用scrapy爬取链家全国以上房源分类的信息：
路径：
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 LianItem(scrapy.Item):
# define the fields for your item here like:
# name = scrapy.Field()
pass class ErShouFangItem(scrapy.Item):
# 省份
province = scrapy.Field()
# 城市
city = scrapy.Field()
# 总价
total_price = scrapy.Field()
# 单价
single_price = scrapy.Field()
# 楼层
room_info = scrapy.Field()
# 住宅位置
region = scrapy.Field()
# 房屋朝向及装修情况
direction = scrapy.Field()
# 建筑面积
area = scrapy.Field()
# 建筑类型
house_struct = scrapy.Field()
# 房屋户型
huxing = scrapy.Field()
# 购买时间
buy_time = scrapy.Field()
# url
ershou_detail_url = scrapy.Field() class NewHouseItem(scrapy.Item):
# 省份
province = scrapy.Field()
# 城市
city = scrapy.Field()
# 标题
title = scrapy.Field()
# 位置
region = scrapy.Field()
# 房屋信息
room_info = scrapy.Field()
# 建筑面积
area = scrapy.Field()
# 价格
price = scrapy.Field()
# 详情页
newHouse_detail_url = scrapy.Field() class RentHouseItem(scrapy.Item):
# 省份
province = scrapy.Field()
# 城市
city = scrapy.Field()
# 标题
title = scrapy.Field()
# 价格
price = scrapy.Field()
# 房间信息（房源户型、朝向、面积、租赁方式）
house_info = scrapy.Field() # 发布时间
pub_time = scrapy.Field()
# 入住：
in_time = scrapy.Field()
# 租期
lease = scrapy.Field()
# 楼层
floor = scrapy.Field()
# 电梯：
lift = scrapy.Field()
# 车位：
carport = scrapy.Field()
# 用水：
use_water = scrapy.Field()
# 用电：
use_electricity = scrapy.Field()
# 燃气：
use_gas = scrapy.Field()
# url
rent_detail_url = scrapy.Field() class OfficeHouseItem(scrapy.Item):
# 省份
province = scrapy.Field()
# 城市
city = scrapy.Field()
# 标题
title = scrapy.Field()
# 价格
price = scrapy.Field()
# 数量
num = scrapy.Field()
# 面积
area = scrapy.Field()
# url
office_detail_url = scrapy.Field() class XiaoquHouseItem(scrapy.Item):
# 省份
province = scrapy.Field()
# 城市
city = scrapy.Field()
# 标题
title = scrapy.Field()
# 地区
region = scrapy.Field()
# 单价
single_price = scrapy.Field()
# 建筑年代
build_time = scrapy.Field()
# 建筑类型
house_struct = scrapy.Field()
# 物业费用
service_fees = scrapy.Field()
# 物业公司
service_company = scrapy.Field()
# 开发商
build_company = scrapy.Field()
# 楼栋数
building_nums = scrapy.Field()
# 房屋总数
house_nums = scrapy.Field()
# url
xiaoqu_detail_url = scrapy.Field()
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
from scrapy.exporters import JsonLinesItemExporter
from lian.items import ErShouFangItem, NewHouseItem,RentHouseItem,OfficeHouseItem,XiaoquHouseItem # 已经导入成功，不用管 class LianPipeline(object):
def __init__(self):
self.ershoufang_fp = open('ershoufang.json', 'wb')
self.ershoufang_exporter = JsonLinesItemExporter(self.ershoufang_fp, ensure_ascii=False) self.newhouse_fp = open('newhouse.json', 'wb')
self.newhouse_exporter = JsonLinesItemExporter(self.newhouse_fp, ensure_ascii=False) self.renthouse_fp = open('renthouse.json', 'wb')
self.renthouse_exporter = JsonLinesItemExporter(self.renthouse_fp, ensure_ascii=False) self.officehouse_fp = open('officehouse.json', 'wb')
self.officehouse_exporter = JsonLinesItemExporter(self.officehouse_fp, ensure_ascii=False) self.xiaoquhouse_fp = open('xiaoquhouse.json', 'wb')
self.xiaoquhouse_exporter = JsonLinesItemExporter(self.xiaoquhouse_fp, ensure_ascii=False) def process_item(self, item, spider):
if isinstance(item, ErShouFangItem):
self.ershoufang_exporter.export_item(item)
elif isinstance(item, NewHouseItem):
self.newhouse_exporter.export_item(item)
elif isinstance(item, RentHouseItem):
self.renthouse_exporter.export_item(item)
elif isinstance(item ,OfficeHouseItem):
self.officehouse_exporter.export_item(item)
else:
self.xiaoquhouse_exporter.export_item(item)
return item def close_spider(self, spider):
self.ershoufang_fp.close()
self.newhouse_fp.close()
self.renthouse_fp.close()
# self.officehouse_fp.closed()
self.xiaoquhouse_fp.close()
lian_spider.py
# -*- coding: utf-8 -*-
import scrapy
import re
from lian.items import ErShouFangItem,NewHouseItem,RentHouseItem,OfficeHouseItem,XiaoquHouseItem # 已经导入成功，不用管
class LianSpiderSpider(scrapy.Spider):
name = 'lian_spider'
allowed_domains = ['lianjia.com']
start_urls = ['https://www.lianjia.com/city/']
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36',
'Cookie': 'select_city=510700; lianjia_uuid=8bd3d017-2c99-49a5-826e-986f56ce99b9; _smt_uid=5cd3cd13.44c49764; UM_distinctid=16a9b59145a158-0442ba7704d667-3b654406-c0000-16a9b59146011e; _jzqckmp=1; _ga=GA1.2.822868133.1557384475; _gid=GA1.2.801531476.1557384475; all-lj=ed5a77c9e9ec3809d0c1321ec78803ae; lianjia_ssid=50fd11a7-d48c-4dde-b281-287224c40487; TY_SESSION_ID=ae45e1a4-b6d9-46bb-81c8-7cff32931953; Hm_lvt_9152f8221cb6243a53c83b956842be8a=1557384618,1557389971,1557392984,1557446598; _jzqc=1; _jzqy=1.1557384468.1557446599.1.jzqsr=baidu|jzqct=%E9%93%BE%E5%AE%B6.-; _qzjc=1; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%2216a9b5916632a6-01ac8dcdbbb8a7-3b654406-786432-16a9b59166452e%22%2C%22%24device_id%22%3A%2216a9b5916632a6-01ac8dcdbbb8a7-3b654406-786432-16a9b59166452e%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_referrer%22%3A%22%22%2C%22%24latest_referrer_host%22%3A%22%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%7D%7D; _jzqa=1.1500973956232310800.1557384468.1557451920.1557454945.6; _jzqx=1.1557451920.1557454945.2.jzqsr=mianyang%2Elianjia%2Ecom|jzqct=/ershoufang/pag1/.jzqsr=mianyang%2Elianjia%2Ecom|jzqct=/ershoufang/; CNZZDATA1255604082=609852050-1557381958-https%253A%252F%252Fwww.baidu.com%252F%7C1557455869; CNZZDATA1254525948=1645681089-1557382543-https%253A%252F%252Fwww.baidu.com%252F%7C1557458144; CNZZDATA1255633284=262578687-1557381275-https%253A%252F%252Fwww.baidu.com%252F%7C1557458627; Hm_lpvt_9152f8221cb6243a53c83b956842be8a=1557459240; _qzja=1.677427564.1557384472885.1557451920228.1557454945305.1557459200351.1557459240226.0.0.0.62.6; _qzjb=1.1557454945305.13.0.0.0; _qzjto=33.3.0; _jzqb=1.13.10.1557454945.1'
} # 每个城市
def parse(self, response):
lis = response.xpath('//div[@class="city_list_section"]/ul/li')
city_links = []
for li in lis:
province = li.xpath('.//div[@class="city_list_tit c_b"]/text()').extract_first()
# print(province)
lis2 = li.xpath('.//div[@class="city_province"]/ul/li')
city_info = {}
for l in lis2:
city_info['city'] = l.xpath('./a/text()').extract_first()
city_info['city_link'] = l.xpath('./a/@href').extract_first()
city_links.append(city_info)
# print(city_info)
yield scrapy.Request(
url=city_info['city_link'],
headers=self.headers,
callback=self.parse_rent_type,
meta={'city_name': (province,city_info['city'])}
)
# 爬取海外房源，所有城市标题上房有海外房源的信息均为一致，所以只爬取一次
# yield scrapy.Request(
# url='https://i.lianjia.com/us',
# headers=self.headers,
# callback=self.parse_haiwai
# ) # 类型（二手房，新房，租房，商业办公，小区）
def parse_rent_type(self, response):
province,city_name = response.meta.get('city_name')
lis = response.xpath('//div[@class="nav typeUserInfo"]/ul/li')
for li in lis:
type = li.xpath('./a/text()').extract_first()
if type == '二手房':
ershoufang_link = li.xpath('./a/@href').extract_first()
# print("city：{}————————{}".format(city_name,ershoufang_link))
next_urls = [ershoufang_link + '/pg{}/'.format(str(i)) for i in range(1, 101)]
i = 0
for url in next_urls:
i = i+1
yield scrapy.Request(
url=url,
headers=self.headers,
callback=self.parse_ershoufang,
meta={'city_name': (province,city_name,i)}
)
# 不好找页码
elif type == '新房':
xinfang_link = li.xpath('./a/@href').extract_first()
xinfang_link = xinfang_link + '/loupan/'
yield scrapy.Request(
url=xinfang_link,
headers=self.headers,
callback=self.parse_xinfang,
meta={'city_name': (province,city_name)}
)
elif type == '租房':
zufang_link = li.xpath('./a/@href').extract_first()
next_urls = [zufang_link + '/pg{}/'.format(str(i)) for i in range(1, 101)]
i = 0
for url in next_urls:
i = i + 1
yield scrapy.Request(
url=url,
headers=self.headers,
callback=self.parse_zufang,
meta={'city_name': (url,province,city_name,i)}
)
# 不好找页码
elif type == '商业办公': #TODO 有一个重定向，只会爬取一页
shangyebangong_link = li.xpath('./a/@href').extract_first()
shangyebangong_link = str(shangyebangong_link)+"/xzl/rent/mlist"
# print(province, city_name,shangyebangong_link)
if shangyebangong_link == None:
continue
yield scrapy.Request(
url=shangyebangong_link,
headers=self.headers,
callback=self.parse_shangyebangong,
meta={'city_name': (province,city_name)}
)
# 不好找页码
elif type == '小区':
xiaoqu_link = li.xpath('./a/@href').extract_first()
yield scrapy.Request(
url=xiaoqu_link,
headers=self.headers,
callback=self.parse_xiaoqu,
meta={'city_name': (province,city_name)}
) # 获取二手房主页item+
def parse_ershoufang(self, response):
province,city_name,i = response.meta.get('city_name')
lis = response.xpath('//ul[@class="sellListContent"]/li')
for li in lis:
ershou_detail_link = li.xpath('.//div[@class="title"]/a/@href').extract_first()
# 注意有的房屋信息为None
if ershou_detail_link == None:
continue
# print("{}——————{}".format(city_name,ershou_detail_link))
yield scrapy.Request(
url=ershou_detail_link,
headers=self.headers,
callback=self.parse_ershoufang_detail,
meta={'city_name': (ershou_detail_link,province,city_name,i)}
)
# 二手房item详情页
def parse_ershoufang_detail(self, response):
ershou_detail_link,province,city_name,i = response.meta.get('city_name')
title = response.xpath('//div[@class="sellDetailHeader"]//div[@class="title"]/h1/text()').extract_first()
# print("***第{}页*** 城市：{} 二手房 标题：{}".format(i,city_name, title))
total_price = response.xpath('//div[@class="price "]/span[@class="total"]/text()').extract_first() + str(response.xpath('//div[@class="price "]/span[@class="unit"]/span/text()').extract_first()).strip()
single_price = response.xpath('//span[@class="unitPriceValue"]/text()').extract_first() + str(response.xpath('//span[@class="unitPriceValue"]/i/text()').extract_first())
room_info = response.xpath('//div[@class="room"]/div[1]/text()').extract_first() + '-' + response.xpath('//div[@class="room"]/div[2]/text()').extract_first()
region = response.xpath('//div[@class="areaName"]/span[@class="info"]/a[1]/text()').extract_first() + '-' + response.xpath('//div[@class="areaName"]/span[@class="info"]/a[2]/text()').extract_first()
direction = response.xpath('//div[@class="type"]/div[1]/text()').extract_first() + '-' + response.xpath('//div[@class="type"]/div[2]/text()').extract_first()
area = response.xpath('//div[@class="area"]/div[1]/text()').extract_first()
house_struct = response.xpath('//div[@class="area"]/div[2]/text()').extract_first()
huxing = response.xpath('//div[@class="introContent"]/div[1]/div[2]/ul/li[1]/text()').extract_first()
buy_time = response.xpath('//div[@class="transaction"]/div[2]/ul/li[3]/span[2]/text()').extract_first()
print("***第{}页*** 城市：{} 二手房 标题：{} 总价：{} 单价：{} 楼层：{} 住宅位置：{} 房屋朝向：{} 建筑面积：{} 建筑类型：{} 房屋户型：{} 购买时间：{}".format(i, city_name, title,total_price,single_price,room_info,region,direction,area,house_struct,huxing,buy_time)) item = ErShouFangItem(
province = province,
city = city_name,
total_price = total_price,
single_price = single_price,
room_info = room_info,
region = region,
direction = direction,
area = area,
house_struct = house_struct,
huxing = huxing,
buy_time = buy_time,
ershou_detail_url = ershou_detail_link
)
yield item # 新房楼盘主页
def parse_xinfang(self, response):
province,city_name = response.meta.get('city_name')
lis = response.xpath('//ul[@class="resblock-list-wrapper"]/li')
for li in lis:
title = li.xpath('./a[@class="resblock-img-wrapper "]/@title').extract_first() region_infos = li.xpath('.//div[@class="resblock-location"]//text()').extract()
region = ''
for i in region_infos:
region = region + i.replace('\n', '').strip(' ') room_infos = li.xpath('.//a[@class="resblock-room"]/span//text()').extract()
room_info = ''
for i in room_infos:
room_info = room_info + i.strip(' ') area_infos = li.xpath('.//div[@class="main-price"]/span//text()').extract()
area = ''
for i in area_infos:
area = area + i.strip(' ') # 加上单位并去除首尾空格
price = li.xpath('.//div[@class="main-price"]/span[1]/text()').extract_first() + str(li.xpath('.//div[@class="main-price"]/span[2]/text()').extract_first()).strip() newhouse_detail_url = 'https://bj.fang.lianjia.com'+str(li.xpath('./a[@class="resblock-img-wrapper "]/@href').extract_first())
print("城市：{} 新房 {} {}".format(city_name,title, newhouse_detail_url))
item = NewHouseItem(
province=province,
city = city_name,
title = title,
region = region,
room_info = room_info,
area = area,
price = price,
newHouse_detail_url = newhouse_detail_url
)
yield item # 租房首页
def parse_zufang(self, response):
zufang_link, province, city_name, i = response.meta.get('city_name')
# 去掉链接pg页码信息
# print("去掉之前：{}".format(zufang_link))
zufang_link = re.findall('(.*?)/zufang//pg\d+/',zufang_link)[0]
items = response.xpath('//div[@class="content__list"]/div')
for zu in items:
zufang_detail_link = zufang_link + str(zu.xpath('./a[@class="content__list--item--aside"]/@href').extract_first())
# 注意有的房屋信息为None
if zufang_detail_link == None:
continue
# print("{}——————{}".format(city_name,zufang_detail_link))
yield scrapy.Request(
url=zufang_detail_link,
headers=self.headers,
callback=self.parse_zufang_detail,
meta={'city_name': (zufang_detail_link,province,city_name,i)}
)
# 租房信息详情
def parse_zufang_detail(self, response):
zufang_detail_link, province, city_name, i = response.meta.get('city_name')
title = response.xpath('//div[@class="content clear w1150"]/p/text()').extract_first()
price = response.xpath('//div[@class="content__aside fr"]/p/span/text()').extract_first()
house_infos = response.xpath('//ul[@class="content__aside__list"]/p//text()').extract()
house_info = ''
for i in house_infos:
house_info = house_info + i.replace('\n','/').strip(' ')
# 发布时间
pub_time = str(response.xpath('string(//div[@class="content__subtitle"])').extract_first())
pub_time = re.findall('\d{4}-\d{1,2}-\d{1,2}',pub_time)
if pub_time:
pub_time = pub_time[0]
else:
pub_time = None
# 入住时间
in_time = response.xpath('//div[@class="content__article__info"]/ul/li[3]/text()').extract_first()
# 租期
lease = response.xpath('//div[@class="content__article__info"]/ul/li[5]/text()').extract_first()
# 楼层
floor = response.xpath('//div[@class="content__article__info"]/ul/li[8]/text()').extract_first()
# 是否有电梯
lift = response.xpath('//div[@class="content__article__info"]/ul/li[9]/text()').extract_first()
# 是否有停车位
carport = response.xpath('//div[@class="content__article__info"]/ul/li[11]/text()').extract_first()
use_water = response.xpath('//div[@class="content__article__info"]/ul/li[12]/text()').extract_first()
use_electricity = response.xpath('//div[@class="content__article__info"]/ul/li[14]/text()').extract_first()
use_gas = response.xpath('//div[@class="content__article__info"]/ul/li[15]/text()').extract_first() # print(" 城市：{} 租房 {} {} {} {} {} {} {}".format(city_name, lease,floor,lift,carport,use_water,use_electricity,use_gas))
item = RentHouseItem(
province = province,
city = city_name,
title = title,
price = price,
house_info = house_info,
pub_time = pub_time,
in_time = in_time,
lease = lease,
floor = floor,
lift = lift,
carport = carport,
use_water = use_water,
use_electricity = use_electricity,
use_gas = use_gas,
rent_detail_url = zufang_detail_link
)
yield item
print("***第{}页*** 城市：{} 租房 {} {}".format(i, city_name, title, price)) # 海外房源信息
# def parse_haiwai(self,response):
# items = response.xpath('//*[@id="env"]/div[4]/div/div[2]')
# for i in items:
# title = i.xpath('.//div[class="titles"]/a/div/text()').extract_first()
# price = i.xpath('.//span[@class="fr"]/text()').extract_first()
# print("城市：美国 标题：{} 价格：{}".format(title,price)) # 商业办公主页item详情
def parse_shangyebangong(self, response):
province, city_name = response.meta.get('city_name')
items = response.xpath('//div[@class="result__ul"]/a')
for i in items:
office_detail_url = response.xpath('./@href')
title = i.xpath('./div/p[@class="result__li-title"]/text()').extract_first()
area = i.xpath('./div/p[@class="result__li-features"]/text()').extract_first()
nums = i.xpath('./div/p[@class="result__li-other"]/text()').extract_first()
price = i.xpath('./div/p[@class="result__li-price"]/span/text()').extract_first()
item = OfficeHouseItem(
province = province,
city = city_name,
title = title,
price = price,
num = nums,
area = area,
office_detail_url = office_detail_url
)
yield item
print("城市：{} 商业办公 标题：{} 面积：{} 数量：{} 价格：{} url:{}".format(city_name, title, area, nums, price, office_detail_url)) # 小区主页item
def parse_xiaoqu(self, response):
province,city_name = response.meta.get('city_name')
ul = response.xpath('//ul[@class="listContent"]/li')
for li in ul:
xiaoqu_detail_link = li.xpath('.//a[@class="img"]/@href').extract_first()
if xiaoqu_detail_link == None:
continue
yield scrapy.Request(
url=xiaoqu_detail_link,
headers=self.headers,
callback=self.parse_xiaoqu_detail,
meta={'city_name': (xiaoqu_detail_link,province,city_name)}
)
# 小区item详情
def parse_xiaoqu_detail(self, response):
xiaoqu_detail_link,province,city_name = response.meta.get('city_name')
title = response.xpath('//h1[@class="detailTitle"]/text()').extract_first()
region = response.xpath('//div[@class="detailDesc"]/text()').extract_first()
single_price = response.xpath('//span[@class="xiaoquUnitPrice"]/text()').extract_first() # 注意有的房屋没有建成时间信息，影响后面值得获取，需要进行判断后准确取值
build_time = str(response.xpath('//div[@class="xiaoquInfo"]/div[1]/span[2]/text()').extract_first()).strip()
house_struct = None
service_fees = None pattern = re.compile('[0-9]+')
if pattern.findall(build_time):
build_time = build_time
house_struct = response.xpath('//div[@class="xiaoquInfo"]/div[2]/span[2]/text()').extract_first()
service_fees = response.xpath('//div[@class="xiaoquInfo"]/div[3]/span[2]/text()').extract_first()
service_company = response.xpath('//div[@class="xiaoquInfo"]/div[4]/span[2]/text()').extract_first()
build_company = response.xpath('//div[@class="xiaoquInfo"]/div[5]/span[2]/text()').extract_first()
building_nums = response.xpath('//div[@class="xiaoquInfo"]/div[6]/span[2]/text()').extract_first()
house_nums = response.xpath('//div[@class="xiaoquInfo"]/div[7]/span[2]/text()').extract_first()
else:
build_time = None
house_struct = response.xpath('//div[@class="xiaoquInfo"]/div[1]/span[2]/text()').extract_first()
service_fees = response.xpath('//div[@class="xiaoquInfo"]/div[2]/span[2]/text()').extract_first()
service_company = response.xpath('//div[@class="xiaoquInfo"]/div[3]/span[2]/text()').extract_first()
build_company = response.xpath('//div[@class="xiaoquInfo"]/div[4]/span[2]/text()').extract_first()
building_nums = response.xpath('//div[@class="xiaoquInfo"]/div[5]/span[2]/text()').extract_first()
house_nums = response.xpath('//div[@class="xiaoquInfo"]/div[6]/span[2]/text()').extract_first() item = XiaoquHouseItem(
province=province,
city = city_name,
title=title,
region=region,
single_price=single_price,
build_time=build_time,
house_struct=house_struct,
service_fees=service_fees,
service_company=service_company,
build_company=build_company,
building_nums=building_nums,
house_nums=house_nums,
xiaoqu_detail_url=xiaoqu_detail_link
)
yield item
print("省份：{} 城市：{} 小区 {} {} {} {} {} {} {}".format(province, city_name, build_time,house_struct,service_fees,service_company,build_company,building_nums,house_nums))
settings.py
# -*- coding: utf-8 -*- # Scrapy settings for lian 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 = 'lian' SPIDER_MODULES = ['lian.spiders']
NEWSPIDER_MODULE = 'lian.spiders' LOG_LEVEL = "WARNING" # Crawl responsibly by identifying yourself (and your website) on the user-agent
USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.157 Safari/537.36' # Obey robots.txt rules
ROBOTSTXT_OBEY = True DOWNLOAD_FAIL_ON_DATALOSS = 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 = 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 https://doc.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
# 'lian.middlewares.LianSpiderMiddleware': 543,
#} # Enable or disable downloader middlewares
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {
# 'lian.middlewares.LianDownloaderMiddleware': 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 = {
'lian.pipelines.LianPipeline': 300,
} # 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'
结果：