记录python的scrapy爬虫——房天下

需求：爬取房天下网站所有新房、二手房信息

scrapy框架在windows10系统中的部署

安装Visual C++ Build Tools

由于在Scrapy的依赖文件库中，pywin32和Twisted的底层是基于C语言开发的，因此需要安装C语言的编译环境。对于python3.6来说，可以通过安装Visual C++ Build Tools来安装这个环境。下载地址为：https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=152

2.安装pywin32

在Windows系统中搭建Scrapy的环境，有两个第三方库不能用常规方法安装，一个是lxml，另外一个就是pywin32。这两个第三方库都不建议使用pip命令安装，均可以采用.exe安装包进行安装。

3.创建虚拟环境Virtualenv

由于在之后的Twisted和Scrapy安装中，会附带安装大量的依赖库，而这些库仅在Scrapy中会用到，平常的开发中几乎不会用。所以把他们安装到系统中会，会导致python系统的混乱，而且发布爬虫时，也不便于导出涉及的依赖库文件。

因此我们使用Virtualenv创建一个虚拟的python环境来安装Scrapy剩下的部分。

Virtualenv是Python的一个第三方库，使用它可以创造Python的虚拟环境。使用普通方法就可以安装Virtualenv：

pip install virtualenv

要让Virtualenv使用系统Python环境下的第三方库，就需要在CMD中使用下面的命令来创建虚拟环境：

virtualenv --always-copy --system-site-packages venv

创建虚拟环境后，可以使用以下的命令来激活虚拟环境：

venv\scripts\avtivate

不要关闭这个窗口，接下来的所有操作都要在这里进行。

4.安装Twisted和Scrapy

在刚才的窗口中使用pip命令进行安装即可，分别是：

pip install twisted

pip install scrapy

抓取房天下网站房源信息实例

网站url分析

1.获取所有城市url

   http://www.fang.com/SoufunFamily.htm

    例如：http://cq.fang.com/

2.新房url

  http://newhouse.sh.fang.com/house/s/

3.二手房url

  http://esf.sh.fang.com/

4.北京新房和二手房url规则不同

   http://newhouse.fang.com/house/s/

   http://esf.fang.com/

创建项目

在刚才的CMD中输入以下命令

scrapy startproject fang

scrapy genspider sfw_spider "fang.com"

sfw_spider.py

# -*- coding: utf-8 -*-
import scrapy
import re
from fang.items import NewHouseItem,ESFHouseItem

class SfwSpiderSpider(scrapy.Spider):
    name = 'sfw_spider'
    allowed_domains = ['fang.com']
    start_urls = ['http://www.fang.com/SoufunFamily.htm']

    def parse(self, response):
        trs = response.xpath("//div[@class='outCont']//tr")
        provice = None
        for tr in trs:
            #排除掉第一个td，两个第二个和第三个td标签
            tds = tr.xpath(".//td[not(@class)]")
            provice_td = tds[0]
            provice_text = provice_td.xpath(".//text()").get()
            #如果第二个td里面是空值，则使用上个td的省份的值
            provice_text = re.sub(r"\s","",provice_text)
            if provice_text:
                provice = provice_text
            #排除海外城市
            if provice == '其它':
                continue

            city_td = tds[1]
            city_links = city_td.xpath(".//a")
            for city_link in city_links:
                city = city_link.xpath(".//text()").get()
                city_url = city_link.xpath(".//@href").get()
                # print("省份：",provice)
                # print("城市：",city)
                # print("城市链接：",city_url)
                #下面通过获取的city_url拼接出新房和二手房的url链接
                #城市url：http://cq.fang.com/
                #新房url:http://newhouse.cq.fang.com/house/s/
                #二手房：http://esf.cq.fang.com/
                url_module = city_url.split("//")
                scheme = url_module[0]     #http:
                domain = url_module[1]     #cq.fang.com/
                if 'bj' in domain:
                    newhouse_url = ' http://newhouse.fang.com/house/s/'
                    esf_url = ' http://esf.fang.com/'
                else:
                    #新房url
                    newhouse_url = scheme + '//' + "newhouse." + domain + "house/s/"
                    #二手房url
                    esf_url = scheme + '//' + "esf." + domain + "house/s/"
                # print("新房链接：",newhouse_url)
                # print("二手房链接：",esf_url)

                #meta里面可以携带一些参数信息放到Request里面，在callback函数里面通过response获取
                yield scrapy.Request(url=newhouse_url,
                                     callback=self.parse_newhouse,
                                     meta = {'info':(provice,city)}
                                     )

                yield scrapy.Request(url=esf_url,
                                     callback=self.parse_esf,
                                     meta={'info': (provice, city)})


    def parse_newhouse(self,response):
        #新房
        provice,city = response.meta.get('info')
        lis = response.xpath("//div[contains(@class,'nl_con')]/ul/li")
        for li in lis:
            name = li.xpath(".//div[contains(@class,'house_value')]//div[@class='nlcd_name']/a/text()").get()
            if name:
                name = re.sub(r"\s","",name)
                #居室
                house_type_list = li.xpath(".//div[contains(@class,'house_type')]/a/text()").getall()
                house_type_list = list(map(lambda x:re.sub(r"\s","",x),house_type_list))
                rooms = list(filter(lambda x:x.endswith("居"),house_type_list))
                #面积
                area = "".join(li.xpath(".//div[contains(@class,'house_type')]/text()").getall())
                area = re.sub(r"\s|－|/","",area)
                #地址
                address = li.xpath(".//div[@class='address']/a/@title").get()
                address = re.sub(r"[请选择]","",address)
                sale = li.xpath(".//div[contains(@class,'fangyuan')]/span/text()").get()
                price = "".join(li.xpath(".//div[@class='nhouse_price']//text()").getall())
                price = re.sub(r"\s|广告","",price)
                #详情页url
                origin_url = li.xpath(".//div[@class='nlcd_name']/a/@href").get()

                item = NewHouseItem(
                    name=name,
                    rooms=rooms,
                    area=area,
                    address=address,
                    sale=sale,
                    price=price,
                    origin_url=origin_url,
                    provice=provice,
                    city=city
                )
                yield item

        #下一页
        next_url = response.xpath("//div[@class='page']//a[@class='next']/@href").get()
        if next_url:
            yield scrapy.Request(url=response.urljoin(next_url),
                                 callback=self.parse_newhouse,
                                 meta={'info': (provice, city)}
                                 )


    def parse_esf(self,response):
        #二手房
        provice, city = response.meta.get('info')
        dls = response.xpath("//div[@class='shop_list shop_list_4']/dl")
        for dl in dls:
            item = ESFHouseItem(provice=provice,city=city)
            name = dl.xpath(".//span[@class='tit_shop']/text()").get()
            if name:
                infos = dl.xpath(".//p[@class='tel_shop']/text()").getall()
                infos = list(map(lambda x:re.sub(r"\s","",x),infos))
                for info in infos:
                    if "厅" in info:
                        item["rooms"] = info
                    elif '层' in info:
                        item["floor"] = info
                    elif '向' in info:
                        item['toward'] = info
                    elif '㎡' in info:
                        item['area'] = info
                    elif '年建' in info:
                        item['year'] = re.sub("年建","",info)
                item['address'] = dl.xpath(".//p[@class='add_shop']/span/text()").get()
                #总价
                item['price'] = "".join(dl.xpath(".//span[@class='red']//text()").getall())
                #单价
                item['unit'] = dl.xpath(".//dd[@class='price_right']/span[2]/text()").get()
                item['name'] = name
                detail = dl.xpath(".//h4[@class='clearfix']/a/@href").get()
                item['origin_url'] = response.urljoin(detail)
                yield item
        #下一页
        next_url = response.xpath("//div[@class='page_al']/p/a/@href").get()
        if next_url:
            yield scrapy.Request(url=response.urljoin(next_url),
                                 callback=self.parse_esf,
                                 meta={'info': (provice, city)}
                                 )

items.py

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

import scrapy

class NewHouseItem(scrapy.Item):
    #省份
    provice = scrapy.Field()
    # 城市
    city = scrapy.Field()
    # 小区
    name = scrapy.Field()
    # 价格
    price = scrapy.Field()
    # 几居，是个列表
    rooms = scrapy.Field()
    # 面积
    area = scrapy.Field()
    # 地址
    address = scrapy.Field()
    # 是否在售
    sale = scrapy.Field()
    # 房天下详情页面的url
    origin_url = scrapy.Field()

class ESFHouseItem(scrapy.Item):
    # 省份
    provice = scrapy.Field()
    # 城市
    city = scrapy.Field()
    # 小区名字
    name = scrapy.Field()
    # 几室几厅
    rooms = scrapy.Field()
    # 层
    floor = scrapy.Field()
    # 朝向
    toward = scrapy.Field()
    # 年代
    year = scrapy.Field()
    # 地址
    address = scrapy.Field()
    # 建筑面积
    area = scrapy.Field()
    # 总价
    price = scrapy.Field()
    # 单价
    unit = scrapy.Field()
    # 详情页url
    origin_url = scrapy.Field()

pipelines.py

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

from scrapy.exporters import JsonLinesItemExporter

class FangPipeline(object):
    def __init__(self):
        self.newhouse_fp = open('newhouse.json','wb')
        self.esfhouse_fp = open('esfhouse.json','wb')
        self.newhouse_exporter = JsonLinesItemExporter(self.newhouse_fp,ensure_ascii=False)
        self.esfhouse_exporter = JsonLinesItemExporter(self.esfhouse_fp,ensure_ascii=False)

    def process_item(self, item, spider):
        self.newhouse_exporter.export_item(item)
        self.esfhouse_exporter.export_item(item)
        return item

    def close_spider(self,spider):
        self.newhouse_fp.close()
        self.esfhouse_fp.close()

middleware.py 设置随机User-Agent:此处没有用到代理池ip

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

import random

class UserAgentDownloadMiddleware(object):
    USER_AGENTS = [
        'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 Safari/537.36',
        'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0 ',
        'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36',
        'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER',
    ]

    def process_request(self,request,spider):
        user_agent = random.choice(self.USER_AGENTS)
        request.headers['User-Agent'] = user_agent

settings.py

ROBOTSTXT_OBEY = False

DOWNLOAD_DELAY = 1

DOWNLOADER_MIDDLEWARES = {
   'fang.middlewares.UserAgentDownloadMiddleware': 543,
}

ITEM_PIPELINES = {
   'fang.pipelines.FangPipeline': 300,
}

start.py

from scrapy import cmdline

cmdline.execute("scrapy crawl sfw_spider".split())