Python的scrapy之爬取链家网房价信息并保存到本地

2021-09-25 13:45:54

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

Python的scrapy之爬取链家网房价信息并保存到本地Python的scrapy之爬取链家网房价信息并保存到本地​

爬虫结构：

Python的scrapy之爬取链家网房价信息并保存到本地Python的scrapy之爬取链家网房价信息并保存到本地​ 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

Python的scrapy之爬取链家网房价信息并保存到本地

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

　　

Python的scrapy之爬取链家网房价信息并保存到本地

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

　　

Python的scrapy之爬取链家网房价信息并保存到本地

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')

　　

Python的scrapy之爬取链家网房价信息并保存到本地

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'

　　

Python的scrapy之爬取链家网房价信息并保存到本地

最后看starthouse.py

from scrapy.cmdline import execute

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

　　

Python的scrapy之爬取链家网房价信息并保存到本地

代码运行结果

Python的scrapy之爬取链家网房价信息并保存到本地Python的scrapy之爬取链家网房价信息并保存到本地​

保存到本地效果：

Python的scrapy之爬取链家网房价信息并保存到本地Python的scrapy之爬取链家网房价信息并保存到本地​

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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