一、前言：
安居客、链家和房天下是目前网上可以获取小区数据较为精准的网站，之前已经发过链家和房天下的部分区域（仅浦东）获取攻略。这次因为工作原因，需要获取整个上海的所有小区数据（仅别墅和住宅），所以过年这几天在不断的数据分析、获取、清洗和验证。特此记录一下，也把代码和各位分享。

二、爬取思路：
不管是安居客、链家还是房天下，获取数据的思路都是一致的：
1、获取不同行政区的网址
2、获取不同行政区下不同商圈/街镇的网址
3、获取不同行政区下每一个商圈/街镇中所有小区的网址
4、根据3中获得的网址，把需要的页面元素爬下来

三、安居客、房天下和链家对比：

我把三个网站的数据都爬下来了，不过最后只用了安居客的数据

四、链家代码

  1 import requests
  2 from bs4 import BeautifulSoup
  3 import re
  4 import time
  5 import traceback
  6 import math
  7 
  8 headers = {
  9     'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',
 10     'Host': 'sh.lianjia.com',
 11     'Cookie': ''
 12 }
 13 
 14 def read_Lregion_dict():
 15     '''读取行政区域的文件，并输出为字典'''
 16     with open('行政区url.txt', 'r') as f:
 17         large_region_list = f.readlines()
 18     large_region_dict = {}
 19     for ele in large_region_list:
 20         url, region = ele.split(' ')
 21         region = region.replace('\n', '')
 22         large_region_dict[url] = region
 23     return large_region_dict
 24 
 25 def get_jiezhen_urls():
 26     '''获取街镇的url'''
 27     large_region_dict = read_Lregion_dict()
 28     small_region_dict = {}
 29     for k, v in large_region_dict.items():
 30         if v != '上海周边':
 31             url = 'https://sh.lianjia.com' + k
 32             r = requests.get(url=url, headers=headers)
 33             soup = BeautifulSoup(r.text, 'lxml')
 34             a = soup.find(name='div', attrs={'data-role': 'ershoufang'})
 35             esf_urls = a.find_all(name='a')
 36             for ele in esf_urls:
 37                 href = ele.attrs['href']
 38                 name = ele.string
 39                 if name in large_region_dict.values():
 40                     continue
 41                 else:
 42                     small_region_dict[href] = name
 43                     with open('街镇url.txt', 'a', encoding='utf-8') as file:
 44                         file.write(','.join([v, name, href]))
 45                         file.write('\n')
 46                     print(v, name, href)
 47 
 48 def region_total(url):
 49     '''获取该区域的小区数量'''
 50     url = r"https://sh.lianjia.com" + url + '?from=rec'
 51     r = requests.get(url=url, headers=headers)
 52     soup = BeautifulSoup(r.text, 'lxml')
 53     total_find = soup.find(name='h2', attrs={'class': 'total fl'})
 54     total_num = int(total_find.find(name='span').string.strip())
 55     return total_num
 56 
 57 def get_all_urls():
 58     '''获取所有小区名字和链接'''
 59     with open('街镇url.txt', 'r', encoding='utf-8') as f:
 60         small_region_list = f.readlines()
 61     for ele in small_region_list:
 62         l_region, s_region, url = ele.split(',')
 63         url = url.replace('\n', '')
 64         total_num = region_total(url)
 65         pages = int(math.ceil(int(total_num)/30))
 66         for i in range(1, pages+1):
 67             if i == 1:
 68                 i = ""
 69             else:
 70                 i = 'pg' + str(i)
 71             tmp_url = r"https://sh.lianjia.com" + url + i
 72             r = requests.get(url=tmp_url, headers=headers)
 73             soup = BeautifulSoup(r.text, 'lxml')
 74             for j in soup.find_all(name='div', attrs={'class': 'title'}):
 75                 community = str(j)
 76                 if '''target="_blank"''' in community:
 77                     community_list = re.search('''<a href="(.*?)" target="_blank">(.*?)</a>.*?''', community)
 78                     community_url = community_list.group(1)
 79                     community_name = community_list.group(2)
 80                     with open('小区url.txt', 'a', encoding='utf-8') as file:
 81                         file.write(','.join([l_region, s_region, community_name, community_url]))
 82                         file.write('\n')
 83             time.sleep(1)
 84         print('{}, {}总共有{}个小区，共有{}页，已全部url爬取完成！'.format(l_region, s_region, total_num, pages))
 85 
 86 def get_communityInfo(l_region, s_region, community_name, community_url):
 87     '''获取某个小区的信息'''
 88     r = requests.get(url=community_url, headers=headers)
 89     soup = BeautifulSoup(r.text, 'lxml')
 90     try:
 91         unitPrice = soup.find(name='span', attrs={'class': 'xiaoquUnitPrice'}).string #小区均价
 92     except:
 93         unitPrice = '空'
 94     try:
 95         address = soup.find(name='div', attrs={'class': 'detailDesc'}).string #小区地址
 96         address = '"' + address + '"'
 97     except:
 98         address = '空'
 99     try:
100         xiaoquInfo = soup.find_all(name='span', attrs={'class': 'xiaoquInfoContent'}) #小区信息
101         xiaoquInfo_list = [l_region, s_region]
102         community_name = '"' + community_name + '"'
103         xiaoquInfo_list.append(community_name)
104         xiaoquInfo_list.append(address)
105         xiaoquInfo_list.append(unitPrice)
106         for info in xiaoquInfo:
107             xiaoquInfo_list.append(info.string)
108         xiaoquInfo_list.pop()
109         export_communityInfo(xiaoquInfo_list)
110         time.sleep(1)
111         print('已爬取{},{}的{}信息'.format(l_region, s_region, community_name))
112     except:
113         print('{},{}的{}爬取错误，url是{}'.format(l_region, s_region, community_name, community_url))
114 
115 def export_communityInfo(xiaoquInfo_list):
116     '''导出小区信息'''
117     with open('上海地区小区信息.txt', 'a', encoding='utf-8') as file:
118         file.write(','.join(xiaoquInfo_list))
119         file.write('\n')
120 
121 if __name__ == "__main__":
122     # get_jiezhen_urls() #获取街镇的url
123     # get_all_urls() #获取所有小区名字和链接
124     with open('小区url.csv', 'r') as f:
125         xiaoqu_list = f.readlines()
126         for ele in xiaoqu_list:
127             l_region, s_region, community_name, community_url = ele.split(',')
128             community_url = community_url.replace('\n', '')
129             try:
130                 get_communityInfo(l_region, s_region, community_name, community_url)
131             except:
132                 traceback.print_exc()
133                 break

五、房天下代码

  1 import requests
  2 from bs4 import BeautifulSoup
  3 import pandas as pd
  4 import time
  5 import traceback
  6 
  7 headers = {
  8     'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',
  9     'cookie': ''''''
 10 }
 11 
 12 def get_true_url(old_url):
 13     '''获得正确的url'''
 14     r = requests.get(url=old_url, headers=headers)
 15     if r'<title>跳转...</title>' in r.text:
 16         soup = BeautifulSoup(r.text, 'lxml')
 17         new_url = soup.find(name='a', attrs={'class': 'btn-redir'}).attrs['href']
 18         return new_url
 19     return old_url
 20 
 21 def get_region_urls():
 22     '''获得上海行政区中不同街镇的url和名称'''
 23     sh_dict = {'浦东': '25', '嘉定': '29', '宝山': '30', '闵行': '18', '松江': '586', '普陀': '28',
 24                '静安': '21', '黄浦': '24', '虹口': '23', '青浦': '31', '奉贤': '32', '金山': '35',
 25                '杨浦': '26', '徐汇': '19', '长宁': '20', '崇明': '996'}
 26     for l_region_name, l_region_url in sh_dict.items():
 27         url = r"https://sh.esf.fang.com/housing/" + l_region_url + '__0_3_0_0_1_0_0_0/'
 28         true_url = get_true_url(url)
 29         r = requests.get(url=true_url, headers=headers)
 30         soup = BeautifulSoup(r.text, 'lxml')
 31         a = soup.find(name='p', attrs={'id': 'shangQuancontain', 'class': 'contain'})
 32         for i in a.find_all(name='a'):
 33             if i.string != '不限':
 34                 this_url = r"https://sh.esf.fang.com" + i.attrs['href']
 35                 this_url_list = get_region_url(this_url)
 36                 with open('上海地区街镇url.txt', 'a', encoding='utf-8') as file:
 37                     for tmp_url in this_url_list:
 38                         file.write(','.join([l_region_name, i.string, tmp_url]))
 39                         file.write('\n')
 40         print('{}已完成'.format(l_region_name))
 41 
 42 def get_region_url(old_url):
 43     '''获得这个区域的其它page_url'''
 44     true_url = get_true_url(old_url)
 45     r = requests.get(url=true_url, headers=headers)
 46     soup = BeautifulSoup(r.text, 'lxml')
 47     page_url = soup.find(name='div', attrs={'class': 'fanye gray6'})
 48     page_url_list = []
 49     page_url_list.append(old_url)
 50     for j in page_url.find_all(name='a'):
 51         if 'href' in j.attrs:
 52             temp_url = r'https://sh.esf.fang.com/' + j.attrs['href'][1:]
 53             if temp_url not in page_url_list:
 54                 page_url_list.append(temp_url)
 55     return page_url_list
 56 
 57 def get_xiaoqu_url(bigregion, smallregion, old_url):
 58     '''获得某区域某一页的小区信息和url'''
 59     true_url = get_true_url(old_url)
 60     r = requests.get(url=true_url, headers=headers)
 61     soup = BeautifulSoup(r.text, 'lxml')
 62     j = 0
 63     for i in soup.find_all(name='a', attrs={'class': 'plotTit', 'target': '_blank'}):
 64         xiaoqu_type = soup.find('a', text=i.string, attrs={'class': 'plotTit', 'target': '_blank'}).parent.find('span', attrs={'class':'plotFangType'}).string
 65         xiaoqu_name = i.string
 66         xiaoqu_url = 'https://sh.esf.fang.com/' + i.attrs['href'][1:]
 67         xiaoqu_url = xiaoqu_url.replace('.htm', '/housedetail.htm')
 68         print(bigregion, smallregion, xiaoqu_name, xiaoqu_type, xiaoqu_url)
 69         j += 1
 70         with open('上海地区小区url.txt', 'a', encoding='utf-8') as file:
 71             file.write(','.join([bigregion, smallregion, xiaoqu_name, xiaoqu_type, xiaoqu_url]))
 72             file.write('\n')
 73     time.sleep(1)
 74     print(bigregion, smallregion, old_url, '所有小区url获取完毕，共有{}条数据'.format(j))
 75     print('-'*100)
 76 
 77 def get_all_urls(last_url=None):
 78     '''获得所有小区的URL'''
 79     '''获得结果后还需要清洗一下，因为有些小区跨区域，所以会有重复'''
 80     with open('上海地区街镇url.txt', 'r', encoding='utf-8') as f:
 81         region_list = f.readlines()
 82         event_tracking = False
 83         for i in range(len(region_list)):
 84             l_region, s_region, url = region_list[i].split(',')
 85             url = url.replace('\n', '')
 86             if last_url == url:
 87                 event_tracking = True
 88             if event_tracking:
 89                 print(l_region, s_region, url)
 90                 get_xiaoqu_url(l_region, s_region, url)
 91 
 92 def get_total_informations(l_region, s_region, community_name, community_type, community_url):
 93     '''爬取某个小区的有用信息'''
 94     r = requests.get(url=community_url, headers=headers)
 95     soup = BeautifulSoup(r.text, 'lxml')
 96     informations = soup.find(name='div', attrs={'class': 'village_info base_info'})
 97     if not informations:
 98         print('{}, {}, {}, {}爬取失败！'.format(l_region, s_region, community_name, community_url))
 99         return None
100     else:
101         all_info = [l_region, s_region, community_name, community_type]
102         for ele in ['本月均价', '小区地址', '产权描述', '环线位置', '建筑年代', '建筑面积', '占地面积', '房屋总数', '楼栋总数', '绿 化 率', '容 积 率', '物 业 费', '开 发 商', '物业公司']:
103             try:
104                 all_info.append(informations.find('span', text=ele).parent.find(name='p').text.strip().replace('\r', '').replace('\n', '、').replace('\t', '').replace(',', '，'))
105             except:
106                 try:
107                     all_info.append(informations.find('span', text=ele).parent.find(name='a').text.strip().replace('\r', '').replace('\n', '、').replace('\t', '').replace(',', '，'))
108                 except:
109                     all_info.append('')
110         return all_info
111 
112 def get_data(last_url=None):
113     '''主程序，爬所有小区信息'''
114     with open('上海地区小区url.txt', 'r', encoding='utf-8') as f:
115         village_list = f.readlines()
116         error_count = 0
117         if last_url == None:
118             event_tracking = True
119         else:
120             event_tracking = False
121         for i in range(len(village_list)):
122             l_region, s_region, community_name, community_type, community_url = village_list[i].split(',')
123             community_url = community_url.replace('\n', '')
124             if last_url == community_url:
125                 event_tracking = True
126             if event_tracking == True:
127                 if community_type=='住宅' or community_type=='别墅':
128                     # print(l_region, s_region, community_name, community_type,community_url)
129                     try:
130                         with open('上海小区数据.txt', 'a', encoding='utf-8') as file:
131                             back = get_total_informations(l_region, s_region, community_name, community_type, community_url)
132                             if not back:
133                                 if error_count>=2:
134                                     break
135                                 else:
136                                     error_count +=1
137                                     time.sleep(1)
138                                     continue
139                             else:
140                                 error_count = 0
141                                 file.write(','.join(back))
142                                 file.write('\n')
143                                 print('{}, {}, {}, {}爬取成功！'.format(l_region, s_region, community_name, community_type, community_url))
144                                 time.sleep(1)
145                     except:
146                         print('{}, {}, {}, {}爬取失败！'.format(l_region, s_region, community_name, community_url))
147                         traceback.print_exc()
148                         break
149                 else:
150                     continue
151 
152 if __name__ == "__main__":
153     get_region_urls() #得上海行政区中不同街镇的url和名称
154     get_xiaoqu_url() #获得某区域某一页的小区信息和url，这里应该是遍历，代码不完全
155     get_data() #爬取所有小区信息

六、安居客代码

  1 import requests
  2 from bs4 import BeautifulSoup
  3 import re
  4 import time
  5 import traceback
  6 
  7 headers = {
  8     'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',
  9     'Cookie': ''
 10 }
 11 
 12 def get_jiezhen_urls():
 13     '''获取所有街镇的url'''
 14     lregion_dict = {'浦东': 'pudong', '闵行': 'minhang', '松江': 'songjiang', '宝山': 'baoshan', '嘉定':'jiading',
 15                     '徐汇':'xuhui', '青浦':'qingpu', '静安':'jingan', '普陀':'putuo', '杨浦':'yangpu',
 16                     '奉贤': 'fengxian', '黄浦':'huangpu', '虹口':'hongkou', '长宁':'changning','金山':'jinshan',
 17                     '崇明':'chongming'}
 18     for k, v in lregion_dict.items():
 19         url = 'https://shanghai.anjuke.com/community/' + v + '/'
 20         r = requests.get(url=url, headers=headers)
 21         soup = BeautifulSoup(r.text, 'lxml')
 22         a = soup.find_all('li', attrs={'class': 'region-item'})
 23         for i in range(19, len(a)):
 24             temp = a[i].find('a')
 25             with open('街镇url.txt', 'a', encoding='utf-8') as file:
 26                 file.write(','.join([k, temp.text, temp.attrs['href']]))
 27                 file.write('\n')
 28         print('{}区域的url都爬取完毕!'.format(k))
 29         time.sleep(1)
 30 
 31 def region_total(url):
 32     '''获取该区域的小区数量'''
 33     r = requests.get(url=url, headers=headers)
 34     soup = BeautifulSoup(r.text, 'lxml')
 35     # print(soup)
 36     total_find = soup.find(name='span', attrs={'class': 'total-info'})
 37     total_num = int(total_find.text.replace('共找到 ', '').replace(' 个小区', ''))
 38     return total_num
 39 
 40 def get_all_urls():
 41     '''获取所有小区名字和链接'''
 42     with open('街镇url.txt', 'r', encoding='utf-8') as f:
 43         small_region_list = f.readlines()
 44     for ele in small_region_list:
 45         l_region, s_region, url = ele.split(',')
 46         url = url.replace('\n', '')
 47         total_num = region_total(url)
 48         pages = int(math.ceil(int(total_num)/25))
 49         for i in range(1, pages+1):
 50             i = 'p' + str(i) + '/'
 51             tmp_url = url + i
 52             r = requests.get(url=tmp_url, headers=headers)
 53             soup = BeautifulSoup(r.text, 'lxml')
 54             a = soup.find_all('div', attrs={'class': 'li-info'})
 55             for ele in a:
 56                 xiaoqu_name = ele.find('div', attrs={'class': 'li-title'}).text.strip()
 57                 xiaoqu_address = ele.find('div', attrs={'class': 'props nowrap'}).text.split(' - ')[-1].strip()
 58                 xiaoqu_tag = ele.find_all('span', attrs={'class': 'prop-tag'})
 59                 xiaoqu_url = ele.find('span', text='小区解读').parent.find('a').attrs['href']
 60                 xiaoqu_url = xiaoqu_url.replace('props/sale', 'view')
 61                 tag_list = []
 62                 for tag in xiaoqu_tag:
 63                     if 'display:none' in str(tag):
 64                         continue
 65                     else:
 66                         tag_list.append(tag.text)
 67                 with open('小区url.txt', 'a', encoding='utf-8') as file:
 68                     file.write('$'.join([l_region, s_region, xiaoqu_name, xiaoqu_address, str(tag_list), xiaoqu_url]))
 69                     file.write('\n')
 70             time.sleep(1)
 71         print('{}, {}总共有{}个小区，共有{}页，已全部url爬取完成！'.format(l_region, s_region, total_num, pages))
 72 
 73 def get_communityInfo(l_region, s_region, community_name, community_address, community_tag, community_url):
 74     '''获取某个小区的信息'''
 75     r = requests.get(url=community_url, headers=headers)
 76     soup = BeautifulSoup(r.text, 'lxml')
 77     # print(soup)
 78     if '访问验证-ajk' in str(soup):
 79         print('触发反爬机制了！url是', community_url)
 80         exit()
 81     else:
 82         # print('pa虫运行正常！')
 83         try:
 84             unitPrice = soup.find(name='span', attrs={'class': 'average'}).string #小区均价
 85         except:
 86             unitPrice = '暂无均价'
 87         xiaoquInfo = soup.find_all(name='div', attrs={'class': 'hover-inner'}) #小区信息
 88         xiaoquInfo_list = [l_region, s_region, community_name, community_address, community_tag, unitPrice]
 89         for info in xiaoquInfo:
 90             temp = info.find('div', attrs={'class': 'hover-value'})
 91             if temp:
 92                 xiaoquInfo_list.append(temp.text.replace('\n', '').strip())
 93         export_communityInfo(xiaoquInfo_list)
 94         time.sleep(0.5)
 95         print('已pa取{},{}的{}信息'.format(l_region, s_region, community_name))
 96 
 97 def export_communityInfo(xiaoquInfo_list):
 98     '''导出小区信息'''
 99     with open('上海地区小区信息.txt', 'a', encoding='utf-8') as file:
100         file.write('&'.join(xiaoquInfo_list))
101         file.write('\n')
102 
103 if __name__ == "__main__":
104     # get_jiezhen_urls() #获取所有街镇的url
105     # get_all_urls() #获取所有小区名字和链接
106     with open('小区url.txt', 'r', encoding='utf-8') as f:
107         xiaoqu_list = f.readlines()
108         last_url = 'https://shanghai.anjuke.com/community/view/8338/'
109         stop_place = False
110         for ele in xiaoqu_list:
111             l_region, s_region, community_name, community_address, community_tag, community_url = ele.split('$')
112             community_url = community_url.replace('\n', '')
113             if community_url == last_url or last_url == '':
114                 stop_place = True
115             if stop_place:
116                 try:
117                     get_communityInfo(l_region, s_region, community_name, community_address, community_tag, community_url)
118                 except:
119                     print('{}爬取失败，url是：{}'.format(community_name, community_url))
120                     traceback.print_exc()
121                     break

七、数据清洗和特征工程

获取的数据很脏，有重复值需要剔重，有异常值需要修正（比如明显是外环的数据被归纳为内环）；需要根据业务场景，区分小区是否高档；需要根据需要，与内部数据结合…
我这边就举例几种场景，供大家参考（以安居客数据为例）：

1、从标签中判断小区是否靠近地铁

1 data['是否靠近地铁'] = data['标签'].apply(lambda x: '是' if '近地铁' in str(x) or '号线' in str(x) else '否')

2、从标签中判断环线位置

 1 def huanxian_position(text):
 2     '''环线位置'''
 3     if '内环以内' in str(text):
 4         return '内环以内'
 5     elif '内中环之间' in str(text):
 6         return '内中环之间'
 7     elif '郊环以外' in str(text):
 8         return '郊环以外'
 9     elif '外郊环之间' in str(text):
10         return '外郊环之间'
11     elif '中外环之间' in str(text):
12         return '中外环之间'
13     else:
14         return np.nan
15 
16 data['环线位置'] = data['标签'].apply(huanxian_position)

3、纠正环线位置

 1 data_pivot = data.pivot_table(index='所属商圈', columns='环线位置', values='名称', aggfunc='count').reset_index()
 2 data_pivot['环线位置2'] = ''
 3 for i in range(data_pivot.shape[0]):
 4     huan_dict = {}
 5     huan_dict['中外环之间'] = data_pivot.iloc[i,1]
 6     huan_dict['内中环之间'] = data_pivot.iloc[i,2]
 7     huan_dict['内环以内'] = data_pivot.iloc[i,3]
 8     huan_dict['外郊环之间'] = data_pivot.iloc[i,4]
 9     huan_dict['郊环以外'] = data_pivot.iloc[i,5]
10     best_answer = ''
11     best_v = 0
12     for k,v in huan_dict.items():
13         if v == np.nan:
14             continue
15         elif v >= best_v:
16             best_answer = k
17         else:
18             continue
19     data_pivot.iloc[i,6] = best_answer
20 
21 huan_dict = {}
22 for k,v in zip(data_pivot['所属商圈'].values, data_pivot['环线位置2'].values):
23     huan_dict[k] = v
24 
25 data['环线位置'] = data['所属商圈'].map(huan_dict)

4、根据竣工时间判断小区年龄

1 def new_age(text):
2     '''竣工时间推导小区年龄'''
3     if str(text) != 'nan':
4         text = 2022 - int(text.split('、')[0].replace('年',''))
5         return text
6     else:
7         return np.nan
8 
9 data['小区年龄'] = data['竣工时间'].apply(new_age)

5、判断是否商务楼宇、园区等（链家）

1 def if_business(text):
2     '''判断是否商务楼宇、园区等'''
3     for ele in ['商务', '园区', '大厦', '写字楼', '广场']:
4         if ele in text:
5             return '是'
6     else:
7         return '否'
8 
9 data['是否商务楼宇等'] = data['小区名称'].apply(if_business)

6、提取物业费上下限（链家）

 1 def wuyefei_down(text):
 2     '''输出物业费下限'''
 3     if text is np.nan:
 4         return np.nan
 5     elif '至' not in text:
 6         return text.replace('元/平米/月','')
 7     else:
 8         down, up = text.split('至')
 9         return down.replace('元/平米/月','')
10 
11 def wuyefei_up(text):
12     '''输出物业费上限'''
13     if text is np.nan:
14         return np.nan
15     elif '至' not in text:
16         return text.replace('元/平米/月','')
17     else:
18         down, up = text.split('至')
19         return up.replace('元/平米/月','')
20     
21 data['物业费下限'] = data['物业费'].apply(wuyefei_down)    
22 data['物业费上限'] = data['物业费'].apply(wuyefei_up)

7、判断小区名字是否有地址

1 def if_number(text):
2     '''判断小区名称里是否有数字'''
3     if bool(re.search(r'\d', text)):
4         return '是'
5     else:
6         return '否'
7 
8 data['小区名称里是否有数字'] = data['名称'].apply(if_number)

8、匹配百度经纬度

 1 from urllib.request import urlopen, quote
 2 import json
 3 import math
 4 from math import radians, cos, sin, asin, sqrt
 5 import requests
 6 
 7 def getjwd_bd(address):
 8     '''根据地址获得经纬度（百度）'''
 9     try:
10         url = 'http://api.map.baidu.com/geocoding/v3/?address='
11         output = 'json'
12         ak = '******'#需填入自己申请应用后生成的ak
13         add = quote(address) #本文城市变量为中文，为防止乱码，先用quote进行编码
14         url2 = url+add+'&output='+output+"&ak="+ak
15         req = urlopen(url2)
16         res = req.read().decode()
17         temp = json.loads(res)
18         lng = float(temp['result']['location']['lng'])  # 经度 Longitude  简写Lng
19         lat = float(temp['result']['location']['lat'])  # 纬度 Latitude   简写Lat
20         return lng, lat
21     except:
22         return np.nan, np.nan
23 
24 for i in tqdm(range(data.shape[0])):
25     region = data.iloc[i, 0]
26     if region=='浦东':
27         region = '上海市浦东新区'
28     else:
29         region = '上海市'+ region + '区'
30     xiaoqu_name = data.iloc[i, 2]
31     address = data.iloc[i, 3]
32     if str(data.iloc[i, 19]) !='nan':
33         continue
34     else:
35         lng1, lat1 = getjwd_bd(region+address+xiaoqu_name)
36         if 120<=lng1<=122 and 30<=lat1<=32:
37             data.iloc[i, 19] = lng1
38             data.iloc[i, 20] = lat1
39         else:
40             data.iloc[i, 19] = np.nan
41             data.iloc[i, 20] = np.nan

9、计算两个经纬度之间的距离（用于与内部数据匹配）

 1 def get_distance(lng1,lat1,lng2,lat2):
 2     '''计算距离'''
 3     lng1, lat1, lng2, lat2 = map(radians, [float(lng1), float(lat1), float(lng2), float(lat2)])  # 经纬度转换成弧度
 4     dlon = lng2 - lng1
 5     dlat = lat2 - lat1
 6     a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
 7     distance = 2 * asin(sqrt(a)) * 6371393  # 地球平均半径，6371km
 8     distance = round(distance, 0)
 9     return distance
10 
11 for i in tqdm(range(data.shape[0])):
12     xiaoqu_name = data.iloc[i, 2]
13     lng1 = data.iloc[i, 18]
14     lat1 = data.iloc[i, 19]
15     match_wg = data.iloc[i, 24]
16     min_distance = 9999999999
17     tmp_grid_cd = ''
18     tmp_grid_name = ''
19 #     print(xiaoqu_name, lng1, lat1)
20     if str(match_wg) != 'nan':
21         print('{}已匹配，跳过'.format(xiaoqu_name))
22         continue
23     else:
24         for j in range(grid_data.shape[0]):
25             lng2 = grid_data.iloc[j, 19]
26             lat2 = grid_data.iloc[j, 20]
27             grid_cd = grid_data.iloc[j, 0]
28             grid_name = grid_data.iloc[j, 1]
29             if str(lng2) == 'nan':
30                 continue
31             else:
32                 tmp_distance = get_distance(lng1, lat1, lng2, lat2)
33 #                 print(grid_name,tmp_distance)
34                 if tmp_distance == 0:
35                     print('{}精确匹配的网格是{}'.format(xiaoqu_name, grid_name))
36                     data.iloc[i, 24] = grid_cd
37                     print('-'*100)
38                     break
39                 else:
40                     if tmp_distance < min_distance:
41                         min_distance = tmp_distance
42                         tmp_grid_cd = grid_cd
43                         tmp_grid_name = grid_name
44 #                         print(min_distance, tmp_grid_cd, tmp_grid_name)
45                     else:
46                         continue
47         else:
48             data.iloc[i, 24] = tmp_grid_cd
49             print('{}模糊匹配的网格是{}'.format(xiaoqu_name, tmp_grid_name))
50             print(min_distance, tmp_grid_cd, tmp_grid_name)
51             print('-'*100)

10、找出区域内top10%均价的房子

 1 region_dict = data['行政区'].value_counts().to_dict()
 2 top10_list = []
 3 for k, v in region_dict.items():
 4     temp_data = data[data['行政区']==k]
 5     temp_data = temp_data.sort_values(by='均价', ascending=False).reset_index()
 6     temp_top10 = temp_data.iloc[:int(v*0.1), :]
 7     top10_index = temp_top10['index'].to_list()
 8     top10_list.extend(top10_index)
 9     
10 data['是否区域内均价top10%'] = '否'
11 for i in top10_list:
12     data.loc[i, '是否区域内均价top10%'] = '是'

11、判断是否高档小区

 1 def if_upscale(df):
 2     '''判断是否高档小区'''
 3     if df['物业类型'] == '别墅':
 4         return '是'
 5     elif df['均价'] <=30000:
 6         return '否'
 7     elif df['小区年龄'] <= 10 and df['环线位置'] in ('内环以内', '内中环之间', '中外环之间'):
 8         return '是'
 9     elif df['物业费'] >= 3:
10         return '是'
11     elif df['是否区域内均价top10%'] == '是':
12         return '是'
13     else:
14         return '否'
15 
16 data['是否高档小区'] = data.apply(if_upscale, axis=1)