Created
January 25, 2022 08:38
-
-
Save ronfe/9109b8d2c911b0a7840407bc825fe304 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "bd1638fb", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "k = '''\n", | |
| "楼龄20年\n", | |
| "楼龄10年\n", | |
| "\n", | |
| "主南向\n", | |
| "通透\n", | |
| "\n", | |
| "满二\n", | |
| "满五\n", | |
| "唯一\n", | |
| "中高楼层\n", | |
| "有电梯\n", | |
| "已购公房\n", | |
| "板楼\n", | |
| "\n", | |
| "客厅10+W\n", | |
| "卧室4.5x3.5\n", | |
| "卫生间4+\n", | |
| "厨房5+\n", | |
| "落地窗\n", | |
| "花园15M\n", | |
| "露台15M\n", | |
| "\n", | |
| "地铁1000M\n", | |
| "地铁500M\n", | |
| "楼户比150\n", | |
| "楼户比100\n", | |
| "车位\n", | |
| "\n", | |
| "月关注30人+\n", | |
| "总关注300人+\n", | |
| "'''" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "d4747068", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "x = [\n", | |
| " 15, # 楼龄\n", | |
| " 0,0,1,0,0,0, # 楼层 one-hot 0 - 地下室; 1 - 底层; 2 - 低楼层; 3 - 中楼层; 4 - 高楼层; 5 - 顶层\n", | |
| " 0,1,0,0,0, # 总楼层 one-hot 1 - 小于3层 ; 2 - 3-7 层;3 - 8-12层 ; 4 - 13-21层;5 - 22层+\n", | |
| " 0, # 是否有电梯\n", | |
| " 5, # 5 - 满五;2 - 满二; 0 - 不满二\n", | |
| " 1, # 是否唯一\n", | |
| " 0,1,0, # 房屋产权属性,0 - 商品房,1 - 已购公房 ; 2 - 其他\n", | |
| " 1,0,0, # 房屋楼型:1 - 板楼; 2 - 塔楼 ; 3 - 板塔结合\n", | |
| " 1,2, # 梯户比 1梯两户\n", | |
| " 76.25, # 建筑面积\n", | |
| " 68.33, # 套内面积\n", | |
| " 1,10.33,1,1,0,0,0, # 卧室A (按面积排序)\n", | |
| " 0,0,0,0,0,0,0, # 卧室B\n", | |
| " 0,0,0,0,0,0,0, # 卧室C\n", | |
| " 1,13.26,1,0,0,1,0, # 客厅:有无;面积;窗户类型:0-无窗;1-普通窗;2-飘窗;3-落地窗;北东南西四向 one-hot\n", | |
| " 0,0,0,0,0,0,0, # 餐厅\n", | |
| " 1,5.54,1,1,0,0,0, # 厨房\n", | |
| " 1,3.86,0,0,0,0,0, # 卫生间A\n", | |
| " 0,0,0,0,0,0,0, # 卫生间B\n", | |
| " 103.22, # 小区楼户比\n", | |
| " 44195, # 小区均价\n", | |
| " 0.496,0.335,1.276, # 地铁距离,超市距离,公园距离 \n", | |
| "]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "3532867b", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn import decomposition\n", | |
| "from sklearn import datasets" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "id": "0396594e", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "iris = datasets.load_iris()\n", | |
| "X = iris.data\n", | |
| "y = iris.target" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "id": "e18c152c", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "PCA(n_components=3)" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pca = decomposition.PCA(n_components=3)\n", | |
| "pca.fit(X)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "id": "c3cf19ea", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "X = pca.transform(X)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "id": "4f53db82", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "samples = [101113346571, 101113810825, 101113600025, 101114037583, 101114052792, 101113955016, 101113481802, 101112957970, 101112467271, 101113957405, 101113858706, 101113266206, 101112796177, 101113747476, 101113722571, 101113630468, 101113322860, 101113805623, 101113913864, 101112530925, 101114088489, 101113977837, 101113960275, 101113985999, 101114031725, 101113963239, 101114070282, 101114047372, 101114075987, 101114031013]\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "a274968e", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "1026a736", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 117, | |
| "id": "5dd12877", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import requests\n", | |
| "\n", | |
| "url = 'https://bj.lianjia.com/ershoufang/{}.html'.format(samples[0])\n", | |
| "x = requests.get(url)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 132, | |
| "id": "32832ab8", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from bs4 import BeautifulSoup\n", | |
| "import re\n", | |
| "import pycnnum\n", | |
| "import datetime\n", | |
| "\n", | |
| "soup = BeautifulSoup(x.text, 'html')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "24e5f433", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 134, | |
| "id": "f28edb01", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "100\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "result = list()\n", | |
| "\n", | |
| "\n", | |
| "# 楼龄\n", | |
| "t = soup.find('div', {\"class\": \"area\"})\n", | |
| "t = t.find('div', {\"class\": \"subInfo\"}).text\n", | |
| "t = re.match(r'\\d+(?=年建)', t).group()\n", | |
| "t = 2022 - int(t)\n", | |
| "result.append(t)\n", | |
| "\n", | |
| "\n", | |
| "# 基本属性: 楼层 one-hot / 总楼层 one-hot / 是否有电梯 / 梯户比 / 建筑面积\n", | |
| "t = soup.find('div', {\"class\": \"base\"})\n", | |
| "t = t.findChildren('li')\n", | |
| "base_info_dict = dict()\n", | |
| "for each in t:\n", | |
| " y = each.find('span', text=True).text\n", | |
| " base_info_dict[y] = each.contents[-1]\n", | |
| "\n", | |
| "# 楼层 & 总楼层\n", | |
| "floor_one_hot = [0,0,0,0,0,0]\n", | |
| "total_floor = [0,0,0,0,0]\n", | |
| "\n", | |
| "if '所在楼层' in base_info_dict:\n", | |
| " if '地下' in base_info_dict['所在楼层']:\n", | |
| " floor_one_hot[0] = 1\n", | |
| " elif '底层' in base_info_dict['所在楼层']:\n", | |
| " floor_one_hot[1] = 1\n", | |
| " elif '低楼层' in base_info_dict['所在楼层']:\n", | |
| " floor_one_hot[2] = 1\n", | |
| " elif '中楼层' in base_info_dict['所在楼层']:\n", | |
| " floor_one_hot[3] = 1\n", | |
| " elif '高楼层' in base_info_dict['所在楼层']:\n", | |
| " floor_one_hot[4] = 1\n", | |
| " elif '顶层' in base_info_dict['所在楼层']:\n", | |
| " floor_one_hot[5] = 1\n", | |
| "\n", | |
| " total_floors = int(re.findall(r'\\d+', base_info_dict['所在楼层'])[0])\n", | |
| " if total_floors <= 3:\n", | |
| " total_floor[0] = 1\n", | |
| " elif total_floors <= 7:\n", | |
| " total_floor[1] = 1\n", | |
| " elif total_floors <= 12:\n", | |
| " total_floor[2] = 1\n", | |
| " elif total_floors <= 21:\n", | |
| " total_floor[3] = 1\n", | |
| " elif total_floors > 21:\n", | |
| " total_floor[4] = 1\n", | |
| " \n", | |
| "result.append(floor_one_hot)\n", | |
| "result.append(total_floor)\n", | |
| " \n", | |
| "# 电梯\n", | |
| "has_elev = 0\n", | |
| "if '配备电梯' in base_info_dict:\n", | |
| " if '有' in base_info_dict['配备电梯']:\n", | |
| " has_elev = 1\n", | |
| "result.append(has_elev)\n", | |
| "\n", | |
| "\n", | |
| "# 梯户比\n", | |
| "stairs = 0\n", | |
| "rooms = 0\n", | |
| "if '梯户比例' in base_info_dict:\n", | |
| " t1 = re.findall(r'.+(?=梯)', base_info_dict['梯户比例'])[0]\n", | |
| " t2 = re.findall(r'(?<=梯).+(?=户)', base_info_dict['梯户比例'])[0]\n", | |
| " \n", | |
| " stairs = pycnnum.cn2num(t1)\n", | |
| " rooms = pycnnum.cn2num(t2)\n", | |
| "result.append(stairs)\n", | |
| "result.append(rooms)\n", | |
| "\n", | |
| "# 建筑面积\n", | |
| "area = 0\n", | |
| "if '建筑面积' in base_info_dict:\n", | |
| " t1 = re.findall(r'[\\.\\d]+', base_info_dict['建筑面积'])[0]\n", | |
| " area = float(t1)\n", | |
| "result.append(area)\n", | |
| "\n", | |
| "\n", | |
| "## 交易属性\n", | |
| "t = soup.find('div', {\"class\": \"transaction\"})\n", | |
| "t = t.findChildren('li')\n", | |
| "base_info_dict = dict()\n", | |
| "for each in t:\n", | |
| " y = each.findChildren('span')\n", | |
| " base_info_dict[y[0].text] = y[1].text\n", | |
| " \n", | |
| "# 挂牌天数\n", | |
| "sincedays = -1\n", | |
| "if '挂牌时间' in base_info_dict:\n", | |
| " t1 = base_info_dict['挂牌时间']\n", | |
| " t1 = datetime.datetime.strptime(t1, '%Y-%m-%d')\n", | |
| " t1 = datetime.datetime.now() - t1\n", | |
| " sincedays = t1.days\n", | |
| "result.append(sincedays)\n", | |
| "\n", | |
| "# 交易权属\n", | |
| "ownership = [0,0,0]\n", | |
| "if '交易权属' in base_info_dict:\n", | |
| " t1 = base_info_dict['交易权属']\n", | |
| " if t1 == '商品房':\n", | |
| " ownership[0] = 1\n", | |
| " elif t1 == '已购公房':\n", | |
| " ownership[1] = 1\n", | |
| " else:\n", | |
| " ownership[2] = 1\n", | |
| "result.append(ownership)\n", | |
| "\n", | |
| "# 房屋年限\n", | |
| "ownership = [0,0,0]\n", | |
| "if '房屋年限' in base_info_dict:\n", | |
| " t1 = base_info_dict['房屋年限']\n", | |
| " if t1 == '满五年':\n", | |
| " ownership[0] = 1\n", | |
| " elif t1 in ('满两年', '满二年'):\n", | |
| " ownership[1] = 1\n", | |
| " else:\n", | |
| " ownership[2] = 1\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 131, | |
| "id": "95083f23", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'挂牌时间': '2021-10-17',\n", | |
| " '交易权属': '商品房',\n", | |
| " '上次交易': '2016-11-05',\n", | |
| " '房屋用途': '普通住宅',\n", | |
| " '房屋年限': '满五年',\n", | |
| " '产权所属': '非共有',\n", | |
| " '抵押信息': '\\n 有抵押 140万元 商业贷款\\n ',\n", | |
| " '房本备件': '已上传房本照片'}" | |
| ] | |
| }, | |
| "execution_count": 131, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "base_info_dict" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 114, | |
| "id": "13cd2f4d", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "x = [\n", | |
| " 15, # 楼龄\n", | |
| " 0,0,1,0,0,0, # 楼层 one-hot 0 - 地下室; 1 - 底层; 2 - 低楼层; 3 - 中楼层; 4 - 高楼层; 5 - 顶层\n", | |
| " 0,1,0,0,0, # 总楼层 one-hot 1 - 小于3层 ; 2 - 3-7 层;3 - 8-12层 ; 4 - 13-21层;5 - 22层+\n", | |
| " 0, # 是否有电梯\n", | |
| " 1,2, # 梯户比 1梯两户\n", | |
| " 76.25, # 建筑面积\n", | |
| " 56, # 挂牌天数\n", | |
| " 0,1,0, # 房屋产权属性,0 - 商品房,1 - 已购公房 ; 2 - 其他\n", | |
| " 1,0,0, # 5 - 满五;2 - 满二; 0 - 不满二\n", | |
| " \n", | |
| " 1, # 是否唯一\n", | |
| " 1,0,0, # 房屋楼型:1 - 板楼; 2 - 塔楼 ; 3 - 板塔结合\n", | |
| " 68.33, # 套内面积\n", | |
| " 1,10.33,1,1,0,0,0, # 卧室A (按面积排序)\n", | |
| " 0,0,0,0,0,0,0, # 卧室B\n", | |
| " 0,0,0,0,0,0,0, # 卧室C\n", | |
| " 1,13.26,1,0,0,1,0, # 客厅:有无;面积;窗户类型:0-无窗;1-普通窗;2-飘窗;3-落地窗;北东南西四向 one-hot\n", | |
| " 0,0,0,0,0,0,0, # 餐厅\n", | |
| " 1,5.54,1,1,0,0,0, # 厨房\n", | |
| " 1,3.86,0,0,0,0,0, # 卫生间A\n", | |
| " 0,0,0,0,0,0,0, # 卫生间B\n", | |
| " 103.22, # 小区楼户比\n", | |
| " 44195, # 小区均价\n", | |
| " 0.496,0.335,1.276, # 地铁距离,超市距离,公园距离 \n", | |
| "]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 115, | |
| "id": "97628b02", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Collecting pycnnum\n", | |
| " Downloading pycnnum-1.0.1-py3-none-any.whl (6.2 kB)\n", | |
| "Installing collected packages: pycnnum\n", | |
| "Successfully installed pycnnum-1.0.1\n", | |
| "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 21.3.1 is available.\n", | |
| "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.9/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n", | |
| "Note: you may need to restart the kernel to use updated packages.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "pip install pycnnum" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 137, | |
| "id": "bf55f5b0", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "True" | |
| ] | |
| }, | |
| "execution_count": 137, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "'唯一' in soup.text" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 138, | |
| "id": "c2d83ca1", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'\\n\\n\\n\\n\\n满五唯一商品房 精装修 车位充足 户型方正 诚售_北京朝阳十里河周庄嘉园东里C区二手房(北京链家)\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n首页二手房新房租房海外商业办公小区问答工具发布房源企业汇登录/注册热线电话1010-9666\\n\\n\\n在售小区地图找房在售在售成交小区\\xa0\\xa0\\n\\n\\n\\n\\n\\n满五唯一商品房 精装修 车位充足 户型方正 诚售\\n \\n\\n\\n\\n\\n关注房源\\n51人关注\\n \\n\\n\\n\\n下载链家APP\\n房源动态早知道\\n\\n\\n\\n\\n预约看房\\n已加入待看\\n24人看过\\n \\n\\n\\n\\n\\n\\n北京房产网\\xa0>\\xa0北京二手房\\xa0>\\xa0朝阳二手房\\xa0>\\xa0十里河二手房\\xa0>\\xa0周庄嘉园东里C区二手房\\xa0>\\xa0\\xa0当前房源\\xa0\\n设计效果<>本房源已支持VR看房,在线看房,省时省力立即看房分享此房源加入对比322万59997元/平米首付及贷款情况请咨询经纪人1室1厅底层/共6层西北平层/精装53.67平米2006年建根据本栋部分房源产权证中《房屋登记表》上的测图日期统计生成,可能本栋不同房源之间存在差异,仅供参考。/板楼诚心卖,省心买小区名称周庄嘉园东里C区地图所在区域朝阳\\xa0十里河\\xa0三至四环近17号线周家庄站看房时间提前预约随时可看链家编号101113346571举报风险提示链家用户风险提示 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n 康贺楠 \\n 链家\\n \\n\\n\\n\\n 本房信息由我维护,有变化最快得知 \\n\\n\\n\\n\\n\\n 在线问\\n \\n\\n 4008896831 转 24278 \\n\\n\\n 扫描下载APP 随时查看新房源\\n\\n\\n\\n\\n\\n房源信息介绍\\n\\n\\n户型分间\\n\\n\\n税费贷款\\n\\n\\n看房记录\\n\\n\\n小区简介\\n\\n\\n小区成交\\n\\n\\n周边配套\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n基本信息\\n\\n\\n基本属性\\n\\n\\n房屋户型1室1厅1厨1卫\\n所在楼层底层 (共6层)\\n建筑面积53.67㎡\\n户型结构平层\\n套内面积暂无数据\\n建筑类型板楼\\n房屋朝向西北\\n建筑结构混合结构\\n装修情况精装\\n梯户比例一梯三户\\n供暖方式集中供暖\\n配备电梯无\\n\\n\\n\\n\\n交易属性\\n\\n\\n\\n挂牌时间\\n2021-10-17\\n\\n\\n交易权属\\n商品房\\n\\n\\n上次交易\\n2016-11-05\\n\\n\\n房屋用途\\n普通住宅\\n\\n\\n房屋年限\\n满五年\\n\\n\\n产权所属\\n非共有\\n\\n\\n抵押信息\\n\\n 有抵押 140万元 商业贷款\\n \\n\\n\\n房本备件\\n已上传房本照片\\n\\n\\n\\n\\n特别提示:本房源所示信息仅供参考,购房时请以该房屋档案登记信息、产权证信息以及所签订合同条款约定为准;本房源公示信息不做为合同条款,不具有合同约束力。\\n\\n\\n\\n\\n\\n房源特色\\n\\n\\n房源标签\\n\\n\\n 地铁\\n \\n\\n VR看装修\\n \\n\\n 房本满五年\\n \\n\\n 随时看房\\n \\n\\n\\n\\n核心卖点\\n\\n 本房满五年,商品房,精装修,楼龄新,无遮挡采光充足\\n \\n\\n\\n小区介绍\\n\\n 此房所在的小区是周庄嘉园东里2006年的房子,都是6层的板楼,楼间距40米宽,不影响采光情况,居住起来很舒适。小区物业费0.6元平米,物业是金都宏业物业管理有限公司,小区停车费是1200元一年,车位宽松,随停随有,停车方便,水电暖都是市政供暖。\\n \\n\\n\\n户型介绍\\n\\n 一室一厅格局,客厅朝西带落地阳台,采光很好,卧室朝北,窗户大,采光充足,厨房朝北带生活阳台,格局方正,没有浪费面积。本房楼龄也新,2006年建成,贷款时间长。\\n \\n\\n\\n周边配套\\n\\n 周庄嘉园东里小区属于成熟社区,配套设施全,小区有饮食一条街,大地电影院,工商银行,中国银行等。小区位于弘燕路上,公交车有680,513,53路,弘燕路公交总站,地铁10号线和14号线。\\n \\n\\n\\n税费解析\\n\\n 本房已经满五年,商品房本,首套购房仅有1%的契税,产权清晰,业主诚意出售,看房方便。\\n \\n\\n展开更多信息\\n注:1.房源介绍中的周边配套、在建设施、规划设施、地铁信息、绿化率、得房率、容积率等信息为通过物业介绍、房产证、实勘、政府官网等渠道获取,因时间、政策会发生变化,与实际情况可能略有偏差,房源介绍仅供参考。 2.房源介绍中与距离相关的数据均来源于百度地图。 3.土地使用起止年限详见业主土地证明材料或查询相关政府部门的登记文件。\\n\\n\\n\\n\\n\\n房主自荐\\n业主在链家网或“链家APP”发表的内容(包括但不限于评论、房屋信息等)仅供参考,且仅表明其个人的立场和观点。业主发表的任何内容均不代表链家的立场或观点,链家亦未对其进行任何授权、同意或确认。如您发现有任何违法、侵权或其他不适宜内容的,请您及时与链家联系。\\n\\n\\n\\n\\n房源特色:\\n这套房子其实挺舍不得卖的,户型方正,大客厅大阳台,****。阳台窗户防护网往外延伸了快1米,加起来快10个平米了。小户型最怕东西多,像临时来人住的折叠床,不用的乱七八糟的东西都可以放阳台,剩余的空间还可以养养花种种菜。年轻人早上多休息会起来后,阳光充分照进客厅,坐到阳台上晒着日光浴看看书。春天时窗外的樱花盛开,别提多惬意了。家里老人偶尔过来,1层也特别方便,完全没有登高的烦恼,毕竟在家人帮扶下买的房子不能只考虑自己那么自私。住一层还有个好处,车脏了,从厨房接个水管就把车洗了,天气好的时候去户外晒晒被子什么的也方便。\\n\\n\\n小区环境:\\n小区楼间距大,停车方便,养房成本极低,物业6毛/平米,停车1200一年(还可以停南里的地下车库里),每年小区都会对绿化进行整修,楼道里都没有什么小广告。小区每年都提前供暖延后停暖,交通有多路公交,地铁17号线周家庄站,周边商业配套很全,晚上9点出门看电影,看完3**走回家睡觉,威尔士健身北京唯一的VIP店就在门口,小区里就有三甲医院,去合生汇从家出发30米内有公交直达。因为挨着山水文园各种高级配套,但作为平价小区小区本身沿街配套商业也非常不错\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n户型分间\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n查看原图\\n\\n\\n\\n\\n\\n\\n\\n客厅\\n15.71平米\\n西\\n普通窗\\n\\n\\n卧室\\n11.9平米\\n无\\n未知窗户类型\\n\\n\\n厨房\\n5.25平米\\n北\\n普通窗\\n\\n\\n卫生间\\n2.84平米\\n无\\n无窗\\n\\n\\n阳台A\\n4.11平米\\n西 北 南\\n普通窗\\n\\n\\n阳台B\\n1.61平米\\n西 北 东\\n普通窗\\n\\n\\n\\n\\n\\n\\n注:户型图标注的面积不同于房本面积,存在测量误差,购房请以房本面积为准。\\n\\n\\n\\n\\n\\n\\n\\n房源照片\\n\\n\\n\\n\\n客厅\\n\\n\\n\\n客厅\\n\\n\\n\\n客厅\\n\\n\\n\\n户型图\\n\\n\\n\\n卧室\\n\\n\\n\\n厨房\\n\\n\\n\\n卫生间\\n\\n\\n\\n查看更多图片\\n\\n\\n\\n\\n参考首付开始计算本次计算仅作为购房参考,不能作为最终的购房依据。了解更准确的方案,建议咨询经纪人\\n\\n\\n\\n\\n\\n看房记录\\n \\n\\n\\n\\n\\n带看时间\\n带看经纪人\\n本房总带看\\n咨询电话\\n\\n暂无看房记录\\n\\n\\n\\n近7天带看次数\\n0\\n- 30日带看0次 -\\n\\n\\n\\n\\n\\n\\n\\n\\n价格变动\\n当前房源共有过条价格记录\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n小区成交价格\\n\\n数据来源:成交系统数据统计\\n更新日期:\\n\\n\\n\\n\\n\\n全部\\n一居\\n二居\\n三居\\n其他\\n\\n\\n\\n\\n\\n\\n同小区成交记录\\n同商圈成交记录\\n\\n\\n\\n\\n暂无成交记录\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n周边配套\\n\\n\\n\\n\\n\\n交通\\n教育\\n医疗\\n购物\\n生活\\n娱乐\\n\\n\\n\\n\\n\\n努力加载中...\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n真实房源\\n真房源\\xa0假一赔百元\\n\\n\\n\\n\\n\\n\\n\\n交易资金监管\\n保障交易安全\\n\\n\\n\\n\\n\\n\\n\\n最高原价回购\\n辐射/凶宅\\n\\n\\n\\n\\n\\n\\n\\n损失先行垫付\\n物业欠费/签前查封\\n\\n\\n\\n\\n\\n我有房子要卖不知道如何定价到成交频道查看成交明细查成交\\xa0>把房源委托给链家10万+专业经纪人·8000+链家门店发布房源\\xa0>已有房源在链家销售去链家APP业主中心管理委托去APP管理委托\\xa0>去链家APP提升卖房速度APP·移动卖房新体验\\n\\n\\n ×\\n\\n\\n户型分间\\n\\n户型特色\\n 客厅带阳台、 卧室有飘窗、 明厨、 观景飘窗 \\n\\n\\n\\n\\n客厅\\n15.71平米\\n西\\n普通窗\\n\\n\\n卧室\\n11.9平米\\n无\\n未知窗户类型\\n\\n\\n厨房\\n5.25平米\\n北\\n普通窗\\n\\n\\n卫生间\\n2.84平米\\n无\\n无窗\\n\\n\\n阳台A\\n4.11平米\\n西 北 南\\n普通窗\\n\\n\\n阳台B\\n1.61平米\\n西 北 东\\n普通窗\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n风险提示: 以上户型图为房源参考图,其中户型结构及面积并非按国家专业测绘活动所得,与真实现状及产权证明呈现的面积存在差异,仅供参考,请以产权证明或专业机构测量结果为准。\\n\\n\\n\\n\\n\\n\\n风险提示\\n\\n\\n\\n\\n\\n\\n 本站旨在为广大用户提供更丰富的信息,但由于部分信息通过技术手段生成,部分信息由第三方提供,我们持续通过技术和管理手段提升信息的准确度,但我们无法确保信息的真实性、准确性和完整性。房产交易滋事体大,本站信息不应作为您买卖决策的依据,您决策前应与房源业主核实相关信息、并亲自到房屋中核验信息,或以产权证明、政府类网站发布的官方信息为准。本站不对您交易过程中对本网站信息产生的依赖承担任何明示或默示的担保责任或任何责任。\\n\\n请您详细阅读如下声明:\\n\\n\\n1、关于参考户型图\\n本网呈现的户型图为平台根据已拍摄的VR内容/数据绘制而成的非标准的参考户型图,其中户型结构及房屋面积并非按国家标准进行的测绘专业活动取得,我们会持续改进技术,但因为设备、技术、摄影师人为操作偏差等原因,参考户型图与真实现状一定存在差异,我们无法保障户型图准确性和差异率,户型图仅供参考,不应作为您交易的决策依据,房屋面积的准确信息请您与房源业主核实,并请以产权证明或您委托的专业机构测量结果为准。\\n\\n\\n2、关于房屋装修情况\\n本网房源图片、VR效果图、视频等呈现出的房屋装修情况可能因为拍摄时间、拍摄角度等原因和实际场景存在出入,仅供参考,不应作为您交易的决策依据,请以您在看房时房源的实际装修情况为准。\\n\\n\\n3、关于房屋情况\\n本网展示的房源信息、交易信息等(包括但不限于房屋面积、所在楼层、房屋朝向、房屋用途、建成年代、建筑结构、供暖方式、抵押信息、交易权属)由经纪人提供,仅供参考不应作为您交易的决策依据,房源的准确信息请您与房源业主核实,并以房本信息、房屋实际情况、您签订房屋买卖合同中披露的信息为准。\\n\\n\\n4、关于房屋周边配套等\\n房源介绍中的周边配套、在建设施、规划设施、地铁信息、绿化率、得房率等内容均系第三方提供,仅供参考不应作为您交易的决策依据,房屋周边配套请您与房源业主及主管部门核实,并以房本信息、房屋实际情况、您签订房屋买卖合同中披露的信息为准。\\n\\n\\n5、关于距离\\n房源介绍中与距离相关的数据均来源于百度地图。\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n微信“扫一扫”\\n分享好友\\n\\n\\n\\n\\n点击分享到微博\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n关于链家联系我们加入我们隐私声明网站地图友情链接官方客服 1010 9666商圈二手房热门二手房二手房推荐二手房推荐小区推荐楼盘昌平其它二手房东关二手房南邵二手房奥林匹克公园二手房西关环岛二手房鼓楼大街二手房百善镇二手房南口二手房天通苑二手房西三旗二手房小汤山二手房回龙观二手房立水桥二手房北七家二手房沙河二手房霍营二手房安定门二手房东直门二手房中央别墅区二手房和平里二手房牡丹园二手房北苑二手房立水桥二手房广渠门二手房方庄二手房健翔桥二手房东坝二手房工体二手房朝阳其它二手房亮马桥二手房潘家园二手房成寿寺二手房欢乐谷二手房通州北苑二手房常营二手房大望路二手房北工大二手房望京二手房惠新西街二手房酒仙桥二手房梧桐苑栖凤园二手房智学苑二手房车公庄北里二手房民岳家园二手房中信新城西区二手房林校北里二手房同馨家园二手房开园小区二手房碧桂园小区A区二手房富东嘉园二手房裕龙五区二手房领秀翡翠山B区二手房新桥南大街二手房万方景轩二手房学院派二手房金地朗悦朗园二手房阿尔法社区三期二手房北潞华家园二手房中关村二手房西潞园一里二手房长阳国际城二区二手房惠景新苑二手房顺兴街17号院二手房平乐园小区二手房芳群园二区二手房行宫园三里二手房西便门西里二手房下坡屯家园一区二手房三源里街二手房绿城百合公寓玉泉苑二手房海悦公馆二手房世纪村东区二手房燕化星城建德一里二手房中国铁建原香漫谷二区二手房裕龙四区二手房望都新地二手房鹿港嘉苑二手房国瑞城西区二手房长阳国际城一区二手房山水文园五期二手房台州二手房温州二手房绍兴二手房宜昌二手房东莞二手房许昌二手房赣州二手房武汉二手房中山二手房潍坊二手房晋中二手房深圳二手房运城二手房海门二手房大理二手房大连二手房南京二手房昆明二手房三亚二手房资阳二手房安庆二手房通辽二手房雅安二手房常州二手房苏州二手房泉州二手房北海二手房济南二手房佛山二手房上饶二手房广州二手房绵阳二手房合肥二手房吉林二手房济宁二手房惠州二手房盐城二手房邯郸二手房唐山二手房黄石二手房恒大绿洲二手房\\r派克公馆二手房\\r保利香槟国际二手房\\r和记黄埔御峰二手房\\r金科廊桥天都二手房\\r恒大山水城二手房\\r左岸经典二手房\\r万科魅力之城二手房\\r倚山里小区二手房\\r凯旋门二手房\\r弘阳旭日上城三区二手房\\r江岸水城二手房\\r卓达太阳城希望之洲二手房\\r越湖名邸二手房\\r万科未来之城二手房\\r恒大名都二手房\\r碧桂园太阳城二手房\\r中海康城国际一期二手房\\r诚基经贸中心二手房\\r橡树玫瑰城二手房\\r恒大绿洲\\r派克公馆\\r保利香槟国际\\r和记黄埔御峰\\r金科廊桥天都\\r恒大山水城\\r左岸经典\\r万科魅力之城\\r倚山里小区\\r凯旋门\\r弘阳旭日上城三区\\r江岸水城\\r卓达太阳城希望之洲\\r越湖名邸\\r万科未来之城\\r恒大名都\\r碧桂园太阳城\\r中海康城国际一期\\r诚基经贸中心\\r橡树玫瑰城\\r鸿坤跃界\\r华润理想国\\r华润未来城\\r华润西山墅\\r华润西山墅\\r华润悦景湾\\r和棠瑞著\\r和悦华锦\\r华远和墅\\r华远和墅\\r和悦华玺\\r华远裘马四季\\r华远裘马四季\\r华远西红世商业\\r金地华著\\r金地悦风华\\r泰禾金府大院\\r金科果冻\\r金科果冻\\r金科天玺\\r贝壳找房(北京)科技有限公司 | 网络经营许可证 京ICP备16057509号 | © Copyright©2010-2022 贝壳找房版权所有 | 营业执照违法和不良信息举报电话:010-86440676 违法和不良信息举报邮箱:jubaoyouxiang@lianjia.com涉未成年人举报电话:010-86440676 涉未成年人举报邮箱:jubaoyouxiang@ke.com京公网安备 11010802024019号 网上有害信息举报专区\\n举报本套房源已有客户举报,我们正在积极核实中,感谢您对链家的支持与监督查看详细赔付标准>>确定您举报该房源的理由是:(必填)房源不存在房源已售价格不真实图片不真实其他补充说明:(必填)注:“其他”类型不在假一赔百元范围内,查看详细赔付标准>>确定\\n\\n\\n\\n\\n\\n×账号密码登录用户名或密码错误7天内免登录忘记密码登录手机快捷登录登录即代表同意《链家隐私政策》及《链家用户使用协议》7天内免登录忘记密码×手机快捷登录别担心,无账号自动注册不会导致手机号被泄露获取验证码没有收到验证码?发送语音验证码用户名或密码错误7天内免登录登录账号密码登录登录即代表同意《链家隐私政策》及《链家用户使用协议》×手机号码注册已有账号?去登录获取验证码没有收到验证码?发送语音验证码用户名或密码错误我已阅读并同意《链家隐私政策》及《链家用户使用协议》注册×找回密码获取验证码没有收到验证码?发送语音验证码用户名或密码错误修改密码用户名或密码错误修改密码\\n\\n\\n'" | |
| ] | |
| }, | |
| "execution_count": 138, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "soup.text" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "fdf4db9c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.9.5" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment