Scrapy结合Selenium采集数据简单实例

做爬虫的都不难发现，有的页面分页，点击下一页，或者指定某页，网址居然不变，如果是基于scrapy框架采集，那么就无法使用yield迭代url进行页面数据解析采集。
前段时间刚好有用过selenium自动化模拟打开浏览器采集数据，不能能模拟人为的一些鼠标、键盘操作。很强大，照样能跟scrapy结合的很完美！！！
以下就来打一个简单的在百度输入框输入关键词并点击百度一下进行页面的查询操作，然后再解析页面内容：
快捷创建项目：

scrapy startproject test
scrapy genspider crawltest 	www.baidu	

items.py源码:

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

settings.py配置修改（取消注释）：

import random
# user agent 列表
USER_AGET_LIST = [
    	MSIE (MSIE 6.0; X11; Linux; i686) Opera 7.2	,
    	Opera/9.20 (Macintosh; Intel Mac OS X; U; en)	,
    	Opera/9.0 (Macintosh; PPC Mac OS X; U; en)	,
    	iTunes/9.0. (Macintosh; U; Intel Mac OS X 10_6_2; en-ca)	,
    	Mozilla/4.76 [en_jp] (X11; U; SunOS 5.8 sun4u)	,
    	iTunes/4.2 (Macintosh; U; PPC Mac OS X 10.2)	,
    	Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:5.0) Gecko/20100101 Firefox/5.0	,
    	Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:9.0) Gecko/20100101 Firefox/9.0	,
    	Mozilla/5.0 (Macintosh; Intel Mac OS X 10.8; rv:16.0) Gecko/2012081 Firefox/16.0	,
    	Mozilla/4.77 [en] (X11; I; IRIX;64 6.5 IP0)	,
    	Mozilla/4.8 [en] (X11; U; SunOS; 5.7 sun4u)	
]
# 随机生成user agent
USER_AGET = (USER_AGET_LIST)
#USER_AGET = 	test (http://www.yourdomain)	 # 用户代理
ROBOTSTXT_OBEY = False #设置为False便于打印调试
ITEM_PIPELIES = {
   	test.pipelines.JobsPipeline	: 1,
} # 用于输出采集的结果，具体操作在pipelines中

爬虫文件crawltest.py源码：

# -*- coding: utf-8 -*-
import scrapy
from selenium import webdriver
from selenium.webdriver .chrome.opti import Opti
from test.items import TestItem
import lxml.html
import time, random

class CrawlSpider(scrapy.Spider):
    name = 	crawl	
    allowed_domains = [	baidu	]
    start_urls = [	https://www.baidu/	]

    def open_page(self):
        chrome_opti = Opti()
        chrome_opti.add_argument(	--no-sandbox	)
        chrome_opti.add_argument(	--disable-dev-shm-usage	)
        chrome_opti.add_argument(	--headless	)
        browser = webdriver.Chrome(chrome_opti=chrome_opti)
        browser.get(self.start_urls[0])
        browser.implicitly_wait(10)
        return browser

    def parse(self, respe):
        browser = self.open_page()
        doc_souce = lxml.html.document_fromstring(browser.page_source)
        su = respe.xpath(	.//input[@id=su]/@value	).extract()
        es = doc_souce.xpath(	.//input[@id=su]/@value	)
        keywd = browser.find_element_by_xpath(//input[@id=	kw	])
        keywd.send_keys(	scrapy	)
        time.sleep(random.randint(,5))
        browser.find_element_by_xpath(//input[@id=	su	]).click()
        time.sleep(random.randint(,5)) # 点击完最好要停留下时间，等待页面加载就绪
        print(es[0],	ppppppppppppppppp	,su[0]) #两个结果一样吗，也就是说selenium打开网页的结果跟内置获取的数据是一致的
        doc_souce_01 = lxml.html.document_fromstring(browser.page_source)
        result = doc_souce_01.xpath(	//span[@class=nums_text]/text()	)
        print(result,	000000000000000000	)
        item = TestItem()
        item[	title	] = su[0]
        yield item

输出pipelines.py源码：

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

# Define your item pipelines here
#
# Don	t forget to add your pipeline to the ITEM_PIPELIES setting
# See: http://doc.scrapy/en/latest/topics/item-pipeline.html
#写入json
# import codecs
# import json
# from  import DropItem
# class SpiderctoPipeline(object):

# def __init__(self):
    # self.file = open(	data.json	,	w	)
    # self.file = (	data.json	,	w	,encoding=	utf-8	)

# def process_item(self, item, spider):
    # line = json.dumps(dict(item),ensure_ascii=False)  	\n	
    # self.file.write(line)
    # return item
#写入数据库
from twisted.enterprise import adbapi
import pymysql
import pymysql.cursors

class SpiderctoPipeline(object):

    def __init__(self,dbpool):
        self.dbpool = dbpool

    @classmethod
    def from_settings(cls,setting):
        dbpool=adbapi.ConnectionPool(	pymysql	,host=	127.0.0.1	,
        db=	test	,user=	root	,password=	12456	,charset=	utf8	,cursorclass=pymysql.cursors.DictCursor,use_unicode=True)
        return cls(dbpool)

    def process_item(self, item, spider):
        self.dbpool.runInteraction(self.do_insert,item)

    def do_insert(self,cursor,item):
        insert_info = 
            insert into ctolist(title,url,score,hour,student,couse_long,price,updata)
            values (%s,%s,%s,%s,%s,%s,%s,%s)
            
        params = (item[	title	],item[	url	],item[	score	],item[	hour	],item[	student	],item[	couse_long	],item[	price	],item[	updata	])
        cursor.execute(insert_info,params)

大功告成，启动爬虫：

[root@localhost test]# scrapy crawl crawl
...
...
百度一下 ppppppppppppppppp 百度一下
2020-04-1 10:1: [selenium.webdriver.remote.remote_connection] DEBUG: GET http://127.0.0.1:4672/session/1722f62a49abe2970500424794017f/source {}
2020-04-1 10:1:4 [] DEBUG: http://127.0.0.1:4672 GET /session/1722f62a49abe2970500424794017f/source HTTP/1.1 200 426695
2020-04-1 10:1:4 [selenium.webdriver.remote.remote_connection] DEBUG: Finished Request
[	百度为您到相关结果约29,000,000个	] 000000000000000000
...
...