==================导入相干库==================================

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()失去的是byte格局的    html=html1.text    return htmlexcept RequestException:#其余问题    print('第{0}读取网页失败'.format(page))    return None

==========解析网页并保留数据到表格======================

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)        "劣势。"        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 = pd.DataFrame(information, columns=['名称', '类型', '销售状态','大地址','具体地址','劣势','均价','总价'])        information.to_csv('贵阳房价.csv', mode='a+', index=False, header=False)  # mode=[黄金](https://www.gendan5.com/nmetal/gold.html)'a+'追加写入    print('第{0}页存储数据胜利'.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1t2 = threading.Thread(target=pase_page, args=(url2,i+1))#线程2t1.start()t2.start()