Scrapy-Redis分布式抓取麦田二手房租房信息与数据分析准备工作租房爬虫二手房分布式爬虫数据分析及可视化

试着通过抓取一家房产公司的全部信息,研究下北京的房价。文章最后用Pandas进行了分析,并给出了数据可视化。


准备工作

麦田房产二手房页面(http://bj.maitian.cn/esfall/PG1)。

麦田房产租房页面(http://bj.maitian.cn/zfall/PG1)。

用Scrapy shell验证二手房XPath表达式

scrapy shell "http://bj.maitian.cn/esfall/PG1"

title
response.xpath('//div[@class="list_title"]/h1/a/text()').extract()

totalprice
response.xpath('//div[@class="list_title"]/div[@class="the_price"]/ol/strong/span/text()').extract()_first().replace('万元','')

unitprice
response.xpath('//div[@class="list_title"]/div[@class="the_price"]/ol/text()').extract()_first().replace('元/㎡','')

area 
response.xpath('//div[@class="list_title"]/p/span/text()').extract_first()

district
response.xpath('//div[@class="list_title"]/p/text()').re(r'昌平|朝阳|东城|大兴|丰台|海淀|石景山|顺义|通州|西城')

region
response.xpath('//div[@class="list_title"]/p/text()').re(r'[\u4e00-\u9fa5][\u4e00-\u9fa5]')[1]

next page
response.xpath('//div[@id="paging"]/a[@class="down_page"]/@href').extract_first()

租房XPath表达式

scrapy shell "http://bj.maitian.cn/zfall/PG1"

title
response.xpath('//div[@class="list_title"]/h1/a/text()').extract_first().strip().replace('\r\n\r\n','')

price
response.xpath('//div[@class="list_title"]/div[@class="the_price"]/ol/strong/span/text()').extract()

area
response.xpath('//div[@class="list_title"]/p/span/text()').extract_first().replace('㎡','')

district
response.xpath('//div[@class="list_title"]/p[@class="house_hot"]/span/text()').re(r'昌平|朝阳|东城|大兴|丰台|海淀|石景山|顺义|通州|西城')[0]

next page:
response.xpath('//div[@id="paging"]/a[@class="down_page"]/@href').extract_first()

租房爬虫

租房的信息比较少,用一般的Scrapy就行。 在目标文件夹中运行:

scrapy startproject maitian1

先在items.py文件中定义items:

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

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

import scrapy

class MaitianItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    title = scrapy.Field()
    price = scrapy.Field()
    area = scrapy.Field()
    district = scrapy.Field()
    pass

根据上文构造的XPath表达式,写一个租房页面的爬虫zufang_spider.py

import scrapy
from maitian.items import MaitianItem

class MaitianSpider(scrapy.Spider):
    name = "zufang"
    start_urls = ['http://bj.maitian.cn/zfall/PG1']
    def parse(self, response):
        for quote in response.xpath('//div[@class="list_title"]'):
            yield {
                'title': quote.xpath('./h1/a/text()').extract_first().strip().replace('\r\n\r\n',''),
                'price': quote.xpath('./div[@class="the_price"]/ol/strong/span/text()').extract_first(),
                'area': quote.xpath('./p/span/text()').extract_first().replace('㎡',''),
                'district': quote.xpath('./p[@class="house_hot"]/span/text()').re(r'昌平|朝阳|东城|大兴|丰台|海淀|石景山|顺义|通州|西城')[0],
            }

        next_page_url = response.xpath('//div[@id="paging"]/a[@class="down_page"]/@href').extract_first()
        if next_page_url is not None:
            yield scrapy.Request(response.urljoin(next_page_url))

数据库选的是MongoDB,需要使用pipelines.py

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

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

import pymongo

from  scrapy.conf import settings
class MaitianPipeline(object):
  def __init__(self):    
    host = settings['MONGODB_HOST']
    port = settings['MONGODB_PORT']
    db_name = settings['MONGODB_DBNAME']
    client = pymongo.MongoClient(host=host, port=port)
    db = client[db_name]
    self.post = db[settings['MONGODB_DOCNAME']]

  def process_item(self, item, spider):
     zufang = dict(item)
     self.post.insert(zufang)
     return item

settings.py打开pipeline,新的settings.py如下:

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

# Scrapy settings for maitian project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
#     http://doc.scrapy.org/en/latest/topics/settings.html
#     http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html
#     http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html

BOT_NAME = 'maitian'

SPIDER_MODULES = ['maitian.spiders']
NEWSPIDER_MODULE = 'maitian.spiders'


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

# Obey robots.txt rules
ROBOTSTXT_OBEY = True

# Configure maximum concurrent requests performed by Scrapy (default: 16)
#CONCURRENT_REQUESTS = 32

# Configure a delay for requests for the same website (default: 0)
# See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
#DOWNLOAD_DELAY = 3
# 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 http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
#    'maitian.middlewares.MaitianSpiderMiddleware': 543,
#}

# Enable or disable downloader middlewares
# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {
#    'maitian.middlewares.MyCustomDownloaderMiddleware': 543,
#}

# Enable or disable extensions
# See http://scrapy.readthedocs.org/en/latest/topics/extensions.html
#EXTENSIONS = {
#    'scrapy.extensions.telnet.TelnetConsole': None,
#}

# Configure item pipelines
# See http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html
#ITEM_PIPELINES = {
#    'maitian.pipelines.MaitianPipeline': 300,
#}

# Enable and configure the AutoThrottle extension (disabled by default)
# See http://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 http://scrapy.readthedocs.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'

ITEM_PIPELINES = {'maitian.pipelines.MaitianPipeline': 300,}  

MONGODB_HOST = '127.0.0.1'
MONGODB_PORT = 27017
MONGODB_DBNAME = 'maitian1'
MONGODB_DOCNAME = 'zufang'  

maitian1文件夹目录下运行爬虫,同时用命令行参数,将结果保存到zf.json文件,便于之后用Pandas进行分析,别忘了用命令mongod打开MongoDB数据库:

scrapy crawl zufang -o zf.json

会看到生成一个zf.json文件。用mongo命令打开MongoDB终端,然后输入以下命令查看结果:

use maitian1
db.zufang.find()

可以看到


二手房分布式爬虫

二手房信息较多,使用Scrapy-Redis。使用一台Linux作为Redis请求服务器和MongoDB数据库,两台Windows作为爬虫节点。 安装Scrapy-Redis:

pip install scrapy-redis

items.py文件不用做修改。

pipelines.py文件也不用进行修改。

和前面的租房爬虫XPath表达式不同,二手房的爬虫文件ershoufang_spider.py是:

import scrapy
from maitian.items import MaitianItem
from scrapy_redis.spiders import RedisSpider
import redis

r = redis.Redis(host='192.168.0.7', port=6379,db=0)

class MaitianSpider(RedisSpider):
    name = "ershoufang"
    # start_urls = ['http://bj.maitian.cn/esfall/PG1']
    redis_key = 'maitianspider:start_urls'

    def parse(self, response):
        for quote in response.xpath('//div[@class="list_title"]'):
            yield {
                'title': quote.xpath('./h1/a/text()').extract(),
                'totalprice': quote.xpath('./div[@class="the_price"]/ol/strong/span/text()').extract_first().replace('万元',''),
                'unitprice': quote.xpath('./div[@class="the_price"]/ol/text()').extract_first().replace('元/㎡',''),
                'area': quote.xpath('./p/span/text()').extract_first(),
                'district': quote.xpath('./p/text()').re(r'昌平|朝阳|东城|大兴|丰台|海淀|石景山|顺义|通州|西城')[0],
            }

        next_page_url = response.xpath('//div[@id="paging"]/a[@class="down_page"]/@href').extract_first()
        if next_page_url is not None:
            # yield scrapy.Request(response.urljoin(next_page_url))
            true_next_url = 'http://bj.maitian.cn' + next_page_url
            r.lpush('maitianspider:start_urls', true_next_url)

可以看到通过from scrapy_redis.spiders import RedisSpider和新的爬虫类class MaitianSpider(RedisSpider):,选用了scrapy-redis的爬虫。

Scrapy-Redis的核心是使用一个公共的Redis数据库作为请求服务器。它在GitHub的地址是https://github.com/rmax/scrapy-redis

Scrapy-Redis最重要的是它的设置:

# Enables scheduling storing requests queue in redis.
SCHEDULER = "scrapy_redis.scheduler.Scheduler"

# Ensure all spiders share same duplicates filter through redis.
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"

# Default requests serializer is pickle, but it can be changed to any module
# with loads and dumps functions. Note that pickle is not compatible between
# python versions.
# Caveat: In python 3.x, the serializer must return strings keys and support
# bytes as values. Because of this reason the json or msgpack module will not
# work by default. In python 2.x there is no such issue and you can use
# 'json' or 'msgpack' as serializers.
#SCHEDULER_SERIALIZER = "scrapy_redis.picklecompat"

# Don't cleanup redis queues, allows to pause/resume crawls.
#SCHEDULER_PERSIST = True

# Schedule requests using a priority queue. (default)
#SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.PriorityQueue'

# Alternative queues.
#SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.FifoQueue'
#SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.LifoQueue'

# Max idle time to prevent the spider from being closed when distributed crawling.
# This only works if queue class is SpiderQueue or SpiderStack,
# and may also block the same time when your spider start at the first time (because the queue is empty).
#SCHEDULER_IDLE_BEFORE_CLOSE = 10

# Store scraped item in redis for post-processing.
ITEM_PIPELINES = {
    'scrapy_redis.pipelines.RedisPipeline': 300
}

# The item pipeline serializes and stores the items in this redis key.
#REDIS_ITEMS_KEY = '%(spider)s:items'

# The items serializer is by default ScrapyJSONEncoder. You can use any
# importable path to a callable object.
#REDIS_ITEMS_SERIALIZER = 'json.dumps'

# Specify the host and port to use when connecting to Redis (optional).
#REDIS_HOST = 'localhost'
#REDIS_PORT = 6379

# Specify the full Redis URL for connecting (optional).
# If set, this takes precedence over the REDIS_HOST and REDIS_PORT settings.
#REDIS_URL = 'redis://user:pass@hostname:9001'

# Custom redis client parameters (i.e.: socket timeout, etc.)
#REDIS_PARAMS  = {}
# Use custom redis client class.
#REDIS_PARAMS['redis_cls'] = 'myproject.RedisClient'

# If True, it uses redis' ``SPOP`` operation. You have to use the ``SADD``
# command to add URLs to the redis queue. This could be useful if you
# want to avoid duplicates in your start urls list and the order of
# processing does not matter.
#REDIS_START_URLS_AS_SET = False

# Default start urls key for RedisSpider and RedisCrawlSpider.
#REDIS_START_URLS_KEY = '%(name)s:start_urls'

# Use other encoding than utf-8 for redis.
#REDIS_ENCODING = 'latin1'

设置项很多,我们用到的是:

#使用Scrapy-Redis的调度器
SCHEDULER = "scrapy_redis.scheduler.Scheduler"

#利用Redis的集合实现去重
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"

#允许继续爬取
SCHEDULER_PERSIST = True

#设置优先级
SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.SpiderPriorityQueue'

#两个管道,第一个是负责存储到MongoDB
ITEM_PIPELINES = {
    'maitian.pipelines.MaitianPipeline': 300,
    'scrapy_redis.pipelines.RedisPipeline': 400,    
}

#Redis数据库的地址
REDIS_URL = 'redis://root:@192.168.0.7:6379'
#MongoDB数据库的地址
MONGODB_HOST = '192.168.0.7'
MONGODB_PORT = 27017
MONGODB_DBNAME = 'maitian'
MONGODB_DOCNAME = 'ershoufang' 

结合之前的租房爬虫,新的settings.py文件是:

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

# Scrapy settings for maitian project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
#     http://doc.scrapy.org/en/latest/topics/settings.html
#     http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html
#     http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html

BOT_NAME = 'maitian'

SPIDER_MODULES = ['maitian.spiders']
NEWSPIDER_MODULE = 'maitian.spiders'

SCHEDULER = "scrapy_redis.scheduler.Scheduler"

DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"

SCHEDULER_PERSIST = True

SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.SpiderPriorityQueue'

ITEM_PIPELINES = {
    'maitian.pipelines.MaitianPipeline': 300,
    'scrapy_redis.pipelines.RedisPipeline': 400,    
}

REDIS_URL = 'redis://root:@192.168.0.7:6379'

MONGODB_HOST = '192.168.0.7'
MONGODB_PORT = 27017
MONGODB_DBNAME = 'maitian'
MONGODB_DOCNAME = 'ershoufang' 

运行爬虫之前不要忘了打开Linux中的Redis和MongoDB的远程访问。

分别在两台爬虫节点的根目录下运行爬虫:

scrapy crawl ershoufang

这时可以看到:

显示正在监听。

这时需要把redis_key = 'maitianspider:start_urls'插入到Redis数据库中,命令如下:

lpush maitianspider:start_urls http://bj.maitian.cn/esfall/PG1

有了这个种子URL,两个爬虫就可运行起来了。


数据分析及可视化

使用的是Pandas和Matplotlib。

打印出出售(出租)情况的基本信息:

import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt

from pylab import *  
mpl.rcParams['font.sans-serif'] = ['SimHei']

#基本信息
df = pd.read_json("esf.json")
print(df.describe())
#分别统计个数
print(df["district"].value_counts())

生成饼图,查看每个区的占比情况:

import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt

from pylab import *  
mpl.rcParams['font.sans-serif'] = ['SimHei']


labels = '朝阳', '海淀', '昌平', '东城', '大兴', '西城', '丰台', '石景山', '通州'
sizes = [4534, 1612, 540, 530, 376, 155, 105, 74, 1]
explode = (0.1, 0, 0, 0,0,0,0,0,0)  
plt.subplot(121)
plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=-90)
plt.axis('equal')  
plt.title("房屋出售分布")


labels = '朝阳', '海淀', '东城', '西城', '昌平', '石景山', '大兴', '丰台'
sizes = [898, 350, 109, 60, 42, 25, 17, 8]
explode = (0.1, 0, 0, 0,0,0,0,0)  
plt.subplot(122)
plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=-90)
plt.axis('equal')  
plt.title("房屋出租分布")

plt.show()

在二手房销售市场上,总共有房7927套。朝阳区有4534套,占比接近60%。海淀区有1612套,占比20%。二者之和,占到整个销售市场的四分之三以上。 一共有1509套房在整租,其中朝阳区898套,占比仍然近60%,海淀区有350套,占比20%+。二者总共依然占到租赁市场的四分之三以上。

最关心的还是北京房价的分布区间:

import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt

from pylab import *  
mpl.rcParams['font.sans-serif'] = ['SimHei']

df = pd.read_json("esf.json")

unitprice_values = df.unitprice
plt.hist(unitprice_values,bins=10)
plt.title(u"房屋出售每平米价格分布")
plt.xlabel(u'价格(单位:万/平方米)')
plt.ylabel(u'套数')
plt.show() 

房屋单价分布在2万每平米到18万每平米之间。大部分处于6万到12万的区间之内。其中,8万每平米的房屋,在市场最多。所有房屋的平均价格是83860万每平米。

售租比是衡量房屋出售与出租关系的指标之一,售租比越低,说明房屋每平米的出租收益越大,越具有购买价值。房屋的售价租金比是用来判断某一区域房产是否存在价值泡沫的一个衡量指标,也是用来研判某一区域是否具有投资价值的普遍标准。国际上用来衡量一个区域房产运行状况良好的售价租金比一般界定为200:1至300:1。

出租市场需要考虑每平米的每月租金,可以利用现有的数据计算,再添加到DataFrame。

unitprice_values = df.price/df.area
df['unitprice']=unitprice_values

df.groupby("district").mean()可以显示出每个区的均值信息,整理如下:

根据这两张表,可以得到售租比与区的对应关系(因为租售房屋中没有通州,所以略过): 选用barh

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from pylab import *  
mpl.rcParams['font.sans-serif'] = ['SimHei']

district = ('西城', '石景山','东城','海淀','丰台','昌平','大兴','朝阳')

ratio = (935.3099343,895.6966335,857.8477132,852.2141772,836.9530937,748.4055373,738.6935962,702.9053925)

fig, ax = plt.subplots()
district = ('西城', '石景山','东城','海淀','丰台','昌平','大兴','朝阳')
y_pos = np.arange(len(district))
performance = ratio

ax.barh(y_pos, ratio, align='center', color='green', ecolor='black')
ax.set_yticks(y_pos)
ax.set_yticklabels(district)
ax.invert_yaxis()  
ax.set_xlabel('售租比(单位:月)')
ax.set_title('各区房屋售租比')

plt.show()

所有区的售租比均在700以上,也就是说房屋每平米的售价是月租金的700以上。售租比最低的朝阳区的值是703,相当于58年。这是因为朝阳区面积较大,且位于商贸中心,上班族租房需求大。换句话说,买到手的房子出租出去,需要58年才能回本。对比200到300的推荐值(大概20年回本),可见北京的房价售价十分之高,从投资的角度,回报率不大。当然了,根据售租比,(有钱的话)买房要买朝阳的,租房要租西城的。

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