目标任务:爬取腾讯社招信息,需要爬取的内容为:职位名称,职位的详情链接,职位类别,招聘人数,工作地点,发布时间。
scrapy startproject Tencent
命令执行后,会创建一个Tencent文件夹,结构如下
# -*- coding: utf-8 -*-import scrapyclass TencentItem(scrapy.Item): # 职位名 positionname = scrapy.Field() # 详情连接 positionlink = scrapy.Field() # 职位类别 positionType = scrapy.Field() # 招聘人数 peopleNum = scrapy.Field() # 工作地点 workLocation = scrapy.Field() # 发布时间 publishTime = scrapy.Field()
三、编写spider文件
进入Tencent目录,使用命令创建一个基础爬虫类:
# tencentPostion为爬虫名,tencent.com为爬虫作用范围scrapy genspider tencentPostion "tencent.com"
执行命令后会在spiders文件夹中创建一个tencentPostion.py的文件,现在开始对其编写:
# -*- coding: utf-8 -*-import scrapyfrom tencent.items import TencentItemclass TencentpositionSpider(scrapy.Spider): """ 功能:爬取腾讯社招信息 """ # 爬虫名 name = "tencentPosition" # 爬虫作用范围 allowed_domains = ["tencent.com"] url = "http://hr.tencent.com/position.php?&start=" offset = 0 # 起始url start_urls = [url + str(offset)] def parse(self, response): for each in response.xpath("//tr[@class='even'] | //tr[@class='odd']"): # 初始化模型对象 item = TencentItem() # 职位名称 item['positionname'] = each.xpath("./td[1]/a/text()").extract()[0] # 详情连接 item['positionlink'] = each.xpath("./td[1]/a/@href").extract()[0] # 职位类别 item['positionType'] = each.xpath("./td[2]/text()").extract()[0] # 招聘人数 item['peopleNum'] = each.xpath("./td[3]/text()").extract()[0] # 工作地点 item['workLocation'] = each.xpath("./td[4]/text()").extract()[0] # 发布时间 item['publishTime'] = each.xpath("./td[5]/text()").extract()[0] yield item if self.offset < 1680: self.offset += 10 # 每次处理完一页的数据之后,重新发送下一页页面请求 # self.offset自增10,同时拼接为新的url,并调用回调函数self.parse处理Response yield scrapy.Request(self.url + str(self.offset), callback = self.parse)
四、编写pipelines文件
# -*- coding: utf-8 -*-import jsonclass TencentPipeline(object): """ 功能:保存item数据 """ def __init__(self): self.filename = open("tencent.json", "w") def process_item(self, item, spider): text = json.dumps(dict(item), ensure_ascii = False) + ",/n" self.filename.write(text.encode("utf-8")) return item def close_spider(self, spider): self.filename.close()
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