scrapy框架

  pip3 install scrapy

注意: 按顺序依次全部安装

  a. pip3 install wheel b. 下载twisted http://www.lfd.uci.edu/~gohlke/pythonlibs/#twisted c. 进入下载目录，执行 pip3 install Twisted‑17.1.0‑cp35‑cp35m‑win_amd64.whl d. pip3 install pywin32 e. pip3 install scrapy

在Pycharm中运行Scrapy爬虫项目的基本操作
目标在Win7上建立一个Scrapy爬虫项目，以及对其进行基本操作。运行环境：电脑上已经安装了python(环境变量path已经设置好)，

以及scrapy模块，IDE为Pycharm 。操作如下：

 

　　一、建立Scrapy模板。进入自己的工作目录，shift + 鼠标右键进入命令行模式，在命令行模式下， 

输入scrapy startproject 项目名 ,如下：



看到以上的代码说明项目已经在工作目录中建好了。

 

　　二、在Pycharm中scrapy的导入。在Pycharm中打开工作目录中的TestDemo，点击File-> Settings->Project: TestDemo->Project Interpreter。

法一： 如图，



选择红框中右边的下拉菜单点击Show All, 如图：



点击右上角加号，如图：



在红色框体内找到电脑里已经安装的python，比如我的是：

C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\python.exe , 导入即可。

之后，pycharm会自动导入你已经在电脑上安装的scrapy等模块。如图，即红色框体中显示的。



法二：一个不那么麻烦的方法。如图：



点击红色框体，在弹出的框体内另安装一个scrapy, 如图：

 

需要安装的模块，如图：



模块自下而上进行安装，其中可能出现twisted包不能成功安装，出现

Failed building wheel for Twisted

Microsoft Visual C++ 14.0 is required...

的现象，那就搜一解决方案，这里不多说了。

 

三、Pycharm中scrapy的运行设置。

 　　法一：Scrapy爬虫的运行需要到命令行下运行，在pychram中左下角有个Terminal,点开就可以在Pycharm下进入命令行，默认

是在项目目录下的，要运行项目，需要进入下一层目录，使用cd TestDemo 进入下一层目录，然后用scrapy crawl 爬虫名 ， 即可运行爬虫。

如图：





　　法二：在TestDemoSpider目录和scrapy.cfg同级目录下面，新建一个entrypoint.py文件,如图：



其中只需把红色框体内的内容改成相应的爬虫的名字就可以在不同的爬虫项目中使用了，直接运行该文件就能使得Scrapy爬虫运行

 

Tips:在创建爬虫时使用模板更加方便一些，如：

 scrapy genspider [-t template] <name> <domain>   即：scrapy genspider testDemoSpider baidu.com

project_name/
   scrapy.cfg：
   project_name/
       __init__.py
       items.py
       pipelines.py
       settings.py
       spiders/
           __init__.py

scrapy.cfg   项目的主配置信息。（真正爬虫相关的配置信息在settings.py文件中）
items.py     设置数据存储模板，用于结构化数据，如：Django的Model
pipelines    数据持久化处理
settings.py  配置文件，如：递归的层数、并发数，延迟下载等
spiders      爬虫目录，如：创建文件，编写爬虫解析规则

# -*- coding: utf-8 -*- import scrapy class QiubaiSpider(scrapy.Spider): name = 'qiubai' #应用名称 #允许爬取的域名（如果遇到非该域名的url则爬取不到数据） allowed_domains = ['https://www.qiushibaike.com/'] #起始爬取的url start_urls = ['https://www.qiushibaike.com/'] #访问起始URL并获取结果后的回调函数，该函数的response参数就是向起始的url发送请求后，获取的响应对象.该函数返回值必须为可迭代对象或者NUll def parse(self, response): print(response.text) #获取字符串类型的响应内容 print(response.body)#获取字节类型的相应内容

修改内容及其结果如下：
19行：USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36' #伪装请求载体身份 22行：ROBOTSTXT_OBEY = False #可以忽略或者不遵守robots协议

# -*- coding: utf-8 -*- import scrapy class QiubaiSpider(scrapy.Spider): name = 'qiubai' allowed_domains = ['https://www.qiushibaike.com/'] start_urls = ['https://www.qiushibaike.com/'] def parse(self, response): #xpath为response中的方法，可以将xpath表达式直接作用于该函数中 odiv = response.xpath('//div[@id="content-left"]/div') content_list = [] #用于存储解析到的数据 for div in odiv: #xpath函数返回的为列表，列表中存放的数据为Selector类型的数据。我们解析到的内容被封装在了Selector对象中，需要调用extract()函数将解析的内容从Selecor中取出。 author = div.xpath('.//div[@class="author clearfix"]/a/h2/text()')[0].extract() content=div.xpath('.//div[@class="content"]/span/text()')[0].extract() #将解析到的内容封装到字典中 dic={ '作者':author, '内容':content } #将数据存储到content_list这个列表中 content_list.append(dic) return content_list

scrapy crawl 爬虫名称 ：该种执行形式会显示执行的日志信息

scrapy crawl 爬虫名称 --nolog：该种执行形式不会显示执行的日志信息

scrapy crawl 爬虫名称 -o xxx.json scrapy crawl 爬虫名称 -o xxx.xml scrapy crawl 爬虫名称 -o xxx.csv

# -*- coding: utf-8 -*- import scrapy from secondblood.items import SecondbloodItem class QiubaidemoSpider(scrapy.Spider): name = 'qiubaiDemo' allowed_domains = ['www.qiushibaike.com'] start_urls = ['http://www.qiushibaike.com/'] def parse(self, response): odiv = response.xpath('//div[@id="content-left"]/div') for div in odiv: # xpath函数返回的为列表，列表中存放的数据为Selector类型的数据。我们解析到的内容被封装在了Selector对象中，需要调用extract()函数将解析的内容从Selecor中取出。 author = div.xpath('.//div[@class="author clearfix"]//h2/text()').extract_first() author = author.strip('\n')#过滤空行 content = div.xpath('.//div[@class="content"]/span/text()').extract_first() content = content.strip('\n')#过滤空行 #将解析到的数据封装至items对象中 item = SecondbloodItem() item['author'] = author item['content'] = content yield item#提交item到管道文件（pipelines.py）

import scrapy class SecondbloodItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() author = scrapy.Field() #存储作者 content = scrapy.Field() #存储段子内容

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html class SecondbloodPipeline(object): #构造方法 def __init__(self): self.fp = None #定义一个文件描述符属性 　　#下列都是在重写父类的方法： #开始爬虫时，执行一次 def open_spider(self,spider): print('爬虫开始') self.fp = open('./data.txt', 'w') 　　 #因为该方法会被执行调用多次，所以文件的开启和关闭操作写在了另外两个只会各自执行一次的方法中。 def process_item(self, item, spider): #将爬虫程序提交的item进行持久化存储 self.fp.write(item['author'] + ':' + item['content'] + '\n') return item #结束爬虫时，执行一次 def close_spider(self,spider): self.fp.close() print('爬虫结束')

#开启管道 ITEM_PIPELINES = { 'secondblood.pipelines.SecondbloodPipeline': 300, #300表示为优先级，值越小优先级越高 }

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html #导入数据库的类 import pymysql class QiubaiproPipelineByMysql(object): conn = None #mysql的连接对象声明 cursor = None#mysql游标对象声明 def open_spider(self,spider): print('开始爬虫') #链接数据库 self.conn = pymysql.Connect(host='127.0.0.1',port=3306,user='root',password='123456',db='qiubai') #编写向数据库中存储数据的相关代码 def process_item(self, item, spider): #1.链接数据库 #2.执行sql语句 sql = 'insert into qiubai values("%s","%s")'%(item['author'],item['content']) self.cursor = self.conn.cursor() #执行事务 try: self.cursor.execute(sql) self.conn.commit() except Exception as e: print(e) self.conn.rollback() return item def close_spider(self,spider): print('爬虫结束') self.cursor.close() self.conn.close()

ITEM_PIPELINES = {
    'qiubaiPro.pipelines.QiubaiproPipelineByMysql': 300,
}

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html import redis class QiubaiproPipelineByRedis(object): conn = None def open_spider(self,spider): print('开始爬虫') #创建链接对象 self.conn = redis.Redis(host='127.0.0.1',port=6379) def process_item(self, item, spider): dict = { 'author':item['author'], 'content':item['content'] } #写入redis中 self.conn.lpush('data', dict) return item

ITEM_PIPELINES = {
    'qiubaiPro.pipelines.QiubaiproPipelineByRedis': 300,
}

    #下列结构为字典，字典中的键值表示的是即将被启用执行的管道文件和其执行的优先级。 ITEM_PIPELINES = { 'doublekill.pipelines.DoublekillPipeline': 300, 'doublekill.pipelines.DoublekillPipeline_db': 200, } #上述代码中，字典中的两组键值分别表示会执行管道文件中对应的两个管道类中的process_item方法，实现两种不同形式的持久化操作。

- 需求：爬取网易新闻的国内板块下的新闻数据 - 需求分析：当点击国内超链进入国内对应的页面时，会发现当前页面展示的新闻数据是被动态加载出来的，如果直接通过程序对url进行请求，是获取不到动态加载出的新闻数据的。则就需要我们使用selenium实例化一个浏览器对象，在该对象中进行url的请求，获取动态加载的新闻数据。

from scrapy.http import HtmlResponse #参数介绍： #拦截到响应对象（下载器传递给Spider的响应对象） #request：响应对象对应的请求对象 #response：拦截到的响应对象 #spider：爬虫文件中对应的爬虫类的实例 def process_response(self, request, response, spider): #响应对象中存储页面数据的篡改 if request.url in['http://news.163.com/domestic/','http://news.163.com/world/','http://news.163.com/air/','http://war.163.com/']: spider.bro.get(url=request.url) js = 'window.scrollTo(0,document.body.scrollHeight)' spider.bro.execute_script(js) time.sleep(2) #一定要给与浏览器一定的缓冲加载数据的时间 #页面数据就是包含了动态加载出来的新闻数据对应的页面数据 page_text = spider.bro.page_source #篡改响应对象 return HtmlResponse(url=spider.bro.current_url,body=page_text,encoding='utf-8',request=request) else: return response

DOWNLOADER_MIDDLEWARES = {
    'wangyiPro.middlewares.WangyiproDownloaderMiddleware': 543,

}

# -*- coding: utf-8 -*- import scrapy from qiushibaike.items import QiushibaikeItem # scrapy.http import Request class QiushiSpider(scrapy.Spider): name = 'qiushi' allowed_domains = ['www.qiushibaike.com'] start_urls = ['https://www.qiushibaike.com/text/'] #爬取多页 pageNum = 1 #起始页码 url = 'https://www.qiushibaike.com/text/page/%s/' #每页的url def parse(self, response): div_list=response.xpath('//*[@id="content-left"]/div') for div in div_list: #//*[@id="qiushi_tag_120996995"]/div[1]/a[2]/h2 author=div.xpath('.//div[@class="author clearfix"]//h2/text()').extract_first() author=author.strip('\n') content=div.xpath('.//div[@class="content"]/span/text()').extract_first() content=content.strip('\n') item=QiushibaikeItem() item['author']=author item['content']=content yield item #提交item到管道进行持久化 #爬取所有页码数据 if self.pageNum <= 13: #一共爬取13页（共13页） self.pageNum += 1 url = format(self.url % self.pageNum) #递归爬取数据：callback参数的值为回调函数（将url请求后，得到的相应数据继续进行parse解析），递归调用parse函数 yield scrapy.Request(url=url,callback=self.parse)

def start_requests(self): #请求的url post_url = 'http://fanyi.baidu.com/sug' # post请求参数 formdata = { 'kw': 'wolf', } # 发送post请求 yield scrapy.FormRequest(url=post_url, formdata=formdata, callback=self.parse)

# -*- coding: utf-8 -*- import scrapy from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule class ChoutidemoSpider(CrawlSpider): name = 'choutiDemo' #allowed_domains = ['www.chouti.com'] start_urls = ['http://www.chouti.com/'] rules = ( Rule(LinkExtractor(allow=r'Items/'), callback='parse_item', follow=True), ) def parse_item(self, response): i = {} #i['domain_id'] = response.xpath('//input[@id="sid"]/@value').extract() #i['name'] = response.xpath('//div[@id="name"]').extract() #i['description'] = response.xpath('//div[@id="description"]').extract() return i - 2，3行：导入CrawlSpider相关模块 　　 - 7行：表示该爬虫程序是基于CrawlSpider类的 　　 - 12，13，14行：表示为提取Link规则 　　 - 16行：解析方法

　　　　LinkExtractor(

　　　　　　　  allow=r'Items/'，# 满足括号中“正则表达式”的值会被提取，如果为空，则全部匹配。 　　　　　　　　 deny=xxx, # 满足正则表达式的则不会被提取。 　　　　　　　　 restrict_xpaths=xxx, # 满足xpath表达式的值会被提取 　　　　　　　　 restrict_css=xxx, # 满足css表达式的值会被提取 　　　　　　　　 deny_domains=xxx, # 不会被提取的链接的domains。　 　　 ) 　　　　- 作用：提取response中符合规则的链接。

　　　　 Rule(LinkExtractor(allow=r'Items/'), callback='parse_item', follow=True) 　　　　- 参数介绍： 　　　　　　参数1：指定链接提取器 　　　　　　参数2：指定规则解析器解析数据的规则（回调函数） 　　　　　　参数3：是否将链接提取器继续作用到链接提取器提取出的链接网页中。当callback为None,参数3的默认值为true。

　　　　a)爬虫文件首先根据起始url，获取该url的网页内容

　　　　b)链接提取器会根据指定提取规则将步骤a中网页内容中的链接进行提取

　　　　c)规则解析器会根据指定解析规则将链接提取器中提取到的链接中的网页内容根据指定的规则进行解析 　　　　d)将解析数据封装到item中，然后提交给管道进行持久化存储

    # -*- coding: utf-8 -*- import scrapy from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule class CrawldemoSpider(CrawlSpider): name = 'qiubai' #allowed_domains = ['www.qiushibaike.com'] start_urls = ['https://www.qiushibaike.com/pic/'] #连接提取器：会去起始url响应回来的页面中提取指定的url link = LinkExtractor(allow=r'/pic/page/\d+\?') #s=为随机数 link1 = LinkExtractor(allow=r'/pic/$')#爬取第一页 #rules元组中存放的是不同的规则解析器（封装好了某种解析规则) rules = ( #规则解析器：可以将连接提取器提取到的所有连接表示的页面进行指定规则（回调函数）的解析 Rule(link, callback='parse_item', follow=True), Rule(link1, callback='parse_item', follow=True), ) def parse_item(self, response): print(response)

# -*- coding: utf-8 -*- import scrapy from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule from qiubaiBycrawl.items import QiubaibycrawlItem import re class QiubaitestSpider(CrawlSpider): name = 'qiubaiTest' #起始url start_urls = ['http://www.qiushibaike.com/'] #定义链接提取器，且指定其提取规则 page_link = LinkExtractor(allow=r'/8hr/page/\d+/') rules = ( #定义规则解析器，且指定解析规则通过callback回调函数 Rule(page_link, callback='parse_item', follow=True), ) #自定义规则解析器的解析规则函数 def parse_item(self, response): div_list = response.xpath('//div[@id="content-left"]/div') for div in div_list: #定义item item = QiubaibycrawlItem() #根据xpath表达式提取糗百中段子的作者 item['author'] = div.xpath('./div/a[2]/h2/text()').extract_first().strip('\n') #根据xpath表达式提取糗百中段子的内容 item['content'] = div.xpath('.//div[@class="content"]/span/text()').extract_first().strip('\n') yield item #将item提交至管道

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html import scrapy class QiubaibycrawlItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() author = scrapy.Field() #作者 content = scrapy.Field() #内容

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html class QiubaibycrawlPipeline(object): def __init__(self): self.fp = None def open_spider(self,spider): print('开始爬虫') self.fp = open('./data.txt','w') def process_item(self, item, spider): #将爬虫文件提交的item写入文件进行持久化存储 self.fp.write(item['author']+':'+item['content']+'\n') return item def close_spider(self,spider): print('结束爬虫') self.fp.close()

                LOG_LEVEL = ‘指定日志信息种类’即可。 LOG_FILE = 'log.txt'则表示将日志信息写入到指定文件中进行存储。

# -*- coding: utf-8 -*- import scrapy from moviePro.items import MovieproItem class MovieSpider(scrapy.Spider): name = 'movie' allowed_domains = ['www.id97.com'] start_urls = ['http://www.id97.com/'] def parse(self, response): div_list = response.xpath('//div[@class="col-xs-1-5 movie-item"]') for div in div_list: item = MovieproItem() item['name'] = div.xpath('.//h1/a/text()').extract_first() item['score'] = div.xpath('.//h1/em/text()').extract_first() #xpath(string(.))表示提取当前节点下所有子节点中的数据值（.）表示当前节点 item['kind'] = div.xpath('.//div[@class="otherinfo"]').xpath('string(.)').extract_first() item['detail_url'] = div.xpath('./div/a/@href').extract_first() #请求二级详情页面，解析二级页面中的相应内容,通过meta参数进行Request的数据传递 yield scrapy.Request(url=item['detail_url'],callback=self.parse_detail,meta={'item':item}) def parse_detail(self,response): #通过response获取item item = response.meta['item'] item['actor'] = response.xpath('//div[@class="row"]//table/tr[1]/a/text()').extract_first() item['time'] = response.xpath('//div[@class="row"]//table/tr[7]/td[2]/text()').extract_first() item['long'] = response.xpath('//div[@class="row"]//table/tr[8]/td[2]/text()').extract_first() #提交item到管道 yield item

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html import scrapy  class MovieproItem(scrapy.Item): # define the fields for your item here like: name = scrapy.Field() score = scrapy.Field() time = scrapy.Field() long = scrapy.Field() actor = scrapy.Field() kind = scrapy.Field() detail_url = scrapy.Field()

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html import json class MovieproPipeline(object): def __init__(self): self.fp = open('data.txt','w') def process_item(self, item, spider): dic = dict(item) print(dic) json.dump(dic,self.fp,ensure_ascii=False) return item def close_spider(self,spider): self.fp.close()

# -*- coding: utf-8 -*- import scrapy from xiaohua.items import XiaohuaItem class XiahuaSpider(scrapy.Spider): name = 'xiaohua' allowed_domains = ['www.521609.com'] start_urls = ['http://www.521609.com/daxuemeinv/'] pageNum = 1 url = 'http://www.521609.com/daxuemeinv/list8%d.html' def parse(self, response): li_list = response.xpath('//div[@class="index_img list_center"]/ul/li') for li in li_list: school = li.xpath('./a/img/@alt').extract_first() img_url = li.xpath('./a/img/@src').extract_first() item = XiaohuaItem() item['school'] = school item['img_url'] = 'http://www.521609.com' + img_url yield item if self.pageNum < 10: self.pageNum += 1 url = format(self.url % self.pageNum) #print(url) yield scrapy.Request(url=url,callback=self.parse)

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html import scrapy class XiaohuaItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() school=scrapy.Field() img_url=scrapy.Field()

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html import json import os import urllib.request class XiaohuaPipeline(object): def __init__(self): self.fp = None def open_spider(self,spider): print('开始爬虫') self.fp = open('./xiaohua.txt','w') def download_img(self,item): url = item['img_url'] fileName = item['school']+'.jpg' if not os.path.exists('./xiaohualib'): os.mkdir('./xiaohualib') filepath = os.path.join('./xiaohualib',fileName) urllib.request.urlretrieve(url,filepath) print(fileName+"下载成功") def process_item(self, item, spider): obj = dict(item) json_str = json.dumps(obj,ensure_ascii=False) self.fp.write(json_str+'\n') #下载图片 self.download_img(item) return item def close_spider(self,spider): print('结束爬虫') self.fp.close()

USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36' # Obey robots.txt rules ROBOTSTXT_OBEY = False # Configure maximum concurrent requests performed by Scrapy (default: 16) CONCURRENT_REQUESTS = 100 COOKIES_ENABLED = False LOG_LEVEL = 'ERROR' RETRY_ENABLED = False DOWNLOAD_TIMEOUT = 3 # Configure a delay for requests for the same website (default: 0) # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 DOWNLOAD_DELAY = 3

　　　　- 不可以。原因有二。

　　　　　　其一：因为多台机器上部署的scrapy会各自拥有各自的调度器，这样就使得多台机器无法分配start_urls列表中的url。（多台机器无法共享同一个调度器）

　　　　　　其二：多台机器爬取到的数据无法通过同一个管道对数据进行统一的数据持久出存储。（多台机器无法共享同一个管道）

    - scrapy-redis组件中为我们封装好了可以被多台机器共享的调度器和管道，我们可以直接使用并实现分布式数据爬取。

    - 实现方式：

        1.基于该组件的RedisSpider类

        2.基于该组件的RedisCrawlSpider类

# -*- coding: utf-8 -*- import scrapy from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule from redis import Redis from incrementPro.items import IncrementproItem class MovieSpider(CrawlSpider): name = 'movie' # allowed_domains = ['www.xxx.com'] start_urls = ['http://www.4567tv.tv/frim/index7-11.html'] rules = ( Rule(LinkExtractor(allow=r'/frim/index7-\d+\.html'), callback='parse_item', follow=True), ) #创建redis链接对象 conn = Redis(host='127.0.0.1',port=6379) def parse_item(self, response): li_list = response.xpath('//li[@class="p1 m1"]') for li in li_list: #获取详情页的url detail_url = 'http://www.4567tv.tv'+li.xpath('./a/@href').extract_first() #将详情页的url存入redis的set中 ex = self.conn.sadd('urls',detail_url) if ex == 1: print('该url没有被爬取过，可以进行数据的爬取') yield scrapy.Request(url=detail_url,callback=self.parst_detail) else: print('数据还没有更新，暂无新数据可爬取！') #解析详情页中的电影名称和类型，进行持久化存储 def parst_detail(self,response): item = IncrementproItem() item['name'] = response.xpath('//dt[@class="name"]/text()').extract_first() item['kind'] = response.xpath('//div[@class="ct-c"]/dl/dt[4]//text()').extract() item['kind'] = ''.join(item['kind']) yield item

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html from redis import Redis class IncrementproPipeline(object): conn = None def open_spider(self,spider): self.conn = Redis(host='127.0.0.1',port=6379) def process_item(self, item, spider): dic = { 'name':item['name'], 'kind':item['kind'] } print(dic) self.conn.lpush('movieData',dic) return item

# -*- coding: utf-8 -*- import scrapy from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule from incrementByDataPro.items import IncrementbydataproItem from redis import Redis import hashlib class QiubaiSpider(CrawlSpider): name = 'qiubai' # allowed_domains = ['www.xxx.com'] start_urls = ['https://www.qiushibaike.com/text/'] rules = ( Rule(LinkExtractor(allow=r'/text/page/\d+/'), callback='parse_item', follow=True), Rule(LinkExtractor(allow=r'/text/$'), callback='parse_item', follow=True), ) #创建redis链接对象 conn = Redis(host='127.0.0.1',port=6379) def parse_item(self, response): div_list = response.xpath('//div[@id="content-left"]/div') for div in div_list: item = IncrementbydataproItem() item['author'] = div.xpath('./div[1]/a[2]/h2/text() | ./div[1]/span[2]/h2/text()').extract_first() item['content'] = div.xpath('.//div[@class="content"]/span/text()').extract_first() #将解析到的数据值生成一个唯一的标识进行redis存储 source = item['author']+item['content'] source_id = hashlib.sha256(source.encode()).hexdigest() #将解析内容的唯一表示存储到redis的data_id中 ex = self.conn.sadd('data_id',source_id) if ex == 1: print('该条数据没有爬取过，可以爬取......') yield item else: print('该条数据已经爬取过了，不需要再次爬取了!!!')

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html from redis import Redis class IncrementbydataproPipeline(object): conn = None def open_spider(self, spider): self.conn = Redis(host='127.0.0.1', port=6379) def process_item(self, item, spider): dic = { 'author': item['author'], 'content': item['content'] } # print(dic) self.conn.lpush('qiubaiData', dic) return item

# -*- coding: utf-8 -*-
import scrapy
from selenium import webdriver


class WangyiSpider(scrapy.Spider):
    name = 'wangyi'
    # allowed_domains = ['www.xxx.com']
    start_urls = ['https://news.163.com']
    five_model_urls = []

    bro = webdriver.Chrome(executable_path=r'F:\数据采集（爬虫）\爬虫+数据工具包\day04\chromedriver.exe')

    # 用来解析五个板块对应的url，然后对其进行手动请求发送
    def parse(self, response):
        model_index = [3, 4, 6, 7, 8]
        li_list = response.xpath('//*[@id="index2016_wrap"]/div[1]/div[2]/div[2]/div[2]/div[2]/div/ul/li')
        for index in model_index:
            li = li_list[index]
            # 获取了五个板块对应的url
            model_url = li.xpath('./a/@href').extract_first()
            self.five_model_urls.append(model_url)
            # 对每一个板块的url进行手动i请求发送
            yield scrapy.Request(model_url, callback=self.parse_model)
    def parse_model(self,response):

        div_list = response.xpath('/html/body/div[1]/div[3]/div[4]/div[1]/div/div/ul/li/div/div')
        for div in div_list:
            title = div.xpath('./div/div[1]/h3/a/text()').extract_first()
            detail_url = div.xpath('./div/div[1]/h3/a/@href').extract_first()
            from wangyiPro.items import WangyiproItem
            item = WangyiproItem()

            yield scrapy.Request(detail_url,callback=self.parse_new_content,meta={'item':item})
    def parse_new_content(self,response):
        # item = response.meta['item']
        item = response.meta['item']
        content = response.xpath('//*[@id="endText"]//text()').extract()
        content = ''.join(content)

        item['content'] = content

        yield item

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

import scrapy


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

from time import sleep

from scrapy import signals
from scrapy.http import HtmlResponse


class WangyiproSpiderMiddleware(object):
    # Not all methods need to be defined. If a method is not defined,
    # scrapy acts as if the spider middleware does not modify the
    # passed objects.

    @classmethod
    def from_crawler(cls, crawler):
        # This method is used by Scrapy to create your spiders.
        s = cls()
        crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
        return s

    def process_spider_input(self, response, spider):
        # Called for each response that goes through the spider
        # middleware and into the spider.

        # Should return None or raise an exception.
        return None

    def process_spider_output(self, response, result, spider):
        # Called with the results returned from the Spider, after
        # it has processed the response.

        # Must return an iterable of Request, dict or Item objects.
        for i in result:
            yield i

    def process_spider_exception(self, response, exception, spider):
        # Called when a spider or process_spider_input() method
        # (from other spider middleware) raises an exception.

        # Should return either None or an iterable of Request, dict
        # or Item objects.
        pass

    def process_start_requests(self, start_requests, spider):
        # Called with the start requests of the spider, and works
        # similarly to the process_spider_output() method, except
        # that it doesn’t have a response associated.

        # Must return only requests (not items).
        for r in start_requests:
            yield r

    def spider_opened(self, spider):
        spider.logger.info('Spider opened: %s' % spider.name)


class WangyiproDownloaderMiddleware(object):
    # 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.

    @classmethod
    def from_crawler(cls, crawler):
        # This method is used by Scrapy to create your spiders.
        s = cls()
        crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
        return s

    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):
        # Called with the response returned from the downloader.

        # Must either;
        # - return a Response object
        # - return a Request object
        # - or raise IgnoreRequest

        bro = spider.bro
        if request.url in spider.five_model_urls:
            bro.get(request.url)
            sleep(1)
            page_text = bro.page_source
            new_response = HtmlResponse(url=request.url, body=page_text, encoding='utf-8', request=request)
            return new_response

        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

    def spider_opened(self, spider):
        spider.logger.info('Spider opened: %s' % spider.name)

class WangyiproPipeline(object):
    def process_item(self, item, spider):
        print(item)
        return item

# -*- coding: utf-8 -*-

# Scrapy settings for wangyiPro project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
#     https://docs.scrapy.org/en/latest/topics/settings.html
#     https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
#     https://docs.scrapy.org/en/latest/topics/spider-middleware.html

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 = 'wangyiPro (+http://www.yourdomain.com)'
USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36'

# Obey robots.txt rules
ROBOTSTXT_OBEY = False
LOG_LEVEL = "ERROR"

# Configure maximum concurrent requests performed by Scrapy (default: 16)
#CONCURRENT_REQUESTS = 32

# Configure a delay for requests for the same website (default: 0)
# See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
#DOWNLOAD_DELAY = 3
# The download delay setting will honor only one of:
#CONCURRENT_REQUESTS_PER_DOMAIN = 16
#CONCURRENT_REQUESTS_PER_IP = 16

# Disable cookies (enabled by default)
#COOKIES_ENABLED = False

# Disable Telnet Console (enabled by default)
#TELNETCONSOLE_ENABLED = False

# Override the default request headers:
#DEFAULT_REQUEST_HEADERS = {
#   'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
#   'Accept-Language': 'en',
#}

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

# 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,
}

# Enable and configure the AutoThrottle extension (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/autothrottle.html
#AUTOTHROTTLE_ENABLED = True
# The initial download delay
#AUTOTHROTTLE_START_DELAY = 5
# The maximum download delay to be set in case of high latencies
#AUTOTHROTTLE_MAX_DELAY = 60
# The average number of requests Scrapy should be sending in parallel to
# each remote server
#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
# Enable showing throttling stats for every response received:
#AUTOTHROTTLE_DEBUG = False

# Enable and configure HTTP caching (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
#HTTPCACHE_ENABLED = True
#HTTPCACHE_EXPIRATION_SECS = 0
#HTTPCACHE_DIR = 'httpcache'
#HTTPCACHE_IGNORE_HTTP_CODES = []
#HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'