通过scrapy爬取前程无忧招聘数据

创建项目：

scrapy startproject ScrapyDemo
cd ScrapyDemo
scrapy genspider bigqcwy msearch.51job.com

items.py文件添加爬取信息：

class ScrapydemoItem(scrapy.Item):# define the fields for your item here like:# name = scrapy.Field()# 职位名称name = scrapy.Field()# 薪资水平salary = scrapy.Field()# 招聘单位company = scrapy.Field()# 工作地点jobPlace = scrapy.Field()# 工作经验jobExperience = scrapy.Field()# 学历要求education = scrapy.Field()# 工作内容（岗位职责）# jobContent = scrapy.Field()# 任职要求（技能要求）jobRequirement = scrapy.Field()

编辑spider文件bigqcwy.py：
对薪资简单做了清洗

# -*- coding: utf-8 -*-
import scrapy
import time
from ScrapyDemo.items import ScrapydemoItem
import reclass BigqcwySpider(scrapy.Spider):name = 'bigqcwy'allowed_domains = ['msearch.51job.com']custom_settings = {"DEFAULT_REQUEST_HEADERS": {'Cookie':'设置你的cookie',},"AUTOTHROTTLE_ENABLED": True,# "DOWNLOAD_DELAY": 1,# "ScrapyDemo.pipelines.ScrapydemoPipeline": 300,}start_urls = ['https://msearch.51job.com/']def start_requests(self):# 搜索关键词列表list = ['0100%2C7700%2C7200%2C7300%2C7800', '7400%2C2700%2C7900%2C7500%2C6600', '8000%2C6100%2C2600%2C2800%2C3300']for i in list:# 每个关键词有2000页for j in range(1, 2001):time.sleep(2)start_url = 'https://msearch.51job.com/job_list.php?funtype=' + str(i) +'&jobarea=000000&filttertype=loginmore&pageno=' + str(j)if start_url:yield scrapy.Request(url=start_url, callback=self.parse)def parse(self, response):# 保存详情页链接list_url = response.xpath('//*[@id="pageContent"]/div[3]/a')for list in list_url:time.sleep(1)url = list.xpath('@href').extract()[0]url = "https:" + url# print("爬取详情url：", url)if url:yield scrapy.Request(url=url, callback=self.parse_item)def parse_item(self, response):# time.sleep(2)item = ScrapydemoItem()# selector = Selector(response)# 职位名称item['name'] = response.xpath('//*[@id="pageContent"]/div[1]/div[1]/p/text()').extract_first()# 薪资水平try:sa = response.xpath('//*[@id="pageContent"]/div[1]/p/text()').extract_first()num = list(re.findall(r'([0-9]+(\.?[0-9]?)?)-([0-9]+(\.?[0-9]?)?)', sa)[0])if '万' in sa and '月' in sa:sa1 = float(num[0]) * 10sa2 = float(num[2]) * 10sa3 = str(sa1).replace('.0', '')sa4 = str(sa2).replace('.0', '')item['salary'] = sa3 + '-' + sa4 + '千/月'elif '万' in sa and '年' in sa:# 1、换算为万/月sa1 = float(num[0]) / 12sa2 = float(num[2]) / 12n1 = list(re.findall(r'([0-9]+(\.?[0-9]?)?)', str(sa1))[0])n2 = list(re.findall(r'([0-9]+(\.?[0-9]?)?)', str(sa2))[0])sa1 = str(n1[0]).replace('.0', '')sa2 = str(n2[0]).replace('.0', '')# 2、换算为千/月sa3 = float(sa1) * 10sa4 = float(sa2) * 10sa5 = str(sa3).replace('.0', '')sa6 = str(sa4).replace('.0', '')item['salary'] = sa5 + '-' + sa6 + '千/月'else:item['salary'] = saexcept:item['salary'] = '面议'# 招聘单位item['company'] = response.xpath('//*[@id="pageContent"]/div[2]/a[1]/p/text()').extract_first()# city地址try:dizhi = response.xpath('//*[@id="pageContent"]/div[2]/a[2]/span/text()').extract_first().replace('上班地址 : ', ':')except:dizhi = ''# 城市city = response.xpath('//*[@id="pageContent"]/div[1]/div[1]/em/text()').extract_first()# 工作地点try:item['jobPlace'] = city + dizhiexcept:item['jobPlace'] = city# 工作经验try:item['jobExperience'] = response.xpath('//*[@id="pageContent"]/div[1]/div[2]/span[2]/text()').extract_first()except:item['jobExperience'] = '数据缺失'# 学历要求try:item['education'] = response.xpath('//*[@id="pageContent"]/div[1]/div[2]/span[3]/text()').extract_first()except:item['education'] = '数据缺失'# 工作内容（岗位职责）# try:#     # item['jobContent'] = response.xpath('//*[@id="pageContent"]/div[3]/div[3]/article/br//text()').extract_first()#     item['jobContent'] = response.xpath('string(//*[@id="pageContent"]/div[3]/div[3]/article)').extract_first().split('：')[1].split('：')[0]# except:#     item['jobContent'] = '无数据'# 任职要求（技能要求）try:# item['jobRequirement'] = response.xpath('string(//*[@id="pageContent"]/div[3]/div[3]/article)').extract_first().split('：')[1].split('：')[1] //*[@id="pageContent"]/div[3]/div[2]/articlejobR = response.xpath('string(//*[@id="pageContent"]/div[3]/div[3]/article)').extract_first()if jobR != '':item['jobRequirement'] = jobRelse:item['jobRequirement'] = response.xpath('string(//*[@id="pageContent"]/div[3]/div[2]/article)').extract_first()except:item['jobRequirement'] = '数据缺失'# print("职位名称：", item['name'])# print("薪资水平：", item['salary'])# print("招聘单位：", item['company'])# print("工作地点：", item['jobPlace'])# print("工作经验：", item['jobExperience'])# print("学历要求：", item['education'])# print("任职要求（技能要求）：", item['jobRequirement'])return item

编辑pipelines.py：
采用Mongodb数据库存储数据

from pymongo import MongoClientclass ScrapydemoPipeline(object):def open_spider(self, spider):self.db = MongoClient('localhost', 27017).bigqcwy_dbself.collection = self.db.bigqcwy_collectiondef process_item(self, item, spider):self.collection.insert_one(dict(item))def close_spider(self, spider):self.collection.close()

编辑settings.py：

USER_AGENT = '设置user-agent'
ROBOTSTXT_OBEY = False
DOWNLOAD_DELAY = 1
COOKIES_ENABLED = False
ITEM_PIPELINES = {'ScrapyDemo.pipelines.ScrapydemoPipeline': 300,
}

爬取结果：