#================== 导入相关库 ==================================
from bs4 import BeautifulSoup
import numpy as np
import requests
from requests.exceptions import RequestException
import pandas as pd
#============= 读取网页 =========================================
def craw(url,page):
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
html1 = requests.request("GET", url, headers=headers,timeout=10)
html1.encoding ='utf-8' # 加编码，重要！转换为字符串编码， read() 得到的是 byte 格式的
html=html1.text
return html
except RequestException:# 其他问题
print(' 第 {0} 读取网页失败 '.format(page))
return None
#========== 解析网页并保存数据到表格 ======================
def pase_page(url,page):
html=craw(url,page)
html = str(html)
if html is not None:
soup = BeautifulSoup(html, 'lxml')
"-- 先确定房子信息，即 li 标签列表 --"
houses=soup.select('.resblock-list-wrapper li')# 房子列表
"-- 再确定每个房子的信息 --"
for j in range(len(houses)):# 遍历每一个房子
house=houses[j]
" 名字 "
recommend_project=house.select('.resblock-name a.name')
recommend_project=[i.get_text()for i in recommend_project]# 名字 英华天元，斌鑫江南御府 ...
recommend_project=' '.join(recommend_project)
#print(recommend_project)
" 类型 "
house_type=house.select('.resblock-name span.resblock-type')
house_type=[i.get_text()for i in house_type]# 写字楼 , 底商 ...
house_type=' '.join(house_type)
#print(house_type)
" 销售状态 "
sale_status = house.select('.resblock-name span.sale-status')
sale_status=[i.get_text()for i in sale_status]# 在售 , 在售 , 售罄 , 在售 ...
sale_status=' '.join(sale_status)
#print(sale_status)
" 大地址 "
big_address=house.select('.resblock-location span')
big_address=[i.get_text()for i in big_address]#
big_address=''.join(big_address)
#print(big_address)
" 具体地址 "
small_address=house.select('.resblock-location a')
small_address=[i.get_text()for i in small_address]#
small_address=' '.join(small_address)
#print(small_address)
" 优势。 "
advantage=house.select('.resblock-tag span')
advantage=[i.get_text()for i in advantage]#
advantage=' '.join(advantage)
#print(advantage)
" 均价：多少 1 平 "
average_price=house.select('.resblock-price .main-price .number')
average_price=[i.get_text()for i in average_price]#16000,25000, 价格待定 ..
average_price=' '.join(average_price)
#print(average_price)
" 总价 , 单位万 "
total_price=house.select('.resblock-price .second')
total_price=[i.get_text()for i in total_price]# 总价 400 万 / 套，总价 100 万 / 套 '...
total_price=' '.join(total_price)
#print(total_price)
#===================== 写入表格 =================================================
information = [recommend_project, house_type, sale_status,big_address,small_address,advantage,average_price,total_price]
information = np.array(information)
information = information.reshape(-1, 8)
information = 外汇跟单gendan5.compd.DataFrame(information, columns=[' 名称 ', ' 类型 ', ' 销售状态 ',' 大地址 ',' 具体地址 ',' 优势 ',' 均价 ',' 总价 '])
information.to_csv(' 贵阳房价 .csv', mode='a+', index=False, header=False) # mode='a+' 追加写入
print(' 第 {0} 页存储数据成功 '.format(page))
else:
print(' 解析失败 ')
#================== 双线程 =====================================
import threading
for i in range(1,100,2):# 遍历网页 1-101
url1="https://gy.fang.lianjia.com/loupan/pg"+str(i)+"/"
url2 = "https://gy.fang.lianjia.com/loupan/pg" + str(i+1) + "/"
t1 = threading.Thread(target=pase_page, args=(url1,i))# 线程 1
t2 = threading.Thread(target=pase_page, args=(url2,i+1))# 线程 2
t1.start()
t2.start()