python爬虫使用scrapy框架
scrapy框架提升篇
关注公众号“轻松学编程”了解更多
1、创建启动爬虫脚本
在项目目录下创建start.py文件：
添加代码：
#以后只要运行start.py就可以启动爬虫
import scrapy.cmdline
def main():
#mytencent为当前项目爬虫名
scrapy.cmdline.execute(['scrapy', 'crawl', 'mytencent'])
if __name__ == '__main__':
main()
2、自动爬取多页
在spiders文件夹下的mytencent.py中MytencentSpider类要继承CrawlSpider，然后添加规则即可：
import scrapy
from tencent.items import TencentItem
from scrapy.spiders import CrawlSpider, Rule # 爬取规则
from scrapy.linkextractors import LinkExtractor # 提取链接
#爬虫类继承CrawlSpider
class MytencentSpider(CrawlSpider):
name = 'mytencent'
allowed_domains = ['hr.tencent.com']
start_urls = ['https://hr.tencent.com/position.php?keywords=&tid=0&start=10#a']
#添加爬取url规则，url符合正则start=(\d+)#a")就爬取
rules = (Rule(LinkExtractor(allow=("start=(\d+)#a")), callback='get_parse', follow=True),)
# 一定不能用parse()
def get_parse(self, response):
jobList = response.xpath('//tr[@class="even"] | //tr[@class="odd"]')
# 存储对象
item = TencentItem()
for job in jobList:
# .extract()提取文本
jobName = job.xpath('./td[1]/a/text()').extract()[0]
jobType = job.xpath('./td[2]/text()').extract()[0]
item['jobName'] = jobName
item['jobType'] = jobType
yield item
3、使用框架自带的Request()构建请求
在spiders文件夹下的mysina.py中:
import scrapy
from scrapy.spiders import CrawlSpider,Rule #爬取规则
from scrapy.linkextractor import LinkExtractor #提取链接
class MysinaSpider(CrawlSpider):
name = 'mysina'
allowed_domains = ['sina.com.cn']
start_urls = ['http://roll.news.sina.com.cn/news/gnxw/gdxw1/index_1.shtml']
#设置爬取规则,可迭代对象，可设置多个规则
rules = [Rule(LinkExtractor(allow=("index_(\d+).shtml")),callback='get_parse',follow=True)]
def get_parse(self, response):
newsList = response.xpath('//ul[@class="list_009"]/li')
for news in newsList:
# 新闻标题
title = news.xpath('./a/text()').extract()[0]
# 新闻时间
newsTime = news.xpath('./span/text()').extract()[0]
# print('***********',title,'****',newsTime)
#获取正文的url
contentsUrl = news.xpath('./a/@href').extract()[0]
#使用框架自带的Request()构建请求,使用meta传递参数
'''
scrapy.Request()参数列表:
url,
callback=None, 回调函数
meta=None, 数据传递
'''
request = scrapy.Request(url=contentsUrl,callback=self.get_article,)
# 使用meta传递参数 是一个字典, 只能传递一层
request.meta['title'] = title
request.meta['newsTime'] = newsTime
yield request
def get_article(self,response):
contents = response.xpath('//div[@id="article"]//text()')
#新闻内容
newsContent = ""
for content in contents:
newsContent += content.extract().strip()+'\n'
print('*****新闻正文*****',newsContent,'*****新闻正文*****')
item = SinaItem()
# 从meta中获取参数
item['title'] = response.meta['title']
item['newsTime'] = response.meta['newsTime']
item['newsContent'] = newsContent
yield item
4、保存进MySQL数据库模板
在MySQL中建立数据库，表，然后在pipelines.py中编写代码如下：
import pymysql
class TencentPipeline(object):
def __init__(self):
#连接数据库
self.conn = None
#游标
self.cur = None
# 打开爬虫时调用，只调用一次
def open_spider(self,spider):
self.conn = pymysql.connect(host='127.0.0.1',
user='root',
password="123456",
database='tjob', #数据库为tjob
port=3306,
charset='utf8')
self.cur = self.conn.cursor()
def process_item(self, item, spider):
clos,value = zip(*item.items())
sql = "INSERT INTO `%s`(%s) VALUES (%s)" % ('tencentjob', #表名为tencentjob
','.join(clos),
','.join(['%s'] * len(value)))
self.cur.execute(sql, value)
self.conn.commit()
return item
def close_spider(self, spider):
self.cur.close()
self.conn.close()
settings.py中要开启
ITEM_PIPELINES = {
'tencent.pipelines.TencentPipeline': 300,
}
5、使用中间件做UA代理，IP代理
在middlewares.py中添加：
from scrapy import signals
import random
#ip代理
from scrapy.downloadermiddlewares.httpproxy import HttpProxyMiddleware
#UA代理
from scrapy.downloadermiddlewares.useragent import UserAgentMiddleware
from weixinsougou.settings import USER_AGENTS,PROXIES
class RandomUAMiddleware(UserAgentMiddleware):
'''
随机UA代理，中间件
'''
def process_request(self, request, spider):
'''
所有的请求都会经过process_request
:param request:请求
:param spider:爬虫名
:return:
'''
ua = random.choice(USER_AGENTS)
request.headers.setdefault("User-Agent", ua)
class RandomIPMiddleware(HttpProxyMiddleware):
'''
随机IP代理
'''
def process_request(self, request, spider):
proxy = random.choice(PROXIES)
request.meta['proxy'] = 'http://' + proxy['ip_port']
#class RandomCookieMiddleware(CookiesMiddleware):
# '''
# 随机cookie池
# '''
#
# def process_request(self, request, spider):
# cookie = random.choice(COOKIES)
# request.cookies = cookie
在settings.py中添加：
# -*- coding: utf-8 -*-
# Obey robots.txt rules
ROBOTSTXT_OBEY = False
# Disable cookies (enabled by default)
COOKIES_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',
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36',
}
# Enable or disable downloader middlewares
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
#启用中间件
DOWNLOADER_MIDDLEWARES = {
# 'weixinsougou.middlewares.WeixinsougouDownloaderMiddleware': 543,
'weixinsougou.middlewares.RandomUAMiddleware': 543,
'weixinsougou.middlewares.RandomIPMiddleware': 544,
}
#UA池
USER_AGENTS = [
"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 2.0.50727; Media Center PC 6.0)",
"Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.0.3705; .NET CLR 1.1.4322)",
"Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 5.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; InfoPath.2; .NET CLR 3.0.04506.30)",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)",
"Mozilla/5.0 (X11; U; Linux; en-US) AppleWebKit/527+ (KHTML, like Gecko, Safari/419.3) Arora/0.6",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.2pre) Gecko/20070215 K-Ninja/2.1.1",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9) Gecko/20080705 Firefox/3.0 Kapiko/3.0",
"Mozilla/5.0 (X11; Linux i686; U;) Gecko/20070322 Kazehakase/0.4.5"
]
#IP池
PROXIES = [
{'ip_port': '171.38.85.93:8123'},
{'ip_port': '113.67.227.143:8118'},
{'ip_port': '101.236.19.165:8866'},
{'ip_port': '101.236.21.22:8866'},
]
#cookle池
COOKIES = []
# 默认线程数量 10
REACTOR_THREADPOOL_MAXSIZE = 20
# 并发 默认16
CONCURRENT_REQUESTS = 16
# pipelines同时处理数量 默认100
CONCURRENT_ITEMS = 50
# scrapy 深度爬取，默认0 不做深度限制
DEPTH_LIMIT = 4
# 下载超时
DOWNLOAD_TIMEOUT = 180
#####6、使用redis实现分布式爬取
https://blog.csdn.net/lm_is_dc/article/details/81866275
#####7、部署
https://blog.csdn.net/lm_is_dc/article/details/81869508
8、使用gerapy管理爬虫
https://blog.csdn.net/lm_is_dc/article/details/81869508
后记
【后记】为了让大家能够轻松学编程，我创建了一个公众号【轻松学编程】，里面有让你快速学会编程的文章，当然也有一些干货提高你的编程水平，也有一些编程项目适合做一些课程设计等课题。
也可加我微信【1257309054】，拉你进群，大家一起交流学习。
如果文章对您有帮助，请我喝杯咖啡吧！
公众号
关注我，我们一起成长~~
