol_show=False,# 是否显示标签label_opts =opts.LabelOpts(is_show = True),)#设置全局配置项.set_global_opts(title_opts = opts.TitleOpts(title="中国疫情地图",subtitle = "XinXinM"+"\n\n\n全国各省份确诊人数分布\n\n截至: "+lastUpdateTime,),visualmap_opts = opts.VisualMapOpts(# 是否为分段型is_piecewise=True,# 自定义的每一段的范围，以及每一段的文字，以及每一段的特别的样式pieces= [# 不指定 max，表示 max 为无限大（Infinity）。{"max": 0,"label":"0","color":"#ffffff"},{"min": 1, "max": 10,"color":"#ebb4a8"},{"min": 10, "max": 100,"color":"#e09694"},{"min": 100, "max": 500,"color":"#cb8382"},{"min": 500, "max": 1000,"color":"#b27372"},{"min": 1000, "color":"#976461"},],# 是否反转 visualMap 组件is_inverse=True,# visualMap 组件离容器右侧的距离pos_right='right',),tooltip_opts=opts.TooltipOpts(# 提示框浮层的背景颜色。background_color='white',# 提示框浮层的边框宽。border_width=1,# 文字样式配置项，参考 `series_options.TextStyleOpts`textstyle_opts=opts.TextStyleOpts(color='#00C791'),# 回调函数，回调函数格式：# (params: Object|Array) => string# 参数 params 是 formatter 需要的单个数据集。formatter=(JsCode("""function(params){return params.name + ' : ' + '<br/>'+ '现存确诊：'+params.data.value[3] + '<br/>'+ '累积确诊：'+params.data.value[0] + '<br/>'+ '死亡人数：'+params.data.value[1] + '<br/>'+ '治愈人数：'+params.data.value[2];}"""))))# 默认将会在根目录下生成一个 render.html 的文件，支持 path 参数，设置文件保存位置.render("China_-nCov_map.html"),)#system函数可以将字符串转化成命令在服务器上运行，其会创建一个子进程在系统上执行命令行，子进程的执行结果无法影响主进程；os.system("China_-nCov_map.html")
生成的"China_-nCov_map.html"代码：
<!DOCTYPE html><html><head><meta charset="UTF-8"><title>Awesome-pyecharts</title><script type="text/javascript" src="/assets/echarts.min.js"></script><script type="text/javascript" src="/assets/maps/china.js"></script></head><body><div id="486804f5cd794d05b9ce1fcdcef941b8" class="chart-container" style="width:900px; height:500px;"></div><script>var chart_486804f5cd794d05b9ce1fcdcef941b8 = echarts.init(document.getElementById('486804f5cd794d05b9ce1fcdcef941b8'), 'white', {renderer: 'canvas'});var option_486804f5cd794d05b9ce1fcdcef941b8 = {"animation": true,"animationThreshold": 2000,"animationDuration": 1000,"animationEasing": "cubicOut","animationDelay": 0,"animationDurationUpdate": 300,"animationEasingUpdate": "cubicOut","animationDelayUpdate": 0,"color": ["#c23531","#2f4554","#61a0a8","#d48265","#749f83","#ca8622","#bda29a","#6e7074","#546570","#c4ccd3","#f05b72","#ef5b9c","#f47920","#905a3d","#fab27b","#2a5caa","#444693","#726930","#b2d235","#6d8346","#ac6767","#1d953f","#6950a1","#918597"],"series": [{"type": "map","label": {"show": true,"position": "top","margin": 8},"mapType": "china","data": [{"name": "\u5317\u4eac","value": [928,9,595,324]},{"name": "\u9999\u6e2f","value": [1268,7,1156,105]},{"name": "\u4e0a\u6d77","value": [716,7,681,28]},{"name": "\u56db\u5ddd","value": [596,3,581,12]},{"name": "\u7518\u8083","value": [167,2,154,11]},{"name": "\u9655\u897f","value": [320,3,311,6]},{"name": "\u6cb3\u5317","value": [349,6,337,6]},{"name": "\u8fbd\u5b81","value": [156,2,150,4]},{"name": "\u5e7f\u4e1c","value": [1643,8,1631,4]},{"name": "\u53f0\u6e7e","value": [449,7,438,4]},{"name": "\u5929\u6d25","value": [199,3,194,2]},{"name": "\u91cd\u5e86","value": [582,6,574,2]},{"name": "\u798f\u5efa","value": [363,1,360,2]},{"name": "\u6fb3\u95e8","value": [46,0,45,1]},{"name": "\u6d59\u6c5f","value": [1269,1,1267,1]},{"name": "\u5185\u8499\u53e4","value": [239,1,237,1]},{"name": "\u4e91\u5357","value": [186,2,183,1]},{"name": "\u6c5f\u82cf","value": [654,0,653,1]},{"name": "\u9ed1\u9f99\u6c5f","value": [947,13,934,0]},{"name": "\u897f\u85cf","value": [1,0,1,0]},{"name": "\u5e7f\u897f","value": [254,2,252,0]},{"name": "\u65b0\u7586","value": [76,3,73,0]},{"name": "\u5409\u6797","value": [155,2,153,0]},{"name": "\u5c71\u897f","value": [198,0,198,0]},{"name": "\u6e56\u5317","value": [68135,4512,63623,0]},{"name": "\u6e56\u5357","value": [1019,4,1015,0]},{"name": "\u6cb3\u5357","value": [1276,22,1254,0]},{"name": "\u5b81\u590f","value": [75,0,75,0]},{"name": "\u8d35\u5dde","value": [147,2,145,0]},{"name": "\u6d77\u5357","value": [171,6,165,0]},{"name": "\u9752\u6d77","value": [18,0,18,0]},{"name": "\u5b89\u5fbd","value": [991,6,985,0]},{"name": "\u5c71\u4e1c","value": [792,7,785,0]},{"name": "\u6c5f\u897f","value": [932,1,931,0]}],"roam": true,"zoom": 1,"showLegendSymbol": false,"emphasis": {}}],"legend": [{"data": [""],"selected": {"": true},"show": true,"padding": 5,"itemGap": 10,"itemWidth": 25,"itemHeight": 14}],"tooltip": {"show": true,"trigger": "item","triggerOn": "mousemove|click","axisPointer": {"type": "line"},"formatter": function(params){ return params.name + ' : ' + '<br/>'+ '\u73b0\u5b58\u786e\u8bca\uff1a'+params.data.value[3] + '<br/>'+ '\u7d2f\u79ef\u786e\u8bca\uff1a'+params.data.value[0] + '<br/>'+ '\u6b7b\u4ea1\u4eba\u6570\uff1a'+params.data.value[1] + '<br/>'+ '\u6cbb\u6108\u4eba\u6570\uff1a'+params.data.value[2];} ,"textStyle": {"color": "#00C791"},"backgroundColor": "white","borderWidth": 1},"title": [{"text": "\u4e2d\u56fd\u75ab\u60c5\u5730\u56fe","subtext": "XinXinM\n\n\n\u5168\u56fd\u5404\u7701\u4efd\u786e\u8bca\u4eba\u6570\u5206\u5e03\n\n\u622a\u81f3: -07-05 19:41:18","padding": 5,"itemGap": 10}],"visualMap": {"show": true,"type": "piecewise","min": 0,"max": 100,"inRange": {"color": ["#50a3ba","#eac763","#d94e5d"]},"calculable": true,"inverse": true,"splitNumber": 5,"orient": "vertical","right": "right","showLabel": true,"itemWidth": 20,"itemHeight": 14,"borderWidth": 0,"pieces": [{"max": 0,"label": "0","color": "#ffffff"},{"min": 1,"max": 10,"color": "#ebb4a8"},{"min": 10,"max": 100,"color": "#e09694"},{"min": 100,"max": 500,"color": "#cb8382"},{"min": 500,"max": 1000,"color": "#b27372"},{"min": 1000,"color": "#976461"}]}};chart_486804f5cd794d05b9ce1fcdcef941b8.setOption(option_486804f5cd794d05b9ce1fcdcef941b8);</script></body></html>
结果如图所示，爬取-nCoV确诊数据并制作pyecharts可视化地图与丁香园疫情图数据相符合：
可点击地图旁边的图例，点击颜色框框选择是否要在地图该区域范围渲染上颜色：
