招聘信息数据分析及可视化|以51JOB为例进行_基于数据挖掘的线上招聘信息的分析与运用

作者：Monodyee | 2024-06-09 22:42:01

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基于数据挖掘的线上招聘信息的分析与运用

作者主页：编程千纸鹤

作者简介：Java、前端、Python开发多年，做过高程，项目经理，架构师

主要内容：Java项目开发、Python项目开发、大学数据和AI项目开发、单片机项目设计、面试技术整理、最新技术分享

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文末获得源码

项目编号：BS-Python-012

一，环境介绍

语言环境：Python3.7

数据库：Mysql: mysql5.7

开发工具：IDEA或PyCharm

使用技术：Python+Flask+Echart+BeautifulSoup

二，项目简介

本项目基于爬虫实现对51JOB网站的爬取，并将数据存入MYSQL数据库。利用Flask开发WEB应用程序，读取MYSQL数据进行分析，利用Echart进行数据分析展示。

三，系统展示

岗位分布

薪资分布

学历分布

经验要求

公司类型

四，核心代码展示


 
from bs4 import BeautifulSoup  # 导入beautifulsoup库
import re  # 导入re库以使用正则表达式
import urllib.request, urllib.error
from urllib import parse
from clean_Data import CleanData
import time
 
 
class CrawlingData:
    # 第page页的岗位列表
    def get_jobPage(self, key, page):
        try:
            # 定义爬取岗位的第一页url
            #url = "https://search.51job.com/list/000000,000000,0000,00,9,99," + key + ',2,' + str(page) + '.html'
            #url = 'https://search.51job.com/list/000000,000000,0000,00,9,99,数据分析,2,{page}.html?lang=c&postchannel=0000&workyear=99&cotype=99&degreefrom=99&jobterm=99&companysize=99&ord_field=0&dibiaoid=0&line=&welfare='
            url = "https://search.51job.com/list/000000,000000,0000,00,9,99," + key + ",2,{}.html"  # 需要获取的url
            # 伪装header头，以防止网站禁止爬取,这里使用的是火狐浏览器的请求头
            header = {
                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:74.0) Gecko/20100101 Firefox/74.0"
            }
            # headers = {
            #     'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36',
            #     'Cookie': 'guid = f717c64de9a2d88a5cb25651fa2469d1;nsearch = jobarea % 3D % 26 % 7C % 26ord_field % 3D % 26 % 7C % 26recentSearch0 % 3D % 26 % 7C % 26recentSearch1 % 3D % 26 % 7C % 26recentSearch2 % 3D % 26 % 7C % 26recentSearch3 % 3D % 26 % 7C % 26recentSearch4 % 3D % 26 % 7C % 26collapse_expansion % 3D;search = jobarea % 7E % 60000000 % 7C % 21ord_field % 7E % 600 % 7C % 21recentSearch0 % 7E % 60000000 % A1 % FB % A1 % FA000000 % A1 % FB % A1 % FA0000 % A1 % FB % A1 % FA00 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA9 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA % A1 % FB % A1 % FA0 % A1 % FB % A1 % FApython % A1 % FB % A1 % FA2 % A1 % FB % A1 % FA1 % 7C % 21recentSearch1 % 7E % 60000000 % A1 % FB % A1 % FA000000 % A1 % FB % A1 % FA0000 % A1 % FB % A1 % FA00 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA9 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA % A1 % FB % A1 % FA0 % A1 % FB % A1 % FAjava % A1 % FB % A1 % FA2 % A1 % FB % A1 % FA1 % 7C % 21recentSearch2 % 7E % 60000000 % A1 % FB % A1 % FA000000 % A1 % FB % A1 % FA0000 % A1 % FB % A1 % FA00 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA9 % A1 % FB % A1 % FA99 % A1 % FB % A1 % FA % A1 % FB % A1 % FA0 % A1 % FB % A1 % FAPython % A1 % FB % A1 % FA2 % A1 % FB % A1 % FA1 % 7C % 21;ssxmod_itna = QqfxRDyD9iqDqY5DtDXDnD + h + OB58xuA7q28qGNQ43DZDiqAPGhDC + 39bRhR02GGqoYiPC0Ix3WG8heaoHfDRb5ABOG4GLDmKDyKGZeGGfxBYDQxAYDGDDPDocPD1D3qDkD7O1lS9kqi3DbO = Di4D + FLQDmqG0DDUH94G2D7UcW0vcQCdPETbgK0iQ07 = DjwbD / +DW17 = 5T1a5nu0i5 = iW3qGyRPGujwUZSbbDC2 = / Ud0zjDPqtgf3YBoQmD4dCYETR0weW0SLCi5pK4qe9G4zn0xDG4q5nDLxeD;ssxmod_itna2 = QqfxRDyD9iqDqY5DtDXDnD + h + OB58xuA7q24A6TQYQD / YCjDFOniFr2rf1aKAPQYmhOvxQVlQWrdqtizQYuxYoAmv4mtg0MANhr2SL8WkD69P34zRCti6TukvKsghsU59Gok8dNSe + cC8vVmhDxXx2qdCr / 8nEqvSA5Tjid9uoVBT64omNN1RGpnRiPyuK3aLfE + mIbpQ2E7pdOIgbjjEUhf6PhipvOIcybp0cd5zrjIgiYC12936gs20okO4c3NRId2BAOPE9bxVyrmrAPZFC6aHIOkSK2qp7HZTApMcOlNSRf / 6VBu5jtnQ / cWlHBtn7 + aM + IhzP4sxqg5q5YZ8xFCyD0aaQ2plh = Kmq8hOGnY2G42G5 = iaIrWlhKycFlhP0b8Kox0 + FWwNW0WnRbD + qGu79Eef4i8IQaiuD4rXEiD += CEPt8yKRt = DTLgtE91j4oX8dGumeEO / AbtxDKuAUEPN0C4pK2iKja = qBYYYU = OKbixD7 = DYIYwhqzDHRhDD == =;adv = ad_logid_url % 3Dhttps % 253A % 252F % 252Ftrace.51job.com % 252Ftrace.php % 253Fpartner % 253Dsem_pc360s1_29369 % 2526ajp % 253DaHR0cHM6Ly9ta3QuNTFqb2IuY29tL3RnL3NlbS9MUF8yMDIwXzEuaHRtbD9mcm9tPTM2MGFk % 2526k % 253D7d16490a53bc7f778963fbe04432456c % 2526qhclickid % 253Dd9d672a3cdb12ead % 26 % 7C % 26;partner = cn_bing_com;privacy = 1651475693'
            # }
 
            # 发送获取页面的请求
            req = urllib.request.Request(url, headers=header)
            html = ""
            response = urllib.request.urlopen(req, timeout=10)
            html = response.read().decode("utf-8")
            response.close()
            time.sleep(0.5)
            return html
        except:
            print('爬取失败')
 
    # 获取该岗位有的页数
    def get_allPages(self, html):
        try:
            bs = BeautifulSoup(html, "html.parser")
            a_text = bs.select(".td")[0].get_text()
            pages = re.findall(r"\d+", a_text)
            pages = int(pages[0])
            return pages
        except:
            print("error")
 
    # 获取岗位详情页links
    def get_links(self, html, pages):
        try:
            bs = BeautifulSoup(html, "html.parser")
            a_href = bs.select(".el > .t1 > span > a")
            link = []
            for i in a_href:
                link.append(i.get("href"))
            return link
        except:
            print('error')
 
    # 爬取详情页信息
    def get_jobInfo(slef, link):
        header = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:74.0) Gecko/20100101 Firefox/74.0"
        }
        errors = 0
        # 创建cleanData对象
        cle = CleanData()
        try:
            req = urllib.request.Request(link, headers=header)
            html = ""
            response = urllib.request.urlopen(req, timeout=10)
            html = response.read().decode("gbk")
            bs = BeautifulSoup(html, "html.parser")
            # 数据列表
            data = []
            # 获取岗位名称
            job_name = bs.select("h1")[0].get_text()
            job_name = cle.clean_job(job_name)
            data.append(job_name)
            # 获取薪资
            salary = bs.select("h1 ~ strong")[0].get_text()
            # 清洗工资数据，统一格式千/月，为空和面议的，默认为0.0代表面议，或不封顶，
            sals = cle.clean_salary(salary)
            # 获取最低工资并填充到data
            low_salary = sals[0]
            data.append(low_salary)
            # 获取最高工资
            high_salary = sals[1]
            data.append(high_salary)
            # 获取公司名
            company = bs.select(".catn")[0].get_text()
            # 去除公司名的中英括号
            company = cle.clean_company(company)
            data.append(company)
            # 获取信息
            msg = bs.select(".msg")[0].get_text()
            # 获取地区
            area = msg.split("|")[0].strip()
            # 清理地址的区
            area = cle.clean_area(area)
            data.append(area)
            # 获取经验
            experience = msg.split("|")[1].strip()
            # 清理经验格式与薪水类似，分为最低经验和最高经验
            exp = cle.clean_experience(experience)
            # 最低经验
            data.append(exp[0])
            # 最高经验
            data.append(exp[1])
            # 获取学历
            education = msg.split("|")[2].strip()
            # 清理学历
            education = cle.clean_education(education)
            data.append(education)
            # 获取公司类型
            comp_type = bs.select(".at")[0].get_text()
            data.append(comp_type)
            # 获取公司规模(人数)
            comp_size = bs.select(".at")[1].get_text()
            data.append(comp_size)
            # 获取职位信息
            job_infos = bs.select(".job_msg,.job_msg > ol > li > p > span,.job_msg > p")
            job_info = ""
            for i in job_infos:
                job_info += i.get_text().strip()
                job_info = "".join(job_info.split())
 
            data.append(job_info)
            response.close()
            time.sleep(0.5)
            print(data)
            return data
        except:
            print("爬取异常，跳过爬取")
            print("爬取失败链接:%s" % link)

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