Crawling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5db8b910",
   "metadata": {},
   "source": [
    "# How to crawling? - Naver Real Estate Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd47a736",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import custom module\n",
    "import nre.classes as nc\n",
    "import nre.util as nu\n",
    "from IPython.display import clear_output \n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import cv2\n",
    "\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "\n",
    "nu.IS_LOGGING = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3d422e",
   "metadata": {},
   "source": [
    "## 위치을 이용하여 구역 특정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffb15a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loc = nc.NLocation(35.18213, 128.1366) # 진주 충무공동\n",
    "loc = nc.NLocation(37.49911, 127.065463) # 강남 대치동\n",
    "\n",
    "#https://new.land.naver.com/complexes?ms=37.49911,127.065463,16&a=APT:OPST&b=B1:A1&e=RETAIL&s=EE\n",
    "\n",
    "cur_region = nc.NRegion('', loc, '0000000000')\n",
    "\n",
    "# 검색해서 지역 찾기\n",
    "while True:\n",
    "    regions = nu.get_region_list(cur_region.no)\n",
    "    if len(regions) == 0: break\n",
    "    for i, region in enumerate(regions): \n",
    "        print(i, region)\n",
    "        \n",
    "    idx = int(input(\"원하는 도시 인덱스(exit -1) : \"))\n",
    "    clear_output()\n",
    "    if idx == -1: break\n",
    "    cur_region = regions[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e77444e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "서울시 강남구 개포동 1168010300 loc(37.482968 | 127.063400)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdb70043f40>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sector = nu.get_sector(cur_region.loc)\n",
    "dm = sector.map.get_dimension()\n",
    "print(sector)\n",
    "\n",
    "plt.imshow(cv2.rotate(dm.get_bg_img(), cv2.ROTATE_90_COUNTERCLOCKWISE))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77a18a0",
   "metadata": {},
   "source": [
    "## 매물 조건 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b430e202",
   "metadata": {},
   "outputs": [],
   "source": [
    "addon = nc.NAddon(\n",
    "    #direction=nc.NAddon.DIR_EACH, #전 방향 탐색의 경우 사용 - 아래 로직을 바꾸세요\n",
    "    tradeType=[nc.NAddon.TRADE_DEAL, nc.NAddon.TRADE_LEASE], #목표 거래 - 매매, 전세\n",
    "    estateType=[nc.NAddon.ESTATE_APT, nc.NAddon.ESTATE_OPST] #목표 매물 - 아파트, 오피스텔\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1cad866",
   "metadata": {},
   "source": [
    "## 조건과 구역을 이용하여 매물 탐색"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51dfe780",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "매물 수 54\n"
     ]
    }
   ],
   "source": [
    "things = [] # 매물\n",
    "removed = 0\n",
    "for dirr in nc.NAddon.DIR_EACH: # 모든 방향 (남향 등등)\n",
    "    addon.dir = dirr # 방향 조건 선택\n",
    "    things.extend(nu.get_things(sector, addon)) # 매물 저장\n",
    "print('매물 수', len(things))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd65b50",
   "metadata": {},
   "source": [
    "## 구역 주변 편의 시설 탐색"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eada1736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BUS 64\n",
      "METRO 2\n",
      "INFANT 21\n",
      "PRESCHOOL 4\n",
      "SCHOOLPOI 5\n",
      "HOSPITAL 0\n",
      "PARKING 15\n",
      "MART 3\n",
      "CONVENIENCE 24\n",
      "WASHING 20\n",
      "BANK 30\n",
      "OFFICE 9\n",
      "Total 197\n"
     ]
    }
   ],
   "source": [
    "neighbors = [] # 편의시설 기록\n",
    "for nType in nc.NNeighbor.EACH: # 모든 편의시설\n",
    "    values = nu.get_neighborhood(sector, nType)\n",
    "    neighbors.extend(values)\n",
    "    print(nType, len(values))\n",
    "print('Total', len(neighbors))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54166206",
   "metadata": {},
   "source": [
    "# 편의시설과 매물 간의 거리 탐색"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18497c96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BUS 500 m\n",
      "METRO 500 m\n",
      "INFANT 750 m\n",
      "PRESCHOOL 750 m\n",
      "PRI_SCHOOL 1000 m\n",
      "PUB_SCHOOL 1000 m\n",
      "HOSPITAL 2000 m\n",
      "PARKING 500 m\n",
      "MART 500 m\n",
      "CONVENIENCE 300 m\n",
      "WASHING 500 m\n",
      "BANK 750 m\n",
      "OFFICE 1250 m\n"
     ]
    }
   ],
   "source": [
    "# 500 == 걸어서 5~10분 이내, 버스로 1분\n",
    "# 1000 == 버스로 1~2분 \n",
    "\n",
    "standard = nu.get_distance_standard() # 거리 기준\n",
    "\n",
    "for (key, value) in standard.items():\n",
    "    print(key, value, 'm')\n",
    "\n",
    "nu.update_things_intersection(things, neighbors, standard)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baab1ca2",
   "metadata": {},
   "source": [
    "## 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b77b599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dusts 251\n",
      "APT\t(0, 255, 0)\n",
      "ABYG\t(0, 255, 0)\n",
      "OPST\t(255, 0, 0)\n",
      "OBYG\t(255, 0, 0)\n",
      "JGB\t(0, 0, 255)\n",
      "JGC\t(0, 0, 255)\n",
      "BUS\t(0, 255, 255)\n",
      "METRO\t(0, 255, 255)\n",
      "INFANT\t(255, 255, 0)\n",
      "PRESCHOOL\t(255, 255, 0)\n",
      "PRI_SCHOOL\t(0, 0, 0)\n",
      "PUB_SCHOOL\t(0, 0, 0)\n",
      "HOSPITAL\t(0, 0, 0)\n",
      "PARKING\t(0, 0, 0)\n",
      "MART\t(0, 0, 0)\n",
      "CONVENIENCE\t(0, 0, 0)\n",
      "WASHING\t(0, 0, 0)\n",
      "BANK\t(0, 0, 0)\n",
      "OFFICE\t(0, 0, 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdbc0f07970>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm = sector.map.get_dimension()\n",
    "\n",
    "dusts = nu.neighbors_to_dusts(neighbors, dm) # lat, lon -> 좌표 축소\n",
    "dusts.extend(nu.things_to_dusts(things, dm)) # lat, lon -> 좌표 축소\n",
    "\n",
    "color_tag = nc.NDimension.get_default_tag_color() # tag에 컬러를 연결\n",
    "\n",
    "print('Dusts', len(dusts))\n",
    "for key,value in color_tag.items():\n",
    "    print(key, value, sep='\\t')\n",
    "\n",
    "plt.imshow(cv2.rotate(dm.get_img(dusts, color_tag), cv2.ROTATE_90_COUNTERCLOCKWISE)) # 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb8a4ed",
   "metadata": {},
   "source": [
    "## Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2ef8069b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def old_classifier(x): #old 구분\n",
    "    return int(x[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7ba69af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Build</th>\n",
       "      <th>Dir</th>\n",
       "      <th>minArea</th>\n",
       "      <th>maxArea</th>\n",
       "      <th>representativeArea</th>\n",
       "      <th>floorAreaRatio</th>\n",
       "      <th>minDeal</th>\n",
       "      <th>maxDeal</th>\n",
       "      <th>...</th>\n",
       "      <th>MART</th>\n",
       "      <th>CONVENIENCE</th>\n",
       "      <th>WASHING</th>\n",
       "      <th>BANK</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>Old</th>\n",
       "      <th>sector</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>개포래미안포레스트</td>\n",
       "      <td>APT</td>\n",
       "      <td>202009</td>\n",
       "      <td>EE</td>\n",
       "      <td>74.31</td>\n",
       "      <td>174.19</td>\n",
       "      <td>115.0</td>\n",
       "      <td>249</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>460000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>더샵트리에</td>\n",
       "      <td>APT</td>\n",
       "      <td>202112</td>\n",
       "      <td>EE</td>\n",
       "      <td>132.23</td>\n",
       "      <td>135.57</td>\n",
       "      <td>135.0</td>\n",
       "      <td>249</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>디에이치아너힐즈</td>\n",
       "      <td>APT</td>\n",
       "      <td>201908</td>\n",
       "      <td>EE</td>\n",
       "      <td>67.54</td>\n",
       "      <td>144.47</td>\n",
       "      <td>114.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>래미안블레스티지</td>\n",
       "      <td>APT</td>\n",
       "      <td>201902</td>\n",
       "      <td>EE</td>\n",
       "      <td>67.77</td>\n",
       "      <td>169.78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>메트하임</td>\n",
       "      <td>OPST</td>\n",
       "      <td>201812</td>\n",
       "      <td>ES</td>\n",
       "      <td>44.07</td>\n",
       "      <td>60.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>794</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>현대3차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198603</td>\n",
       "      <td>ES</td>\n",
       "      <td>105.86</td>\n",
       "      <td>193.76</td>\n",
       "      <td>193.0</td>\n",
       "      <td>147</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>개포현대(200동)</td>\n",
       "      <td>APT</td>\n",
       "      <td>198607</td>\n",
       "      <td>ES</td>\n",
       "      <td>101.55</td>\n",
       "      <td>101.55</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1986</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>개포래미안포레스트</td>\n",
       "      <td>APT</td>\n",
       "      <td>202009</td>\n",
       "      <td>ES</td>\n",
       "      <td>74.31</td>\n",
       "      <td>174.19</td>\n",
       "      <td>115.0</td>\n",
       "      <td>249</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>460000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>우성8차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198709</td>\n",
       "      <td>ES</td>\n",
       "      <td>98.87</td>\n",
       "      <td>109.35</td>\n",
       "      <td>98.0</td>\n",
       "      <td>192</td>\n",
       "      <td>243000.0</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>우성3차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198412</td>\n",
       "      <td>ES</td>\n",
       "      <td>110.6</td>\n",
       "      <td>182.45</td>\n",
       "      <td>110.0</td>\n",
       "      <td>179</td>\n",
       "      <td>280000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>현대2차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198601</td>\n",
       "      <td>ES</td>\n",
       "      <td>105.78</td>\n",
       "      <td>195.17</td>\n",
       "      <td>195.0</td>\n",
       "      <td>156</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1986</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>더샵트리에</td>\n",
       "      <td>APT</td>\n",
       "      <td>202112</td>\n",
       "      <td>ES</td>\n",
       "      <td>132.23</td>\n",
       "      <td>135.57</td>\n",
       "      <td>135.0</td>\n",
       "      <td>249</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>개포현대2차(220동)</td>\n",
       "      <td>APT</td>\n",
       "      <td>198809</td>\n",
       "      <td>ES</td>\n",
       "      <td>98.54</td>\n",
       "      <td>98.54</td>\n",
       "      <td>98.0</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1988</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>디에이치아너힐즈</td>\n",
       "      <td>APT</td>\n",
       "      <td>201908</td>\n",
       "      <td>ES</td>\n",
       "      <td>67.54</td>\n",
       "      <td>144.47</td>\n",
       "      <td>114.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>렉스힐</td>\n",
       "      <td>OPST</td>\n",
       "      <td>201111</td>\n",
       "      <td>ES</td>\n",
       "      <td>45.22</td>\n",
       "      <td>57.13</td>\n",
       "      <td>45.0</td>\n",
       "      <td>358</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>현대1차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198404</td>\n",
       "      <td>ES</td>\n",
       "      <td>102.73</td>\n",
       "      <td>190.81</td>\n",
       "      <td>190.0</td>\n",
       "      <td>179</td>\n",
       "      <td>280000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>우성6차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198711</td>\n",
       "      <td>ES</td>\n",
       "      <td>61.32</td>\n",
       "      <td>86.52</td>\n",
       "      <td>86.0</td>\n",
       "      <td>106</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경남</td>\n",
       "      <td>APT</td>\n",
       "      <td>198403</td>\n",
       "      <td>ES</td>\n",
       "      <td>97.92</td>\n",
       "      <td>198.33</td>\n",
       "      <td>146.0</td>\n",
       "      <td>174</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>래미안블레스티지</td>\n",
       "      <td>APT</td>\n",
       "      <td>201902</td>\n",
       "      <td>ES</td>\n",
       "      <td>67.77</td>\n",
       "      <td>169.78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>디에이치아너힐즈</td>\n",
       "      <td>APT</td>\n",
       "      <td>201908</td>\n",
       "      <td>WW</td>\n",
       "      <td>67.54</td>\n",
       "      <td>144.47</td>\n",
       "      <td>114.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>래미안블레스티지</td>\n",
       "      <td>APT</td>\n",
       "      <td>201902</td>\n",
       "      <td>WW</td>\n",
       "      <td>67.77</td>\n",
       "      <td>169.78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>개포래미안포레스트</td>\n",
       "      <td>APT</td>\n",
       "      <td>202009</td>\n",
       "      <td>WS</td>\n",
       "      <td>74.31</td>\n",
       "      <td>174.19</td>\n",
       "      <td>115.0</td>\n",
       "      <td>249</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>460000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>우성8차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198709</td>\n",
       "      <td>WS</td>\n",
       "      <td>98.87</td>\n",
       "      <td>109.35</td>\n",
       "      <td>98.0</td>\n",
       "      <td>192</td>\n",
       "      <td>243000.0</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>우성3차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198412</td>\n",
       "      <td>WS</td>\n",
       "      <td>110.6</td>\n",
       "      <td>182.45</td>\n",
       "      <td>110.0</td>\n",
       "      <td>179</td>\n",
       "      <td>280000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>현대2차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198601</td>\n",
       "      <td>WS</td>\n",
       "      <td>105.78</td>\n",
       "      <td>195.17</td>\n",
       "      <td>195.0</td>\n",
       "      <td>156</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1986</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>더샵트리에</td>\n",
       "      <td>APT</td>\n",
       "      <td>202112</td>\n",
       "      <td>WS</td>\n",
       "      <td>132.23</td>\n",
       "      <td>135.57</td>\n",
       "      <td>135.0</td>\n",
       "      <td>249</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>개포현대2차(220동)</td>\n",
       "      <td>APT</td>\n",
       "      <td>198809</td>\n",
       "      <td>WS</td>\n",
       "      <td>98.54</td>\n",
       "      <td>98.54</td>\n",
       "      <td>98.0</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1988</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>디에이치아너힐즈</td>\n",
       "      <td>APT</td>\n",
       "      <td>201908</td>\n",
       "      <td>WS</td>\n",
       "      <td>67.54</td>\n",
       "      <td>144.47</td>\n",
       "      <td>114.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>우성6차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198711</td>\n",
       "      <td>WS</td>\n",
       "      <td>61.32</td>\n",
       "      <td>86.52</td>\n",
       "      <td>86.0</td>\n",
       "      <td>106</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>경남</td>\n",
       "      <td>APT</td>\n",
       "      <td>198403</td>\n",
       "      <td>WS</td>\n",
       "      <td>97.92</td>\n",
       "      <td>198.33</td>\n",
       "      <td>146.0</td>\n",
       "      <td>174</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>래미안블레스티지</td>\n",
       "      <td>APT</td>\n",
       "      <td>201902</td>\n",
       "      <td>WS</td>\n",
       "      <td>67.77</td>\n",
       "      <td>169.78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>메트하임</td>\n",
       "      <td>OPST</td>\n",
       "      <td>201812</td>\n",
       "      <td>SS</td>\n",
       "      <td>44.07</td>\n",
       "      <td>60.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>794</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>현대3차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198603</td>\n",
       "      <td>SS</td>\n",
       "      <td>105.86</td>\n",
       "      <td>193.76</td>\n",
       "      <td>193.0</td>\n",
       "      <td>147</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>개포현대(200동)</td>\n",
       "      <td>APT</td>\n",
       "      <td>198607</td>\n",
       "      <td>SS</td>\n",
       "      <td>101.55</td>\n",
       "      <td>101.55</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1986</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>개포래미안포레스트</td>\n",
       "      <td>APT</td>\n",
       "      <td>202009</td>\n",
       "      <td>SS</td>\n",
       "      <td>74.31</td>\n",
       "      <td>174.19</td>\n",
       "      <td>115.0</td>\n",
       "      <td>249</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>460000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>우성8차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198709</td>\n",
       "      <td>SS</td>\n",
       "      <td>98.87</td>\n",
       "      <td>109.35</td>\n",
       "      <td>98.0</td>\n",
       "      <td>192</td>\n",
       "      <td>243000.0</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>우성3차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198412</td>\n",
       "      <td>SS</td>\n",
       "      <td>110.6</td>\n",
       "      <td>182.45</td>\n",
       "      <td>110.0</td>\n",
       "      <td>179</td>\n",
       "      <td>280000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>현대2차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198601</td>\n",
       "      <td>SS</td>\n",
       "      <td>105.78</td>\n",
       "      <td>195.17</td>\n",
       "      <td>195.0</td>\n",
       "      <td>156</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1986</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>더샵트리에</td>\n",
       "      <td>APT</td>\n",
       "      <td>202112</td>\n",
       "      <td>SS</td>\n",
       "      <td>132.23</td>\n",
       "      <td>135.57</td>\n",
       "      <td>135.0</td>\n",
       "      <td>249</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>개포현대2차(220동)</td>\n",
       "      <td>APT</td>\n",
       "      <td>198809</td>\n",
       "      <td>SS</td>\n",
       "      <td>98.54</td>\n",
       "      <td>98.54</td>\n",
       "      <td>98.0</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1988</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>디에이치아너힐즈</td>\n",
       "      <td>APT</td>\n",
       "      <td>201908</td>\n",
       "      <td>SS</td>\n",
       "      <td>67.54</td>\n",
       "      <td>144.47</td>\n",
       "      <td>114.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>현대1차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198404</td>\n",
       "      <td>SS</td>\n",
       "      <td>102.73</td>\n",
       "      <td>190.81</td>\n",
       "      <td>190.0</td>\n",
       "      <td>179</td>\n",
       "      <td>280000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>우성6차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198711</td>\n",
       "      <td>SS</td>\n",
       "      <td>61.32</td>\n",
       "      <td>86.52</td>\n",
       "      <td>86.0</td>\n",
       "      <td>106</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>경남</td>\n",
       "      <td>APT</td>\n",
       "      <td>198403</td>\n",
       "      <td>SS</td>\n",
       "      <td>97.92</td>\n",
       "      <td>198.33</td>\n",
       "      <td>146.0</td>\n",
       "      <td>174</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>개포상지리츠빌</td>\n",
       "      <td>APT</td>\n",
       "      <td>200303</td>\n",
       "      <td>SS</td>\n",
       "      <td>178.74</td>\n",
       "      <td>178.74</td>\n",
       "      <td>178.0</td>\n",
       "      <td>202</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>래미안블레스티지</td>\n",
       "      <td>APT</td>\n",
       "      <td>201902</td>\n",
       "      <td>SS</td>\n",
       "      <td>67.77</td>\n",
       "      <td>169.78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>더샵트리에</td>\n",
       "      <td>APT</td>\n",
       "      <td>202112</td>\n",
       "      <td>EN</td>\n",
       "      <td>132.23</td>\n",
       "      <td>135.57</td>\n",
       "      <td>135.0</td>\n",
       "      <td>249</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>래미안블레스티지</td>\n",
       "      <td>APT</td>\n",
       "      <td>201902</td>\n",
       "      <td>EN</td>\n",
       "      <td>67.77</td>\n",
       "      <td>169.78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>메트하임</td>\n",
       "      <td>OPST</td>\n",
       "      <td>201812</td>\n",
       "      <td>NN</td>\n",
       "      <td>44.07</td>\n",
       "      <td>60.18</td>\n",
       "      <td>44.0</td>\n",
       "      <td>794</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>우성3차</td>\n",
       "      <td>APT</td>\n",
       "      <td>198412</td>\n",
       "      <td>NN</td>\n",
       "      <td>110.6</td>\n",
       "      <td>182.45</td>\n",
       "      <td>110.0</td>\n",
       "      <td>179</td>\n",
       "      <td>280000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>래미안블레스티지</td>\n",
       "      <td>APT</td>\n",
       "      <td>201902</td>\n",
       "      <td>NN</td>\n",
       "      <td>67.77</td>\n",
       "      <td>169.78</td>\n",
       "      <td>113.0</td>\n",
       "      <td>249</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>개포래미안포레스트</td>\n",
       "      <td>APT</td>\n",
       "      <td>202009</td>\n",
       "      <td>WN</td>\n",
       "      <td>74.31</td>\n",
       "      <td>174.19</td>\n",
       "      <td>115.0</td>\n",
       "      <td>249</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>460000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>경남</td>\n",
       "      <td>APT</td>\n",
       "      <td>198403</td>\n",
       "      <td>WN</td>\n",
       "      <td>97.92</td>\n",
       "      <td>198.33</td>\n",
       "      <td>146.0</td>\n",
       "      <td>174</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>개포동</td>\n",
       "      <td>서울시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Type   Build Dir minArea maxArea  representativeArea  \\\n",
       "0      개포래미안포레스트   APT  202009  EE   74.31  174.19               115.0   \n",
       "1          더샵트리에   APT  202112  EE  132.23  135.57               135.0   \n",
       "2       디에이치아너힐즈   APT  201908  EE   67.54  144.47               114.0   \n",
       "3       래미안블레스티지   APT  201902  EE   67.77  169.78               113.0   \n",
       "4           메트하임  OPST  201812  ES   44.07   60.18                44.0   \n",
       "5           현대3차   APT  198603  ES  105.86  193.76               193.0   \n",
       "6     개포현대(200동)   APT  198607  ES  101.55  101.55               101.0   \n",
       "7      개포래미안포레스트   APT  202009  ES   74.31  174.19               115.0   \n",
       "8           우성8차   APT  198709  ES   98.87  109.35                98.0   \n",
       "9           우성3차   APT  198412  ES   110.6  182.45               110.0   \n",
       "10          현대2차   APT  198601  ES  105.78  195.17               195.0   \n",
       "11         더샵트리에   APT  202112  ES  132.23  135.57               135.0   \n",
       "12  개포현대2차(220동)   APT  198809  ES   98.54   98.54                98.0   \n",
       "13      디에이치아너힐즈   APT  201908  ES   67.54  144.47               114.0   \n",
       "14           렉스힐  OPST  201111  ES   45.22   57.13                45.0   \n",
       "15          현대1차   APT  198404  ES  102.73  190.81               190.0   \n",
       "16          우성6차   APT  198711  ES   61.32   86.52                86.0   \n",
       "17            경남   APT  198403  ES   97.92  198.33               146.0   \n",
       "18      래미안블레스티지   APT  201902  ES   67.77  169.78               113.0   \n",
       "19      디에이치아너힐즈   APT  201908  WW   67.54  144.47               114.0   \n",
       "20      래미안블레스티지   APT  201902  WW   67.77  169.78               113.0   \n",
       "21     개포래미안포레스트   APT  202009  WS   74.31  174.19               115.0   \n",
       "22          우성8차   APT  198709  WS   98.87  109.35                98.0   \n",
       "23          우성3차   APT  198412  WS   110.6  182.45               110.0   \n",
       "24          현대2차   APT  198601  WS  105.78  195.17               195.0   \n",
       "25         더샵트리에   APT  202112  WS  132.23  135.57               135.0   \n",
       "26  개포현대2차(220동)   APT  198809  WS   98.54   98.54                98.0   \n",
       "27      디에이치아너힐즈   APT  201908  WS   67.54  144.47               114.0   \n",
       "28          우성6차   APT  198711  WS   61.32   86.52                86.0   \n",
       "29            경남   APT  198403  WS   97.92  198.33               146.0   \n",
       "30      래미안블레스티지   APT  201902  WS   67.77  169.78               113.0   \n",
       "31          메트하임  OPST  201812  SS   44.07   60.18                44.0   \n",
       "32          현대3차   APT  198603  SS  105.86  193.76               193.0   \n",
       "33    개포현대(200동)   APT  198607  SS  101.55  101.55               101.0   \n",
       "34     개포래미안포레스트   APT  202009  SS   74.31  174.19               115.0   \n",
       "35          우성8차   APT  198709  SS   98.87  109.35                98.0   \n",
       "36          우성3차   APT  198412  SS   110.6  182.45               110.0   \n",
       "37          현대2차   APT  198601  SS  105.78  195.17               195.0   \n",
       "38         더샵트리에   APT  202112  SS  132.23  135.57               135.0   \n",
       "39  개포현대2차(220동)   APT  198809  SS   98.54   98.54                98.0   \n",
       "40      디에이치아너힐즈   APT  201908  SS   67.54  144.47               114.0   \n",
       "41          현대1차   APT  198404  SS  102.73  190.81               190.0   \n",
       "42          우성6차   APT  198711  SS   61.32   86.52                86.0   \n",
       "43            경남   APT  198403  SS   97.92  198.33               146.0   \n",
       "44       개포상지리츠빌   APT  200303  SS  178.74  178.74               178.0   \n",
       "45      래미안블레스티지   APT  201902  SS   67.77  169.78               113.0   \n",
       "46         더샵트리에   APT  202112  EN  132.23  135.57               135.0   \n",
       "47      래미안블레스티지   APT  201902  EN   67.77  169.78               113.0   \n",
       "48          메트하임  OPST  201812  NN   44.07   60.18                44.0   \n",
       "49          우성3차   APT  198412  NN   110.6  182.45               110.0   \n",
       "50      래미안블레스티지   APT  201902  NN   67.77  169.78               113.0   \n",
       "51     개포래미안포레스트   APT  202009  WN   74.31  174.19               115.0   \n",
       "52            경남   APT  198403  WN   97.92  198.33               146.0   \n",
       "\n",
       "    floorAreaRatio   minDeal   maxDeal  ...  MART  CONVENIENCE  WASHING  BANK  \\\n",
       "0              249  175000.0  460000.0  ...     0            3        1     5   \n",
       "1              249  300000.0  380000.0  ...     2            2       10    24   \n",
       "2              249  180000.0  530000.0  ...     2            1        2     5   \n",
       "3              249  180000.0  500000.0  ...     2            2        6     2   \n",
       "4              794   32000.0   62000.0  ...     0            4        3     4   \n",
       "5              147  300000.0  400000.0  ...     0            0        4    12   \n",
       "6              157       NaN       NaN  ...     1            3        3    12   \n",
       "7              249  175000.0  460000.0  ...     0            3        1     5   \n",
       "8              192  243000.0  260000.0  ...     0            0        3     6   \n",
       "9              179  280000.0  400000.0  ...     1            2        5    24   \n",
       "10             156  270000.0  410000.0  ...     1            2        2    25   \n",
       "11             249  300000.0  380000.0  ...     2            2       10    24   \n",
       "12             185       NaN       NaN  ...     1            3        2     6   \n",
       "13             249  180000.0  530000.0  ...     2            1        2     5   \n",
       "14             358   50000.0   55000.0  ...     2            4        7     5   \n",
       "15             179  280000.0  400000.0  ...     1            3        3    18   \n",
       "16             106  188000.0  265000.0  ...     0            1        0     4   \n",
       "17             174  265000.0  420000.0  ...     1            3        6    24   \n",
       "18             249  180000.0  500000.0  ...     2            2        6     2   \n",
       "19             249  180000.0  530000.0  ...     2            1        2     5   \n",
       "20             249  180000.0  500000.0  ...     2            2        6     2   \n",
       "21             249  175000.0  460000.0  ...     0            3        1     5   \n",
       "22             192  243000.0  260000.0  ...     0            0        3     6   \n",
       "23             179  280000.0  400000.0  ...     1            2        5    24   \n",
       "24             156  270000.0  410000.0  ...     1            2        2    25   \n",
       "25             249  300000.0  380000.0  ...     2            2       10    24   \n",
       "26             185       NaN       NaN  ...     1            3        2     6   \n",
       "27             249  180000.0  530000.0  ...     2            1        2     5   \n",
       "28             106  188000.0  265000.0  ...     0            1        0     4   \n",
       "29             174  265000.0  420000.0  ...     1            3        6    24   \n",
       "30             249  180000.0  500000.0  ...     2            2        6     2   \n",
       "31             794   32000.0   62000.0  ...     0            4        3     4   \n",
       "32             147  300000.0  400000.0  ...     0            0        4    12   \n",
       "33             157       NaN       NaN  ...     1            3        3    12   \n",
       "34             249  175000.0  460000.0  ...     0            3        1     5   \n",
       "35             192  243000.0  260000.0  ...     0            0        3     6   \n",
       "36             179  280000.0  400000.0  ...     1            2        5    24   \n",
       "37             156  270000.0  410000.0  ...     1            2        2    25   \n",
       "38             249  300000.0  380000.0  ...     2            2       10    24   \n",
       "39             185       NaN       NaN  ...     1            3        2     6   \n",
       "40             249  180000.0  530000.0  ...     2            1        2     5   \n",
       "41             179  280000.0  400000.0  ...     1            3        3    18   \n",
       "42             106  188000.0  265000.0  ...     0            1        0     4   \n",
       "43             174  265000.0  420000.0  ...     1            3        6    24   \n",
       "44             202  390000.0  400000.0  ...     0            0        8    18   \n",
       "45             249  180000.0  500000.0  ...     2            2        6     2   \n",
       "46             249  300000.0  380000.0  ...     2            2       10    24   \n",
       "47             249  180000.0  500000.0  ...     2            2        6     2   \n",
       "48             794   32000.0   62000.0  ...     0            4        3     4   \n",
       "49             179  280000.0  400000.0  ...     1            2        5    24   \n",
       "50             249  180000.0  500000.0  ...     2            2        6     2   \n",
       "51             249  175000.0  460000.0  ...     0            3        1     5   \n",
       "52             174  265000.0  420000.0  ...     1            3        6    24   \n",
       "\n",
       "    OFFICE  PRI_SCHOOL  PUB_SCHOOL   Old  sector  city  \n",
       "0        8           2           2  2020     개포동   서울시  \n",
       "1        9           2           1  2021     개포동   서울시  \n",
       "2        6           1           2  2019     개포동   서울시  \n",
       "3        8           1           1  2019     개포동   서울시  \n",
       "4        7           0           2  2018     개포동   서울시  \n",
       "5        7           1           1  1986     개포동   서울시  \n",
       "6        8           2           2  1986     개포동   서울시  \n",
       "7        8           2           2  2020     개포동   서울시  \n",
       "8        7           1           1  1987     개포동   서울시  \n",
       "9        8           2           2  1984     개포동   서울시  \n",
       "10       8           2           2  1986     개포동   서울시  \n",
       "11       9           2           1  2021     개포동   서울시  \n",
       "12       8           2           2  1988     개포동   서울시  \n",
       "13       6           1           2  2019     개포동   서울시  \n",
       "14       9           2           1  2011     개포동   서울시  \n",
       "15       8           2           2  1984     개포동   서울시  \n",
       "16       7           2           1  1987     개포동   서울시  \n",
       "17       8           2           1  1984     개포동   서울시  \n",
       "18       8           1           1  2019     개포동   서울시  \n",
       "19       6           1           2  2019     개포동   서울시  \n",
       "20       8           1           1  2019     개포동   서울시  \n",
       "21       8           2           2  2020     개포동   서울시  \n",
       "22       7           1           1  1987     개포동   서울시  \n",
       "23       8           2           2  1984     개포동   서울시  \n",
       "24       8           2           2  1986     개포동   서울시  \n",
       "25       9           2           1  2021     개포동   서울시  \n",
       "26       8           2           2  1988     개포동   서울시  \n",
       "27       6           1           2  2019     개포동   서울시  \n",
       "28       7           2           1  1987     개포동   서울시  \n",
       "29       8           2           1  1984     개포동   서울시  \n",
       "30       8           1           1  2019     개포동   서울시  \n",
       "31       7           0           2  2018     개포동   서울시  \n",
       "32       7           1           1  1986     개포동   서울시  \n",
       "33       8           2           2  1986     개포동   서울시  \n",
       "34       8           2           2  2020     개포동   서울시  \n",
       "35       7           1           1  1987     개포동   서울시  \n",
       "36       8           2           2  1984     개포동   서울시  \n",
       "37       8           2           2  1986     개포동   서울시  \n",
       "38       9           2           1  2021     개포동   서울시  \n",
       "39       8           2           2  1988     개포동   서울시  \n",
       "40       6           1           2  2019     개포동   서울시  \n",
       "41       8           2           2  1984     개포동   서울시  \n",
       "42       7           2           1  1987     개포동   서울시  \n",
       "43       8           2           1  1984     개포동   서울시  \n",
       "44       9           1           1  2003     개포동   서울시  \n",
       "45       8           1           1  2019     개포동   서울시  \n",
       "46       9           2           1  2021     개포동   서울시  \n",
       "47       8           1           1  2019     개포동   서울시  \n",
       "48       7           0           2  2018     개포동   서울시  \n",
       "49       8           2           2  1984     개포동   서울시  \n",
       "50       8           1           1  2019     개포동   서울시  \n",
       "51       8           2           2  2020     개포동   서울시  \n",
       "52       8           2           1  1984     개포동   서울시  \n",
       "\n",
       "[53 rows x 38 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df = pd.DataFrame(data=[t.get_list() for t in things], columns=nc.NThing.HEADER)\n",
    "total_df['Old'] = total_df['Build'].apply(old_classifier)\n",
    "total_df['sector'] = sector.name\n",
    "total_df['city'] = sector.city\n",
    "\n",
    "total_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8dededd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53 entries, 0 to 52\n",
      "Data columns (total 38 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Name                53 non-null     object \n",
      " 1   Type                53 non-null     object \n",
      " 2   Build               53 non-null     object \n",
      " 3   Dir                 53 non-null     object \n",
      " 4   minArea             53 non-null     object \n",
      " 5   maxArea             53 non-null     object \n",
      " 6   representativeArea  53 non-null     float64\n",
      " 7   floorAreaRatio      53 non-null     int64  \n",
      " 8   minDeal             48 non-null     float64\n",
      " 9   maxDeal             48 non-null     float64\n",
      " 10  medianDeal          48 non-null     float64\n",
      " 11  minLease            51 non-null     float64\n",
      " 12  maxLease            51 non-null     float64\n",
      " 13  medianLease         46 non-null     float64\n",
      " 14  minDealUnit         48 non-null     float64\n",
      " 15  maxDealUnit         48 non-null     float64\n",
      " 16  medianDealUnit      48 non-null     float64\n",
      " 17  minLeaseUnit        51 non-null     float64\n",
      " 18  maxLeaseUnit        51 non-null     float64\n",
      " 19  medianLeaseUnit     46 non-null     float64\n",
      " 20  Lat                 53 non-null     float64\n",
      " 21  Lon                 53 non-null     float64\n",
      " 22  BUS                 53 non-null     int64  \n",
      " 23  METRO               53 non-null     int64  \n",
      " 24  INFANT              53 non-null     int64  \n",
      " 25  PRESCHOOL           53 non-null     int64  \n",
      " 26  HOSPITAL            53 non-null     int64  \n",
      " 27  PARKING             53 non-null     int64  \n",
      " 28  MART                53 non-null     int64  \n",
      " 29  CONVENIENCE         53 non-null     int64  \n",
      " 30  WASHING             53 non-null     int64  \n",
      " 31  BANK                53 non-null     int64  \n",
      " 32  OFFICE              53 non-null     int64  \n",
      " 33  PRI_SCHOOL          53 non-null     int64  \n",
      " 34  PUB_SCHOOL          53 non-null     int64  \n",
      " 35  Old                 53 non-null     int64  \n",
      " 36  sector              53 non-null     object \n",
      " 37  city                53 non-null     object \n",
      "dtypes: float64(15), int64(15), object(8)\n",
      "memory usage: 15.9+ KB\n"
     ]
    }
   ],
   "source": [
    "total_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "96cec499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>representativeArea</th>\n",
       "      <th>floorAreaRatio</th>\n",
       "      <th>minDeal</th>\n",
       "      <th>maxDeal</th>\n",
       "      <th>medianDeal</th>\n",
       "      <th>minLease</th>\n",
       "      <th>maxLease</th>\n",
       "      <th>medianLease</th>\n",
       "      <th>minDealUnit</th>\n",
       "      <th>maxDealUnit</th>\n",
       "      <th>...</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>PARKING</th>\n",
       "      <th>MART</th>\n",
       "      <th>CONVENIENCE</th>\n",
       "      <th>WASHING</th>\n",
       "      <th>BANK</th>\n",
       "      <th>OFFICE</th>\n",
       "      <th>PRI_SCHOOL</th>\n",
       "      <th>PUB_SCHOOL</th>\n",
       "      <th>Old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.00000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>120.547170</td>\n",
       "      <td>239.90566</td>\n",
       "      <td>219041.666667</td>\n",
       "      <td>389083.333333</td>\n",
       "      <td>295416.666667</td>\n",
       "      <td>90803.921569</td>\n",
       "      <td>175215.686275</td>\n",
       "      <td>131456.521739</td>\n",
       "      <td>7207.041667</td>\n",
       "      <td>9442.875000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.867925</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>2.075472</td>\n",
       "      <td>4.075472</td>\n",
       "      <td>11.660377</td>\n",
       "      <td>7.735849</td>\n",
       "      <td>1.547170</td>\n",
       "      <td>1.509434</td>\n",
       "      <td>2002.433962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.130629</td>\n",
       "      <td>145.42686</td>\n",
       "      <td>77153.055491</td>\n",
       "      <td>125978.017896</td>\n",
       "      <td>89951.720699</td>\n",
       "      <td>28492.117933</td>\n",
       "      <td>80440.615046</td>\n",
       "      <td>42215.166586</td>\n",
       "      <td>1616.770605</td>\n",
       "      <td>2112.422192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000363</td>\n",
       "      <td>0.820191</td>\n",
       "      <td>1.106781</td>\n",
       "      <td>2.772454</td>\n",
       "      <td>9.114535</td>\n",
       "      <td>0.812189</td>\n",
       "      <td>0.606568</td>\n",
       "      <td>0.504695</td>\n",
       "      <td>16.815183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>106.00000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>55000.000000</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>28000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>2404.000000</td>\n",
       "      <td>3416.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>174.00000</td>\n",
       "      <td>180000.000000</td>\n",
       "      <td>380000.000000</td>\n",
       "      <td>290000.000000</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>95000.000000</td>\n",
       "      <td>6786.000000</td>\n",
       "      <td>8866.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>202.00000</td>\n",
       "      <td>215500.000000</td>\n",
       "      <td>405000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>90000.000000</td>\n",
       "      <td>160000.000000</td>\n",
       "      <td>155000.000000</td>\n",
       "      <td>7346.000000</td>\n",
       "      <td>9536.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>135.000000</td>\n",
       "      <td>249.00000</td>\n",
       "      <td>280000.000000</td>\n",
       "      <td>470000.000000</td>\n",
       "      <td>342500.000000</td>\n",
       "      <td>90000.000000</td>\n",
       "      <td>240000.000000</td>\n",
       "      <td>165000.000000</td>\n",
       "      <td>8336.000000</td>\n",
       "      <td>10484.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>794.00000</td>\n",
       "      <td>390000.000000</td>\n",
       "      <td>530000.000000</td>\n",
       "      <td>400000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>280000.000000</td>\n",
       "      <td>170000.000000</td>\n",
       "      <td>8881.000000</td>\n",
       "      <td>12167.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       representativeArea  floorAreaRatio        minDeal        maxDeal  \\\n",
       "count           53.000000        53.00000      48.000000      48.000000   \n",
       "mean           120.547170       239.90566  219041.666667  389083.333333   \n",
       "std             38.130629       145.42686   77153.055491  125978.017896   \n",
       "min             44.000000       106.00000   32000.000000   55000.000000   \n",
       "25%            101.000000       174.00000  180000.000000  380000.000000   \n",
       "50%            114.000000       202.00000  215500.000000  405000.000000   \n",
       "75%            135.000000       249.00000  280000.000000  470000.000000   \n",
       "max            195.000000       794.00000  390000.000000  530000.000000   \n",
       "\n",
       "          medianDeal       minLease       maxLease    medianLease  \\\n",
       "count      48.000000      51.000000      51.000000      46.000000   \n",
       "mean   295416.666667   90803.921569  175215.686275  131456.521739   \n",
       "std     89951.720699   28492.117933   80440.615046   42215.166586   \n",
       "min     35000.000000   28000.000000   30000.000000   30000.000000   \n",
       "25%    290000.000000   80000.000000  100000.000000   95000.000000   \n",
       "50%    300000.000000   90000.000000  160000.000000  155000.000000   \n",
       "75%    342500.000000   90000.000000  240000.000000  165000.000000   \n",
       "max    400000.000000  150000.000000  280000.000000  170000.000000   \n",
       "\n",
       "       minDealUnit   maxDealUnit  ...  HOSPITAL    PARKING       MART  \\\n",
       "count    48.000000     48.000000  ...      53.0  53.000000  53.000000   \n",
       "mean   7207.041667   9442.875000  ...       0.0   1.867925   1.018868   \n",
       "std    1616.770605   2112.422192  ...       0.0   2.000363   0.820191   \n",
       "min    2404.000000   3416.000000  ...       0.0   0.000000   0.000000   \n",
       "25%    6786.000000   8866.000000  ...       0.0   0.000000   0.000000   \n",
       "50%    7346.000000   9536.000000  ...       0.0   1.000000   1.000000   \n",
       "75%    8336.000000  10484.750000  ...       0.0   4.000000   2.000000   \n",
       "max    8881.000000  12167.000000  ...       0.0   6.000000   2.000000   \n",
       "\n",
       "       CONVENIENCE    WASHING       BANK     OFFICE  PRI_SCHOOL  PUB_SCHOOL  \\\n",
       "count    53.000000  53.000000  53.000000  53.000000   53.000000   53.000000   \n",
       "mean      2.075472   4.075472  11.660377   7.735849    1.547170    1.509434   \n",
       "std       1.106781   2.772454   9.114535   0.812189    0.606568    0.504695   \n",
       "min       0.000000   0.000000   2.000000   6.000000    0.000000    1.000000   \n",
       "25%       1.000000   2.000000   5.000000   7.000000    1.000000    1.000000   \n",
       "50%       2.000000   3.000000   6.000000   8.000000    2.000000    2.000000   \n",
       "75%       3.000000   6.000000  24.000000   8.000000    2.000000    2.000000   \n",
       "max       4.000000  10.000000  25.000000   9.000000    2.000000    2.000000   \n",
       "\n",
       "               Old  \n",
       "count    53.000000  \n",
       "mean   2002.433962  \n",
       "std      16.815183  \n",
       "min    1984.000000  \n",
       "25%    1986.000000  \n",
       "50%    2003.000000  \n",
       "75%    2019.000000  \n",
       "max    2021.000000  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb04c384",
   "metadata": {},
   "source": [
    "## 시각화 라벨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e38b2ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_lbl = 'minDeal'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fde98a4",
   "metadata": {},
   "source": [
    "## 방향에 따른 가격"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07b9f9e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dir', ylabel='minDeal'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Dir', y = target_lbl, hue='Type', data=total_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aebd8d5",
   "metadata": {},
   "source": [
    "## 오피스텔과 아파트 차이"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d9dbf0e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Type', ylabel='minDeal'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Type', y = target_lbl, data=total_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb51f0a",
   "metadata": {},
   "source": [
    "## 연식에 따른 차이"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ccab3df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Old', ylabel='minDeal'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Old', y = target_lbl, hue='Type', data=total_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01a6ef6",
   "metadata": {},
   "source": [
    "## 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ab2895c",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df.to_csv('./csv/%s - %s house_price.csv' % (sector.name, sector.city), sep=',', na_rep='NaN', index=False)"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}