Scrapy学习——爬取智联招聘网站案例

安装scrapy

如果直接使用pip安装会在安装Twisted报错，所以我们需要手动安装。

下载

安装scrapy需要手动下载Twisted、pyOpenSSL。
Twisted：https://www.lfd.uci.edu/~gohlke/pythonlibs/
pyOpenSSL：https://pypi.org/simple/pyopenssl/

注：选择与自己python版本一致的安装包

安装

pip install 下载的文件名

准备

创建项目：

scrapy startproject zhaopin

这里是引用

创建爬虫：

scrapy genspider zp zhaopin.com

分析

我们爬取一个网站就得需要分析他的网页源代码
我分析之后发现，可以直接爬取每个城市的每页，他的所有信息都是可以直接在每页的找到，下面就是每个职位的所有信息

通过xpath来获取标签

智联招聘网站需要登陆才能爬取，我使用的是cookie登陆，先登陆之后将cookie复制下来

将cookie复制下来，然后使用复制的cookice进行登陆

代码

zhaopin爬虫文件代码

import scrapy
import re,json
import time
import requests
class ZhaopinSpider(scrapy.Spider):
    name = 'zhaopin'
    allowed_domains = ['zhaopin.com']
    
    def start_requests(self):
        cookies = 浏览器上面的cookie复制粘贴在这里
        self.cookies = {
     i.split("=")[0]: i.split("=")[1] for i in cookies.split("; ")}
        self.headers = {
     'User-Agent':"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)",}
        yield scrapy.Request(
            'https://www.zhaopin.com/',
            callback=self.parse,
            cookies=self.cookies,
            headers=self.headers
        )

    def parse(self, response):
        # 自动添加第一次请求的cookie
        start_city = 480
        end_city = 950
        print("开始爬取")
        for i in range(start_city,end_city):
            print("城市ID：",i)
            url_city = "https://sou.zhaopin.com/?jl={0}".format(i)
            yield scrapy.Request(
                url=url_city,
                callback=self.parse_page,
                    cookies=self.cookies,
                    headers=self.headers
            )

    def parse_page(self, response):
        page = 1
        next_page = response.xpath("//div[@class='pagination clearfix']//div[@class='pagination__pages']//button[@class='btn soupager__btn soupager__btn--disable']/text()").extract_first()
        if next_page == "下一页":
            print("正在爬取1：")
            yield scrapy.Request(
                url=response.request.url,
                callback=self.parse_zp,
                cookies=self.cookies,
                headers=self.headers
            )
        elif response.xpath("//div[@class='sou-main']//div[@class='sou-main__center clearfix']//div[@class='positionList-hook']//div[@class='page-empty__tips']//span/text()").extract_first() != None:
            print("未搜索到:",response.request.url)
            return
        else:
            print("正在爬取2：")
            for i in range(2, 40, 1):
                url_page = response.request.url + "&p={0}".format(page)
                page += 1
                yield scrapy.Request(
                    url=url_page,
                    callback=self.parse_zp,
                    cookies=self.cookies,
                    headers=self.headers
                )

    def parse_zp(self, response):
        item = {
     }
        list_body = response.xpath("//div[@class='joblist-box__item clearfix']")
        print("URL：",response.request.url)
        for body in list_body:
            #工作名字
            item['title'] = body.xpath(".//div[@class='iteminfo__line iteminfo__line1']//div[@class='iteminfo__line1__jobname']//span[@class='iteminfo__line1__jobname__name']/text()").extract_first()
            list_li = body.xpath(".//div[@class='iteminfo__line iteminfo__line2']//ul//li")
            #学历
            item['Education'] = list_li[2].xpath("./text()").extract_first()
            # 工作地点
            item['job_location'] = list_li[0].xpath("./text()").extract_first()
            #工作时间
            item['job_time'] = list_li[1].xpath("./text()").extract_first()
            #工资
            money = body.xpath(".//div[@class='iteminfo__line iteminfo__line2']//div[@class='iteminfo__line2__jobdesc']//p/text()").extract_first()
            item['money'] = money.split()
            #工作需要
            info = body.xpath(".//div[@class='iteminfo__line iteminfo__line3']//div[@class='iteminfo__line3__welfare']//div")
            info_list = []
            for i in info:
                info_list.append(i.xpath("./text()").extract_first())
            item['job_info'] = " ".join(info_list)
            # #公司名
            item['Company_name'] = body.xpath("//div[@class='iteminfo__line iteminfo__line1']//div[@class='iteminfo__line1__compname']//span[@class='iteminfo__line1__compname__name']/text()").extract_first()
            company = body.xpath(".//div[@class='iteminfo__line iteminfo__line2']//div[@class='iteminfo__line2__compdesc']//span")
            # 公司人数
            item['company_number'] = company[1].xpath("./text()").extract()
            # 公司类型
            item['company_type'] = company[0].xpath("./text()").extract()
            yield item

先在items写入以下代码

# Define here the models for your scraped items
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html

import scrapy
class RecruitmentItem(scrapy.Item):
    # 工作名字
    title = scrapy.Field()
    # 工资
    money = scrapy.Field()
    # 学历
    Education = scrapy.Field()
    # 工作描述
    # job_desc = scrapy.Field()
    #工作时间
    job_time = scrapy.Field()
    #工作需要
    job_info = scrapy.Field()
    # 工作地点
    job_location = scrapy.Field()
    # 公司名
    Company_name = scrapy.Field()
    # 公司类型
    company_type = scrapy.Field()
    # 公司人数
    company_number = scrapy.Field()

pipelines代码

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html


# useful for handling different item types with a single interface
from itemadapter import ItemAdapter

class RecruitmentPipeline:
    def process_item(self, item, spider):
        print(item)
        with open("智联招聘.txt", "a+", encoding="utf-8") as f: 
            for i in dict(item).items():
                f.write("".join(i[1])+"\t")
            f.write("\n")
        # return item
    def open_spider(self,spider):
        with open("智联招聘.txt", "w", encoding="utf-8") as f: 
            f.write("工作名\t学历\t工作地点\t工作时间\t工资\t公司名\t企业人数\t企业类型\n")

settings.py

需要在配置文件中放开几个配置

#管道文件
ITEM_PIPELINES = {
‘Recruitment.pipelines.RecruitmentPipeline’: 300,
}
#是否使用自定义的cookies
COOKIES_ENABLED = True
#是否遵循robots.txt规则
ROBOTSTXT_OBEY = False

结果

结语 ：scrapy爬虫之爬取智联招聘笔记