整站40万条房价数据并行抓取，可更换抓取城市

写在前面

这次的爬虫是关于房价信息的抓取，目的在于练习10万以上的数据处理及整站式抓取。

数据量的提升最直观的感觉便是对函数逻辑要求的提高，针对Python的特性，谨慎的选择数据结构。以往小数据量的抓取，即使函数逻辑部分重复，I/O请求频率密集，循环套嵌过深，也不过是1~2s的差别，而随着数据规模的提高，这1~2s的差别就有可能扩展成为1~2h。

因此对于要抓取数据量较多的网站，可以从两方面着手降低抓取信息的时间成本。

1）优化函数逻辑，选择适当的数据结构，符合Pythonic的编程习惯。例如，字符串的合并，使用join（）要比“+”节省内存空间。

2）依据I/O密集与CPU密集，选择多线程、多进程并行的执行方式，提高执行效率。

一、获取索引

包装请求request，设置超时timeout

# 获取列表页面
def get_page(url):
    headers = {
 'User-Agent': r'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '
 r'Chrome/45.0.2454.85 Safari/537.36 115Browser/6.0.3',
 'Referer': r'http://bj.fangjia.com/ershoufang/',
 'Host': r'bj.fangjia.com',
 'Connection': 'keep-alive'
    }
    timeout = 60
    socket.setdefaulttimeout(timeout)  # 设置超时
    req = request.Request(url, headers=headers)
    response = request.urlopen(req).read()
    page = response.decode('utf-8')
    return page

一级位置：区域信息

二级位置：板块信息（根据区域位置得到板块信息，以key_value对的形式存储在dict中）

以dict方式存储，可以快速的查询到所要查找的目标。-> {'朝阳'：{'工体'，'安贞'，'健翔桥'......}}

三级位置：地铁信息（搜索地铁周边房源信息）

将所属位置地铁信息，添加至dict中。 -> {'朝阳'：{'工体'：{'5号线'，'10号线' , '13号线'}，'安贞'，'健翔桥'......}}

对应的url：http://bj.fangjia.com/ershoufang/--r-%E6%9C%9D%E9%98%B3%7Cw-5%E5%8F%B7%E7%BA%BF%7Cb-%E6%83%A0%E6%96%B0%E8%A5%BF%E8%A1%97

解码后的url：http://bj.fangjia.com/ershoufang/--r-朝阳|w-5号线|b-惠新西街

根据url的参数模式，可以有两种方式获取目的url：
1）根据索引路径获得目的url

# 获取房源信息列表(嵌套字典遍历)
def get_info_list(search_dict, layer, tmp_list, search_list):
    layer += 1  # 设置字典层级
    for i in range(len(search_dict)):
 tmp_key = list(search_dict.keys())[i]  # 提取当前字典层级key
 tmp_list.append(tmp_key)   # 将当前key值作为索引添加至tmp_list
 tmp_value = search_dict[tmp_key]
 if isinstance(tmp_value, str):   # 当键值为url时
     tmp_list.append(tmp_value)   # 将url添加至tmp_list
     search_list.append(copy.deepcopy(tmp_list))   # 将tmp_list索引url添加至search_list
     tmp_list = tmp_list[:layer]  # 根据层级保留索引
 elif tmp_value == '':   # 键值为空时跳过
     layer -= 2    # 跳出键值层级
     tmp_list = tmp_list[:layer]   # 根据层级保留索引
 else:
     get_info_list(tmp_value, layer, tmp_list, search_list)  # 当键值为列表时，迭代遍历
     tmp_list = tmp_list[:layer]
    return search_list

2）根据dict信息包装url

{'朝阳'：{'工体'：{'5号线'}}}
参数：

——　　r-朝阳

——　　b-工体

——　　w-5号线

组装参数：http://bj.fangjia.com/ershoufang/--r-朝阳|w-5号线|b-工体

1 # 根据参数创建组合url
2 def get_compose_url(compose_tmp_url, tag_args,  key_args):
3     compose_tmp_url_list = [compose_tmp_url, '|' if tag_args != 'r-' else '', tag_args, parse.quote(key_args), ]
4     compose_url = ''.join(compose_tmp_url_list)
5     return compose_url

二、获取索引页最大页数

# 获取当前索引页面页数的url列表
def get_info_pn_list(search_list):
    fin_search_list = []
    for i in range(len(search_list)):
 print('>>>正在抓取%s' % search_list[i][:3])
 search_url = search_list[i][5]
 try:
     page = get_page(search_url)
 except:
     print('获取页面超时')
     continue
 soup = BS(page, 'lxml')
 # 获取最大页数
 pn_num = soup.select('span[class="mr5"]')[0].get_text()
 rule = re.compile(r'd+')
 max_pn = int(rule.findall(pn_num)[1])
 # 组装url
 for pn in range(1, max_pn+1):
     print('************************正在抓取%s页************************' % pn)
     pn_rule = re.compile('[|]')
     fin_url = pn_rule.sub(r'|e-%s|' % pn, search_url, 1)
     tmp_url_list = copy.deepcopy(search_list[i][:3])
     tmp_url_list.append(fin_url)
     fin_search_list.append(tmp_url_list)
    return fin_search_list

三、抓取房源信息Tag
这是我们要抓取的Tag：

['区域', '板块', '地铁', '标题', '位置', '平米', '户型', '楼层', '总价', '单位平米价格']

# 获取tag信息
def get_info(fin_search_list, process_i):
    print('进程%s开始' % process_i)
    fin_info_list = []
    for i in range(len(fin_search_list)):
 url = fin_search_list[i][7]
 try:
     page = get_page(url)
 except:
     print('获取tag超时')
     continue
 soup = BS(page, 'lxml')
 title_list = soup.select('a[class="h_name"]')
 address_list = soup.select('span[class="address]')
 attr_list = soup.select('span[class="attribute"]')
 price_list = soup.find_all(attrs={"class": "xq_aprice xq_esf_width"})  # select对于某些属性值（属性值中间包含空格）无法识别，可以用find_all(attrs={})代替
 for num in range(20):
     tag_tmp_list = []
     try:
  title = title_list[num].attrs["title"]
  print(r'************************正在获取%s************************' % title)
  address = re.sub('n', '', address_list[num].get_text())   
  area = re.search('d+[u4E00-u9FA5]{2}', attr_list[num].get_text()).group(0)  
  layout = re.search('d[^0-9]d.', attr_list[num].get_text()).group(0)
  floor = re.search('d/d', attr_list[num].get_text()).group(0)
  price = re.search('d+[u4E00-u9FA5]', price_list[num].get_text()).group(0)
  unit_price = re.search('d+[u4E00-u9FA5]/.', price_list[num].get_text()).group(0)
  tag_tmp_list = copy.deepcopy(fin_search_list[i][:3])
  for tag in [title, address, area, layout, floor, price, unit_price]:
      tag_tmp_list.append(tag)
  fin_info_list.append(tag_tmp_list)
     except:
  print('【抓取失败】')
  continue
    print('进程%s结束' % process_i)
    return fin_info_list

四、分配任务，并行抓取

对任务列表进行分片，设置进程池，并行抓取。

# 分配任务
def assignment_search_list(fin_search_list, project_num):  # project_num每个进程包含的任务数，数值越小，进程数越多
    assignment_list = []
    fin_search_list_len = len(fin_search_list)
    for i in range(0, fin_search_list_len, project_num):
 start = i
 end = i+project_num
 assignment_list.append(fin_search_list[start: end])  # 获取列表碎片
    return assignment_list

p = Pool(4)  # 设置进程池
    assignment_list = assignment_search_list(fin_info_pn_list, 3)  # 分配任务，用于多进程
    result = []  # 多进程结果列表
    for i in range(len(assignment_list)):
 result.append(p.apply_async(get_info, args=(assignment_list[i], i)))
    p.close()
    p.join()
    for result_i in range(len(result)):
 fin_info_result_list = result[result_i].get()
 fin_save_list.extend(fin_info_result_list)  # 将各个进程获得的列表合并

通过设置进程池并行抓取，时间缩短为单进程抓取时间的3/1，总计时间3h。

电脑为4核，经过测试，任务数为3时，在当前电脑运行效率最高。

五、将抓取结果存储到excel中，等待可视化数据化处理

# 存储抓取结果
def save_excel(fin_info_list, file_name):
    tag_name = ['区域', '板块', '地铁', '标题', '位置', '平米', '户型', '楼层', '总价', '单位平米价格']
    book = xlsxwriter.Workbook(r'C:UsersAdministratorDesktop%s.xls' % file_name)  # 默认存储在桌面上
    tmp = book.add_worksheet()
    row_num = len(fin_info_list)
    for i in range(1, row_num):
 if i == 1:
     tag_pos = 'A%s' % i
     tmp.write_row(tag_pos, tag_name)
 else:
     con_pos = 'A%s' % i
     content = fin_info_list[i-1]  # -1是因为被表格的表头所占
     tmp.write_row(con_pos, content)
    book.close()

#! -*-coding:utf-8-*-
# Function: 房价调查
# Author：蘭兹

from urllib import parse, request
from bs4 import BeautifulSoup as BS
from multiprocessing import Pool
import re
import lxml
import datetime
import cProfile
import socket
import copy
import xlsxwriter

starttime = datetime.datetime.now()

base_url = r'http://bj.fangjia.com/ershoufang/'

test_search_dict = {'昌平': {'霍营': {'13号线': 'http://bj.fangjia.com/ershoufang/--r-%E6%98%8C%E5%B9%B3|w-13%E5%8F%B7%E7%BA%BF|b-%E9%9C%8D%E8%90%A5'}}}

search_list = []  # 房源信息url列表
tmp_list = []  # 房源信息url缓存列表
layer = -1

# 获取列表页面
def get_page(url):
    headers = {
 'User-Agent': r'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '
 r'Chrome/45.0.2454.85 Safari/537.36 115Browser/6.0.3',
 'Referer': r'http://bj.fangjia.com/ershoufang/',
 'Host': r'bj.fangjia.com',
 'Connection': 'keep-alive'
    }
    timeout = 60
    socket.setdefaulttimeout(timeout)  # 设置超时
    req = request.Request(url, headers=headers)
    response = request.urlopen(req).read()
    page = response.decode('utf-8')
    return page

# 获取查询关键词dict
def get_search(page, key):
    soup = BS(page, 'lxml')
    search_list = soup.find_all(href=re.compile(key), target='')
    search_dict = {}
    for i in range(len(search_list)):
 soup = BS(str(search_list[i]), 'lxml')
 key = soup.select('a')[0].get_text()
 value = soup.a.attrs['href']
 search_dict[key] = value
    return search_dict

# 获取房源信息列表(嵌套字典遍历)
def get_info_list(search_dict, layer, tmp_list, search_list):
    layer += 1  # 设置字典层级
    for i in range(len(search_dict)):
 tmp_key = list(search_dict.keys())[i]  # 提取当前字典层级key
 tmp_list.append(tmp_key)   # 将当前key值作为索引添加至tmp_list
 tmp_value = search_dict[tmp_key]
 if isinstance(tmp_value, str):   # 当键值为url时
     tmp_list.append(tmp_value)   # 将url添加至tmp_list
     search_list.append(copy.deepcopy(tmp_list))   # 将tmp_list索引url添加至search_list
     tmp_list = tmp_list[:layer]  # 根据层级保留索引
 elif tmp_value == '':   # 键值为空时跳过
     layer -= 2    # 跳出键值层级
     tmp_list = tmp_list[:layer]   # 根据层级保留索引
 else:
     get_info_list(tmp_value, layer, tmp_list, search_list)  # 当键值为列表时，迭代遍历
     tmp_list = tmp_list[:layer]
    return search_list

# 获取房源信息详情
def get_info_pn_list(search_list):
    fin_search_list = []
    for i in range(len(search_list)):
 print('>>>正在抓取%s' % search_list[i][:3])
 search_url = search_list[i][9]
 try:
     page = get_page(search_url)
 except:
     print('获取页面超时')
     continue
 soup = BS(page, 'lxml')
 # 获取最大页数
 pn_num = soup.select('span[class="mr5"]')[0].get_text()
 rule = re.compile(r'd+')
 max_pn = int(rule.findall(pn_num)[1])
 # 组装url
 for pn in range(1, max_pn+1):
     print('************************正在抓取%s页************************' % pn)
     pn_rule = re.compile('[|]')
     fin_url = pn_rule.sub(r'|e-%s|' % pn, search_url, 1)
     tmp_url_list = copy.deepcopy(search_list[i][:3])
     tmp_url_list.append(fin_url)
     fin_search_list.append(tmp_url_list)
    return fin_search_list

# 获取tag信息
def get_info(fin_search_list, process_i):
    print('进程%s开始' % process_i)
    fin_info_list = []
    for i in range(len(fin_search_list)):
 url = fin_search_list[i][10]
 try:
     page = get_page(url)
 except:
     print('获取tag超时')
     continue
 soup = BS(page, 'lxml')
 title_list = soup.select('a[class="h_name"]')
 address_list = soup.select('span[class="address]')
 attr_list = soup.select('span[class="attribute"]')
 price_list = soup.find_all(attrs={"class": "xq_aprice xq_esf_width"})  # select对于某些属性值（属性值中间包含空格）无法识别，可以用find_all(attrs={})代替
 for num in range(20):
     tag_tmp_list = []
     try:
  title = title_list[num].attrs["title"]
  print(r'************************正在获取%s************************' % title)
  address = re.sub('n', '', address_list[num].get_text())
  area = re.search('d+[u4E00-u9FA5]{2}', attr_list[num].get_text()).group(0)
  layout = re.search('d[^0-9]d.', attr_list[num].get_text()).group(0)
  floor = re.search('d/d', attr_list[num].get_text()).group(0)
  price = re.search('d+[u4E00-u9FA5]', price_list[num].get_text()).group(0)
  unit_price = re.search('d+[u4E00-u9FA5]/.', price_list[num].get_text()).group(0)
  tag_tmp_list = copy.deepcopy(fin_search_list[i][:3])
  for tag in [title, address, area, layout, floor, price, unit_price]:
      tag_tmp_list.append(tag)
  fin_info_list.append(tag_tmp_list)
     except:
  print('【抓取失败】')
  continue
    print('进程%s结束' % process_i)
    return fin_info_list

# 分配任务
def assignment_search_list(fin_search_list, project_num):  # project_num每个进程包含的任务数，数值越小，进程数越多
    assignment_list = []
    fin_search_list_len = len(fin_search_list)
    for i in range(0, fin_search_list_len, project_num):
 start = i
 end = i+project_num
 assignment_list.append(fin_search_list[start: end])  # 获取列表碎片
    return assignment_list

# 存储抓取结果
def save_excel(fin_info_list, file_name):
    tag_name = ['区域', '板块', '地铁', '标题', '位置', '平米', '户型', '楼层', '总价', '单位平米价格']
    book = xlsxwriter.Workbook(r'C:UsersAdministratorDesktop%s.xls' % file_name)  # 默认存储在桌面上
    tmp = book.add_worksheet()
    row_num = len(fin_info_list)
    for i in range(1, row_num):
 if i == 1:
     tag_pos = 'A%s' % i
     tmp.write_row(tag_pos, tag_name)
 else:
     con_pos = 'A%s' % i
     content = fin_info_list[i-1]  # -1是因为被表格的表头所占
     tmp.write_row(con_pos, content)
    book.close()

if __name__ == '__main__':
    file_name = input(r'抓取完成，输入文件名保存：')
    fin_save_list = []  # 抓取信息存储列表
    # 一级筛选
    page = get_page(base_url)
    search_dict = get_search(page, 'r-')
    # 二级筛选
    for k in search_dict:
 print(r'************************一级抓取：正在抓取【%s】************************' % k)
 url = search_dict[k]
 second_page = get_page(url)
 second_search_dict = get_search(second_page, 'b-')
 search_dict[k] = second_search_dict
    # 三级筛选
    for k in search_dict:
 second_dict = search_dict[k]
 for s_k in second_dict:
     print(r'************************二级抓取：正在抓取【%s】************************' % s_k)
     url = second_dict[s_k]
     third_page = get_page(url)
     third_search_dict = get_search(third_page, 'w-')
     print('%s>%s' % (k, s_k))
     second_dict[s_k] = third_search_dict
    fin_info_list = get_info_list(search_dict, layer, tmp_list, search_list)
    fin_info_pn_list = get_info_pn_list(fin_info_list)
    p = Pool(4)  # 设置进程池
    assignment_list = assignment_search_list(fin_info_pn_list, 2)  # 分配任务，用于多进程
    result = []  # 多进程结果列表
    for i in range(len(assignment_list)):
 result.append(p.apply_async(get_info, args=(assignment_list[i], i)))
    p.close()
    p.join()
    for result_i in range(len(result)):
 fin_info_result_list = result[result_i].get()
 fin_save_list.extend(fin_info_result_list)  # 将各个进程获得的列表合并
    save_excel(fin_save_list, file_name)
    endtime = datetime.datetime.now()
    time = (endtime - starttime).seconds
    print('总共用时：%s s' % time)

总结：
当抓取数据规模越大，对程序逻辑要求就愈严谨，对python语法要求就越熟练。如何写出更加pythonic的语法，也需要不断学习掌握的。
推荐阅读《编写高质量代码 改善Python程序的91个建议》
大家可以尝试抓取一下，分析一下房价的走势也是蛮有意思的ლ(^o^ლ)
欢迎交流，~ (^ _ ^)/~~

原文链接：http://www.cnblogs.com/Lands-ljk/p/5467236.html