利用python爬取某殼的房產資料

阿亮發表於2024-05-05
Python

以無錫的某殼為例進行資料爬取，現在房子的價格起伏很快，買房是人生一個大事，瞭解本地的房價走勢來判斷是否應該入手。

（建議是近2年不買，本人在21年高位拋了一套房，基本是透過貝殼資料判斷房價已經到頂，希望此爬蟲能夠幫到各位。）

這裡只爬了必看好房的資料，貝殼有放抓機制，無法跑全所有資料，有心的可以拿過去擴充套件一番。

import requests
from pyquery import PyQuery as pq
import json
import pandas as pd
import datetime,time
columns = ['id','title','place','msg', 'price', 'per_meter','area','city']
areas=['濱湖區','梁溪區','新吳區','惠山區','錫山區','江陰市','宜興市']
# 爬取某網頁
def get_a_page(url,area):
    result = requests.get(url)
    doc = pq(result.text)
    ul = doc('.sellListContent')
    divs = ul.children('.clear .info.clear').items()
    count = 0
    realids=[]
    titles = []
    places = []
    msgs = []
    prices = []
    per_meters = []
    realarea=[]
    citys=[]
    for div in divs:
        count += 1
        realid=div.children('.address .priceInfo .unitPrice').attr('data-hid')
        title = div.children('.title a').text()
        place = div.children('.address .flood .positionInfo a').text()
        msg = div.children('.address .houseInfo').text()
        price = div.children('.address .priceInfo .totalPrice span').text()
        per_meter = div.children('.address .priceInfo .unitPrice span').text()
        city='無錫'
        dict = {
            'id':realid,
            'title': title,
            'place': place,
            'msg': msg,
            'price': price,
            'per_meter': per_meter,
            'area':areas[area],
            'city':'無錫'
        }
        realids.append(realid)
        titles.append(title)
        places.append(place)
        msgs.append(msg)
        prices.append(price)
        per_meters.append(per_meter)
        realarea.append(areas[area])
        citys.append(city)
        print(str(count) + ':' + json.dumps(dict, ensure_ascii=False))
    datas={
        'id':realids,
        'title': titles,
        'place': places,
        'msg': msgs,
        'price': prices,
        'per_meter': per_meters,
        'area':realarea,
        'city':citys
    }
    df = pd.DataFrame(data=datas, columns=columns)
    df.to_csv('wx'+time.strftime('%Y-%m-%d')+'.csv', mode='a', index=False, header=False)

if __name__ == '__main__':
    quyu=['binhu','liangxi','xinwu','huishan','xishan','jiangyinshi','yixingshi']
    index=0
    for qy in quyu:
        #print (index)
        #print (qy)
        for i in range(1, 20):
            get_a_page(f'https://wx.ke.com/ershoufang/{qy}/pg{i}tt9/',index)
        index=index+1

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