因为有在北京租房的打算,于是上网浏览了一下链家网站的房价,想将他们爬取下来,并保存到本地。

先看链家网的源码。。房价信息 都保存在 ul 下的li 里面

 

爬虫结构:

​ 其中封装了一个数据库处理模块,还有一个user-agent池。。

 

先看mylianjia.py

# -*- coding: utf-8 -*-
import scrapy
from ..items import LianjiaItem
from scrapy.http import Request
from parsel import Selector
import requests
import os


class MylianjiaSpider(scrapy.Spider):
    name = \'mylianjia\'
    #allowed_domains = [\'lianjia.com\']
    start_urls = [\'https://bj.lianjia.com/ershoufang/chaoyang/pg\']

    def start_requests(self):
        for i in range(1, 101):  #100页的所有信息
            url1 = self.start_urls + list(str(i))
            #print(url1)
            url = \'\'
            for j in url1:
                url += j + \'\'
            yield Request(url, self.parse)

    def parse(self, response):
        print(response.url)

        \'\'\'
        response1 = requests.get(response.url, params={\'search_text\': \'粉墨\', \'cat\': 1001})
        if response1.status_code == 200:
            print(response1.text)
        dirPath = os.path.join(os.getcwd(), \'data\')
        if not os.path.exists(dirPath):
            os.makedirs(dirPath)
        with open(os.path.join(dirPath, \'lianjia.html\'), \'w\', encoding=\'utf-8\')as fp:
            fp.write(response1.text)
            print(\'网页源码写入完毕\')
        \'\'\'

        infoall=response.xpath("//div[4]/div[1]/ul/li")
        #infos = response.xpath(\'//div[@class="info clear"]\')
        #print(infos)
        #info1 = infoall.xpath(\'div/div[1]/a/text()\').extract_first()
        #print(infoall)
        for info in infoall:
            item =LianjiaItem()
            #print(info)
            info1 = info.xpath(\'div/div[1]/a/text()\').extract_first()
            info1_url = info.xpath(\'div/div[1]/a/@href\').extract_first()
            #info2 = info.xpath(\'div/div[2]/div/text()\').extract_first()
            info2_dizhi = info.xpath(\'div/div[2]/div/a/text()\').extract_first()
            info2_xiangxi= info.xpath(\'div/div[2]/div/text()\').extract()
            #info3 = info.xpath(\'div/div[3]/div/a/text()\').extract_first()
            #info4 = info.xpath(\'div/div[4]/text()\').extract_first()
            price = info.xpath(\'div/div[4]/div[2]/div/span/text()\').extract_first()
            perprice = info.xpath(\'div/div[4]/div[2]/div[2]/span/text()\').extract_first()
            #print(info1,\'--\',info1_url,\'--\',info2_dizhi,\'--\',info2_xiangxi,\'--\',info4,\'--\',price,perprice)
            info2_xiangxi1 = \'\'
            for j1 in info2_xiangxi:
                info2_xiangxi1 += j1 + \'\'
            #print(info2_xiangxi1)  #化为字符串

            item[\'houseinfo\']=info1
            item[\'houseurl\']=info1_url
            item[\'housedizhi\']=info2_dizhi
            item[\'housexiangxi\']=info2_xiangxi1
            item[\'houseprice\']=price
            item[\'houseperprice\']=perprice

            yield item

 

 再看items.py

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

# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html

import scrapy


class LianjiaItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    houseinfo=scrapy.Field()
    houseurl=scrapy.Field()
    housedizhi=scrapy.Field()
    housexiangxi=scrapy.Field()
    houseprice=scrapy.Field()
    houseperprice=scrapy.Field()
    pass

  

接下来看pipelines.py

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

# Define your item pipelines here
#
# Don\'t forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html


class LianjiaPipeline(object):
    def process_item(self, item, spider):
        print(\'房屋信息:\',item[\'houseinfo\'])
        print(\'房屋链接:\', item[\'houseurl\'])
        print(\'房屋位置:\', item[\'housedizhi\'])
        print(\'房屋详细信息:\', item[\'housexiangxi\'])
        print(\'房屋总价:\', item[\'houseprice\'],\'万\')
        print(\'平方米价格:\', item[\'houseperprice\'])
        print(\'====\'*10)
        return item

  

接下来看csvpipelines.py

import os
print(os.getcwd())
class LianjiaPipeline(object):
    def process_item(self, item, spider):
        with open(\'G:\pythonAI\爬虫大全\lianjia\data\house.txt\', \'a+\', encoding=\'utf-8\') as fp:
            name=str(item[\'houseinfo\'])
            dizhi=str(item[\'housedizhi\'])
            info=str(item[\'housexiangxi\'])
            price=str(item[\'houseprice\'])
            perprice=str(item[\'houseperprice\'])
            fp.write(name + dizhi + info+ price +perprice+ \'\n\')
            fp.flush()
            fp.close()
        return item

    print(\'写入文件成功\')

  

 接下来看 settings.py

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

# Scrapy settings for lianjia 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 = \'lianjia\'

SPIDER_MODULES = [\'lianjia.spiders\']
NEWSPIDER_MODULE = \'lianjia.spiders\'


# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = \'lianjia (+http://www.yourdomain.com)\'

# 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 = 0.5
# 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 = {
    \'lianjia.middlewares.LianjiaSpiderMiddleware\': 543,
    #\'scrapy.contrib.downloadermiddleware.useragent.UserAgentMiddleware\' : None,
	#\'lianjia.rotate_useragent.RotateUserAgentMiddleware\' :400
}

# Enable or disable downloader middlewares
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
DOWNLOADER_MIDDLEWARES = {
    \'lianjia.middlewares.LianjiaDownloaderMiddleware\': 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 = {
    \'lianjia.pipelines.LianjiaPipeline\': 300,
    #\'lianjia.iopipelines.LianjiaPipeline\': 301,
    \'lianjia.csvpipelines.LianjiaPipeline\':302,
}

# 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\'


LOG_LEVEL=\'INFO\'
LOG_FILE=\'lianjia.log\'

  

 最后看starthouse.py

from scrapy.cmdline import execute

execute([\'scrapy\', \'crawl\', \'mylianjia\'])

  

代码运行结果

保存到本地效果:

完成,事后可以分析一下房价和每平方米的方剂,,因为是海淀区的,,可以看到 都是好几万一平米,总价也得几百万了 而且是二手房,,,可以看出来 ,在北京买房太难。。。。

 

源码 tyutltf/lianjia: 爬取链家北京房价并保存txt文档  https://github.com/tyutltf/lianjia

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