Python的scrapy之爬取鏈家網房價資訊並儲存到本地
阿新 • • 發佈:2018-11-24
因為有在北京租房的打算,於是上網瀏覽了一下鏈家網站的房價,想將他們爬取下來,並儲存到本地。
先看鏈家網的原始碼。。房價資訊 都儲存在 ul 下的li 裡面
爬蟲結構:
先看mylianjia.py
# -*- coding: utf-8 -*- import scrapy from ..items import LianjiaItem from scrapy.http import Request from parsel import Selectorimport 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頁的所有資訊 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('網頁原始碼寫入完畢')''' 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) #化為字串 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
接下來看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('房屋資訊:',item['houseinfo']) print('房屋連結:', item['houseurl']) print('房屋位置:', item['housedizhi']) print('房屋詳細資訊:', item['housexiangxi']) print('房屋總價:', item['houseprice'],'萬') print('平方米價格:', item['houseperprice']) print('===='*10) return item
接下來看csvpipelines.py
import os print(os.getcwd()) class LianjiaPipeline(object): def process_item(self, item, spider): with open('G:\pythonAI\爬蟲大全\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('寫入檔案成功')
接下來看 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'
最後看starthouse.py
from scrapy.cmdline import execute execute(['scrapy', 'crawl', 'mylianjia'])
程式碼執行結果
儲存到本地效果:
完成,事後可以分析一下房價和每平方米的方劑,,因為是海淀區的,,可以看到 都是好幾萬一平米,總價也得幾百萬了 而且是二手房,,,可以看出來 ,在北京買房太難。。。。
原始碼 tyutltf/lianjia: 爬取鏈家北京房價並儲存txt文件 https://github.com/tyutltf/lianjia