python爬虫–scrapy（再探）

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发布于 2021-04-21 11:02:58

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python爬虫–scrapy（再探）

scrapy项目创建

请移步这里

基于scrapy的全站数据爬取

—需求：爬取校花网中全部图片的名称

http://www.521609.com/meinvxiaohua/

实现方式：

将所有页面的ur L添加到start_ urls列表(不推荐)
自行手动进行请求发送(推荐) 手动请求发送: yield scrapy. Request (url, callback) : callback专用做于数据解析

创建scrapy以及基于管道的持久化存储：请点击此处查看

import scrapy
from meinvNetwork.items import MeinvnetworkItem

class MnspiderSpider(scrapy.Spider):
    name = 'mnSpider'
    #allowed_domains = ['www.xxx.com']
    start_urls = ['http://www.521609.com/meinvxiaohua/']
    url = 'http://www.521609.com/meinvxiaohua/list12%d.html'
    page_num = 2
    def parse(self, response):
        li_list = response.xpath('//*[@id="content"]/div[2]/div[2]/ul/li')
        for li in li_list:
            name = li.xpath('./a[2]/b/text() | ./a[2]/text()').extract_first()
            item = MeinvnetworkItem(name=name)
            yield item
        if self.page_num <= 11:
            new_url = format(self.url%self.page_num)
            self.page_num += 1
            yield scrapy.Request(url=new_url,callback=self.parse)

使用终端命令执行项目：scrapy crawl mnSpider 效果图

五大核心组件

引擎（Scrapy）

用来处理整个系统的数据流处理，触发事务（框架核心）

调度器（Scheduler）

用来接收引擎发过来的请求，压入队列中，并在引擎再次请求的时候返回。可以想象成一个URL（抓取网页的网址或者说是链接）的优先队列，由他来决定下一个要抓取的网址是什么，同时去除重复的网址。

下载器（DownLoader）

用于下载网页内容，并将网页内容返回给蜘蛛（Scrapy下载器是建立在twisted这个搞笑的异步模型上的）

爬虫（spiders）

爬虫是主要干活的，用于从特定的网页中提取自己需要的信息，即所谓的实体（Item）。用户也可以从中提取出链接，让Scrapy继续抓取下一个页面。

项目管道（Pipeline）

负责处理爬虫从网页中抽取的实体，主要的功能是持久化实体，验证实体的有效性、清除不需要的信息。当页面被爬虫解析后，将被发送到项目管理，并经过几个特定的持续处理数据。

请求传参

使用场景:如果爬取解析的数据不在同一张页面中。(深度爬取)

详见案例：爬取网易新闻

scrapy图片爬取

图片数据爬取（ImagesPipeline）

基于scrapy爬取字符串类型的数据和爬取图片类型的数据区别

— 字符串：只需要基于小path进行解析且提交管道进行持久化存储

— 图片：xpath解析出图片src的属性值。单独的对图片地址发起请求获取图片二进制类型的数据。

使用流程：

— 数据解析（图片地址）

— 将存储图片地址的item提交到指定的管道类

— 在管道文件中自制一个机遇ImagesPipeline的管道类

		— def get_media_requests(self,item,info):#根据图片地址进行数据请求

		— def file_path(self,request,response=None,info=None):#指定图片存储类型

		—def item_completed(self,results,item,info):#返回给下一个即将执行的管道类

— 在配置文件中：

		— 指定图片存储的目录：IMAGES_STORE = './img_temp'

		— 指定开启的管道：自制定的管道类

目录层级

img.py

import scrapy
from imgsPro.items import ImgsproItem

class ImgSpider(scrapy.Spider):
    name = 'img'
    # allowed_domains = ['www.xxx.com']
    start_urls = ['https://sc.chinaz.com/tupian/']

    def parse(self, response):
        div_list = response.xpath('//div[@id="container"]/div')
        for div in div_list:
            #注意伪属性
            img_url = 'https:' + div.xpath('./div/a/img/@src2').extract()[0]
            item = ImgsproItem(img_url=img_url)
            yield item

items.py

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

import scrapy


class ImgsproItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    img_url = scrapy.Field()
    #pass

pipeline.py

# 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 ImgsproPipeline:
# def process_item(self, item, spider):
# return item
from scrapy.pipelines.images import ImagesPipeline
import scrapy

class imgsPipeLine(ImagesPipeline):
    #根据图片地址进行数据请求
    def get_media_requests(self,item,info):

        yield scrapy.Request(item['img_url'])
    #指定图片存储类型
    def file_path(self,request,response=None,info=None):
        imgName = request.url.split('/')[-1]
        return imgName

    # def item_completed(self,results,item,info):
    # return item #返回给下一个即将执行的管道类

setting.py

BOT_NAME = 'imgsPro'

SPIDER_MODULES = ['imgsPro.spiders']
NEWSPIDER_MODULE = 'imgsPro.spiders'

LOG_LEVEL = 'ERROR'
# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'imgsPro (+http://www.yourdomain.com)'
USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36'
# Obey robots.txt rules
ROBOTSTXT_OBEY = False


# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
   
   'imgsPro.pipelines.imgsPipeLine': 300,
}

#指定图片存储路径
IMAGES_STORE = './img_temp'

效果图

中间件的使用

下载中间件

位置：引擎和下载器之间
作用：批量拦截到整个工程中的所有请求和响应
拦截请求：
- UA伪装
- 代理IP
拦截响应：篡改响应数据，响应对象。

中间件案例：网易新闻

https://news.163.com/

需求:爬取网易新闻中的新闻数据(标题和内容)

1.通过网易新闻的首页解析出五大板块对应的详情页的url (没有动态加载)
2.每一个板块对应的新闻标题都是动态加载出来的(动态加载)
3.通过解析出每一条新闻详情页的url获取详情页的页面源码，解析出新闻内容

目录层级

wangyi.py

import scrapy
from selenium import webdriver
from wangyiPro.items import WangyiproItem

class WangyiSpider(scrapy.Spider):
    name = 'wangyi'
    # allowed_domains = ['www.xxx.com']
    start_urls = ['https://news.163.com/']
    model_urls = []
    def __init__(self):
        self.bro = webdriver.Chrome(executable_path=r"E:\google\Chrome\Application\chromedriver.exe")
    def parse(self, response):
        li_list = response.xpath('//*[@id="index2016_wrap"]/div[1]/div[2]/div[2]/div[2]/div[2]/div/ul/li')
        alist = [3,4,6,7,8]
        for i in alist:
            model_url = li_list[i].xpath('./a/@href').extract_first()
            self.model_urls.append(model_url)
        for url in self.model_urls:
            yield scrapy.Request(url,callback=self.model_parse)

    def model_parse(self,response):
        div_list = response.xpath('/html/body/div/div[3]/div[4]/div[1]/div[1]/div/ul/li/div/div')
        for div in div_list:
            title = div.xpath('./div/div[1]/h3/a/text()').extract_first()
            new_detail_url = div.xpath('./div/div[1]/h3/a/@href').extract_first()
            if new_detail_url == None:
                continue
            item = WangyiproItem()
            item['title'] = title
            yield scrapy.Request(url=new_detail_url,callback=self.parse_detail,meta={
   'item':item})
    def parse_detail(self,response):
        content = response.xpath('//*[@id="content"]/div[2]//text()').extract()
        content = ''.join(content)
        item = response.meta['item']
        item['content'] = content
        yield item

    def closed(self,spider):
        self.bro.quit()

items.py

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

import scrapy


class WangyiproItem(scrapy.Item):
    # define the fields for your item here like:
    title = scrapy.Field()
    content = scrapy.Field()

middlewares.py

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

from scrapy import signals

# useful for handling different item types with a single interface
from itemadapter import is_item, ItemAdapter
from scrapy.http import HtmlResponse
from time import sleep


class WangyiproDownloaderMiddleware:
    # Not all methods need to be defined. If a method is not defined,
    # scrapy acts as if the downloader middleware does not modify the
    # passed objects.



    def process_request(self, request, spider):
        # Called for each request that goes through the downloader
        # middleware.

        # Must either:
        # - return None: continue processing this request
        # - or return a Response object
        # - or return a Request object
        # - or raise IgnoreRequest: process_exception() methods of
        # installed downloader middleware will be called
        return None

    def process_response(self, request, response, spider):
        bro = spider.bro
        if request.url in spider.model_urls:
            bro.get(request.url)
            sleep(2)
            page_text = bro.page_source
            new_response = HtmlResponse(url=request.url,body=page_text,encoding='utf-8',request=request)
            return new_response
        else:

            return response

    def process_exception(self, request, exception, spider):
        # Called when a download handler or a process_request()
        # (from other downloader middleware) raises an exception.

        # Must either:
        # - return None: continue processing this exception
        # - return a Response object: stops process_exception() chain
        # - return a Request object: stops process_exception() chain
        pass

pipelines.py

# 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 WangyiproPipeline:
    fp = None

    # 重写父类的一个方法：该方法只在爬虫开始的时候被调用一次
    def open_spider(self, spider):
        print('开始爬虫。。。。')
        self.fp = open('./wangyi.txt', 'w', encoding='utf-8')

    def close_spider(self, spider):
        print('爬虫结束!!!')
        self.fp.close()

    def process_item(self, item, spider):
        title = item['title']
        content = item['content']
        self.fp.write(title+content + '\n')
        return item

setting.py

BOT_NAME = 'wangyiPro'

SPIDER_MODULES = ['wangyiPro.spiders']
NEWSPIDER_MODULE = 'wangyiPro.spiders'


# Crawl responsibly by identifying yourself (and your website) on the user-agent
USER_AGENT = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11'
LOG_LEVEL = 'ERROR'
# Obey robots.txt rules
ROBOTSTXT_OBEY = False


# Enable or disable downloader middlewares
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
DOWNLOADER_MIDDLEWARES = {
   
   'wangyiPro.middlewares.WangyiproDownloaderMiddleware': 543,
}

# Enable or disable extensions
# See https://docs.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {
   
# 'scrapy.extensions.telnet.TelnetConsole': None,
#}

# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
   
   'wangyiPro.pipelines.WangyiproPipeline': 300,
}

效果图

CrawlSpider的全站数据爬取

CrawlSpider是Spider的一个子类全站数据爬取方式：

基于Spider：手动请求
基于CrawlSpider：

CrawlSpider的使用:

创建一个工程

cd XXX

创建爬虫文件(CrawlSpider) :

scrapy genspider -t crawl xxx www.xxx.com

链接提取器:
- 作用:根据指定的规则(allow) 进行指定链接的提取
规则解析器:
- 作用:将链接提取器提取到的链接进行指定规则(callback) 的解析

例子：

http://wz.sun0769.com/political/index/politicsNewest?id=1&page=1

sun.py

import scrapy
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule


class SunSpider(CrawlSpider):
    name = 'sun'
    #allowed_domains = ['www.xxx.com']
    start_urls = ['http://wz.sun0769.com/political/index/politicsNewest?id=1&page=1']
    link = LinkExtractor(allow=r'id=1&page=\d+')
    rules = (
        Rule(link, callback='parse_item', follow=True),
    )

    def parse_item(self, response):
        item = {
   }
        #item['domain_id'] = response.xpath('//input[@id="sid"]/@value').get()
        #item['name'] = response.xpath('//div[@id="name"]').get()
        #item['description'] = response.xpath('//div[@id="description"]').get()
        #return item
        print(response)

因为该网站更新技术，所以只能显示10页的数据（IP惨遭封禁）