python 房价分析算法 基于python的房价分析论文

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'......}}

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

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][3]
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][3]
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:\Users\Administrator\Desktop\%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()

附上源码
#! -*-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][3]
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][3]
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:\Users\Administrator\Desktop\%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。