闸北区网站建设网,株洲企业网站建设工作室,微信公众号可以自动导入wordpress,html制作新闻信息展示页面大家好#xff0c;作为一名互联网行业的小白#xff0c;写博客只是为了巩固自己学习的知识#xff0c;但由于水平有限#xff0c;博客中难免会有一些错误出现#xff0c;有不妥之处恳请各位大佬指点一二#xff01; 博客主页#xff1a;链接: https://blog.csdn.net/wei… 大家好作为一名互联网行业的小白写博客只是为了巩固自己学习的知识但由于水平有限博客中难免会有一些错误出现有不妥之处恳请各位大佬指点一二 博客主页链接: https://blog.csdn.net/weixin_52720197?spm1018.2118.3001.5343 博文声明该项目参考B站视频https://www.bilibili.com/video/BV177411j7qJ 本人基于原项目做了补充和完善 1.项目准备
1.1.简介
基于PythonFlaskEcharts打造一个疫情监控系统涉及技术有
Python爬虫 使用Python与Mysql数据库交互 使用Flask构建Web项目 基于Echarts数据可视化展示 在linux上部署Web项目及爬虫 项目架构
数据获取爬虫数据持久化MySQLflask搭建Web后台数据可视化(h5echarts)
1.2.项目环境
Python 3.
MySQL
PyCharm(Python IDE)
Jupyter notebook(Python IDE)
Hbuilder(前端IDEhttps://www.dcloud.io/hbuilderx.html)
Linux主机后期项目部署
1.2.1.Jupyter Notebook
Jupyter Notebook此前被称为IPython notebook是一个基于网页的用于交互计算的应用程序在数据可续额领域很受欢迎。
简言之notebook是以网页的形式打开可以在code类型单元格中直接编写代码和运行代码代码的运行结果也会直接在代码块下显示。如在编程过程中需要编写说明文档可在md类型的单元格中直接编写便于及时的说明和解释。
安装
pip install notebook启动
jupyter notebook2.数据获取
2.1.爬虫概述
爬虫就是给网站发起请求并从响应中提取需要的数据自动化程序 发起请求获取响应 通过http库对目标站点进行请求。等同于自己打开浏览器输入网址 常用库urllib、urllib3、requests 服务器会返回请求的内容一般为HTML、二进制文件视频、音频、文档、JSON字符串等 解析内容 寻找自己需要的信息就是利用正则表达式或者其他库提取目标信息
常用库re、beautifulsoup4 3. 保存数据 将解析得到的数据持久化到文件或者数据库中
2.2.request发送请求
2.2.1.demo 先安装pip install requests requests.get()
import requestsurlhttp://www.baidu.com
# 发起请求
resrequests.get(url)# 查看编码
print(res.encoding)
# 查看请求头
print(res.headers)
# 查看访问的地址
print(res.url)res.headers返回结果里面 如果没有Content-Type ,那么encoding就是utf-8 否则 如果设置了charset就以设置的为准否则就是ISO-8859-1
查看网页html源码
print(res.text)发现出现了乱码用res.encodingutf-8来解决
res.encodingutf-8
print(res.text) 解决反爬
同样这里也把url改成点评
import requestsurlhttp://www.dianping.com
header{User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36
}
resrequests.get(url,headersheader)print(res.encoding)
print(res.headers)
print(res.url)
print(res.status_code) #此时的状态码是200说明可以正常访问了2.3.爬取腾讯疫情数据 有了爬虫基础后我们可以自行去全国各地的卫健委网站上爬取数据不过部分网站反爬虫手段很高明需要更专业的反爬手段 我们也可以去各大平台直接爬取最终数据比如 https://voice.baidu.com/act/newpneumonia/newpneumonia/?fromosari_pc_1 https://news.qq.com//zt2020/page/feiyan.htm 浏览腾讯的发现有意外收获 有全国的数据我们可以直接拿来使用
这里有个问题如果腾讯的大兄弟偷懒的话怎么办
解打开google Browser开发者工具networkjs一般js、json格式的都在这里面找get开头的一般这种请求的数据都是以get命名开头
一个一个筛选找到是这个
import requests
urlhttps://view.inews.qq.com/g2/getOnsInfo?namedisease_h5
#使用requests请求
resrequests.get(url)
print(res.text)执行返回json格式的数据
拿到json格式的数据后我们把它转换为字典
import json
djson.loads(res.text)
print(d)里面主要两个ret、data
打印一下data 看看data的数据类型是什么
print(type(d[data]))再用json模块加载data里的数据到新的变量data_all即str加载成字典格式
data_alljson.loads(d[data])
print(type(data_all))看一下这个字典格式的data里面有什么
data_all.keys()打印一下前几个看看怎么样
print(data_all[lastUpdateTime]) # 上次数据更新时间
print(data_all[chinaTotal]) # 当前我国汇总数据
print(data_all[chinaAdd]) # 新增数据看一下areaTree
print(data_all[areaTree])print(len(data_all[areaTree]))可以看到长度是一只有中国之前有其他国家的腾讯后台删除了
看一下有几个key
# [0]代表查看第一个数据
print(data_all[areaTree][0].keys())分别查看
print(data_all[areaTree][0][name])
print(data_all[areaTree][0][today])
print(data_all[areaTree][0][total])children比较长另起一个单元格
print(data_all[areaTree][0][children])#写个循环遍历到省级
for i in data_all[areaTree][0][children]:print(i[name]) #拿到各省名字依次类推可以遍历找出各市级县级。。
下面附上主要字段的数据结构 下面贴上完整的分析代码
在这里插入代码片2.3.1.数据存储
创建数据库cov,然后建两张表
history 表存储每日总数据
CREATE TABLE history (
ds datetime NOT NULL COMMENT 日期,
confirm int(11) DEFAULT NULL COMMENT 累计确诊,
confirm_add int(11) DEFAULT NULL COMMENT 当日新增确诊,
suspect int(11) DEFAULT NULL COMMENT 剩余疑似,
suspect_add int(11) DEFAULT NULL COMMENT 当日新增疑似,
heal int(11) DEFAULT NULL COMMENT 累计治愈,
heal_add int(11) DEFAULT NULL COMMENT 当日新增治愈,
dead int(11) DEFAULT NULL COMMENT 累计死亡,
dead_add int(11) DEFAULT NULL COMMENT 当日新增死亡,
PRIMARY KEY (ds) USING BTREE ) ENGINEInnoDB DEFAULT CHARSETutf8mb4;
details 表存储每日详细数据
CREATE TABLE details (
id int(11) NOT NULL AUTO_INCREMENT,
update_time datetime DEFAULT NULL COMMENT 数据最后更新时间,
province varchar(50) DEFAULT NULL COMMENT 省,
city varchar(50) DEFAULT NULL COMMENT 市,
confirm int(11) DEFAULT NULL COMMENT 累计确诊,
confirm_add int(11) DEFAULT NULL COMMENT 新增确诊,
heal int(11) DEFAULT NULL COMMENT 累计治愈,
dead int(11) DEFAULT NULL COMMENT 累计死亡,
PRIMARY KEY (id) ) ENGINEInnoDB DEFAULT CHARSETutf8mb4;使用 pymysql 模块与数据库交互
安装 pip install pymysql
① 建立连接 ② 创建游标 ③ 执行操作 ④ 关闭连接
2.3.1.1.pymysql基础测试
随便插入一条数据测试一下
插入没报错的话下面进行查询测试 存储操作
def get_conn()::return: 连接游标# 创建连接conn pymysql.connect(host127.0.0.1,userroot,password123456,dbcov,charsetutf8)# 创建游标cursor conn.cursor() # 执行完毕返回的结果集默认以元组显示return conn, cursordef close_conn(conn, cursor):if cursor:cursor.close()if conn:conn.close()def update_details():更新 details 表:return:cursor Noneconn Nonetry:li get_tencent_data()[1] # 0 是历史数据字典,1 最新详细数据列表conn, cursor get_conn()sql insert into details(update_time,province,city,confirm,confirm_add,heal,dead) values(%s,%s,%s,%s,%s,%s,%s)sql_query select %s(select update_time from details order by id desc limit 1) #对比当前最大时间戳cursor.execute(sql_query,li[0][0])if not cursor.fetchone()[0]:print(f{time.asctime()}开始更新最新数据)for item in li:cursor.execute(sql, item)conn.commit() # 提交事务 update delete insert操作print(f{time.asctime()}更新最新数据完毕)else:print(f{time.asctime()}已是最新数据)except:traceback.print_exc()finally:close_conn(conn, cursor)def insert_history():插入历史数据:return:cursor Noneconn Nonetry:dic get_tencent_data()[0] # 0 是历史数据字典,1 最新详细数据列表print(f{time.asctime()}开始插入历史数据)conn, cursor get_conn()sql insert into history values(%s,%s,%s,%s,%s,%s,%s,%s,%s)for k, v in dic.items():# item 格式 {2020-01-13: {confirm: 41, suspect: 0, heal: 0, dead: 1}cursor.execute(sql, [k, v.get(confirm), v.get(confirm_add), v.get(suspect),v.get(suspect_add), v.get(heal), v.get(heal_add),v.get(dead), v.get(dead_add)])conn.commit() # 提交事务 update delete insert操作print(f{time.asctime()}插入历史数据完毕)except:traceback.print_exc()finally:close_conn(conn, cursor)def update_history():更新历史数据:return:cursor Noneconn Nonetry:dic get_tencent_data()[0] # 0 是历史数据字典,1 最新详细数据列表print(f{time.asctime()}开始更新历史数据)conn, cursor get_conn()sql insert into history values(%s,%s,%s,%s,%s,%s,%s,%s,%s)sql_query select confirm from history where ds%sfor k, v in dic.items():# item 格式 {2020-01-13: {confirm: 41, suspect: 0, heal: 0, dead: 1}if not cursor.execute(sql_query, k):cursor.execute(sql, [k, v.get(confirm), v.get(confirm_add), v.get(suspect),v.get(suspect_add), v.get(heal), v.get(heal_add),v.get(dead), v.get(dead_add)])conn.commit() # 提交事务 update delete insert操作print(f{time.asctime()}历史数据更新完毕)except:traceback.print_exc()finally:close_conn(conn, cursor)#插入历史数据
insert_history()
Mon Feb 17 01:43:37 2020开始插入历史数据
Mon Feb 17 01:43:40 2020插入历史数据完毕这里在2020三月份后会发现你的history表为空只有当日的数据details表中可以获取是因为腾讯把东西挪地方了我下面又重新爬了一遍
还是按照上次教的步骤来先分析一遍然后上完整源码
这次把1月13-3月24的历史数据搞了过来日后有小伙伴使用时如果爬不到的话可以将下面这个历史数据和上面爬取那个结合起来赋给一个变量直接拿去做可视化
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firm\:182,\deadRate\:\0.92\,\healRate\:\10.08\}},{\date\:\02.23\,\all\:{\confirm\:1895,\dead\:22,\heal\:155,\newAddConfirm\:386,\deadRate\:\1.16\,\healRate\:\8.18\}},{\date\:\02.24\,\all\:{\confirm\:2286,\dead\:32,\heal\:173,\newAddConfirm\:300,\deadRate\:\1.40\,\healRate\:\7.57\}},{\date\:\02.25\,\all\:{\confirm\:2575,\dead\:40,\heal\:192,\newAddConfirm\:365,\deadRate\:\1.55\,\healRate\:\7.46\}},{\date\:\02.26\,\all\:{\confirm\:3085,\dead\:51,\heal\:220,\newAddConfirm\:459,\deadRate\:\1.65\,\healRate\:\7.13\}},{\date\:\02.27\,\all\:{\confirm\:3945,\dead\:64,\heal\:310,\newAddConfirm\:736,\deadRate\:\1.62\,\healRate\:\7.86\}},{\date\:\02.28\,\all\:{\confirm\:4947,\dead\:80,\heal\:360,\newAddConfirm\:1030,\deadRate\:\1.62\,\healRate\:\7.28\}},{\date\:\02.29\,\all\:{\confirm\:6305,\dead\:95,\heal\:455,\newAddConfirm\:1309,\deadRate\:\1.51\,\healRate\:\7.22\}},{\date\:\03.01\,\all\:{\confirm\:7342,\dead\:112,\heal\:1020,\newAddConfirm\:1161,\deadRate\:\1.53\,\healRate\:\13.89\}},{\date\:\03.02\,\all\:{\confirm\:9628,\dead\:152,\heal\:558,\newAddConfirm\:1589,\deadRate\:\1.58\,\healRate\:\5.80\}},{\date\:\03.03\,\all\:{\confirm\:12047,\dead\:184,\heal\:895,\newAddConfirm\:1800,\deadRate\:\1.53\,\healRate\:\7.43\}},{\date\:\03.04\,\all\:{\confirm\:13897,\dead\:236,\heal\:909,\newAddConfirm\:2078,\deadRate\:\1.70\,\healRate\:\6.54\}},{\date\:\03.05\,\all\:{\confirm\:16118,\dead\:294,\heal\:1087,\newAddConfirm\:4166,\deadRate\:\1.82\,\healRate\:\6.74\}},{\date\:\03.06\,\all\:{\confirm\:19378,\dead\:365,\heal\:1556,\newAddConfirm\:2743,\deadRate\:\1.88\,\healRate\:\8.03\}},{\date\:\03.07\,\all\:{\confirm\:22946,\dead\:451,\heal\:1720,\newAddConfirm\:3641,\deadRate\:\1.97\,\healRate\:\7.50\}},{\date\:\03.08\,\all\:{\confirm\:26913,\dead\:561,\heal\:3208,\newAddConfirm\:3706,\deadRate\:\2.08\,\healRate\:\11.92\}},{\date\:\03.09\,\all\:{\confirm\:30760,\dead\:763,\heal\:3614,\newAddConfirm\:4063,\deadRate\:\2.48\,\healRate\:\11.75\}},{\date\:\03.10\,\all\:{\confirm\:35713,\dead\:977,\heal\:4212,\newAddConfirm\:3972,\deadRate\:\2.74\,\healRate\:\11.79\}},{\date\:\03.11\,\all\:{\confirm\:41078,\dead\:1228,\heal\:5274,\newAddConfirm\:4596,\deadRate\:\2.99\,\healRate\:\12.84\}},{\date\:\03.12\,\all\:{\confirm\:48535,\dead\:1577,\heal\:5779,\newAddConfirm\:6702,\deadRate\:\3.25\,\healRate\:\11.91\}},{\date\:\03.13\,\all\:{\confirm\:58151,\dead\:1933,\heal\:6544,\newAddConfirm\:7485,\deadRate\:\3.32\,\healRate\:\11.25\}},{\date\:\03.14\,\all\:{\confirm\:67416,\dead\:2354,\heal\:7947,\newAddConfirm\:9750,\deadRate\:\3.49\,\healRate\:\11.79\}},{\date\:\03.15\,\all\:{\confirm\:79182,\dead\:2751,\heal\:9304,\newAddConfirm\:11511,\deadRate\:\3.47\,\healRate\:\11.75\}},{\date\:\03.16\,\all\:{\confirm\:92121,\dead\:3452,\heal\:10413,\newAddConfirm\:14579,\deadRate\:\3.75\,\healRate\:\11.30\}},{\date\:\03.17\,\all\:{\confirm\:105519,\dead\:4261,\heal\:11680,\newAddConfirm\:11665,\deadRate\:\4.04\,\healRate\:\11.07\}},{\date\:\03.18\,\all\:{\confirm\:122951,\dead\:4955,\heal\:13483,\newAddConfirm\:15084,\deadRate\:\4.03\,\healRate\:\10.97\}},{\date\:\03.19\,\all\:{\confirm\:146643,\dead\:6038,\heal\:15388,\newAddConfirm\:17888,\deadRate\:\4.12\,\healRate\:\10.49\}},{\date\:\03.20\,\all\:{\confirm\:171675,\dead\:7154,\heal\:17687,\newAddConfirm\:24120,\deadRate\:\4.17\,\healRate\:\10.30\}},{\date\:\03.21\,\all\:{\confirm\:202690,\dead\:8589,\heal\:20704,\newAddConfirm\:28268,\deadRate\:\4.24\,\healRate\:\10.21\}},{\date\:\03.22\,\all\:{\confirm\:234460,\dead\:10344,\heal\:23721,\newAddConfirm\:32665,\deadRate\:\4.41\,\healRate\:\10.12\}},{\date\:\03.23\,\all\:{\confirm\:269064,\dead\:12071,\heal\:26708,\newAddConfirm\:40678,\deadRate\:\4.49\,\healRate\:\9.93\}},{\date\:\03.24\,\all\:{\confirm\:308511,\dead\:13818,\heal\:29281,\newAddConfirm\:0,\deadRate\:\4.48\,\healRate\:\9.49\}}]}}import json
djson.loads(res.text) #和上次一样先将json格式转字典
# print(d)print(type(d[data])) #查看key为data的数据还是class strdata_historyjson.loads(d[data]) #strdict
print(type(data_history)) #返回class dict
data_history.keys() #查看keys们都有谁这下我们终于有chinaDayList和它的兄弟了快看看长啥样 先看这俩吧反正长得都还行
下面正式上源码和上面原理一样就是history需要从这个url上获取加上就OK啦
然后接下来就不用说了吧其他代码照搬上次的就OK了
最后看一下欧不欧凯 3.web
3.1.Flask快速入门
Flask 是一个使用 Python 编写的轻量级 Web 应用框架。其 WSGIPython Web Server Gateway Interface 工具包采用 Werkzeug 模板引擎则使 用 Jinja2 是目前十分流行的 web 框架。
安装pip install flask
创建Flask项目 创建后会有一个helloword模板可以直接运行这个mainif __name__ __main__: app.run()
from flask import Flaskapp Flask(__name__)app.route(/abc)
def hello_world1():return form账号inputbr密码inputbr/formif __name__ __main__:app.run()运行后打开浏览器输入网址http://127.0.0.1:5000/abc
使用模板
3.2.使用 Ajax 局部刷新页面
Ajax 是 Asynchronous JavaScript and XML 的简称通过 Ajax 向服务 器发送请求接收服务器返回的 json 数据然后使用 JavaScript 修改网 页的来实现页面局部数据更新
使用 jquery 框架可方便的编写 ajax 代码需要 jquery.js 文件 先下载jquery.min.js文件放到static/js/下
可视化大屏模板制作
3.3.echarts快速入门
ECharts缩写来自 Enterprise Charts商业级数据图表是百度的一个开源的数据可视 化工具提供了丰富的图表库能够在 PC 端和移动设备上流畅运行
官网网站https://echarts.apache.org/zh/index.html
5分钟上手echarts
上手这个五分钟的案例我们就可以搞个差不多了
echarts绘制图表 全国疫情地图实现
复制中国地图 option导入 china.js获取数据
def get_c2_data()::return: 返回各省数据# 因为会更新多次数据取时间戳最新的那组数据sql select province,sum(confirm) from details \where update_time(select update_time from details \order by update_time desc limit 1) \group by provinceres query(sql)return res全国累计趋势 复制折线图 option 获取数据
def get_l1_data()::return:返回每天历史累计数据sql select ds,confirm,suspect,heal,dead from historyres query(sql)return res全国新增趋势 复制折线图 option 获取数据
def get_l2_data()::return:返回每天新增确诊和疑似数据sql select ds,confirm_add,suspect_add from historyres query(sql)return res非湖北地区TOP5
复制柱状图 option获取数据
def get_r1_data()::return: 返回非湖北地区城市确诊人数前5名sql SELECT city,confirm FROM \(select city,confirm from details \where update_time(select update_time from details order by update_time desc limit 1) \and province not in (湖北,北京,上海,天津,重庆) \union all \select province as city,sum(confirm) as confirm from details \where update_time(select update_time from details order by update_time desc limit 1) \and province in (北京,上海,天津,重庆) group by province) as a \ORDER BY confirm DESC LIMIT 5res query(sql)return res疫情热搜
复制词云图 option导入wordcloud.js获取数据使用 jieba 获取关键字
def get_r2_data()::return: 返回最近的20条热搜sql select content from hotsearch order by id desc limit 20res query(sql) # 格式 ((民警抗疫一线奋战16天牺牲1037364,), (四川再派两批医疗队1537382,)return respycharm运行项目效果
4.linux部署Flask
4.1.部署模式
开发模式部署 修改 app.run() 的 host 为 0.0.0.0 生产模式部署 部署 Flask 应用时通常都是使用一种 WSGI 应用服务器搭配 Nginx 作为反向代理
常用的 WSGI 服务器 gunicorn、uwsgi
反向代理和正向代理 生产模式部署
安装 Nginxyum install nginx (
安装 Gunicorn: pip install gunicorn
启动 Gunicorn gunicorn -b 127.0.0.1:8080 -D my_app:app
编辑 Nginx 配置文件 vim /etc/nginx/nginx.conf
启动 Nginx : /usr/sbin/nginx 4.2.部署定时爬虫
• 获取脚本参数
sys.argv
sys.argv[0] 是脚本所在绝对路径
根据不同参数调用不用方法
• Linux 安装 chrome
yum install https://dl.google.com/linux/direct/google-chromestable_current_x86_64.rpm
• 下载 chromedriver
http://npm.taobao.org/mirrors/chromedriver/ 项目中使用的 79.0.3945.36/
获取crontab 定时调度 crontab –l 列出当前任务 crontab –e 编辑任务 格式* * * * * 指令 五个星号分别代表 分 、时 、日 、月、周
pycharm先给大家看一下我的项目结构
