爬取静态页面内容加分页内容

实验网站http://books.toscrape.com/爬取

# -*- coding: utf-8 -*-
import scrapy


class Book(scrapy.Spider):
    name = 'book'    #爬虫标识名称
    # allowed_domains = ['example.com']
    start_urls = ['http://books.toscrape.com/']   #开始爬取的位置
    def parse(self, response):
        for book in response.xpath("//article[@class='product_pod']"):
            book_name = book.xpath("./h3/a/@title").extract()
            book_price = book.xpath("./div[@class='product_price']/p[@class='price_color']/text()").extract()
            yield{
                'name':book_name,
                'price':book_price,
            }
            #获取下一页代码实现所有页数的数据的爬取
            url = response.xpath("//li[@class='next']/a/@href").extract_first()
            url2 = response.urljoin(url)
            yield scrapy.Request(url2,callback=self.parse)

这个地址不是全部内容所以要拼截url2 = response.urljoin(url)
最后通过yield获取全部路径

启动文件内容

运行后显示的结果

到此显示book网页的对书名与书价的静态数据爬取与分页的显示所有内容完成

例子2：
爬取51cto中的静态数据数据中输入关键字python,爬取，职位名，公司名，工作地点，薪资，发布时间

# -*- coding:utf-8 -*-
import scrapy

class Job51(scrapy.Spider):
    name = 'job51'   #爬虫名称
    start_urls = ['https://search.51job.com/list/150200,000000,0000,00,9,99,python,2,1.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare=']
    def parse(self, response):
        for jobinfo in response.xpath("//div[@class='dw_wp']/div[@class='dw_table']/div[@class='el']"):
            jobname = jobinfo.xpath("./p/span/a/@title").extract()
            companyname = jobinfo.xpath("./span[@class='t2']/a/@title").extract()
            place = jobinfo.xpath("./span[@class='t3']/text()").extract()
            salary = jobinfo.xpath("./span[@class='t4']/text()").extract()
            time = jobinfo.xpath("./span[@class='t5']/text()").extract()
            yield{
                'jobname':jobname,
                ' companyname':companyname,
                'place':place,
                'salary':salary,
                'time':time,
            }

结果

第三部分例子

爬取51job中的分页内容 我们输入关键字数据科学家


共有4页将爬取所有的数据

# -*- coding:utf-8 -*-
import scrapy

class MathSubject(scrapy.Spider):
    name = 'mathsubject'   #爬虫名称
    start_urls = ['https://search.51job.com/list/000000,000000,0000,00,9,99,%25E6%2595%25B0%25E6%258D%25AE%25E7%25A7%2591%25E5%25AD%25A6%25E5%25AE%25B6,2,1.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare=']
    def parse(self, response):
        for jobinfo in response.xpath("//div[@class='dw_wp']/div[@class='dw_table']/div[@class='el']"):
            jobname = jobinfo.xpath("./p/span/a/@title").extract()
            companyname = jobinfo.xpath("./span[@class='t2']/a/@title").extract()
            place = jobinfo.xpath("./span[@class='t3']/text()").extract()
            salary = jobinfo.xpath("./span[@class='t4']/text()").extract()
            time = jobinfo.xpath("./span[@class='t5']/text()").extract()
            yield{
                'jobname':jobname,
                ' companyname':companyname,
                'place':place,
                'salary':salary,
                'time':time,
            }
            #上面是获取一页中的内容
            #下面是获取下一页的网页地址路径
        url = response.xpath("//li[@class='bk']")[1].xpath("./a/@href").extract_first()
        yield scrapy.Request(url,callback=self.parse)

结果

实例3：对51job中的数据中的详细信息进行获取

# -*- coding:utf-8 -*-
import scrapy

class JobDetailsInfo(scrapy.Spider):
    name = 'jobdetailsinfo'   #爬虫名称
    start_urls = ['https://search.51job.com/list/000000,000000,0000,00,9,99,%25E6%2595%25B0%25E6%258D%25AE%25E7%25A7%2591%25E5%25AD%25A6%25E5%25AE%25B6,2,1.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare=']
    def parse(self, response):
       #解析多行集合
       lines = response.xpath("//div[@class='dw_wp']/div[@class='dw_table']/div[@class='el']")
       #遍历每行数据
       for line in lines:
            jobinfo_url =line.xpath("./*[@class='t1 ']/span/a/@href").extract_first()
            yield scrapy.Request( jobinfo_url,callback=self.parseInfo )
       url = response.xpath("//li[@class='bk']")[1].xpath("./a/@href").extract_first()
       yield scrapy.Request(url,callback=self.parse)

    #定义函数：目的解析详情页数据
    def parseInfo(self, response):
           zw = response.xpath("//div[@class='cn']/h1/@title").extract_first()
           jinfo = response.xpath("//p[@class='msg ltype']/@title").extract()
           company = response.xpath("//div[@class='cn']/p[@class='cname']/a/@title").extract()
           zyinfo = response.xpath("//div[@class='bmsg job_msg inbox']/p/text()").extract()
           yield{

               '职业要求':jinfo,
               'zw':zw,
               '公司':company,
               '岗位职责任':zyinfo
           }

启动验证结果