day26-爬虫-scrapy框架初识

 1.框架了解：高性能的异步下载、解析、持久化存储
    2.下载安装，创建项目-----------
        pip install wheel
        Twisted 5步安装！

二.安装

　　Linux：

      pip3 install scrapy

 

　　Windows：

      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

 

        scrapy startproject 项目名称
    3.项目使用--5步听视频总结：
        1.新建工程 scrapy startproject fristBlood
        2.cd fristBlood 新建爬虫文件scrapy genspider chouti www.chouti.com(在spiders中会新增一个chouti.py,注意名称、start_url，注释#allowed_domains)       
        3.在chouti.py中进行parse方法的编写
        4.配置文件的配置：在settings中进行UA伪装、ROBOTSTXT_OBEY = False     
        5.配置完后，在cmd中执行：scarpy crawl 爬虫文件名称

1.爬取chouti  fristBlood

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


class ChoutiSpider(scrapy.Spider):
    #爬虫文件的名称：可以指定某一个具体的爬虫文件
    name = 'chouti'
    #允许的域名：
    #allowed_domains = ['www.chouti.com']
    #起始url列表：工程被执行后就可以获取该列表中url所对应的页面数据
    start_urls = ['https://dig.chouti.com/']
    
    #该方法作用:就是讲起始url列表中指定url对应的页面数据进行解析操作
    #response参数：就是对起始url发起请求后对应的响应对象
    def parse(self, response):
        print(response)

chouti.py

2.爬取糗百 ---注意parse中   qiubaiPro 
    #extract()可以将selector对象中存储的文本内容获取
    封装一个可迭代类型
    基于终端指令执行 scarpy crawl -o data.csv qiubai --nolog---不常用

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


class QiubaiSpider(scrapy.Spider):
    name = 'qiubai'
    #allowed_domains = ['www.fdsfds.com']
    start_urls = ['https://www.qiushibaike.com/text/']

    def parse(self, response):
        #xpath返回的列表元素类型为Selecor类型
        div_list = response.xpath('//div[@id="content-left"]/div')
        #声明一个用于存储解析到数据的列表
        all_data = []
        
        for div in div_list:
            #extract()可以将selector对象中存储的文本内容获取
            #author = div.xpath('./div[1]/a[2]/h2/text()')[0].extract()
            author = div.xpath('./div[1]/a[2]/h2/text()').extract_first() #取出第一个元素，不用[0]了--意义同上行
            content = div.xpath('.//div[@class="content"]/span//text()').extract() #//text获取的内容不止一个，extract()获取多个列表内容
            content = "".join(content) #将列表转化成字符串
            
            dict = {
                'author':author,
                'content':content
            }
            all_data.append(dict)
            
        return all_data
            #持久化存储方式：
                #1.基于终端指令：必须保证parse方法有一个可迭代类型对象的返回
                #2.基于管道

qiubai.py

3.爬取糗百--基于管道执行--注意item  pipeLinepro
pipelines.py编写
在settings中开启ITEM_PIPELINES 67-69行
    ITEM_PIPELINES数值越小，优先级越高（管道中） 
    一个写到磁盘，一个写到数据库中

    屏蔽日志信息 scarpy crawl chouti --nolog
    cls清屏

import scrapy


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

items.py

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

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

BOT_NAME = 'pipeLinePro'

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


# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'pipeLinePro (+http://www.yourdomain.com)'
USER_AGENT = 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'
# Obey robots.txt rules
ROBOTSTXT_OBEY = False

# 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://doc.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://doc.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
#    'pipeLinePro.middlewares.PipelineproSpiderMiddleware': 543,
#}

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

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

# Configure item pipelines
# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
    'pipeLinePro.pipelines.PipelineproPipeline': 300,
    'pipeLinePro.pipelines.MyPipeline': 301,
}

# Enable and configure the AutoThrottle extension (disabled by default)
# See https://doc.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://doc.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'

settings.py

 

# -*- coding: utf-8 -*-
import scrapy
from pipeLinePro.items import PipelineproItem

class QiubaiSpider(scrapy.Spider):
    name = 'qiubai'
    #allowed_domains = ['www.ds.com']
    start_urls = ['https://www.qiushibaike.com/text/']

    def parse(self, response):
        # xpath返回的列表元素类型为Selecor类型
        div_list = response.xpath('//div[@id="content-left"]/div')
        # 声明一个用于存储解析到数据的列表
        all_data = []
    
        for div in div_list:
            # extract()可以将selector对象中存储的文本内容获取
            # author = div.xpath('./div[1]/a[2]/h2/text()')[0].extract()
            author = div.xpath('./div[1]/a[2]/h2/text()').extract_first()
            content = div.xpath('.//div[@class="content"]/span//text()').extract()
            content = "".join(content)
            
            #实例化item对象
            item = PipelineproItem()
            #将解析到的数据值存储到item对象中
            item['author'] = author
            item['content'] = content
            
            #将item对象提交给管道
            yield item
            
        # 持久化存储方式：
        # 1.基于终端指令：必须保证parse方法有一个可迭代类型对象的返回
        # 2.基于管道:
            #1.items.py:对该文件中的类进行实例化操作（item对象：存储解析到的数据值）。
            #2.pipeline.py:管道，作用就是接受爬虫文件提交的item对象，然后将该对象中的数据值进行持久化存储操作
        

qiubai.py-管道

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

import pymysql

class PipelineproPipeline(object):
    #作用：每当爬虫文件向管道提交一次item，该方法就会被调用一次。item参数就是接受到爬虫文件给提交过来的item对象
    #该方法只有在开始爬虫的时候被调用一次
    fp = None
    def open_spider(self,spider): #父类的方法
        print('开始爬虫')
        self.fp = open('./qiubai_data.txt', 'w', encoding='utf-8')
        
    def process_item(self, item, spider): #父类的方法
        author = item['author']
        content = item['content']
        self.fp.write(author+":"+content)
        
        return item

    #该方法只有在爬虫结束后被调用一次
    def close_spider(self,spider):  #父类的方法
        print('爬虫结束')
        self.fp.close()
        
class MyPipeline(object):
    conn = None
    cursor = None
    # 作用：每当爬虫文件向管道提交一次item，该方法就会被调用一次。item参数就是接受到爬虫文件给提交过来的item对象
    def open_spider(self,spider):
        self.conn = pymysql.Connect(host="192.168.12.65", port=3306, db="scrapyDB", charset="utf8", user="root")
        self.cursor = self.conn.cursor()
        print('mysql')
        
    def process_item(self, item, spider):
        author = item['author']
        content = item['content']
        
        sql = "insert into qiubai values('%s','%s')" % (author,content)  #qiubai是表名
        try:
            self.cursor.execute(sql) #执行sql
            self.conn.commit() #事务的处理，没有问题提交，有问题回滚
        except Exception as e:
            print(e)
            self.conn.rollback()
        return item

pipelines.py

    管道操作4步---听视频自己总结：
        前提要在parse方法中获取解析到的数据，
        1.将解析到的数据值存储到item对象中（前提item中要进行属性的声明）,
        2.使用yield关键字将item对象提交给管道
        3.在pipelines.py中进行PipelineproPipeline方法的编写，编写process_item
        4.在配置文件中开启管道

        1.#实例化item对象
            item = PipelineproItem()
        2.在items.py中声明属性
        3.#将解析到的数据值存储到item对象中
            item['author'] = author
            item['content'] = content           
        4.#将item对象提交给管道
            yield item

将数据写入到数据库：新建数据库、表

select * from qiubai 查看写入的内容

转载于:https://www.cnblogs.com/lijie123/p/9998441.html