==================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 html
except 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1
t2 = threading.Thread(target=pase_page, args=(url2,i+1))#线程2
t1.start()
t2.start()

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