前段时间小编发了一篇有关于Python数据类型的文章，由于只是介绍了数据类型，我觉得远远不够，所以呢我现在写一篇用Python爬取数据的文章来补充。

首先我会介绍如何使用scrapy抓取二手房数据，然后我会将抓下来的数据进行了一些简单的分析和可视化。最后奉上数据，感兴趣的朋友可以深入分析

Github地址：https://github.com/HunterChao/Crawler

使用scrapy抓取二手房数据，如下所示：

文章目录结构

D:.

│ run.py

│ scrapy.cfg

│

└─LianJia

│ items.py

│ pipelines.py

│ settings.py

│ __init__.py

│

├─spiders

│ lianjia.py

│ __init__.py

lianjia.py是程序的主要运行文件，run.py为程序启动文件。在pycharm下执行run.py即可启动程序。

项目分析：

链接的构造：我们通过抓取首页可以获得北京市各城区的名称(如：东城、西城、朝阳)及对应的拼音，进一步通过遍历每个城区对应的页码数(Pn)即可构造出各城区的二手房链接。

信息的抓取：在进入各个城区的二手房页面时，可匹配出每个房源的详细信息。这里需要注意的是，由于我想将各房源的经纬度信息获取以便可视化到地图上，需要找到每个房源的详情页链接，进入该链接，匹配出经纬度相关的字段。(resblockPosition)数据字段：item.py

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

import scrapy

class LianjiaItem(scrapy.Item):

# 标签 小区 户型 面积 关注人数 观看人数 发布时间 价格 均价 详情链接 经纬度 城区

title = scrapy.Field()

community = scrapy.Field()

model = scrapy.Field()

area = scrapy.Field()

focus_num = scrapy.Field()

watch_num = scrapy.Field()

time = scrapy.Field()

price = scrapy.Field()

average_price = scrapy.Field()

link = scrapy.Field()

Latitude = scrapy.Field()

city = scrapy.Field()

主要运行函数：lianjia.py

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

import scrapy

import requests

import re

import time

from lxml import etree

from ..items import LianjiaItem

from scrapy_redis.spiders import RedisSpider

class LianjiaSpider(RedisSpider):

name = ‘lianjiaspider‘

redis_key = ‘lianjiaspider:urls‘

start_urls = ‘http://bj.lianjia.com/ershoufang/‘

def start_requests(self):

user_agent = ‘Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.22

Safari/537.36 SE 2.X MetaSr 1.0‘

headers = {‘User-Agent‘: user_agent}

yield scrapy.Request(url=self.start_urls, headers=headers, method=‘GET‘, callback=self.parse)

def parse(self, response):

user_agent = ‘Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.22

Safari/537.36 SE 2.X MetaSr 1.0‘

headers = {‘User-Agent‘: user_agent}

lists = response.body.decode(‘utf-8‘)

selector = etree.HTML(lists)

area_list = selector.xpath(‘/html/body/div[3]/div[2]/dl[2]/dd/div[1]/div/a‘)

for area in area_list:

try:

area_han = area.xpath(‘text()‘).pop() # 地点

area_pin = area.xpath(‘@href‘).pop().split(‘/‘)[2] # 拼音

area_url = ‘http://bj.lianjia.com/ershoufang/{}/‘.format(area_pin)

print(area_url)

yield scrapy.Request(url=area_url, headers=headers, callback=self.detail_url, meta={"id1":area_han,"id2":area_pin} )

except Exception:

pass

def get_latitude(self,url): # 进入每个房源链接抓经纬度

p = requests.get(url)

contents = etree.HTML(p.content.decode(‘utf-8‘))

latitude = contents.xpath(‘/ html / body / script[19]/text()‘).pop()

time.sleep(3)

regex = ‘‘‘resblockPosition(.+)‘‘‘

items = re.search(regex, latitude)

content = items.group()[:-1] # 经纬度

longitude_latitude = content.split(‘:‘)[1]

return longitude_latitude[1:-1]

def detail_url(self,response):

‘http://bj.lianjia.com/ershoufang/dongcheng/pg2/‘

for i in range(1,101):

url = ‘http://bj.lianjia.com/ershoufang/{}/pg{}/‘.format(response.meta["id2"],str(1))

time.sleep(2)

try:

contents = requests.get(url)

contents = etree.HTML(contents.content.decode(‘utf-8‘))

houselist = contents.xpath(‘/html/body/div[4]/div[1]/ul/li‘)

for house in houselist:

try:

item = LianjiaItem()

item[‘title‘] = house.xpath(‘div[1]/div[1]/a/text()‘).pop()

item[‘community‘] = house.xpath(‘div[1]/div[2]/div/a/text()‘).pop()

item[‘model‘] = house.xpath(‘div[1]/div[2]/div/text()‘).pop().split(‘|‘)[1]

item[‘area‘] = house.xpath(‘div[1]/div[2]/div/text()‘).pop().split(‘|‘)[2]

item[‘focus_num‘] = house.xpath(‘div[1]/div[4]/text()‘).pop().split(‘/‘)[0]

item[‘watch_num‘] = house.xpath(‘div[1]/div[4]/text()‘).pop().split(‘/‘)[1]

item[‘time‘] = house.xpath(‘div[1]/div[4]/text()‘).pop().split(‘/‘)[2]

item[‘price‘] = house.xpath(‘div[1]/div[6]/div[1]/span/text()‘).pop()

item[‘average_price‘] = house.xpath(‘div[1]/div[6]/div[2]/span/text()‘).pop()

item[‘link‘] = house.xpath(‘div[1]/div[1]/a/@href‘).pop()

item[‘city‘] = response.meta["id1"]

self.url_detail = house.xpath(‘div[1]/div[1]/a/@href‘).pop()

item[‘Latitude‘] = self.get_latitude(self.url_detail)

except Exception:

pass

yield item

except Exception:

pass

抓取效果：

以上就是如何抓取数据了，大家是否成功将数据爬取下来了呢？假如还有不懂的地方或者需要源码，可以加群959997225即可获得！

北京二手房数据：https://pan.baidu.com/share/init?surl=nuCeVrV，密码：rfli

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