Python使用爬虫爬取贵阳房价的方法详解

#==========导 包=============
import requests
#=====step_1 : 指 定 url=========
url = 'https://gy.fang.lianjia.com/ /'
#=====step_2 : 发 起 请 求 :======
#使 用 get 方 法 发 起 get 请 求 ， 该 方 法 会 返 回 一 个 响 应 对 象 。 参 数 url 表 示 请 求 对 应 的 url
response = requests . get ( url = url )
#=====step_3 : 获 取 响 应 数 据 :===
#通 过 调 用 响 应 对 象 的 text 属 性 ， 返 回 响 应 对 象 中 存 储 的 字 符 串 形 式 的 响 应 数 据 （ 页 面 源 码数 据 ）
page_text = response . text
#====step_4 : 持 久 化 存 储=======
with open ('贵阳房价 . html ','w', encoding ='utf -8') as fp:
    fp.write ( page_text )
print (' 爬 取 数 据 完 毕 !!!')

爬 取 数 据 完 毕 !!!
Process finished with exit code 0

#==================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 = pd.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()v