使用scrapy框架爬取51job全国数据分析职位信息并做简单分析

使用scrapy框架爬取51job全国数据分析职位信息并做简单分析

工具：scrapy，MongoDB，Excel，tableau

1.分析网页链接，里面包含有【keyword=数据分析师&keywordtype=2&curr_page=1】这些关键信息，可以看出换页操作只需要更改curr_page的值。
2.查看网页源代码，发现职位信息都藏在源代码里，这样直接从源代码里提取职位信息就好了
3.先编写items，这里提取职位名称，薪资，公司名称，公司类型，公司规模，公司标签，所在城市，学历要求，工作经验，福利，招聘要求标签

class Job61Item(scrapy.Item):
    jobname=scrapy.Field()
    salary=scrapy.Field()
    company=scrapy.Field()
    companytype=scrapy.Field()
    companyscale=scrapy.Field()
    companytag=scrapy.Field()
    city=scrapy.Field()
    record=scrapy.Field()
    workyear=scrapy.Field()
    welfare=scrapy.Field()
    requirements=scrapy.Field()
    pass

4.再编写pipelines，把获取到的数据保存到MongoDB

import pymongo

class MongoPipeline(object):

    collection_name = 'scrapy_items'

    def __init__(self, mongo_uri, mongo_db):
        self.mongo_uri = mongo_uri
        self.mongo_db = mongo_db

    @classmethod
    def from_crawler(cls, crawler):
        return cls(
            mongo_uri=crawler.settings.get('MONGO_URI'),
            mongo_db=crawler.settings.get('MONGO_DATABASE')
        )

    def open_spider(self, spider):
        self.client = pymongo.MongoClient(self.mongo_uri)
        self.db = self.client[self.mongo_db]

    def close_spider(self, spider):
        self.client.close()

    def process_item(self, item, spider):
        self.db[self.collection_name].insert_one(dict(item))
        return item

5.配置settings

MONGO_URI='localhost'
MONGO_DATABASE='job51'   

ITEM_PIPELINES = {
   'job61.pipelines.MongoPipeline':300,
}

6.开始编写爬虫，首先使用循环获取每个职位的详情页面的url，从里面再提取具体的职位信息

 def start_requests(self):
        for i in range(1,185):
            print('正在爬取第'+str(i)+'页')
            url='https://search.51job.com/jobsearch/search_result.php?keyword=%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E5%B8%88&keywordtype=2&curr_page='+str(i)
            r=requests.get(url)
            r.encoding='gbk'
            urllist=re.compile('href="(.*?)" οnmοusedοwn=""').findall(r.text)
            for j in range(0,len(urllist)):
                joburl=urllist[j]
                yield scrapy.Request(joburl,callback=self.parse)

7.使用css选择器从网页源代码提取职位信息

parse(self, response):
        item = Job61Item()
        doc=pq(response.text)
        items=doc('body > div.tCompanyPage > div.tCompany_center.clearfix').items()
        for ite in items:    
            item['jobname']=ite.find('div.tHeader.tHjob > div > div.cn > h1').text()                 
            item['salary']=ite.find('div.tHeader.tHjob > div > div.cn > strong').text()
            item['company']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_msg > a > p').text()
            item['companytype']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_tag > p:nth-child(1)').text()
            item['companyscale']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_tag > p:nth-child(2)').text()
            item['companytag']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_tag > p:nth-child(3)').text()
            item['city']=(ite.find('div.tHeader.tHjob > div > div.cn > p.msg.ltype').text()).split('|')[0] 
            item['record']=(ite.find('div.tHeader.tHjob > div > div.cn > p.msg.ltype').text()).split('|')[2]
            item['workyear']=(ite.find('div.tHeader.tHjob > div > div.cn > p.msg.ltype').text()).split('|')[1]  
            item['welfare']=ite.find('div.tHeader.tHjob > div > div.cn > div > div').text()
            item['requirements']=ite.find('div.tCompany_main > div:nth-child(1) > div > div.mt10 > p:nth-child(2)').text()
            yield item
        pass

8.贴上完整代码

# -*- coding: utf-8 -*-
import scrapy
from job61.items import Job61Item
from pyquery import PyQuery as pq
import pymongo
from pymongo import MongoClient
import re
import requests

class JobSpider(scrapy.Spider):
    name = 'job'
    allowed_domains = ['51job.com']
    start_urls = ['https://51job.com/']

    def start_requests(self):
        for i in range(1,185):
            print('正在爬取第'+str(i)+'页')
            url='https://search.51job.com/jobsearch/search_result.php?keyword=%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E5%B8%88&keywordtype=2&curr_page='+str(i)
            r=requests.get(url)
            r.encoding='gbk'
            urllist=re.compile('href="(.*?)" οnmοusedοwn=""').findall(r.text)
            for j in range(0,len(urllist)):
                joburl=urllist[j]
                yield scrapy.Request(joburl,callback=self.parse)

    def parse(self, response):
        item = Job61Item()
        doc=pq(response.text)
        items=doc('body > div.tCompanyPage > div.tCompany_center.clearfix').items()
        for ite in items:    
            item['jobname']=ite.find('div.tHeader.tHjob > div > div.cn > h1').text()                 
            item['salary']=ite.find('div.tHeader.tHjob > div > div.cn > strong').text()
            item['company']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_msg > a > p').text()
            item['companytype']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_tag > p:nth-child(1)').text()
            item['companyscale']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_tag > p:nth-child(2)').text()
            item['companytag']=ite.find('div.tCompany_sidebar > div:nth-child(1) > div.com_tag > p:nth-child(3)').text()
            item['city']=(ite.find('div.tHeader.tHjob > div > div.cn > p.msg.ltype').text()).split('|')[0] 
            item['record']=(ite.find('div.tHeader.tHjob > div > div.cn > p.msg.ltype').text()).split('|')[2]
            item['workyear']=(ite.find('div.tHeader.tHjob > div > div.cn > p.msg.ltype').text()).split('|')[1]  
            item['welfare']=ite.find('div.tHeader.tHjob > div > div.cn > div > div').text()
            item['requirements']=ite.find('div.tCompany_main > div:nth-child(1) > div > div.mt10 > p:nth-child(2)').text()
            yield item
        pass

对爬取到的数据作简单分析

1.将MongoDB数据导出成csv文件，一共是9826条数据，在Excel中对其进行数据整理和清洗，结果如下所示

然后再将这个文件导入到tableau进行简单可视化分析
2.不同类型公司对数据分析职位的需求量

3.数据分析职位在各城市的薪资水平

4.工资经验与薪资的对比情况

5.对学历要求占比

6.招聘要求关键词词云