【scrapy实践】_爬取安居客_广州_新楼盘数据

需求：爬取【安居客—广州—新楼盘】的数据，具体到每个楼盘的详情页的若干字段。

难点：楼盘类型各式各样：住宅 别墅 商住 商铺 写字楼，不同楼盘字段的名称不一样。然后同一种类型，比如住宅，又分为不同的情况，比如分为期房在售，现房在售，待售，尾盘。其他类型也有类似情况。所以字段不能设置固定住。

解决方案：目前想到的解决方案，第一种：scrapy中items.py中不设置字段，spider中爬的时候自动识别字段（也就是有啥字段就保留下来），然后返回字典存起来。第二种，不同字段的网页分别写规则单独抓取。显然不可取。我采用的是第一种方案。还有其他方案的朋友们，欢迎交流哈。

目标网址为：http://gz.fang.anjuke.com/ 该网页下的楼盘数据

示例楼盘网址：http://gz.fang.anjuke.com/loupan/canshu-298205.html?from=loupan_tab

开始编写scrapy脚本。建立工程步骤略过。

1、count.py

__author__ = 'Oscar_Yang'
#-*- coding= utf-8 -*-
"""
    查看mongodb存储状况的脚本count.py
"""
import time
import pymongo
client = pymongo.MongoClient("localhost", 27017)
db = client["SCRAPY_anjuke_gz"]
sheet = db["anjuke_doc1"]

while True:
    print(sheet.find().count())
    print("____________________________________")
    time.sleep(3)

"""
    entrypoint.py
"""
from scrapy.cmdline import execute
execute(['scrapy', 'crawl', 'anjuke_gz'])

# -*- coding: utf-8 -*-
"""
    settings.py
"""

# Scrapy settings for anjuke_gz 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 = 'anjuke_gz'

SPIDER_MODULES = ['anjuke_gz.spiders']
NEWSPIDER_MODULE = 'anjuke_gz.spiders'
MONGODB_HOST = "127.0.0.1"
MONGODB_PORT = 27017
MONGODB_DBNAME="SCRAPY_anjuke_gz"
MONGODB_DOCNAME="anjuke_doc1"

# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'anjuke_gz (+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 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 = {
#    'anjuke_gz.middlewares.AnjukeGzSpiderMiddleware': 543,
#}

# Enable or disable downloader middlewares
# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {
#    'anjuke_gz.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 = {
   'anjuke_gz.pipelines.AnjukeGzPipeline': 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'

接下来，是items。因为没有设置字段，为默认的代码。

# -*- 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 AnjukeGzItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    pass

接下来，是piplines.py。在中设置了mongodb的配置。

# -*- 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 AnjukeGzPipeline(object):
    def __init__(self):
        host=settings["MONGODB_HOST"]
        port=settings["MONGODB_PORT"]
        dbname=settings["MONGODB_DBNAME"]
        client=pymongo.MongoClient(port=port,host=host)
        tdb = client[dbname]
        self.post=tdb[settings["MONGODB_DOCNAME"]]
    def process_item(self,item,spider):
        info = dict(item)
        self.post.insert(info)
        return item

最后，是最主要的spider.py

from scrapy.http import Request
import scrapy
from bs4 import BeautifulSoup
import re
import requests
"""
    spider脚本
"""
class Myspider(scrapy.Spider):
    name = 'anjuke_gz'
    allowed_domains = ['http://gz.fang.anjuke.com/loupan/']
    start_urls = ["http://gz.fang.anjuke.com/loupan/all/p{}/".format(i) for i in range(39)]

    def parse(self, response):
        soup = BeautifulSoup(response.text,"lxml")
        content=soup.find_all(class_="items-name") #返回每个楼盘的对应数据
        for item in content:
            code=item["href"].split("/")[-1][:6]
            real_href="http://gz.fang.anjuke.com/loupan/canshu-{}.html?from=loupan_tab".format(code) #拼凑出楼盘详情页的url
            res=requests.get(real_href)
            soup = BeautifulSoup(res.text,"lxml")
            a = re.findall(r'<div class="name">(.*?)</div>', str(soup))
            b = soup.find_all(class_="des")
            data = {}
            for (i, j) in zip(range(len(b)), a):
                data[j] = b[i].text.strip().strip("	")
                data["url"] = real_href
            yield data

下面是存入mongodb的情况。

　　因为针对不同的网页结构，爬取的规则是一个，所以爬取的时候就不能针对每个字段进行爬取，所以存到库里的数据如果要是分析的话还需要清洗。

在python中使用mongodb的查询语句，再配合使用pandas应该就很方便清洗了。