gwangjin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.starbucks 거리 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>Store Number</th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7982</td>\n",
       "      <td>19176-199455</td>\n",
       "      <td>Wolgae Emart</td>\n",
       "      <td>333-1 Wolgae3dong, Nowongu, Seoul</td>\n",
       "      <td>127.06</td>\n",
       "      <td>37.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7983</td>\n",
       "      <td>23428-230106</td>\n",
       "      <td>Olympic Park North Gate</td>\n",
       "      <td>468 Seongnae-dong,Gangdong-gu</td>\n",
       "      <td>127.13</td>\n",
       "      <td>37.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7984</td>\n",
       "      <td>49283-267704</td>\n",
       "      <td>Mia Newtown</td>\n",
       "      <td>812-2, Mia-dong, Gangbuk-gu</td>\n",
       "      <td>127.02</td>\n",
       "      <td>37.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7985</td>\n",
       "      <td>21320-211245</td>\n",
       "      <td>Yangjae Station</td>\n",
       "      <td>14-4 Yangjae-dong, Seocho-gu</td>\n",
       "      <td>127.04</td>\n",
       "      <td>37.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7986</td>\n",
       "      <td>15565-163169</td>\n",
       "      <td>Apgujeong Center</td>\n",
       "      <td>639-7, Shinsa-Dong, Gangnam-Gu, Yonsei Foundat...</td>\n",
       "      <td>127.04</td>\n",
       "      <td>37.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>8082</td>\n",
       "      <td>48547-263608</td>\n",
       "      <td>Apgujeong</td>\n",
       "      <td>609 sinsadong, gangnamgu</td>\n",
       "      <td>127.03</td>\n",
       "      <td>37.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>8083</td>\n",
       "      <td>25341-240585</td>\n",
       "      <td>Yaksu Station</td>\n",
       "      <td>374-27, Sindang-dong, Jung-gu,</td>\n",
       "      <td>127.01</td>\n",
       "      <td>37.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>8084</td>\n",
       "      <td>48732-264885</td>\n",
       "      <td>mapo yeomri</td>\n",
       "      <td>168-9 Yeomri-dong, Mapo-gu, Garden 5 Tool Pavi...</td>\n",
       "      <td>126.95</td>\n",
       "      <td>37.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>8085</td>\n",
       "      <td>15987-167305</td>\n",
       "      <td>Garden 5 E-Mart</td>\n",
       "      <td>280 Munjung-Dong, Songpa-Gu, B1F</td>\n",
       "      <td>127.12</td>\n",
       "      <td>37.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>8086</td>\n",
       "      <td>21446-211225</td>\n",
       "      <td>Sindang Station</td>\n",
       "      <td>132, Hungindong, Junggu</td>\n",
       "      <td>127.01</td>\n",
       "      <td>37.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Store Number               Store Name  \\\n",
       "0         7982  19176-199455             Wolgae Emart   \n",
       "1         7983  23428-230106  Olympic Park North Gate   \n",
       "2         7984  49283-267704              Mia Newtown   \n",
       "3         7985  21320-211245          Yangjae Station   \n",
       "4         7986  15565-163169         Apgujeong Center   \n",
       "..         ...           ...                      ...   \n",
       "95        8082  48547-263608                Apgujeong   \n",
       "96        8083  25341-240585            Yaksu Station   \n",
       "97        8084  48732-264885              mapo yeomri   \n",
       "98        8085  15987-167305          Garden 5 E-Mart   \n",
       "99        8086  21446-211225          Sindang Station   \n",
       "\n",
       "                                       Street Address  Longitude  Latitude  \n",
       "0                   333-1 Wolgae3dong, Nowongu, Seoul     127.06     37.63  \n",
       "1                       468 Seongnae-dong,Gangdong-gu     127.13     37.53  \n",
       "2                         812-2, Mia-dong, Gangbuk-gu     127.02     37.62  \n",
       "3                        14-4 Yangjae-dong, Seocho-gu     127.04     37.48  \n",
       "4   639-7, Shinsa-Dong, Gangnam-Gu, Yonsei Foundat...     127.04     37.53  \n",
       "..                                                ...        ...       ...  \n",
       "95                           609 sinsadong, gangnamgu     127.03     37.53  \n",
       "96                     374-27, Sindang-dong, Jung-gu,     127.01     37.56  \n",
       "97  168-9 Yeomri-dong, Mapo-gu, Garden 5 Tool Pavi...     126.95     37.54  \n",
       "98                   280 Munjung-Dong, Songpa-Gu, B1F     127.12     37.48  \n",
       "99                            132, Hungindong, Junggu     127.01     37.34  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sb_data = pd.read_csv(\"starbucks_seoul.csv\")\n",
    "sb_data.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from haversine import haversine\n",
    "\n",
    "# 출처: https://stricky.tistory.com/284 [The DataBase that i am good at]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.274422812902493"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 위경도 입력 \n",
    "data1 = (127.06, 37.63) #Latitude, Longitude \n",
    "data2 = (127.13, 37.53) \n",
    "\n",
    "# 거리 계산 \n",
    "\n",
    "haversine(data1, data2, unit = 'km')\n",
    "\n",
    "#출처: https://stricky.tistory.com/284 [The DataBase that i am good at]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 아파트 근처 외부시설 유무 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "apt_data = pd.read_csv(\"gwangjin.csv\")\n",
    "apt_data.drop([\"Unnamed: 0\"], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>apt</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>37.566785</td>\n",
       "      <td>127.078428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>에스케이</td>\n",
       "      <td>37.568871</td>\n",
       "      <td>127.086509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>중곡1단지</td>\n",
       "      <td>37.566399</td>\n",
       "      <td>127.078881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>성원</td>\n",
       "      <td>37.571190</td>\n",
       "      <td>127.079631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>에스케이</td>\n",
       "      <td>37.568871</td>\n",
       "      <td>127.086509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>일성파크</td>\n",
       "      <td>37.551274</td>\n",
       "      <td>127.072640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>동양파라곤</td>\n",
       "      <td>37.548573</td>\n",
       "      <td>127.070232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>일성파크</td>\n",
       "      <td>37.551274</td>\n",
       "      <td>127.072640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>일성파크</td>\n",
       "      <td>37.551274</td>\n",
       "      <td>127.072640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>동양파라곤</td>\n",
       "      <td>37.548573</td>\n",
       "      <td>127.070232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2062 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        apt   latitude   longitude\n",
       "0     중곡2단지  37.566785  127.078428\n",
       "1      에스케이  37.568871  127.086509\n",
       "2     중곡1단지  37.566399  127.078881\n",
       "3        성원  37.571190  127.079631\n",
       "4      에스케이  37.568871  127.086509\n",
       "...     ...        ...         ...\n",
       "2057   일성파크  37.551274  127.072640\n",
       "2058  동양파라곤  37.548573  127.070232\n",
       "2059   일성파크  37.551274  127.072640\n",
       "2060   일성파크  37.551274  127.072640\n",
       "2061  동양파라곤  37.548573  127.070232\n",
       "\n",
       "[2062 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 아파트 위치 위도, 경도 계산\n",
    "\n",
    "apt_data[[\"apt\", \"latitude\", \"longitude\"]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wolgae Emart</td>\n",
       "      <td>37.63</td>\n",
       "      <td>127.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Olympic Park North Gate</td>\n",
       "      <td>37.53</td>\n",
       "      <td>127.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mia Newtown</td>\n",
       "      <td>37.62</td>\n",
       "      <td>127.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yangjae Station</td>\n",
       "      <td>37.48</td>\n",
       "      <td>127.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apgujeong Center</td>\n",
       "      <td>37.53</td>\n",
       "      <td>127.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Hapjung Xi</td>\n",
       "      <td>37.55</td>\n",
       "      <td>126.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Seocho Jungangro</td>\n",
       "      <td>37.50</td>\n",
       "      <td>127.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Seogang</td>\n",
       "      <td>37.55</td>\n",
       "      <td>126.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Kkachisan-station</td>\n",
       "      <td>37.53</td>\n",
       "      <td>126.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Gunja Station</td>\n",
       "      <td>37.33</td>\n",
       "      <td>127.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Store Name  Latitude  Longitude\n",
       "0               Wolgae Emart     37.63     127.06\n",
       "1    Olympic Park North Gate     37.53     127.13\n",
       "2                Mia Newtown     37.62     127.02\n",
       "3            Yangjae Station     37.48     127.04\n",
       "4           Apgujeong Center     37.53     127.04\n",
       "..                       ...       ...        ...\n",
       "238               Hapjung Xi     37.55     126.91\n",
       "239         Seocho Jungangro     37.50     127.01\n",
       "240                  Seogang     37.55     126.93\n",
       "241        Kkachisan-station     37.53     126.85\n",
       "242            Gunja Station     37.33     127.44\n",
       "\n",
       "[243 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 외부 요인들 위도, 경도 계산\n",
    "\n",
    "sb_data[[\"Store Name\", \"Latitude\", \"Longitude\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gu</th>\n",
       "      <th>dong</th>\n",
       "      <th>apt</th>\n",
       "      <th>addr</th>\n",
       "      <th>area</th>\n",
       "      <th>completion</th>\n",
       "      <th>transaction_ym</th>\n",
       "      <th>floor</th>\n",
       "      <th>price</th>\n",
       "      <th>price/pyeong</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>중곡동 190-26 중곡2단지</td>\n",
       "      <td>55.87</td>\n",
       "      <td>1976</td>\n",
       "      <td>201701</td>\n",
       "      <td>1</td>\n",
       "      <td>25000</td>\n",
       "      <td>1477</td>\n",
       "      <td>37.566785</td>\n",
       "      <td>127.078428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>에스케이</td>\n",
       "      <td>중곡동 292 에스케이</td>\n",
       "      <td>84.60</td>\n",
       "      <td>1999</td>\n",
       "      <td>201701</td>\n",
       "      <td>17</td>\n",
       "      <td>45500</td>\n",
       "      <td>1775</td>\n",
       "      <td>37.568871</td>\n",
       "      <td>127.086509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>중곡1단지</td>\n",
       "      <td>중곡동 191-77 중곡1단지</td>\n",
       "      <td>48.46</td>\n",
       "      <td>1976</td>\n",
       "      <td>201701</td>\n",
       "      <td>5</td>\n",
       "      <td>19000</td>\n",
       "      <td>1294</td>\n",
       "      <td>37.566399</td>\n",
       "      <td>127.078881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>성원</td>\n",
       "      <td>중곡동 681 성원</td>\n",
       "      <td>59.82</td>\n",
       "      <td>1998</td>\n",
       "      <td>201701</td>\n",
       "      <td>14</td>\n",
       "      <td>29600</td>\n",
       "      <td>1633</td>\n",
       "      <td>37.571190</td>\n",
       "      <td>127.079631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>에스케이</td>\n",
       "      <td>중곡동 292 에스케이</td>\n",
       "      <td>84.60</td>\n",
       "      <td>1999</td>\n",
       "      <td>201701</td>\n",
       "      <td>15</td>\n",
       "      <td>43300</td>\n",
       "      <td>1689</td>\n",
       "      <td>37.568871</td>\n",
       "      <td>127.086509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>일성파크</td>\n",
       "      <td>군자동 99 일성파크</td>\n",
       "      <td>84.96</td>\n",
       "      <td>1996</td>\n",
       "      <td>201710</td>\n",
       "      <td>19</td>\n",
       "      <td>49500</td>\n",
       "      <td>1923</td>\n",
       "      <td>37.551274</td>\n",
       "      <td>127.072640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>동양파라곤</td>\n",
       "      <td>군자동 360-20 동양파라곤</td>\n",
       "      <td>124.65</td>\n",
       "      <td>2008</td>\n",
       "      <td>201710</td>\n",
       "      <td>8</td>\n",
       "      <td>65400</td>\n",
       "      <td>1731</td>\n",
       "      <td>37.548573</td>\n",
       "      <td>127.070232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>일성파크</td>\n",
       "      <td>군자동 99 일성파크</td>\n",
       "      <td>59.96</td>\n",
       "      <td>1996</td>\n",
       "      <td>201711</td>\n",
       "      <td>6</td>\n",
       "      <td>39500</td>\n",
       "      <td>2174</td>\n",
       "      <td>37.551274</td>\n",
       "      <td>127.072640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>일성파크</td>\n",
       "      <td>군자동 99 일성파크</td>\n",
       "      <td>59.96</td>\n",
       "      <td>1996</td>\n",
       "      <td>201711</td>\n",
       "      <td>7</td>\n",
       "      <td>39900</td>\n",
       "      <td>2196</td>\n",
       "      <td>37.551274</td>\n",
       "      <td>127.072640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>동양파라곤</td>\n",
       "      <td>군자동 360-20 동양파라곤</td>\n",
       "      <td>84.98</td>\n",
       "      <td>2008</td>\n",
       "      <td>201711</td>\n",
       "      <td>5</td>\n",
       "      <td>54700</td>\n",
       "      <td>2124</td>\n",
       "      <td>37.548573</td>\n",
       "      <td>127.070232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2062 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gu dong    apt              addr    area  completion  transaction_ym  \\\n",
       "0     광진구  중곡동  중곡2단지  중곡동 190-26 중곡2단지   55.87        1976          201701   \n",
       "1     광진구  중곡동   에스케이      중곡동 292 에스케이   84.60        1999          201701   \n",
       "2     광진구  중곡동  중곡1단지  중곡동 191-77 중곡1단지   48.46        1976          201701   \n",
       "3     광진구  중곡동     성원        중곡동 681 성원   59.82        1998          201701   \n",
       "4     광진구  중곡동   에스케이      중곡동 292 에스케이   84.60        1999          201701   \n",
       "...   ...  ...    ...               ...     ...         ...             ...   \n",
       "2057  광진구  군자동   일성파크       군자동 99 일성파크   84.96        1996          201710   \n",
       "2058  광진구  군자동  동양파라곤  군자동 360-20 동양파라곤  124.65        2008          201710   \n",
       "2059  광진구  군자동   일성파크       군자동 99 일성파크   59.96        1996          201711   \n",
       "2060  광진구  군자동   일성파크       군자동 99 일성파크   59.96        1996          201711   \n",
       "2061  광진구  군자동  동양파라곤  군자동 360-20 동양파라곤   84.98        2008          201711   \n",
       "\n",
       "      floor  price  price/pyeong   latitude   longitude  \n",
       "0         1  25000          1477  37.566785  127.078428  \n",
       "1        17  45500          1775  37.568871  127.086509  \n",
       "2         5  19000          1294  37.566399  127.078881  \n",
       "3        14  29600          1633  37.571190  127.079631  \n",
       "4        15  43300          1689  37.568871  127.086509  \n",
       "...     ...    ...           ...        ...         ...  \n",
       "2057     19  49500          1923  37.551274  127.072640  \n",
       "2058      8  65400          1731  37.548573  127.070232  \n",
       "2059      6  39500          2174  37.551274  127.072640  \n",
       "2060      7  39900          2196  37.551274  127.072640  \n",
       "2061      5  54700          2124  37.548573  127.070232  \n",
       "\n",
       "[2062 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### 첫 번째 아파트와 스벅 매장들과의 거리\n",
    "\n",
    "\n",
    "distance_list=[]\n",
    "# 첫번째 아파트\n",
    "data1 = (apt_data[\"latitude\"][0], apt_data[\"longitude\"][0]) \n",
    "\n",
    "# 스벅 모든 매장\n",
    "j = 0\n",
    "for j in range(0, len(sb_data)):\n",
    "    data2 = (sb_data[\"Latitude\"][j], sb_data[\"Longitude\"][j]) \n",
    "    \n",
    "    #아파트 한곳이랑 스벅 전체 매장의 거리\n",
    "    distance = haversine(data1, data2, unit = 'm')\n",
    "    \n",
    "    distance_list.append(distance)\n",
    "\n",
    "distance_list\n",
    "    \n",
    "# 0번 아파트와 200개 스벅 매장 중 가장 가까운 거리 찾기 + 첫번째 로우의 셀 자료가 될 것\n",
    "# distance는 소수점 1자리까지 반올림해서 정리\n",
    "\n",
    "distance_0 = round(min(distance_list), 1)\n",
    "distance_0\n",
    "\n",
    "# 아파트당 최단거리 데이터가 모이면 그 데이터가 모여 \"거리\" 컬럼이 된다.\n",
    "\n",
    "apt_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>중곡2단지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>에스케이</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>중곡1단지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>성원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>욱현하이브Ⅲ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>일성파크</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>호동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>동양파라곤</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>동양파라곤(2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>서원힐탑</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          apt\n",
       "0       중곡2단지\n",
       "1        에스케이\n",
       "2       중곡1단지\n",
       "3          성원\n",
       "4      욱현하이브Ⅲ\n",
       "..        ...\n",
       "115      일성파크\n",
       "116        호동\n",
       "117     동양파라곤\n",
       "118  동양파라곤(2)\n",
       "119      서원힐탑\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ap_data = apt_data[[\"apt\"]].drop_duplicates(keep='first') # 중복되는 아파트 제거\n",
    "ap_data = ap_data.reset_index(drop=True) # index 값 재정렬 \n",
    "ap_data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ap_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       " ...]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 아파트와 스벅 매장간 거리 계산 for문 만들기\n",
    "\n",
    "distance_list=[]\n",
    "\n",
    "i = 0\n",
    "j = 0\n",
    "\n",
    "for i in range(0, len(ap_data)):\n",
    "    \n",
    "    for j in range(0, len(sb_data)):\n",
    "        data1 = (apt_data[\"latitude\"][i], apt_data[\"longitude\"][i]) \n",
    "        data2 = (sb_data[\"Latitude\"][j], sb_data[\"Longitude\"][j]) \n",
    "       \n",
    "        distance = haversine(data1, data2, unit = 'm')\n",
    "        \n",
    "        distance_list.append(distance)\n",
    "\n",
    "distance_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29160"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(distance_list) # 전체 데이터 개수 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9477"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 243 * 39 # 행과 열 개수 곱하여서 함수로 구한 데이터와 개수가 맞는지 확인(개수가 맞아야 array로 변경 가능)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1058.75881949,  1788.71200277,  2126.9307184 , ...,\n",
       "        56974.51741456, 66606.15703244,            nan],\n",
       "       [ 1275.13061119,  1757.9568036 ,  2120.8398654 , ...,\n",
       "        57707.80914059, 66062.30812521,            nan],\n",
       "       [ 1057.85328559,  1802.0936848 ,  2070.14367884, ...,\n",
       "        56987.80204802, 66551.71988965,            nan],\n",
       "       ...,\n",
       "       [  356.38305195,   732.35916585,  1554.06748315, ...,\n",
       "        56932.30022469, 63723.11241891,            nan],\n",
       "       [  393.36207806,   540.78006066,  1366.42966998, ...,\n",
       "        56854.76158408, 63918.57035859,            nan],\n",
       "       [  356.38305195,   732.35916585,  1554.06748315, ...,\n",
       "        56932.30022469, 63723.11241891,            nan]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_data = np.reshape(distance_list, (len(ap_data),len(sb_data))) # list > array로 변환 \n",
    "distance_data = np.sort(distance_data, axis=1) # 구역마다 오름차순 정렬\n",
    "distance_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>56387.531649</td>\n",
       "      <td>57707.809141</td>\n",
       "      <td>66062.308125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>393.362078</td>\n",
       "      <td>540.780061</td>\n",
       "      <td>1366.429670</td>\n",
       "      <td>1408.886254</td>\n",
       "      <td>1408.886254</td>\n",
       "      <td>2131.604835</td>\n",
       "      <td>2287.419050</td>\n",
       "      <td>2968.857171</td>\n",
       "      <td>3011.719429</td>\n",
       "      <td>3011.719429</td>\n",
       "      <td>...</td>\n",
       "      <td>49550.123593</td>\n",
       "      <td>51765.648440</td>\n",
       "      <td>52032.898141</td>\n",
       "      <td>53352.061345</td>\n",
       "      <td>53491.027189</td>\n",
       "      <td>55280.687273</td>\n",
       "      <td>55567.934631</td>\n",
       "      <td>56854.761584</td>\n",
       "      <td>63918.570359</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>361.502380</td>\n",
       "      <td>915.495672</td>\n",
       "      <td>1065.554279</td>\n",
       "      <td>1447.575329</td>\n",
       "      <td>1447.575329</td>\n",
       "      <td>2215.272428</td>\n",
       "      <td>2442.194225</td>\n",
       "      <td>2528.596427</td>\n",
       "      <td>3369.706250</td>\n",
       "      <td>3369.706250</td>\n",
       "      <td>...</td>\n",
       "      <td>49820.429914</td>\n",
       "      <td>52450.035049</td>\n",
       "      <td>52753.108118</td>\n",
       "      <td>53720.753739</td>\n",
       "      <td>54079.773235</td>\n",
       "      <td>55540.029007</td>\n",
       "      <td>55801.521732</td>\n",
       "      <td>57099.123865</td>\n",
       "      <td>64555.601804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>356.383052</td>\n",
       "      <td>732.359166</td>\n",
       "      <td>1554.067483</td>\n",
       "      <td>1581.304749</td>\n",
       "      <td>1581.304749</td>\n",
       "      <td>1993.195381</td>\n",
       "      <td>2453.473689</td>\n",
       "      <td>2763.115154</td>\n",
       "      <td>2868.446931</td>\n",
       "      <td>2868.446931</td>\n",
       "      <td>...</td>\n",
       "      <td>49623.095010</td>\n",
       "      <td>51566.839045</td>\n",
       "      <td>51831.825417</td>\n",
       "      <td>53150.549830</td>\n",
       "      <td>53571.404986</td>\n",
       "      <td>55355.482578</td>\n",
       "      <td>55647.519081</td>\n",
       "      <td>56932.300225</td>\n",
       "      <td>63723.112419</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>393.362078</td>\n",
       "      <td>540.780061</td>\n",
       "      <td>1366.429670</td>\n",
       "      <td>1408.886254</td>\n",
       "      <td>1408.886254</td>\n",
       "      <td>2131.604835</td>\n",
       "      <td>2287.419050</td>\n",
       "      <td>2968.857171</td>\n",
       "      <td>3011.719429</td>\n",
       "      <td>3011.719429</td>\n",
       "      <td>...</td>\n",
       "      <td>49550.123593</td>\n",
       "      <td>51765.648440</td>\n",
       "      <td>52032.898141</td>\n",
       "      <td>53352.061345</td>\n",
       "      <td>53491.027189</td>\n",
       "      <td>55280.687273</td>\n",
       "      <td>55567.934631</td>\n",
       "      <td>56854.761584</td>\n",
       "      <td>63918.570359</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>356.383052</td>\n",
       "      <td>732.359166</td>\n",
       "      <td>1554.067483</td>\n",
       "      <td>1581.304749</td>\n",
       "      <td>1581.304749</td>\n",
       "      <td>1993.195381</td>\n",
       "      <td>2453.473689</td>\n",
       "      <td>2763.115154</td>\n",
       "      <td>2868.446931</td>\n",
       "      <td>2868.446931</td>\n",
       "      <td>...</td>\n",
       "      <td>49623.095010</td>\n",
       "      <td>51566.839045</td>\n",
       "      <td>51831.825417</td>\n",
       "      <td>53150.549830</td>\n",
       "      <td>53571.404986</td>\n",
       "      <td>55355.482578</td>\n",
       "      <td>55647.519081</td>\n",
       "      <td>56932.300225</td>\n",
       "      <td>63723.112419</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 243 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0            1            2            3            4    \\\n",
       "0    1058.758819  1788.712003  2126.930718  2981.538354  2981.538354   \n",
       "1    1275.130611  1757.956804  2120.839865  3225.063508  3261.218247   \n",
       "2    1057.853286  1802.093685  2070.143679  2937.049979  2937.049979   \n",
       "3    1339.672417  1506.231531  2527.213018  2714.351046  3331.317907   \n",
       "4    1275.130611  1757.956804  2120.839865  3225.063508  3261.218247   \n",
       "..           ...          ...          ...          ...          ...   \n",
       "115   393.362078   540.780061  1366.429670  1408.886254  1408.886254   \n",
       "116   361.502380   915.495672  1065.554279  1447.575329  1447.575329   \n",
       "117   356.383052   732.359166  1554.067483  1581.304749  1581.304749   \n",
       "118   393.362078   540.780061  1366.429670  1408.886254  1408.886254   \n",
       "119   356.383052   732.359166  1554.067483  1581.304749  1581.304749   \n",
       "\n",
       "             5            6            7            8            9    ...  \\\n",
       "0    3049.757819  3069.583414  3148.177325  3470.112169  3533.651209  ...   \n",
       "1    3261.218247  3313.128859  3423.551106  3475.001414  3475.001414  ...   \n",
       "2    3038.015231  3094.707993  3107.394906  3475.974896  3500.180153  ...   \n",
       "3    3331.317907  3468.271079  3468.271079  3570.532051  3586.535715  ...   \n",
       "4    3261.218247  3313.128859  3423.551106  3475.001414  3475.001414  ...   \n",
       "..           ...          ...          ...          ...          ...  ...   \n",
       "115  2131.604835  2287.419050  2968.857171  3011.719429  3011.719429  ...   \n",
       "116  2215.272428  2442.194225  2528.596427  3369.706250  3369.706250  ...   \n",
       "117  1993.195381  2453.473689  2763.115154  2868.446931  2868.446931  ...   \n",
       "118  2131.604835  2287.419050  2968.857171  3011.719429  3011.719429  ...   \n",
       "119  1993.195381  2453.473689  2763.115154  2868.446931  2868.446931  ...   \n",
       "\n",
       "              233           234           235           236           237  \\\n",
       "0    49769.303291  53565.857489  54579.327562  54935.545078  55453.547420   \n",
       "1    50497.235252  54068.582087  54300.778634  54449.047467  55791.000785   \n",
       "2    49780.703377  53579.767468  54523.658171  54879.128772  55465.913435   \n",
       "3    50118.820293  53899.243214  54723.420846  55100.202333  55796.838184   \n",
       "4    50497.235252  54068.582087  54300.778634  54449.047467  55791.000785   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "115  49550.123593  51765.648440  52032.898141  53352.061345  53491.027189   \n",
       "116  49820.429914  52450.035049  52753.108118  53720.753739  54079.773235   \n",
       "117  49623.095010  51566.839045  51831.825417  53150.549830  53571.404986   \n",
       "118  49550.123593  51765.648440  52032.898141  53352.061345  53491.027189   \n",
       "119  49623.095010  51566.839045  51831.825417  53150.549830  53571.404986   \n",
       "\n",
       "              238           239           240           241  242  \n",
       "0    55652.859540  56272.606510  56974.517415  66606.157032  NaN  \n",
       "1    56184.310524  56387.531649  57707.809141  66062.308125  NaN  \n",
       "2    55666.673114  56216.053400  56987.802048  66551.719890  NaN  \n",
       "3    55986.819818  56441.294953  57311.883825  66720.313319  NaN  \n",
       "4    56184.310524  56387.531649  57707.809141  66062.308125  NaN  \n",
       "..            ...           ...           ...           ...  ...  \n",
       "115  55280.687273  55567.934631  56854.761584  63918.570359  NaN  \n",
       "116  55540.029007  55801.521732  57099.123865  64555.601804  NaN  \n",
       "117  55355.482578  55647.519081  56932.300225  63723.112419  NaN  \n",
       "118  55280.687273  55567.934631  56854.761584  63918.570359  NaN  \n",
       "119  55355.482578  55647.519081  56932.300225  63723.112419  NaN  \n",
       "\n",
       "[120 rows x 243 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(distance_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "               0\n",
       "0    1058.758819\n",
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       "4    1275.130611\n",
       "..           ...\n",
       "115   393.362078\n",
       "116   361.502380\n",
       "117   356.383052\n",
       "118   393.362078\n",
       "119   356.383052\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[[0]]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>starbucks_d(m)</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "</table>\n",
       "<p>120 rows × 1 columns</p>\n",
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      "text/plain": [
       "     starbucks_d(m)\n",
       "0       1058.758819\n",
       "1       1275.130611\n",
       "2       1057.853286\n",
       "3       1339.672417\n",
       "4       1275.130611\n",
       "..              ...\n",
       "115      393.362078\n",
       "116      361.502380\n",
       "117      356.383052\n",
       "118      393.362078\n",
       "119      356.383052\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.rename(columns={0: \"starbucks_d(m)\"}, inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>4</th>\n",
       "      <td>욱현하이브Ⅲ</td>\n",
       "      <td>1275.130611</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>393.362078</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>호동</td>\n",
       "      <td>361.502380</td>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>동양파라곤</td>\n",
       "      <td>356.383052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>동양파라곤(2)</td>\n",
       "      <td>393.362078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>서원힐탑</td>\n",
       "      <td>356.383052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          apt  starbucks_d(m)\n",
       "0       중곡2단지     1058.758819\n",
       "1        에스케이     1275.130611\n",
       "2       중곡1단지     1057.853286\n",
       "3          성원     1339.672417\n",
       "4      욱현하이브Ⅲ     1275.130611\n",
       "..        ...             ...\n",
       "115      일성파크      393.362078\n",
       "116        호동      361.502380\n",
       "117     동양파라곤      356.383052\n",
       "118  동양파라곤(2)      393.362078\n",
       "119      서원힐탑      356.383052\n",
       "\n",
       "[120 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data = pd.merge(ap_data, df1, left_index=True, right_index=True, how='left')\n",
    "result_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Subway 거리 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>역이름</th>\n",
       "      <th>역지역</th>\n",
       "      <th>호선</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>낙성대역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.477090</td>\n",
       "      <td>126.963506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>구룡역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>분당선</td>\n",
       "      <td>37.487027</td>\n",
       "      <td>127.059475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>서울대입구역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.481285</td>\n",
       "      <td>126.952695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>불광역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.610044</td>\n",
       "      <td>126.930302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>월롱역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>경의선</td>\n",
       "      <td>37.796204</td>\n",
       "      <td>126.792563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>안국역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.576493</td>\n",
       "      <td>126.985419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>부발역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>경강선</td>\n",
       "      <td>37.260392</td>\n",
       "      <td>127.490308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>강변역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.535118</td>\n",
       "      <td>127.094723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>영등포구청역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.525831</td>\n",
       "      <td>126.896668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>동대입구역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.559050</td>\n",
       "      <td>127.005297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>잠실새내역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.511608</td>\n",
       "      <td>127.086301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>당산역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.534774</td>\n",
       "      <td>126.902607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>약수역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.554491</td>\n",
       "      <td>127.010887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>충정로역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.559748</td>\n",
       "      <td>126.964484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>녹번역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.600803</td>\n",
       "      <td>126.935814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>종합운동장역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.511130</td>\n",
       "      <td>127.073836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>합정역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.549901</td>\n",
       "      <td>126.914447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>금호역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.548256</td>\n",
       "      <td>127.015820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>홍대입구역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.556864</td>\n",
       "      <td>126.923765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>홍제역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.588788</td>\n",
       "      <td>126.944158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>옥수역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.541552</td>\n",
       "      <td>127.017380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>압구정역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.526485</td>\n",
       "      <td>127.028486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>삼성역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.508812</td>\n",
       "      <td>127.063021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>신답역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.569835</td>\n",
       "      <td>127.047067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>신사역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.516431</td>\n",
       "      <td>127.020293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>가정중앙시장역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>인천2호선</td>\n",
       "      <td>37.517523</td>\n",
       "      <td>126.676805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>선릉역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.504494</td>\n",
       "      <td>127.048948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>신정네거리역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.520216</td>\n",
       "      <td>126.852876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>지축역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.648145</td>\n",
       "      <td>126.913836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>구파발역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.636418</td>\n",
       "      <td>126.918835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>연신내역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.619192</td>\n",
       "      <td>126.921096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>무악재역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.582556</td>\n",
       "      <td>126.950231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>독립문역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.574468</td>\n",
       "      <td>126.957911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>경복궁역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.575788</td>\n",
       "      <td>126.973669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>종로3가역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.570421</td>\n",
       "      <td>126.992144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>을지로3가역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>03호선</td>\n",
       "      <td>37.566276</td>\n",
       "      <td>126.990967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>동오역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>의정부경전철</td>\n",
       "      <td>37.745252</td>\n",
       "      <td>127.056980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>역삼역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.500660</td>\n",
       "      <td>127.036460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>강남역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.498085</td>\n",
       "      <td>127.027978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>교대역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.492736</td>\n",
       "      <td>127.013863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>방배역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.481456</td>\n",
       "      <td>126.997535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>사당역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.476553</td>\n",
       "      <td>126.981550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>봉천역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.482469</td>\n",
       "      <td>126.941682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>신림역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.484264</td>\n",
       "      <td>126.929741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>신대방역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.487641</td>\n",
       "      <td>126.913501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>구로디지털단지역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.485253</td>\n",
       "      <td>126.901473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>신도림역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.508905</td>\n",
       "      <td>126.891308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>문래역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.517972</td>\n",
       "      <td>126.894756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>신촌역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.555195</td>\n",
       "      <td>126.936963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>이대역</td>\n",
       "      <td>수도권</td>\n",
       "      <td>02호선</td>\n",
       "      <td>37.556804</td>\n",
       "      <td>126.946423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0       역이름  역지역      호선         위도          경도\n",
       "0            0      낙성대역  수도권    02호선  37.477090  126.963506\n",
       "1            1       구룡역  수도권     분당선  37.487027  127.059475\n",
       "2            2    서울대입구역  수도권    02호선  37.481285  126.952695\n",
       "3            3       불광역  수도권    03호선  37.610044  126.930302\n",
       "4            4       월롱역  수도권     경의선  37.796204  126.792563\n",
       "5            5       안국역  수도권    03호선  37.576493  126.985419\n",
       "6            6       부발역  수도권     경강선  37.260392  127.490308\n",
       "7            7       강변역  수도권    02호선  37.535118  127.094723\n",
       "8            8    영등포구청역  수도권    02호선  37.525831  126.896668\n",
       "9            9     동대입구역  수도권    03호선  37.559050  127.005297\n",
       "10          10     잠실새내역  수도권    02호선  37.511608  127.086301\n",
       "11          11       당산역  수도권    02호선  37.534774  126.902607\n",
       "12          12       약수역  수도권    03호선  37.554491  127.010887\n",
       "13          13      충정로역  수도권    02호선  37.559748  126.964484\n",
       "14          14       녹번역  수도권    03호선  37.600803  126.935814\n",
       "15          15    종합운동장역  수도권    02호선  37.511130  127.073836\n",
       "16          16       합정역  수도권    02호선  37.549901  126.914447\n",
       "17          17       금호역  수도권    03호선  37.548256  127.015820\n",
       "18          18     홍대입구역  수도권    02호선  37.556864  126.923765\n",
       "19          19       홍제역  수도권    03호선  37.588788  126.944158\n",
       "20          20       옥수역  수도권    03호선  37.541552  127.017380\n",
       "21          21      압구정역  수도권    03호선  37.526485  127.028486\n",
       "22          22       삼성역  수도권    02호선  37.508812  127.063021\n",
       "23          23       신답역  수도권    02호선  37.569835  127.047067\n",
       "24          24       신사역  수도권    03호선  37.516431  127.020293\n",
       "25          25   가정중앙시장역  수도권   인천2호선  37.517523  126.676805\n",
       "26          26       선릉역  수도권    02호선  37.504494  127.048948\n",
       "27          27    신정네거리역  수도권    02호선  37.520216  126.852876\n",
       "28          28       지축역  수도권    03호선  37.648145  126.913836\n",
       "29          29      구파발역  수도권    03호선  37.636418  126.918835\n",
       "30          30      연신내역  수도권    03호선  37.619192  126.921096\n",
       "31          31      무악재역  수도권    03호선  37.582556  126.950231\n",
       "32          32      독립문역  수도권    03호선  37.574468  126.957911\n",
       "33          33      경복궁역  수도권    03호선  37.575788  126.973669\n",
       "34          34     종로3가역  수도권    03호선  37.570421  126.992144\n",
       "35          35    을지로3가역  수도권    03호선  37.566276  126.990967\n",
       "36          36       동오역  수도권  의정부경전철  37.745252  127.056980\n",
       "37          37       역삼역  수도권    02호선  37.500660  127.036460\n",
       "38          38       강남역  수도권    02호선  37.498085  127.027978\n",
       "39          39       교대역  수도권    02호선  37.492736  127.013863\n",
       "40          40       방배역  수도권    02호선  37.481456  126.997535\n",
       "41          41       사당역  수도권    02호선  37.476553  126.981550\n",
       "42          42       봉천역  수도권    02호선  37.482469  126.941682\n",
       "43          43       신림역  수도권    02호선  37.484264  126.929741\n",
       "44          44      신대방역  수도권    02호선  37.487641  126.913501\n",
       "45          45  구로디지털단지역  수도권    02호선  37.485253  126.901473\n",
       "46          46      신도림역  수도권    02호선  37.508905  126.891308\n",
       "47          47       문래역  수도권    02호선  37.517972  126.894756\n",
       "48          48       신촌역  수도권    02호선  37.555195  126.936963\n",
       "49          49       이대역  수도권    02호선  37.556804  126.946423"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw_data = pd.read_csv(\"seoul_sbway.csv\")\n",
    "sw_data.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>역이름</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>낙성대역</td>\n",
       "      <td>37.477090</td>\n",
       "      <td>126.963506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>구룡역</td>\n",
       "      <td>37.487027</td>\n",
       "      <td>127.059475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울대입구역</td>\n",
       "      <td>37.481285</td>\n",
       "      <td>126.952695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>불광역</td>\n",
       "      <td>37.610044</td>\n",
       "      <td>126.930302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>월롱역</td>\n",
       "      <td>37.796204</td>\n",
       "      <td>126.792563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>종각역</td>\n",
       "      <td>37.570222</td>\n",
       "      <td>126.983134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>화계역</td>\n",
       "      <td>37.634084</td>\n",
       "      <td>127.017492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>삼양역</td>\n",
       "      <td>37.626912</td>\n",
       "      <td>127.018148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>회기역</td>\n",
       "      <td>37.589778</td>\n",
       "      <td>127.058039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>가좌역</td>\n",
       "      <td>37.568751</td>\n",
       "      <td>126.914811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>728 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        역이름         위도          경도\n",
       "0      낙성대역  37.477090  126.963506\n",
       "1       구룡역  37.487027  127.059475\n",
       "2    서울대입구역  37.481285  126.952695\n",
       "3       불광역  37.610044  126.930302\n",
       "4       월롱역  37.796204  126.792563\n",
       "..      ...        ...         ...\n",
       "723     종각역  37.570222  126.983134\n",
       "724     화계역  37.634084  127.017492\n",
       "725     삼양역  37.626912  127.018148\n",
       "726     회기역  37.589778  127.058039\n",
       "727     가좌역  37.568751  126.914811\n",
       "\n",
       "[728 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 외부 요인들 위도, 경도 계산\n",
    "\n",
    "sw_data[[\"역이름\", \"위도\", \"경도\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       " 22865.98830122427,\n",
       " ...]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 아파트와 스벅 매장간 거리 계산 for문 만들기\n",
    "\n",
    "distance_list_1=[]\n",
    "\n",
    "i = 0\n",
    "j = 0\n",
    "\n",
    "for i in range(0, len(ap_data)):\n",
    "    \n",
    "    for j in range(0, len(sw_data)):\n",
    "        data3 = (apt_data[\"latitude\"][i], apt_data[\"longitude\"][i]) \n",
    "        data4 = (sw_data[\"위도\"][j], sw_data[\"경도\"][j]) \n",
    "       \n",
    "        distance_1 = haversine(data3, data4, unit = 'm')\n",
    "        \n",
    "        distance_list_1.append(distance_1)\n",
    "\n",
    "distance_list_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87360"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(distance_list_1) # 전체 데이터 개수 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "728"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28392"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 728 * 39 # 행과 열 개수 곱하여서 함수로 구한 데이터와 개수가 맞는지 확인(개수가 맞아야 array로 변경 가능)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   523.8639573 ,   1063.49913488,   1075.47290953, ...,\n",
       "         89352.16899226, 190321.00518893, 291793.92178048],\n",
       "       [   388.43260215,    537.91662716,   1352.09961393, ...,\n",
       "         89675.11817123, 189731.79653623, 291667.78394061],\n",
       "       [   477.53424917,   1029.85210563,   1029.85210563, ...,\n",
       "         89314.66803034, 190268.75103279, 291737.30526508],\n",
       "       ...,\n",
       "       [   361.01530444,   1010.89999892,   1019.22320987, ...,\n",
       "         86351.30289092, 187628.21674555, 288114.24705884],\n",
       "       [   403.95910338,    871.98009431,   1000.55821779, ...,\n",
       "         86463.42249975, 187818.76527246, 288307.07113068],\n",
       "       [   361.01530444,   1010.89999892,   1019.22320987, ...,\n",
       "         86351.30289092, 187628.21674555, 288114.24705884]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_data_1 = np.reshape(distance_list_1, (len(ap_data),len(sw_data))) # list > array로 변환 \n",
    "distance_data_1 = np.sort(distance_data_1, axis=1) # 구역마다 오름차순 정렬\n",
    "distance_data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>86109.301555</td>\n",
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       "      <td>89675.118171</td>\n",
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       "      <td>1029.852106</td>\n",
       "      <td>1029.852106</td>\n",
       "      <td>1068.566645</td>\n",
       "      <td>1440.966957</td>\n",
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       "      <td>2085.744483</td>\n",
       "      <td>2322.274468</td>\n",
       "      <td>2512.866700</td>\n",
       "      <td>...</td>\n",
       "      <td>81721.305374</td>\n",
       "      <td>84434.424752</td>\n",
       "      <td>85212.417113</td>\n",
       "      <td>85849.503239</td>\n",
       "      <td>86000.210960</td>\n",
       "      <td>87640.881283</td>\n",
       "      <td>87730.270801</td>\n",
       "      <td>89314.668030</td>\n",
       "      <td>190268.751033</td>\n",
       "      <td>291737.305265</td>\n",
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       "      <td>687.469866</td>\n",
       "      <td>718.609747</td>\n",
       "      <td>1334.898475</td>\n",
       "      <td>1561.203480</td>\n",
       "      <td>1561.203480</td>\n",
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       "      <td>2066.512077</td>\n",
       "      <td>2281.543337</td>\n",
       "      <td>2433.156905</td>\n",
       "      <td>2621.867322</td>\n",
       "      <td>...</td>\n",
       "      <td>82247.487669</td>\n",
       "      <td>84961.108696</td>\n",
       "      <td>85740.299538</td>\n",
       "      <td>86380.332480</td>\n",
       "      <td>86529.558696</td>\n",
       "      <td>88177.115844</td>\n",
       "      <td>88264.575177</td>\n",
       "      <td>89851.473532</td>\n",
       "      <td>190389.636544</td>\n",
       "      <td>292177.820671</td>\n",
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       "      <th>4</th>\n",
       "      <td>388.432602</td>\n",
       "      <td>537.916627</td>\n",
       "      <td>1352.099614</td>\n",
       "      <td>1442.579498</td>\n",
       "      <td>1442.579498</td>\n",
       "      <td>1868.674537</td>\n",
       "      <td>2167.260645</td>\n",
       "      <td>2206.232847</td>\n",
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       "      <td>3001.102297</td>\n",
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       "      <td>81947.007688</td>\n",
       "      <td>84663.348570</td>\n",
       "      <td>85449.525746</td>\n",
       "      <td>86109.301555</td>\n",
       "      <td>86248.062717</td>\n",
       "      <td>87969.155030</td>\n",
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       "      <td>89675.118171</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>115</th>\n",
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       "      <td>1612.708772</td>\n",
       "      <td>2113.135757</td>\n",
       "      <td>2140.193875</td>\n",
       "      <td>2360.003326</td>\n",
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       "      <td>...</td>\n",
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       "      <td>81257.561208</td>\n",
       "      <td>82051.512240</td>\n",
       "      <td>82735.726463</td>\n",
       "      <td>82861.068527</td>\n",
       "      <td>84695.710979</td>\n",
       "      <td>84698.452613</td>\n",
       "      <td>86463.422500</td>\n",
       "      <td>187818.765272</td>\n",
       "      <td>288307.071131</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>577.814154</td>\n",
       "      <td>962.799688</td>\n",
       "      <td>1336.932760</td>\n",
       "      <td>1424.246605</td>\n",
       "      <td>1582.006798</td>\n",
       "      <td>1582.006798</td>\n",
       "      <td>1626.245191</td>\n",
       "      <td>2148.277337</td>\n",
       "      <td>2263.428758</td>\n",
       "      <td>2263.428758</td>\n",
       "      <td>...</td>\n",
       "      <td>79577.861154</td>\n",
       "      <td>82296.146754</td>\n",
       "      <td>83087.729870</td>\n",
       "      <td>83764.469911</td>\n",
       "      <td>83893.923027</td>\n",
       "      <td>85693.674866</td>\n",
       "      <td>85712.919365</td>\n",
       "      <td>87442.370146</td>\n",
       "      <td>188392.683060</td>\n",
       "      <td>289329.883489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>361.015304</td>\n",
       "      <td>1010.899999</td>\n",
       "      <td>1019.223210</td>\n",
       "      <td>1796.908978</td>\n",
       "      <td>1935.782690</td>\n",
       "      <td>2165.255562</td>\n",
       "      <td>2320.945878</td>\n",
       "      <td>2320.945878</td>\n",
       "      <td>2341.604547</td>\n",
       "      <td>2528.715267</td>\n",
       "      <td>...</td>\n",
       "      <td>78392.798986</td>\n",
       "      <td>81112.562273</td>\n",
       "      <td>81908.318488</td>\n",
       "      <td>82597.722587</td>\n",
       "      <td>82720.292183</td>\n",
       "      <td>84568.980072</td>\n",
       "      <td>84574.796388</td>\n",
       "      <td>86351.302891</td>\n",
       "      <td>187628.216746</td>\n",
       "      <td>288114.247059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>403.959103</td>\n",
       "      <td>871.980094</td>\n",
       "      <td>1000.558218</td>\n",
       "      <td>1612.708772</td>\n",
       "      <td>2113.135757</td>\n",
       "      <td>2140.193875</td>\n",
       "      <td>2360.003326</td>\n",
       "      <td>2360.806888</td>\n",
       "      <td>2360.806888</td>\n",
       "      <td>2471.814253</td>\n",
       "      <td>...</td>\n",
       "      <td>78538.445646</td>\n",
       "      <td>81257.561208</td>\n",
       "      <td>82051.512240</td>\n",
       "      <td>82735.726463</td>\n",
       "      <td>82861.068527</td>\n",
       "      <td>84695.710979</td>\n",
       "      <td>84698.452613</td>\n",
       "      <td>86463.422500</td>\n",
       "      <td>187818.765272</td>\n",
       "      <td>288307.071131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>361.015304</td>\n",
       "      <td>1010.899999</td>\n",
       "      <td>1019.223210</td>\n",
       "      <td>1796.908978</td>\n",
       "      <td>1935.782690</td>\n",
       "      <td>2165.255562</td>\n",
       "      <td>2320.945878</td>\n",
       "      <td>2320.945878</td>\n",
       "      <td>2341.604547</td>\n",
       "      <td>2528.715267</td>\n",
       "      <td>...</td>\n",
       "      <td>78392.798986</td>\n",
       "      <td>81112.562273</td>\n",
       "      <td>81908.318488</td>\n",
       "      <td>82597.722587</td>\n",
       "      <td>82720.292183</td>\n",
       "      <td>84568.980072</td>\n",
       "      <td>84574.796388</td>\n",
       "      <td>86351.302891</td>\n",
       "      <td>187628.216746</td>\n",
       "      <td>288114.247059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 728 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0            1            2            3            4    \\\n",
       "0    523.863957  1063.499135  1075.472910  1075.472910  1420.806940   \n",
       "1    388.432602   537.916627  1352.099614  1442.579498  1442.579498   \n",
       "2    477.534249  1029.852106  1029.852106  1068.566645  1440.966957   \n",
       "3    687.469866   718.609747  1334.898475  1561.203480  1561.203480   \n",
       "4    388.432602   537.916627  1352.099614  1442.579498  1442.579498   \n",
       "..          ...          ...          ...          ...          ...   \n",
       "115  403.959103   871.980094  1000.558218  1612.708772  2113.135757   \n",
       "116  577.814154   962.799688  1336.932760  1424.246605  1582.006798   \n",
       "117  361.015304  1010.899999  1019.223210  1796.908978  1935.782690   \n",
       "118  403.959103   871.980094  1000.558218  1612.708772  2113.135757   \n",
       "119  361.015304  1010.899999  1019.223210  1796.908978  1935.782690   \n",
       "\n",
       "             5            6            7            8            9    ...  \\\n",
       "0    1805.454128  1891.658309  2121.327586  2281.517770  2482.224833  ...   \n",
       "1    1868.674537  2167.260645  2206.232847  2551.256887  3001.102297  ...   \n",
       "2    1824.075978  1834.261668  2085.744483  2322.274468  2512.866700  ...   \n",
       "3    1763.739879  2066.512077  2281.543337  2433.156905  2621.867322  ...   \n",
       "4    1868.674537  2167.260645  2206.232847  2551.256887  3001.102297  ...   \n",
       "..           ...          ...          ...          ...          ...  ...   \n",
       "115  2140.193875  2360.003326  2360.806888  2360.806888  2471.814253  ...   \n",
       "116  1582.006798  1626.245191  2148.277337  2263.428758  2263.428758  ...   \n",
       "117  2165.255562  2320.945878  2320.945878  2341.604547  2528.715267  ...   \n",
       "118  2140.193875  2360.003326  2360.806888  2360.806888  2471.814253  ...   \n",
       "119  2165.255562  2320.945878  2320.945878  2341.604547  2528.715267  ...   \n",
       "\n",
       "              718           719           720           721           722  \\\n",
       "0    81767.165498  84480.108151  85257.657585  85893.474885  86044.857862   \n",
       "1    81947.007688  84663.348570  85449.525746  86109.301555  86248.062717   \n",
       "2    81721.305374  84434.424752  85212.417113  85849.503239  86000.210960   \n",
       "3    82247.487669  84961.108696  85740.299538  86380.332480  86529.558696   \n",
       "4    81947.007688  84663.348570  85449.525746  86109.301555  86248.062717   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "115  78538.445646  81257.561208  82051.512240  82735.726463  82861.068527   \n",
       "116  79577.861154  82296.146754  83087.729870  83764.469911  83893.923027   \n",
       "117  78392.798986  81112.562273  81908.318488  82597.722587  82720.292183   \n",
       "118  78538.445646  81257.561208  82051.512240  82735.726463  82861.068527   \n",
       "119  78392.798986  81112.562273  81908.318488  82597.722587  82720.292183   \n",
       "\n",
       "              723           724           725            726            727  \n",
       "0    87680.610567  87772.127488  89352.168992  190321.005189  291793.921780  \n",
       "1    87969.155030  88025.474648  89675.118171  189731.796536  291667.783941  \n",
       "2    87640.881283  87730.270801  89314.668030  190268.751033  291737.305265  \n",
       "3    88177.115844  88264.575177  89851.473532  190389.636544  292177.820671  \n",
       "4    87969.155030  88025.474648  89675.118171  189731.796536  291667.783941  \n",
       "..            ...           ...           ...            ...            ...  \n",
       "115  84695.710979  84698.452613  86463.422500  187818.765272  288307.071131  \n",
       "116  85693.674866  85712.919365  87442.370146  188392.683060  289329.883489  \n",
       "117  84568.980072  84574.796388  86351.302891  187628.216746  288114.247059  \n",
       "118  84695.710979  84698.452613  86463.422500  187818.765272  288307.071131  \n",
       "119  84568.980072  84574.796388  86351.302891  187628.216746  288114.247059  \n",
       "\n",
       "[120 rows x 728 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = pd.DataFrame(distance_data_1)\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "              0\n",
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       "[120 rows x 1 columns]"
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     "execution_count": 27,
     "metadata": {},
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   ],
   "source": [
    "df_2 = df_1[[0]]\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>subway_d(m)</th>\n",
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       "<p>120 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     subway_d(m)\n",
       "0     523.863957\n",
       "1     388.432602\n",
       "2     477.534249\n",
       "3     687.469866\n",
       "4     388.432602\n",
       "..           ...\n",
       "115   403.959103\n",
       "116   577.814154\n",
       "117   361.015304\n",
       "118   403.959103\n",
       "119   361.015304\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.rename(columns={0: \"subway_d(m)\"}, inplace=True)\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>apt</th>\n",
       "      <th>starbucks_d(m)</th>\n",
       "      <th>subway_d(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>에스케이</td>\n",
       "      <td>1275.130611</td>\n",
       "      <td>388.432602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>중곡1단지</td>\n",
       "      <td>1057.853286</td>\n",
       "      <td>477.534249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>성원</td>\n",
       "      <td>1339.672417</td>\n",
       "      <td>687.469866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>욱현하이브Ⅲ</td>\n",
       "      <td>1275.130611</td>\n",
       "      <td>388.432602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>115</th>\n",
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       "      <td>393.362078</td>\n",
       "      <td>403.959103</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>호동</td>\n",
       "      <td>361.502380</td>\n",
       "      <td>577.814154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>동양파라곤</td>\n",
       "      <td>356.383052</td>\n",
       "      <td>361.015304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>동양파라곤(2)</td>\n",
       "      <td>393.362078</td>\n",
       "      <td>403.959103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>서원힐탑</td>\n",
       "      <td>356.383052</td>\n",
       "      <td>361.015304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          apt  starbucks_d(m)  subway_d(m)\n",
       "0       중곡2단지     1058.758819   523.863957\n",
       "1        에스케이     1275.130611   388.432602\n",
       "2       중곡1단지     1057.853286   477.534249\n",
       "3          성원     1339.672417   687.469866\n",
       "4      욱현하이브Ⅲ     1275.130611   388.432602\n",
       "..        ...             ...          ...\n",
       "115      일성파크      393.362078   403.959103\n",
       "116        호동      361.502380   577.814154\n",
       "117     동양파라곤      356.383052   361.015304\n",
       "118  동양파라곤(2)      393.362078   403.959103\n",
       "119      서원힐탑      356.383052   361.015304\n",
       "\n",
       "[120 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data = pd.merge(result_data, df_2, left_index=True, right_index=True, how='left')\n",
    "result_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.서울 마켓 거리 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>자치구</th>\n",
       "      <th>시장명</th>\n",
       "      <th>주소</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>종로구</td>\n",
       "      <td>광장시장</td>\n",
       "      <td>종로구 창경궁로 88\\n(종로구 예지동 6-1)</td>\n",
       "      <td>37.570113</td>\n",
       "      <td>126.999707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>종로구</td>\n",
       "      <td>통인시장</td>\n",
       "      <td>종로구 필운대로 6길3 일대\\n(종로구 통인동 44 일대)</td>\n",
       "      <td>37.580834</td>\n",
       "      <td>126.968816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>종로구</td>\n",
       "      <td>광장전통시장</td>\n",
       "      <td>종로구 창경궁로 88 일대\\n(종로구 예지동 6-1)</td>\n",
       "      <td>37.570113</td>\n",
       "      <td>126.999707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>종로구</td>\n",
       "      <td>세운상가가동</td>\n",
       "      <td>종로구 청계천로 159\\n(종로구 장사동 116-4)</td>\n",
       "      <td>37.569284</td>\n",
       "      <td>126.995240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문종합시장</td>\n",
       "      <td>종로구 종로 266\\n(종로구 종로6가 262-1)</td>\n",
       "      <td>37.570677</td>\n",
       "      <td>127.007314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문쇼핑타운</td>\n",
       "      <td>종로구 종로272\\n(종로구 종로6가 289-57)</td>\n",
       "      <td>37.570827</td>\n",
       "      <td>127.008122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문종합시장D동상가</td>\n",
       "      <td>종로구 종로 266\\n(종로구 종로6가 262-1)</td>\n",
       "      <td>37.570677</td>\n",
       "      <td>127.007314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동문시장</td>\n",
       "      <td>종로구 종로44길 14\\n(종로구 창신동 436-41)</td>\n",
       "      <td>37.570495</td>\n",
       "      <td>127.011252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>종로구</td>\n",
       "      <td>신설종합시장</td>\n",
       "      <td>종로구 난계로27길 51\\n(종로구 숭인동 206-9)</td>\n",
       "      <td>37.572670</td>\n",
       "      <td>127.020347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>종로구</td>\n",
       "      <td>종로신진시장</td>\n",
       "      <td>종로구 종로38길16 일대\\n(종로구 종로5가 225-21 일대)</td>\n",
       "      <td>37.570229</td>\n",
       "      <td>127.004946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>종로구</td>\n",
       "      <td>한일상가</td>\n",
       "      <td>종로구 청계천로 229\\n(종로구 종로5가 314-7)</td>\n",
       "      <td>37.570135</td>\n",
       "      <td>127.003043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>종로구</td>\n",
       "      <td>세종마을 음식문화거리\\n(구. 금천교시장)</td>\n",
       "      <td>종로구 자하문로 1길 일대\\n(종로구 내자동 1-2 일대)</td>\n",
       "      <td>37.576325</td>\n",
       "      <td>126.972289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>종로구</td>\n",
       "      <td>낙원상가</td>\n",
       "      <td>종로구 삼일대로 428\\n(종로구 낙원동 288)</td>\n",
       "      <td>37.572765</td>\n",
       "      <td>126.987919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>종로구</td>\n",
       "      <td>낙원지하시장(대일상가)</td>\n",
       "      <td>종로구 삼일대로 428\\n(종로구 낙원동 288)</td>\n",
       "      <td>37.572765</td>\n",
       "      <td>126.987919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>종로구</td>\n",
       "      <td>충신시장</td>\n",
       "      <td>종로구 율곡로22길 일대\\n(종로구 충신동 55-4 일대)</td>\n",
       "      <td>37.575494</td>\n",
       "      <td>126.981812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>종로구</td>\n",
       "      <td>창신골목시장</td>\n",
       "      <td>종로구 종로51길 18 일대\\n(종로구 창신동 142-33 일대)</td>\n",
       "      <td>37.572802</td>\n",
       "      <td>127.011887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문문구완구종합시장</td>\n",
       "      <td>종로구 종로52길 36 일대\\n(종로구 창신동 390-29 일대)</td>\n",
       "      <td>37.571243</td>\n",
       "      <td>127.014461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문상가A동</td>\n",
       "      <td>종로구 청계천로 295\\n(종로구 창신동 436-79)</td>\n",
       "      <td>37.570063</td>\n",
       "      <td>127.010692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문상가B동</td>\n",
       "      <td>종로구 청계천로 307\\n(종로구 창신동 436-78)</td>\n",
       "      <td>37.570072</td>\n",
       "      <td>127.011985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문상가C동</td>\n",
       "      <td>종로구 청계천로 319\\n(종로구 창신동 436-65)</td>\n",
       "      <td>37.570115</td>\n",
       "      <td>127.013368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>종로구</td>\n",
       "      <td>종각지하쇼핑센터</td>\n",
       "      <td>종로구 종로 지하 73\\n(종로구 종로2가 11-1 일대)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>종로구</td>\n",
       "      <td>종로4가지하쇼핑센터</td>\n",
       "      <td>종로구 창경궁로 지하 81\\n(종로구 예지동 187-2 일대)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>종로구</td>\n",
       "      <td>종오지하쇼핑센터</td>\n",
       "      <td>종로구 종로 지하 200\\n(종로구 종로4가 176-1 일대)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>종로구</td>\n",
       "      <td>마전교지하쇼핑센터\\n(구한일상가)</td>\n",
       "      <td>종로구 동호로 지하 398\\n(종로구 종로5가 332-18 일대)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동대문지하쇼핑센터</td>\n",
       "      <td>종로구 율곡로 지하 308\\n(종로구 종로6가 287-1 일대)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>종로구</td>\n",
       "      <td>동묘시장</td>\n",
       "      <td>종로구 종로 58길40\\n(종로구 숭인동 241-1 일대)</td>\n",
       "      <td>37.571598</td>\n",
       "      <td>127.018431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>중구</td>\n",
       "      <td>남대문시장</td>\n",
       "      <td>중구 남대문시장 4길 21                  \\n(남창동 49번지)</td>\n",
       "      <td>37.559193</td>\n",
       "      <td>126.977664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>중구</td>\n",
       "      <td>숭례문수입상가</td>\n",
       "      <td>중구 소월로 3                                      ...</td>\n",
       "      <td>37.559374</td>\n",
       "      <td>126.976219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>중구</td>\n",
       "      <td>중부시장</td>\n",
       "      <td>중구 을지로 36길 35                                 ...</td>\n",
       "      <td>37.565088</td>\n",
       "      <td>127.001922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>중구</td>\n",
       "      <td>방산종합시장</td>\n",
       "      <td>중구 동호로 37길 20                                 ...</td>\n",
       "      <td>37.568725</td>\n",
       "      <td>127.000823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>중구</td>\n",
       "      <td>서울중앙시장</td>\n",
       "      <td>중구 퇴계로 85길 22                     \\n(황학동 409번지)</td>\n",
       "      <td>37.566799</td>\n",
       "      <td>127.019806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>중구</td>\n",
       "      <td>신중앙시장</td>\n",
       "      <td>중구 황학동 442번지 일대</td>\n",
       "      <td>37.567396</td>\n",
       "      <td>127.019781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>중구</td>\n",
       "      <td>평화시장</td>\n",
       "      <td>중구 청계천로 274                   \\n(을지로6가 17)</td>\n",
       "      <td>37.569431</td>\n",
       "      <td>127.008050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>중구</td>\n",
       "      <td>통일상가</td>\n",
       "      <td>청계천로 274(을지로6가)</td>\n",
       "      <td>37.569431</td>\n",
       "      <td>127.008050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>중구</td>\n",
       "      <td>동화상가</td>\n",
       "      <td>중구 장충단로 13길 34              \\n(을지로6가 17-145)</td>\n",
       "      <td>37.568649</td>\n",
       "      <td>127.006919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>중구</td>\n",
       "      <td>남평화시장</td>\n",
       "      <td>중구 장충단로 280-10                      \\n(신당동 772)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>중구</td>\n",
       "      <td>제일평화시장</td>\n",
       "      <td>중구 마장로 13                           \\n(신당동 775)</td>\n",
       "      <td>37.568573</td>\n",
       "      <td>127.011383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>중구</td>\n",
       "      <td>에리어식스\\n(벨포스트)</td>\n",
       "      <td>중구 마장로 19                        \\n(신당동 776)</td>\n",
       "      <td>37.568154</td>\n",
       "      <td>127.011997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>중구</td>\n",
       "      <td>광희패션몰</td>\n",
       "      <td>중구 마장로 1길 21                                (신...</td>\n",
       "      <td>37.568673</td>\n",
       "      <td>127.011916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>중구</td>\n",
       "      <td>청평화시장</td>\n",
       "      <td>중구 청계천로 334\\n(신당동 217-91)</td>\n",
       "      <td>37.569522</td>\n",
       "      <td>127.014987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>중구</td>\n",
       "      <td>삼익패션타운</td>\n",
       "      <td>중구 남대문시장 8길 7                                 ...</td>\n",
       "      <td>37.560169</td>\n",
       "      <td>126.978947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>중구</td>\n",
       "      <td>신중부시장</td>\n",
       "      <td>중구 을지로 30길 29(오장동)</td>\n",
       "      <td>37.565548</td>\n",
       "      <td>126.999431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>중구</td>\n",
       "      <td>자유상가</td>\n",
       "      <td>중구 회현동1가 202                                  ...</td>\n",
       "      <td>37.561217</td>\n",
       "      <td>126.980143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>중구</td>\n",
       "      <td>신평화패션타운</td>\n",
       "      <td>중구 청계천로 298                        \\n(신당동 217-1)</td>\n",
       "      <td>37.569457</td>\n",
       "      <td>127.011096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>중구</td>\n",
       "      <td>동평화시장</td>\n",
       "      <td>청계천로 318(신당동)</td>\n",
       "      <td>37.569335</td>\n",
       "      <td>127.013772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>중구</td>\n",
       "      <td>인현시장</td>\n",
       "      <td>중구 퇴계로 41길 45                        \\n(인현동 2가...</td>\n",
       "      <td>37.563853</td>\n",
       "      <td>126.995440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>중구</td>\n",
       "      <td>방산시장</td>\n",
       "      <td>중구 을지로 33길 18-1                               ...</td>\n",
       "      <td>37.567427</td>\n",
       "      <td>126.999725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>중구</td>\n",
       "      <td>약수시장</td>\n",
       "      <td>중구 다산로 10길 12                          \\n(신당동 ...</td>\n",
       "      <td>37.553288</td>\n",
       "      <td>127.010708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>중구</td>\n",
       "      <td>소공지하도상가</td>\n",
       "      <td>중구 소공로지하 102                        \\n(소공동 87)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>중구</td>\n",
       "      <td>을지로지하보도쇼핑센터</td>\n",
       "      <td>중구 을지로 88/131/218                             ...</td>\n",
       "      <td>37.566153</td>\n",
       "      <td>126.986869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  자치구                      시장명  \\\n",
       "0            0  종로구                     광장시장   \n",
       "1            1  종로구                     통인시장   \n",
       "2            2  종로구                   광장전통시장   \n",
       "3            3  종로구                   세운상가가동   \n",
       "4            4  종로구                  동대문종합시장   \n",
       "5            5  종로구                  동대문쇼핑타운   \n",
       "6            6  종로구              동대문종합시장D동상가   \n",
       "7            7  종로구                     동문시장   \n",
       "8            8  종로구                   신설종합시장   \n",
       "9            9  종로구                   종로신진시장   \n",
       "10          10  종로구                     한일상가   \n",
       "11          11  종로구  세종마을 음식문화거리\\n(구. 금천교시장)   \n",
       "12          12  종로구                     낙원상가   \n",
       "13          13  종로구             낙원지하시장(대일상가)   \n",
       "14          14  종로구                     충신시장   \n",
       "15          15  종로구                   창신골목시장   \n",
       "16          16  종로구              동대문문구완구종합시장   \n",
       "17          17  종로구                  동대문상가A동   \n",
       "18          18  종로구                  동대문상가B동   \n",
       "19          19  종로구                  동대문상가C동   \n",
       "20          20  종로구                 종각지하쇼핑센터   \n",
       "21          21  종로구               종로4가지하쇼핑센터   \n",
       "22          22  종로구                 종오지하쇼핑센터   \n",
       "23          23  종로구       마전교지하쇼핑센터\\n(구한일상가)   \n",
       "24          24  종로구                동대문지하쇼핑센터   \n",
       "25          25  종로구                     동묘시장   \n",
       "26          26   중구                    남대문시장   \n",
       "27          27   중구                  숭례문수입상가   \n",
       "28          28   중구                     중부시장   \n",
       "29          29   중구                   방산종합시장   \n",
       "30          30   중구                   서울중앙시장   \n",
       "31          31   중구                    신중앙시장   \n",
       "32          32   중구                     평화시장   \n",
       "33          33   중구                     통일상가   \n",
       "34          34   중구                     동화상가   \n",
       "35          35   중구                    남평화시장   \n",
       "36          36   중구                   제일평화시장   \n",
       "37          37   중구            에리어식스\\n(벨포스트)   \n",
       "38          38   중구                    광희패션몰   \n",
       "39          39   중구                    청평화시장   \n",
       "40          40   중구                   삼익패션타운   \n",
       "41          41   중구                    신중부시장   \n",
       "42          42   중구                     자유상가   \n",
       "43          43   중구                  신평화패션타운   \n",
       "44          44   중구                    동평화시장   \n",
       "45          45   중구                     인현시장   \n",
       "46          46   중구                     방산시장   \n",
       "47          47   중구                     약수시장   \n",
       "48          48   중구                  소공지하도상가   \n",
       "49          49   중구              을지로지하보도쇼핑센터   \n",
       "\n",
       "                                                   주소         위도          경도  \n",
       "0                          종로구 창경궁로 88\\n(종로구 예지동 6-1)  37.570113  126.999707  \n",
       "1                    종로구 필운대로 6길3 일대\\n(종로구 통인동 44 일대)  37.580834  126.968816  \n",
       "2                       종로구 창경궁로 88 일대\\n(종로구 예지동 6-1)  37.570113  126.999707  \n",
       "3                       종로구 청계천로 159\\n(종로구 장사동 116-4)  37.569284  126.995240  \n",
       "4                        종로구 종로 266\\n(종로구 종로6가 262-1)  37.570677  127.007314  \n",
       "5                        종로구 종로272\\n(종로구 종로6가 289-57)  37.570827  127.008122  \n",
       "6                        종로구 종로 266\\n(종로구 종로6가 262-1)  37.570677  127.007314  \n",
       "7                      종로구 종로44길 14\\n(종로구 창신동 436-41)  37.570495  127.011252  \n",
       "8                      종로구 난계로27길 51\\n(종로구 숭인동 206-9)  37.572670  127.020347  \n",
       "9                종로구 종로38길16 일대\\n(종로구 종로5가 225-21 일대)  37.570229  127.004946  \n",
       "10                     종로구 청계천로 229\\n(종로구 종로5가 314-7)  37.570135  127.003043  \n",
       "11                   종로구 자하문로 1길 일대\\n(종로구 내자동 1-2 일대)  37.576325  126.972289  \n",
       "12                        종로구 삼일대로 428\\n(종로구 낙원동 288)  37.572765  126.987919  \n",
       "13                        종로구 삼일대로 428\\n(종로구 낙원동 288)  37.572765  126.987919  \n",
       "14                   종로구 율곡로22길 일대\\n(종로구 충신동 55-4 일대)  37.575494  126.981812  \n",
       "15               종로구 종로51길 18 일대\\n(종로구 창신동 142-33 일대)  37.572802  127.011887  \n",
       "16               종로구 종로52길 36 일대\\n(종로구 창신동 390-29 일대)  37.571243  127.014461  \n",
       "17                     종로구 청계천로 295\\n(종로구 창신동 436-79)  37.570063  127.010692  \n",
       "18                     종로구 청계천로 307\\n(종로구 창신동 436-78)  37.570072  127.011985  \n",
       "19                     종로구 청계천로 319\\n(종로구 창신동 436-65)  37.570115  127.013368  \n",
       "20                   종로구 종로 지하 73\\n(종로구 종로2가 11-1 일대)        NaN         NaN  \n",
       "21                 종로구 창경궁로 지하 81\\n(종로구 예지동 187-2 일대)        NaN         NaN  \n",
       "22                 종로구 종로 지하 200\\n(종로구 종로4가 176-1 일대)        NaN         NaN  \n",
       "23               종로구 동호로 지하 398\\n(종로구 종로5가 332-18 일대)        NaN         NaN  \n",
       "24                종로구 율곡로 지하 308\\n(종로구 종로6가 287-1 일대)        NaN         NaN  \n",
       "25                   종로구 종로 58길40\\n(종로구 숭인동 241-1 일대)  37.571598  127.018431  \n",
       "26       중구 남대문시장 4길 21                  \\n(남창동 49번지)  37.559193  126.977664  \n",
       "27  중구 소월로 3                                      ...  37.559374  126.976219  \n",
       "28  중구 을지로 36길 35                                 ...  37.565088  127.001922  \n",
       "29  중구 동호로 37길 20                                 ...  37.568725  127.000823  \n",
       "30    중구 퇴계로 85길 22                     \\n(황학동 409번지)  37.566799  127.019806  \n",
       "31                                    중구 황학동 442번지 일대  37.567396  127.019781  \n",
       "32         중구 청계천로 274                   \\n(을지로6가 17)  37.569431  127.008050  \n",
       "33                                    청계천로 274(을지로6가)  37.569431  127.008050  \n",
       "34       중구 장충단로 13길 34              \\n(을지로6가 17-145)  37.568649  127.006919  \n",
       "35    중구 장충단로 280-10                      \\n(신당동 772)        NaN         NaN  \n",
       "36    중구 마장로 13                           \\n(신당동 775)  37.568573  127.011383  \n",
       "37       중구 마장로 19                        \\n(신당동 776)  37.568154  127.011997  \n",
       "38  중구 마장로 1길 21                                (신...  37.568673  127.011916  \n",
       "39                          중구 청계천로 334\\n(신당동 217-91)  37.569522  127.014987  \n",
       "40  중구 남대문시장 8길 7                                 ...  37.560169  126.978947  \n",
       "41                                 중구 을지로 30길 29(오장동)  37.565548  126.999431  \n",
       "42  중구 회현동1가 202                                  ...  37.561217  126.980143  \n",
       "43   중구 청계천로 298                        \\n(신당동 217-1)  37.569457  127.011096  \n",
       "44                                      청계천로 318(신당동)  37.569335  127.013772  \n",
       "45  중구 퇴계로 41길 45                        \\n(인현동 2가...  37.563853  126.995440  \n",
       "46  중구 을지로 33길 18-1                               ...  37.567427  126.999725  \n",
       "47  중구 다산로 10길 12                          \\n(신당동 ...  37.553288  127.010708  \n",
       "48     중구 소공로지하 102                        \\n(소공동 87)        NaN         NaN  \n",
       "49  중구 을지로 88/131/218                             ...  37.566153  126.986869  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_data = pd.read_csv(\"seoul_market.csv\")\n",
    "sm_data.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from haversine import haversine\n",
    "\n",
    "# 출처: https://stricky.tistory.com/284 [The DataBase that i am good at]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ap_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6948.001982858507,\n",
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       " ...]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 아파트와 스벅 매장간 거리 계산 for문 만들기\n",
    "\n",
    "distance_list_2=[]\n",
    "\n",
    "i = 0\n",
    "j = 0\n",
    "\n",
    "for i in range(0, len(ap_data)):\n",
    "    \n",
    "    for j in range(0, len(sm_data)):\n",
    "        data5 = (apt_data[\"latitude\"][i], apt_data[\"longitude\"][i]) \n",
    "        data6 = (sm_data[\"위도\"][j], sm_data[\"경도\"][j]) \n",
    "       \n",
    "        distance_2 = haversine(data5, data6, unit = 'm')\n",
    "        \n",
    "        distance_list_2.append(distance_2)\n",
    "\n",
    "distance_list_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42240"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(distance_list_2) # 전체 데이터 개수 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "352"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sm_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13728"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 352 * 39 # 행과 열 개수 곱하여서 함수로 구한 데이터와 개수가 맞는지 확인(개수가 맞아야 array로 변경 가능)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 409.61409531,  428.85468936,  436.45765719, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [ 544.75012251,  694.45750883,  716.34009205, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [ 351.15340775,  371.00085849,  377.80470006, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       ...,\n",
       "       [1206.74101645, 1469.1514182 , 1927.60213148, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [1002.39193498, 1369.31174469, 1853.0035766 , ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [1206.74101645, 1469.1514182 , 1927.60213148, ...,           nan,\n",
       "                  nan,           nan]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_data_2 = np.reshape(distance_list_2, (len(ap_data),len(sm_data))) # list > array로 변환 \n",
    "distance_data_2 = np.sort(distance_data_2, axis=1) # 구역마다 오름차순 정렬\n",
    "distance_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2404.970586</td>\n",
       "      <td>2504.070533</td>\n",
       "      <td>2605.152683</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>482.643468</td>\n",
       "      <td>1132.113494</td>\n",
       "      <td>1195.284146</td>\n",
       "      <td>1863.051194</td>\n",
       "      <td>2041.032827</td>\n",
       "      <td>2042.291026</td>\n",
       "      <td>2063.476707</td>\n",
       "      <td>2101.022486</td>\n",
       "      <td>2152.083998</td>\n",
       "      <td>2277.796474</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1206.741016</td>\n",
       "      <td>1469.151418</td>\n",
       "      <td>1927.602131</td>\n",
       "      <td>1950.431427</td>\n",
       "      <td>2362.017372</td>\n",
       "      <td>2425.327852</td>\n",
       "      <td>2562.520095</td>\n",
       "      <td>2587.699833</td>\n",
       "      <td>2620.424042</td>\n",
       "      <td>2659.480378</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1002.391935</td>\n",
       "      <td>1369.311745</td>\n",
       "      <td>1853.003577</td>\n",
       "      <td>2084.912530</td>\n",
       "      <td>2183.759250</td>\n",
       "      <td>2248.093903</td>\n",
       "      <td>2377.530812</td>\n",
       "      <td>2404.970586</td>\n",
       "      <td>2504.070533</td>\n",
       "      <td>2605.152683</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1206.741016</td>\n",
       "      <td>1469.151418</td>\n",
       "      <td>1927.602131</td>\n",
       "      <td>1950.431427</td>\n",
       "      <td>2362.017372</td>\n",
       "      <td>2425.327852</td>\n",
       "      <td>2562.520095</td>\n",
       "      <td>2587.699833</td>\n",
       "      <td>2620.424042</td>\n",
       "      <td>2659.480378</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 352 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0            1            2            3            4    \\\n",
       "0     409.614095   428.854689   436.457657   579.004679  1288.151953   \n",
       "1     544.750123   694.457509   716.340092   752.900325   947.731404   \n",
       "2     351.153408   371.000858   377.804700   602.840632  1269.140260   \n",
       "3     148.339099   807.685594   823.497390   833.741773   855.387929   \n",
       "4     544.750123   694.457509   716.340092   752.900325   947.731404   \n",
       "..           ...          ...          ...          ...          ...   \n",
       "115  1002.391935  1369.311745  1853.003577  2084.912530  2183.759250   \n",
       "116   482.643468  1132.113494  1195.284146  1863.051194  2041.032827   \n",
       "117  1206.741016  1469.151418  1927.602131  1950.431427  2362.017372   \n",
       "118  1002.391935  1369.311745  1853.003577  2084.912530  2183.759250   \n",
       "119  1206.741016  1469.151418  1927.602131  1950.431427  2362.017372   \n",
       "\n",
       "             5            6            7            8            9    ...  \\\n",
       "0    1327.796584  1377.094590  1515.195341  1587.335380  1714.487254  ...   \n",
       "1    1193.685576  1221.311272  1287.988693  1381.923825  1883.613632  ...   \n",
       "2    1311.095687  1318.481004  1573.407041  1609.238806  1755.566946  ...   \n",
       "3    1107.309313  1237.169322  1397.029728  1666.552591  1725.531346  ...   \n",
       "4    1193.685576  1221.311272  1287.988693  1381.923825  1883.613632  ...   \n",
       "..           ...          ...          ...          ...          ...  ...   \n",
       "115  2248.093903  2377.530812  2404.970586  2504.070533  2605.152683  ...   \n",
       "116  2042.291026  2063.476707  2101.022486  2152.083998  2277.796474  ...   \n",
       "117  2425.327852  2562.520095  2587.699833  2620.424042  2659.480378  ...   \n",
       "118  2248.093903  2377.530812  2404.970586  2504.070533  2605.152683  ...   \n",
       "119  2425.327852  2562.520095  2587.699833  2620.424042  2659.480378  ...   \n",
       "\n",
       "     342  343  344  345  346  347  348  349  350  351  \n",
       "0    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "115  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "116  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "117  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "118  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "119  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[120 rows x 352 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3 = pd.DataFrame(distance_data_2)\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "               0\n",
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       "\n",
       "[120 rows x 1 columns]"
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     "execution_count": 39,
     "metadata": {},
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   ],
   "source": [
    "df_3 = df_3[[0]]\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     market_d(m)\n",
       "0     409.614095\n",
       "1     544.750123\n",
       "2     351.153408\n",
       "3     148.339099\n",
       "4     544.750123\n",
       "..           ...\n",
       "115  1002.391935\n",
       "116   482.643468\n",
       "117  1206.741016\n",
       "118  1002.391935\n",
       "119  1206.741016\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_3.rename(columns={0: \"market_d(m)\"}, inplace=True)\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>apt</th>\n",
       "      <th>starbucks_d(m)</th>\n",
       "      <th>subway_d(m)</th>\n",
       "      <th>market_d(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
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       "      <th>1</th>\n",
       "      <td>에스케이</td>\n",
       "      <td>1275.130611</td>\n",
       "      <td>388.432602</td>\n",
       "      <td>544.750123</td>\n",
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       "      <th>2</th>\n",
       "      <td>중곡1단지</td>\n",
       "      <td>1057.853286</td>\n",
       "      <td>477.534249</td>\n",
       "      <td>351.153408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>성원</td>\n",
       "      <td>1339.672417</td>\n",
       "      <td>687.469866</td>\n",
       "      <td>148.339099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>욱현하이브Ⅲ</td>\n",
       "      <td>1275.130611</td>\n",
       "      <td>388.432602</td>\n",
       "      <td>544.750123</td>\n",
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       "      <td>393.362078</td>\n",
       "      <td>403.959103</td>\n",
       "      <td>1002.391935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>호동</td>\n",
       "      <td>361.502380</td>\n",
       "      <td>577.814154</td>\n",
       "      <td>482.643468</td>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>동양파라곤</td>\n",
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       "      <td>361.015304</td>\n",
       "      <td>1206.741016</td>\n",
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       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>동양파라곤(2)</td>\n",
       "      <td>393.362078</td>\n",
       "      <td>403.959103</td>\n",
       "      <td>1002.391935</td>\n",
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       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>서원힐탑</td>\n",
       "      <td>356.383052</td>\n",
       "      <td>361.015304</td>\n",
       "      <td>1206.741016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          apt  starbucks_d(m)  subway_d(m)  market_d(m)\n",
       "0       중곡2단지     1058.758819   523.863957   409.614095\n",
       "1        에스케이     1275.130611   388.432602   544.750123\n",
       "2       중곡1단지     1057.853286   477.534249   351.153408\n",
       "3          성원     1339.672417   687.469866   148.339099\n",
       "4      욱현하이브Ⅲ     1275.130611   388.432602   544.750123\n",
       "..        ...             ...          ...          ...\n",
       "115      일성파크      393.362078   403.959103  1002.391935\n",
       "116        호동      361.502380   577.814154   482.643468\n",
       "117     동양파라곤      356.383052   361.015304  1206.741016\n",
       "118  동양파라곤(2)      393.362078   403.959103  1002.391935\n",
       "119      서원힐탑      356.383052   361.015304  1206.741016\n",
       "\n",
       "[120 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data = pd.merge(result_data, df_3, left_index=True, right_index=True, how='left')\n",
    "result_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>addr</th>\n",
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       "      <th>price/pyeong</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>starbucks_d(m)</th>\n",
       "      <th>subway_d(m)</th>\n",
       "      <th>market_d(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>중곡동 190-26 중곡2단지</td>\n",
       "      <td>55.87</td>\n",
       "      <td>1976</td>\n",
       "      <td>201701</td>\n",
       "      <td>1</td>\n",
       "      <td>25000</td>\n",
       "      <td>1477</td>\n",
       "      <td>37.566785</td>\n",
       "      <td>127.078428</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>중곡동 190-26 중곡2단지</td>\n",
       "      <td>55.87</td>\n",
       "      <td>1976</td>\n",
       "      <td>201702</td>\n",
       "      <td>2</td>\n",
       "      <td>24700</td>\n",
       "      <td>1459</td>\n",
       "      <td>37.566785</td>\n",
       "      <td>127.078428</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>중곡동 190-26 중곡2단지</td>\n",
       "      <td>55.87</td>\n",
       "      <td>1976</td>\n",
       "      <td>201702</td>\n",
       "      <td>1</td>\n",
       "      <td>25000</td>\n",
       "      <td>1477</td>\n",
       "      <td>37.566785</td>\n",
       "      <td>127.078428</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>중곡동 190-26 중곡2단지</td>\n",
       "      <td>55.87</td>\n",
       "      <td>1976</td>\n",
       "      <td>201703</td>\n",
       "      <td>2</td>\n",
       "      <td>22500</td>\n",
       "      <td>1329</td>\n",
       "      <td>37.566785</td>\n",
       "      <td>127.078428</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>중곡2단지</td>\n",
       "      <td>중곡동 190-26 중곡2단지</td>\n",
       "      <td>55.87</td>\n",
       "      <td>1976</td>\n",
       "      <td>201704</td>\n",
       "      <td>4</td>\n",
       "      <td>25000</td>\n",
       "      <td>1477</td>\n",
       "      <td>37.566785</td>\n",
       "      <td>127.078428</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>동양파라곤</td>\n",
       "      <td>군자동 360-20 동양파라곤</td>\n",
       "      <td>84.98</td>\n",
       "      <td>2008</td>\n",
       "      <td>201711</td>\n",
       "      <td>5</td>\n",
       "      <td>54700</td>\n",
       "      <td>2124</td>\n",
       "      <td>37.548573</td>\n",
       "      <td>127.070232</td>\n",
       "      <td>356.383052</td>\n",
       "      <td>361.015304</td>\n",
       "      <td>1206.741016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>동양파라곤(2)</td>\n",
       "      <td>군자동 358-9 동양파라곤(2)</td>\n",
       "      <td>78.83</td>\n",
       "      <td>2008</td>\n",
       "      <td>201702</td>\n",
       "      <td>15</td>\n",
       "      <td>48700</td>\n",
       "      <td>2039</td>\n",
       "      <td>37.548481</td>\n",
       "      <td>127.069738</td>\n",
       "      <td>393.362078</td>\n",
       "      <td>403.959103</td>\n",
       "      <td>1002.391935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>서원힐탑</td>\n",
       "      <td>군자동 100-109 서원힐탑</td>\n",
       "      <td>59.88</td>\n",
       "      <td>1994</td>\n",
       "      <td>201704</td>\n",
       "      <td>1</td>\n",
       "      <td>34400</td>\n",
       "      <td>1896</td>\n",
       "      <td>37.552169</td>\n",
       "      <td>127.071891</td>\n",
       "      <td>356.383052</td>\n",
       "      <td>361.015304</td>\n",
       "      <td>1206.741016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>서원힐탑</td>\n",
       "      <td>군자동 100-109 서원힐탑</td>\n",
       "      <td>84.57</td>\n",
       "      <td>1994</td>\n",
       "      <td>201704</td>\n",
       "      <td>4</td>\n",
       "      <td>40000</td>\n",
       "      <td>1561</td>\n",
       "      <td>37.552169</td>\n",
       "      <td>127.071891</td>\n",
       "      <td>356.383052</td>\n",
       "      <td>361.015304</td>\n",
       "      <td>1206.741016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>광진구</td>\n",
       "      <td>군자동</td>\n",
       "      <td>서원힐탑</td>\n",
       "      <td>군자동 100-109 서원힐탑</td>\n",
       "      <td>59.85</td>\n",
       "      <td>1994</td>\n",
       "      <td>201708</td>\n",
       "      <td>8</td>\n",
       "      <td>37900</td>\n",
       "      <td>2090</td>\n",
       "      <td>37.552169</td>\n",
       "      <td>127.071891</td>\n",
       "      <td>356.383052</td>\n",
       "      <td>361.015304</td>\n",
       "      <td>1206.741016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2062 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gu dong       apt                addr   area  completion  \\\n",
       "0     광진구  중곡동     중곡2단지    중곡동 190-26 중곡2단지  55.87        1976   \n",
       "1     광진구  중곡동     중곡2단지    중곡동 190-26 중곡2단지  55.87        1976   \n",
       "2     광진구  중곡동     중곡2단지    중곡동 190-26 중곡2단지  55.87        1976   \n",
       "3     광진구  중곡동     중곡2단지    중곡동 190-26 중곡2단지  55.87        1976   \n",
       "4     광진구  중곡동     중곡2단지    중곡동 190-26 중곡2단지  55.87        1976   \n",
       "...   ...  ...       ...                 ...    ...         ...   \n",
       "2057  광진구  군자동     동양파라곤    군자동 360-20 동양파라곤  84.98        2008   \n",
       "2058  광진구  군자동  동양파라곤(2)  군자동 358-9 동양파라곤(2)  78.83        2008   \n",
       "2059  광진구  군자동      서원힐탑    군자동 100-109 서원힐탑  59.88        1994   \n",
       "2060  광진구  군자동      서원힐탑    군자동 100-109 서원힐탑  84.57        1994   \n",
       "2061  광진구  군자동      서원힐탑    군자동 100-109 서원힐탑  59.85        1994   \n",
       "\n",
       "      transaction_ym  floor  price  price/pyeong   latitude   longitude  \\\n",
       "0             201701      1  25000          1477  37.566785  127.078428   \n",
       "1             201702      2  24700          1459  37.566785  127.078428   \n",
       "2             201702      1  25000          1477  37.566785  127.078428   \n",
       "3             201703      2  22500          1329  37.566785  127.078428   \n",
       "4             201704      4  25000          1477  37.566785  127.078428   \n",
       "...              ...    ...    ...           ...        ...         ...   \n",
       "2057          201711      5  54700          2124  37.548573  127.070232   \n",
       "2058          201702     15  48700          2039  37.548481  127.069738   \n",
       "2059          201704      1  34400          1896  37.552169  127.071891   \n",
       "2060          201704      4  40000          1561  37.552169  127.071891   \n",
       "2061          201708      8  37900          2090  37.552169  127.071891   \n",
       "\n",
       "      starbucks_d(m)  subway_d(m)  market_d(m)  \n",
       "0        1058.758819   523.863957   409.614095  \n",
       "1        1058.758819   523.863957   409.614095  \n",
       "2        1058.758819   523.863957   409.614095  \n",
       "3        1058.758819   523.863957   409.614095  \n",
       "4        1058.758819   523.863957   409.614095  \n",
       "...              ...          ...          ...  \n",
       "2057      356.383052   361.015304  1206.741016  \n",
       "2058      393.362078   403.959103  1002.391935  \n",
       "2059      356.383052   361.015304  1206.741016  \n",
       "2060      356.383052   361.015304  1206.741016  \n",
       "2061      356.383052   361.015304  1206.741016  \n",
       "\n",
       "[2062 rows x 15 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.merge(apt_data, result_data, on =\"apt\")\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gu</th>\n",
       "      <th>dong</th>\n",
       "      <th>apt</th>\n",
       "      <th>area</th>\n",
       "      <th>completion</th>\n",
       "      <th>transaction_ym</th>\n",
       "      <th>floor</th>\n",
       "      <th>price</th>\n",
       "      <th>price/pyeong</th>\n",
       "      <th>starbucks_d(m)</th>\n",
       "      <th>subway_d(m)</th>\n",
       "      <th>market_d(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>욱현하이브Ⅲ</td>\n",
       "      <td>15.69</td>\n",
       "      <td>2012</td>\n",
       "      <td>201704</td>\n",
       "      <td>3</td>\n",
       "      <td>14000</td>\n",
       "      <td>2945</td>\n",
       "      <td>1275.130611</td>\n",
       "      <td>388.432602</td>\n",
       "      <td>544.750123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>광진구</td>\n",
       "      <td>화양동</td>\n",
       "      <td>씨즈 건대힐스</td>\n",
       "      <td>14.23</td>\n",
       "      <td>2017</td>\n",
       "      <td>201711</td>\n",
       "      <td>3</td>\n",
       "      <td>15400</td>\n",
       "      <td>3571</td>\n",
       "      <td>444.600794</td>\n",
       "      <td>376.423566</td>\n",
       "      <td>948.351892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>광진구</td>\n",
       "      <td>중곡동</td>\n",
       "      <td>욱현하이브Ⅲ</td>\n",
       "      <td>17.71</td>\n",
       "      <td>2012</td>\n",
       "      <td>201702</td>\n",
       "      <td>2</td>\n",
       "      <td>15400</td>\n",
       "      <td>2870</td>\n",
       "      <td>1275.130611</td>\n",
       "      <td>388.432602</td>\n",
       "      <td>544.750123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>광진구</td>\n",
       "      <td>화양동</td>\n",
       "      <td>광진코지웰</td>\n",
       "      <td>15.63</td>\n",
       "      <td>2014</td>\n",
       "      <td>201709</td>\n",
       "      <td>4</td>\n",
       "      <td>15600</td>\n",
       "      <td>3294</td>\n",
       "      <td>393.362078</td>\n",
       "      <td>403.959103</td>\n",
       "      <td>1002.391935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>광진구</td>\n",
       "      <td>화양동</td>\n",
       "      <td>광진코지웰</td>\n",
       "      <td>15.63</td>\n",
       "      <td>2014</td>\n",
       "      <td>201710</td>\n",
       "      <td>5</td>\n",
       "      <td>15800</td>\n",
       "      <td>3336</td>\n",
       "      <td>393.362078</td>\n",
       "      <td>403.959103</td>\n",
       "      <td>1002.391935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>광진구</td>\n",
       "      <td>광장동</td>\n",
       "      <td>워커힐</td>\n",
       "      <td>226.45</td>\n",
       "      <td>1978</td>\n",
       "      <td>201701</td>\n",
       "      <td>5</td>\n",
       "      <td>175000</td>\n",
       "      <td>2550</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>광진구</td>\n",
       "      <td>광장동</td>\n",
       "      <td>워커힐</td>\n",
       "      <td>226.45</td>\n",
       "      <td>1978</td>\n",
       "      <td>201710</td>\n",
       "      <td>6</td>\n",
       "      <td>179500</td>\n",
       "      <td>2616</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>광진구</td>\n",
       "      <td>자양동</td>\n",
       "      <td>더샵스타시티</td>\n",
       "      <td>163.53</td>\n",
       "      <td>2007</td>\n",
       "      <td>201707</td>\n",
       "      <td>36</td>\n",
       "      <td>185000</td>\n",
       "      <td>3733</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>광진구</td>\n",
       "      <td>광장동</td>\n",
       "      <td>워커힐</td>\n",
       "      <td>226.45</td>\n",
       "      <td>1978</td>\n",
       "      <td>201710</td>\n",
       "      <td>6</td>\n",
       "      <td>190000</td>\n",
       "      <td>2769</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>광진구</td>\n",
       "      <td>자양동</td>\n",
       "      <td>더샵스타시티</td>\n",
       "      <td>163.53</td>\n",
       "      <td>2007</td>\n",
       "      <td>201705</td>\n",
       "      <td>35</td>\n",
       "      <td>200000</td>\n",
       "      <td>4036</td>\n",
       "      <td>1058.758819</td>\n",
       "      <td>523.863957</td>\n",
       "      <td>409.614095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2062 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gu dong      apt    area  completion  transaction_ym  floor   price  \\\n",
       "57    광진구  중곡동   욱현하이브Ⅲ   15.69        2012          201704      3   14000   \n",
       "2018  광진구  화양동  씨즈 건대힐스   14.23        2017          201711      3   15400   \n",
       "56    광진구  중곡동   욱현하이브Ⅲ   17.71        2012          201702      2   15400   \n",
       "1983  광진구  화양동    광진코지웰   15.63        2014          201709      4   15600   \n",
       "1984  광진구  화양동    광진코지웰   15.63        2014          201710      5   15800   \n",
       "...   ...  ...      ...     ...         ...             ...    ...     ...   \n",
       "854   광진구  광장동      워커힐  226.45        1978          201701      5  175000   \n",
       "872   광진구  광장동      워커힐  226.45        1978          201710      6  179500   \n",
       "1220  광진구  자양동   더샵스타시티  163.53        2007          201707     36  185000   \n",
       "874   광진구  광장동      워커힐  226.45        1978          201710      6  190000   \n",
       "1188  광진구  자양동   더샵스타시티  163.53        2007          201705     35  200000   \n",
       "\n",
       "      price/pyeong  starbucks_d(m)  subway_d(m)  market_d(m)  \n",
       "57            2945     1275.130611   388.432602   544.750123  \n",
       "2018          3571      444.600794   376.423566   948.351892  \n",
       "56            2870     1275.130611   388.432602   544.750123  \n",
       "1983          3294      393.362078   403.959103  1002.391935  \n",
       "1984          3336      393.362078   403.959103  1002.391935  \n",
       "...            ...             ...          ...          ...  \n",
       "854           2550     1058.758819   523.863957   409.614095  \n",
       "872           2616     1058.758819   523.863957   409.614095  \n",
       "1220          3733     1058.758819   523.863957   409.614095  \n",
       "874           2769     1058.758819   523.863957   409.614095  \n",
       "1188          4036     1058.758819   523.863957   409.614095  \n",
       "\n",
       "[2062 rows x 12 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df_sorted_by_values = final_df.sort_values(by='price', ascending=True)\n",
    "final_df1 = final_df_sorted_by_values.drop(['latitude', 'longitude', 'addr'], axis=1)\n",
    "final_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67189.39476236663"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df1['price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>gu</th>\n",
       "      <th>dong</th>\n",
       "      <th>apt</th>\n",
       "      <th>area</th>\n",
       "      <th>completion</th>\n",
       "      <th>transaction_ym</th>\n",
       "      <th>floor</th>\n",
       "      <th>price</th>\n",
       "      <th>price/pyeong</th>\n",
       "      <th>starbucks_d(m)</th>\n",
       "      <th>subway_d(m)</th>\n",
       "      <th>market_d(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>광진구</td>\n",
       "      <td>구의동</td>\n",
       "      <td>구의현대2단지</td>\n",
       "      <td>84.91</td>\n",
       "      <td>1996</td>\n",
       "      <td>201705</td>\n",
       "      <td>7</td>\n",
       "      <td>68000</td>\n",
       "      <td>2643</td>\n",
       "      <td>901.479701</td>\n",
       "      <td>271.960054</td>\n",
       "      <td>459.554898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gu dong      apt   area  completion  transaction_ym  floor  price  \\\n",
       "302  광진구  구의동  구의현대2단지  84.91        1996          201705      7  68000   \n",
       "\n",
       "     price/pyeong  starbucks_d(m)  subway_d(m)  market_d(m)  \n",
       "302          2643      901.479701   271.960054   459.554898  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df1.loc[[302]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.상관관계 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 212,
       "width": 1189
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#sns.set_style('darkgrid')\n",
    "sns.set(rc={'figure.figsize':(20,3)})\n",
    "fig, ax = plt.subplots(ncols=3, nrows=1)\n",
    "sns.regplot(y='price/pyeong', x='starbucks_d(m)', data=final_df, ax=ax[0], )\n",
    "sns.regplot(y='price/pyeong',x='subway_d(m)', data=final_df, ax=ax[1])\n",
    "sns.regplot(y='price/pyeong',x='market_d(m)', data=final_df, ax=ax[2]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 538,
       "width": 627
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "corr_mat = final_df.corr().round(1)\n",
    "sns.set(rc={\"figure.figsize\":(10,8)})\n",
    "sns.heatmap(data=corr_mat, annot=True, cmap=\"bwr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
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   "execution_count": null,
   "metadata": {},
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  {
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  {
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  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
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 "nbformat": 4,
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}