Scrapy实战篇（二）之爬取链家网成交房源数据（下）

在上一小节中，我们已经提取到了房源的具体信息，这一节中，我们主要是对提取到的数据进行后续的处理，以及进行相关的设置。

数据处理

我们这里以把数据存储到mongo数据库为例。
编写pipelines.py文件

import pymongoclass MongoPipeline(object):collection = 'lianjia_house'  #数据库collection名称def __init__(self, mongo_uri, mongo_db):self.mongo_uri = mongo_uriself.mongo_db = mongo_db    @classmethoddef from_crawler(cls,crawler):return cls(mongo_uri = crawler.settings.get('MONGO_URI'),mongo_db = crawler.settings.get('MONGO_DB'))def open_spider(self,spider):self.client = pymongo.MongoClient(self.mongo_uri)self.db = self.client[self.mongo_db]def close(self, spider):self.client.close()def process_item(self, item, spider):table = self.db[self.collection]data = dict(item)table.insert_one(data)return item

非常简单的几步，就实现了将数据保存到mongo数据库中，所以说mongo数据库还是非常好用的。
由于之前的学习篇中已经学习过数据的存储相关的内容，在这里就不多赘述。

设置随机User-Agent

这个内容在之前的学习篇中也已经学习过了，这里就直接拿过来用。
编写middlewares.py文件。

import scrapy
import random
from scrapy.downloadermiddlewares.useragent import UserAgentMiddlewareclass MyUserAgentMiddleware(UserAgentMiddleware):def __init__(self, agents):self.agents = agents    @classmethoddef from_crawler(cls, crawler):return cls(agents=crawler.settings.get('USER_AGENTS'))def process_request(self, request, spider):agent = random.choice(self.agents)request.headers['User-Agent'] = agent

设置（settings）

最后一步就是在settings.py文件中，进行我们的设置和应用我们的相关的组件。
内容如下：

BOT_NAME = 'lianjia'SPIDER_MODULES = ['lianjia.spiders']
NEWSPIDER_MODULE = 'lianjia.spiders'ROBOTSTXT_OBEY = FalseUSER_AGENTS = ["Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)","Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Acoo Browser; SLCC1; .NET CLR 2.0.50727; Media Center PC 5.0; .NET CLR 3.0.04506)","Mozilla/4.0 (compatible; MSIE 7.0; AOL 9.5; AOLBuild 4337.35; Windows NT 5.1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)","Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)","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","Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.8) Gecko Fedora/1.9.0.8-1.fc10 Kazehakase/0.5.6","Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11","Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3) AppleWebKit/535.20 (KHTML, like Gecko) Chrome/19.0.1036.7 Safari/535.20","Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; fr) Presto/2.9.168 Version/11.52","Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.11 TaoBrowser/2.0 Safari/536.11","Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER","Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; LBBROWSER)","Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E; LBBROWSER)","Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.84 Safari/535.11 LBBROWSER","Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E)","Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; QQBrowser/7.0.3698.400)","Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)","Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SV1; QQDownload 732; .NET4.0C; .NET4.0E; 360SE)","Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)","Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E)","Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.89 Safari/537.1","Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.89 Safari/537.1","Mozilla/5.0 (iPad; U; CPU OS 4_2_1 like Mac OS X; zh-cn) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8C148 Safari/6533.18.5","Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:2.0b13pre) Gecko/20110307 Firefox/4.0b13pre","Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:16.0) Gecko/20100101 Firefox/16.0","Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11","Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10) Gecko/20100922 Ubuntu/10.10 (maverick) Firefox/3.6.10","Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36",]MONGO_URI = 'mongodb://localhost:27017'
MONGO_DB = "lianjia"DOWNLOAD_DELAY = 2DEFAULT_REQUEST_HEADERS = {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8','Accept-Language': 'zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3','Connection':'keep-alive'
}DOWNLOADER_MIDDLEWARES = {'lianjia.middlewares.MyUserAgentMiddleware': 400,
}ITEM_PIPELINES = {'lianjia.pipelines.MongoPipeline': 300,
}

总结

由于我们爬取得数据量比较大，请求比较多，如果你直接运行的话，肯定是很快就会被封掉的，你可以选择设置ip代理，具体的设置方法你可以参照scrapy学习篇里面的设置ip代理，这里就不多演示，当然了，如果你想看一下效果的话，你可以选择只爬取某一个区的数据，比如鼓楼区。其效果如下面所示。

另外，你可以在你的项目根目录下创建一个run.py文件，里面添加如下的内容：

from scrapy import cmdline
cmdline.execute("scrapy crawl lianjia".split())

其中，lianjia是你spider里面定义的名字，这样，你只需要使用python run.py就可以运行这个项目了。

这里提醒一下，如果你不是着急获取这个数据的话，可以将设置里面的下载延迟设置的稍微大一些，一方面防止我们爬虫被办，另一方面以减轻对方服务器的压力。

github地址: https://github.com/cnkai/lianjia.git