correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "# import geopandas as gpd\n",
    "# from shapely.geometry import Polygon, LineString, Point\n",
    "# import fiona\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import seaborn as sns\n",
    "import lightgbm as lgb\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import font_manager, rc\n",
    "font_path = \"data/THEdog.ttf\"   #폰트파일의 위치\n",
    "font_name = font_manager.FontProperties(fname=font_path).get_name()\n",
    "rc('font', family=font_name)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"output/그리드 전처리 완료/PM사고/서초강남PM교통사고전처리완료.csv\")\n",
    "df2 = pd.read_csv(\"output/그리드 전처리 완료/격자인구/서초강남2030대인구전처리완료.csv\")\n",
    "df3 = pd.read_csv(\"output/그리드 전처리 완료/교통시설물/서초강남과속방지턱전처리완료.csv\")\n",
    "df4 = pd.read_csv(\"output/그리드 전처리 완료/교통시설물/서초강남무인교통단속카메라전처리완료.csv\")\n",
    "df5 = pd.read_csv(\"output/그리드 전처리 완료/교통시설물/서초강남횡단보도부착대전처리완료.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = pd.read_csv(\"output/그리드 전처리 완료/버스승하차/서초강남버스아침승차.csv\")\n",
    "df7 = pd.read_csv(\"output/그리드 전처리 완료/버스승하차/서초강남버스아침하차.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df8 = pd.read_csv(\"output/그리드 전처리 완료/버스승하차/서초강남버스저녁승차.csv\")\n",
    "df9 = pd.read_csv(\"output/그리드 전처리 완료/버스승하차/서초강남버스저녁하차.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df10 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남관광여가오락전처리완료.csv\")\n",
    "df11 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남부동산전처리완료.csv\")\n",
    "df12 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남생활서비스전처리완료.csv\")\n",
    "df13 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남소매점전처리완료.csv\")\n",
    "df14 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남숙박전처리완료.csv\")\n",
    "df15 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남스포츠전처리완료.csv\")\n",
    "df16 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남음식점전처리완료.csv\")\n",
    "df17 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남학교전처리완료.csv\")\n",
    "df18 = pd.read_csv(\"output/그리드 전처리 완료/상권학교/서초강남학문교육전처리완료.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df19 = pd.read_csv(\"output/그리드 전처리 완료/자전거이용/서초강남출근시간자전거.csv\")\n",
    "df20 = pd.read_csv(\"output/그리드 전처리 완료/자전거이용/서초강남퇴근시간자전거.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df21 = pd.read_csv(\"output/그리드 전처리 완료/지하철승하차/서초강남지하철아침승차.csv\")\n",
    "df22 = pd.read_csv(\"output/그리드 전처리 완료/지하철승하차/서초강남지하철아침하차.csv\")\n",
    "df23 = pd.read_csv(\"output/그리드 전처리 완료/지하철승하차/서초강남지하철저녁승차.csv\")\n",
    "df24 = pd.read_csv(\"output/그리드 전처리 완료/지하철승하차/서초강남지하철저녁하차.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>gid</th>\n",
       "      <th>acci_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>다사542434</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>다사547430</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>다사549419</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>다사551417</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사564435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>다사566432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>다사567450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>다사568465</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>다사569441</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>다사570474</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>다사572430</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>다사572439</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>다사573440</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>다사574471</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>다사575434</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>다사575464</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>다사575465</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>다사576439</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>다사576472</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>다사577436</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>다사577455</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>다사578453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>다사578455</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>다사579427</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>다사579466</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>다사580447</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>다사581443</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>다사581444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>다사581448</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>다사583441</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>다사586434</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>다사587462</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>다사587471</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>다사589426</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>다사589446</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>다사589455</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>다사590447</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>다사590448</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>다사591453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>다사591457</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>다사592441</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>다사592475</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>다사594467</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>다사595463</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>다사596449</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>다사601450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>다사603464</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>다사606456</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>다사608454</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>다사608468</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>다사609443</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>다사610452</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>다사611453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>다사612444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>다사613445</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>다사613446</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>다사619457</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>다사648432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         gid  acci_cnt\n",
       "0   다사542434         1\n",
       "1   다사547430         1\n",
       "2   다사549419         1\n",
       "3   다사551417         1\n",
       "4   다사564435         1\n",
       "5   다사566432         1\n",
       "6   다사567450         1\n",
       "7   다사568465         1\n",
       "8   다사569441         1\n",
       "9   다사570474         1\n",
       "10  다사572430         1\n",
       "11  다사572439         1\n",
       "12  다사573440         1\n",
       "13  다사574471         1\n",
       "14  다사575434         1\n",
       "15  다사575464         1\n",
       "16  다사575465         1\n",
       "17  다사576439         1\n",
       "18  다사576472         1\n",
       "19  다사577436         1\n",
       "20  다사577455         1\n",
       "21  다사578453         1\n",
       "22  다사578455         1\n",
       "23  다사579427         1\n",
       "24  다사579466         1\n",
       "25  다사580447         1\n",
       "26  다사581443         1\n",
       "27  다사581444         1\n",
       "28  다사581448         1\n",
       "29  다사583441         1\n",
       "30  다사586434         1\n",
       "31  다사587462         1\n",
       "32  다사587471         1\n",
       "33  다사589426         1\n",
       "34  다사589446         2\n",
       "35  다사589455         1\n",
       "36  다사590447         1\n",
       "37  다사590448         1\n",
       "38  다사591453         1\n",
       "39  다사591457         1\n",
       "40  다사592441         1\n",
       "41  다사592475         1\n",
       "42  다사594467         1\n",
       "43  다사595463         1\n",
       "44  다사596449         1\n",
       "45  다사601450         1\n",
       "46  다사603464         1\n",
       "47  다사606456         1\n",
       "48  다사608454         1\n",
       "49  다사608468         2\n",
       "50  다사609443         1\n",
       "51  다사610452         1\n",
       "52  다사611453         1\n",
       "53  다사612444         1\n",
       "54  다사613445         1\n",
       "55  다사613446         1\n",
       "56  다사619457         1\n",
       "57  다사648432         1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df1[[\"gid\",\"TL_cnt\"]]\n",
    "df1.columns = [\"gid\",\"acci_cnt\"]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>9128</th>\n",
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       "      <th>9129</th>\n",
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       "<p>9133 rows × 2 columns</p>\n",
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       "           gid  2030val\n",
       "0     다사579454     77.0\n",
       "1     다사613448     66.0\n",
       "2     다사585452      0.0\n",
       "3     다사583456    227.0\n",
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       "\n",
       "[9133 rows x 2 columns]"
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     },
     "execution_count": 23,
     "metadata": {},
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   "source": [
    "df2 = df2[[\"gid\",\"2030val\"]]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "         gid  과속방지턱\n",
       "0   다사571457      2\n",
       "1   다사575448      1\n",
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       "9   다사617396      2\n",
       "10  다사620399      2\n",
       "11  다사620401      1\n",
       "12  다사620404      1\n",
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       "14  다사621399      1\n",
       "15  다사621401      1\n",
       "16  다사621402      1\n",
       "17  다사621403      1\n",
       "18  다사621404      1\n",
       "19  다사622401      1\n",
       "20  다사630403      3\n",
       "21  다사632405      1\n",
       "22  다사633407      1\n",
       "23  다사633425      1\n",
       "24  다사634425      1\n",
       "25  다사634426      2\n",
       "26  다사634427      2\n",
       "27  다사635407      1\n",
       "28  다사637403      1\n",
       "29  다사653407      1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df3[[\"gid\",\"TL_cnt\"]]\n",
    "df3.columns = [\"gid\",\"과속방지턱\"]\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df4[[\"gid\",\"TL_cnt\"]]\n",
    "df4.columns = [\"gid\",\"무인카메라\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>94 rows × 2 columns</p>\n",
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      "text/plain": [
       "         gid  무인카메라\n",
       "0   다사542430      1\n",
       "1   다사542436      1\n",
       "2   다사543443      1\n",
       "3   다사546413      1\n",
       "4   다사546431      1\n",
       "..       ...    ...\n",
       "89  다사647407      1\n",
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       "92  다사653419      1\n",
       "93  다사654417      1\n",
       "\n",
       "[94 rows x 2 columns]"
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     },
     "execution_count": 32,
     "metadata": {},
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    "df4"
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  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>1295 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "           gid  신호등\n",
       "0     다사541420   21\n",
       "1     다사541421    1\n",
       "2     다사541424    6\n",
       "3     다사541425    4\n",
       "4     다사541427    5\n",
       "...        ...  ...\n",
       "1290  다사663411    2\n",
       "1291  다사663413    2\n",
       "1292  다사664409    3\n",
       "1293  다사664410    2\n",
       "1294  다사664412    5\n",
       "\n",
       "[1295 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df5[[\"gid\",\"TL_cnt\"]]\n",
    "df5.columns = [\"gid\",\"신호등\"]\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>다사655420</td>\n",
       "      <td>1901.504267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>다사658421</td>\n",
       "      <td>1662.390390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>다사660408</td>\n",
       "      <td>25.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>다사662409</td>\n",
       "      <td>47.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>다사664410</td>\n",
       "      <td>234.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>778 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid       버스아침승차\n",
       "0    다사540452    95.915000\n",
       "1    다사541419  2123.280992\n",
       "2    다사541421   933.513889\n",
       "3    다사541423   957.306122\n",
       "4    다사542418   200.000000\n",
       "..        ...          ...\n",
       "773  다사655420  1901.504267\n",
       "774  다사658421  1662.390390\n",
       "775  다사660408    25.277778\n",
       "776  다사662409    47.888889\n",
       "777  다사664410   234.888889\n",
       "\n",
       "[778 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df6[[\"gid\",\"아침총승객수\"]]\n",
    "df6.columns = [\"gid\", \"버스아침승차\"]\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>773</th>\n",
       "      <td>다사655420</td>\n",
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       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>다사658421</td>\n",
       "      <td>1662.390390</td>\n",
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       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>다사660408</td>\n",
       "      <td>25.277778</td>\n",
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       "      <th>776</th>\n",
       "      <td>다사662409</td>\n",
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       "      <th>777</th>\n",
       "      <td>다사664410</td>\n",
       "      <td>234.888889</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>778 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid       버스아침승차\n",
       "0    다사540452    95.915000\n",
       "1    다사541419  2123.280992\n",
       "2    다사541421   933.513889\n",
       "3    다사541423   957.306122\n",
       "4    다사542418   200.000000\n",
       "..        ...          ...\n",
       "773  다사655420  1901.504267\n",
       "774  다사658421  1662.390390\n",
       "775  다사660408    25.277778\n",
       "776  다사662409    47.888889\n",
       "777  다사664410   234.888889\n",
       "\n",
       "[778 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>다사655420</td>\n",
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       "      <th>774</th>\n",
       "      <td>다사658421</td>\n",
       "      <td>495.613363</td>\n",
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       "      <th>775</th>\n",
       "      <td>다사660408</td>\n",
       "      <td>138.805556</td>\n",
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       "      <th>776</th>\n",
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       "      <th>777</th>\n",
       "      <td>다사664410</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>778 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid       버스아침하차\n",
       "0    다사540452   290.648333\n",
       "1    다사541419  1642.181818\n",
       "2    다사541421  4109.416667\n",
       "3    다사541423  1936.224490\n",
       "4    다사542418  2066.666667\n",
       "..        ...          ...\n",
       "773  다사655420   438.883357\n",
       "774  다사658421   495.613363\n",
       "775  다사660408   138.805556\n",
       "776  다사662409   171.111111\n",
       "777  다사664410   547.638889\n",
       "\n",
       "[778 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df7[[\"gid\",\"아침총승객수\"]]\n",
    "df7.columns = [\"gid\", \"버스아침하차\"]\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3695.930556</td>\n",
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       "      <td>다사542418</td>\n",
       "      <td>125.583333</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>다사655420</td>\n",
       "      <td>678.780228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>다사658421</td>\n",
       "      <td>635.659910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>다사660408</td>\n",
       "      <td>74.444444</td>\n",
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       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>다사662409</td>\n",
       "      <td>145.805556</td>\n",
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       "      <th>777</th>\n",
       "      <td>다사664410</td>\n",
       "      <td>629.305556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>778 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid       버스저녁승차\n",
       "0    다사540452   530.146667\n",
       "1    다사541419  1891.380165\n",
       "2    다사541421  3695.930556\n",
       "3    다사541423  1792.285714\n",
       "4    다사542418   125.583333\n",
       "..        ...          ...\n",
       "773  다사655420   678.780228\n",
       "774  다사658421   635.659910\n",
       "775  다사660408    74.444444\n",
       "776  다사662409   145.805556\n",
       "777  다사664410   629.305556\n",
       "\n",
       "[778 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = df8[[\"gid\",\"저녁총승객수\"]]\n",
    "df8.columns = [\"gid\", \"버스저녁승차\"]\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <th>773</th>\n",
       "      <td>다사655420</td>\n",
       "      <td>1905.945235</td>\n",
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       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>다사658421</td>\n",
       "      <td>1263.244745</td>\n",
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       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>다사660408</td>\n",
       "      <td>58.083333</td>\n",
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       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>다사662409</td>\n",
       "      <td>63.138889</td>\n",
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       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>다사664410</td>\n",
       "      <td>253.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>778 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid       버스저녁하차\n",
       "0    다사540452   507.370000\n",
       "1    다사541419   848.768595\n",
       "2    다사541421  1874.736111\n",
       "3    다사541423  1591.408163\n",
       "4    다사542418  2929.583333\n",
       "..        ...          ...\n",
       "773  다사655420  1905.945235\n",
       "774  다사658421  1263.244745\n",
       "775  다사660408    58.083333\n",
       "776  다사662409    63.138889\n",
       "777  다사664410   253.250000\n",
       "\n",
       "[778 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df9[[\"gid\",\"저녁총승객수\"]]\n",
    "df9.columns = [\"gid\", \"버스저녁하차\"]\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>633</th>\n",
       "      <td>다사648432</td>\n",
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       "      <th>634</th>\n",
       "      <td>다사649416</td>\n",
       "      <td>1</td>\n",
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       "      <th>635</th>\n",
       "      <td>다사649431</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>다사656419</td>\n",
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       "      <th>637</th>\n",
       "      <td>다사661406</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>638 rows × 2 columns</p>\n",
       "</div>"
      ],
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       "          gid  관광여가오락\n",
       "0    다사542420       1\n",
       "1    다사542421       3\n",
       "2    다사542422       2\n",
       "3    다사542423       1\n",
       "4    다사542424       1\n",
       "..        ...     ...\n",
       "633  다사648432       1\n",
       "634  다사649416       1\n",
       "635  다사649431       8\n",
       "636  다사656419       1\n",
       "637  다사661406       1\n",
       "\n",
       "[638 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = df10[[\"gid\",\"TL_cnt\"]]\n",
    "df10.columns = [\"gid\",\"관광여가오락\"]\n",
    "df10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1228</th>\n",
       "      <td>다사656419</td>\n",
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       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>다사662413</td>\n",
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       "    <tr>\n",
       "      <th>1230</th>\n",
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       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>다사663409</td>\n",
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       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>다사663411</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1233 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  부동산\n",
       "0     다사542417    2\n",
       "1     다사542418    1\n",
       "2     다사542424    1\n",
       "3     다사542425    1\n",
       "4     다사542427    4\n",
       "...        ...  ...\n",
       "1228  다사656419    2\n",
       "1229  다사662413    2\n",
       "1230  다사663408    1\n",
       "1231  다사663409    1\n",
       "1232  다사663411    1\n",
       "\n",
       "[1233 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11 = df11[[\"gid\",\"TL_cnt\"]]\n",
    "df11.columns = [\"gid\",\"부동산\"]\n",
    "df11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>다사663408</td>\n",
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       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>다사663411</td>\n",
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       "    <tr>\n",
       "      <th>2268</th>\n",
       "      <td>다사664410</td>\n",
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       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>다사664412</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>2270 rows × 2 columns</p>\n",
       "</div>"
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      "text/plain": [
       "           gid  생활서비스\n",
       "0     다사542417      4\n",
       "1     다사542418      3\n",
       "2     다사542422      2\n",
       "3     다사542423      2\n",
       "4     다사542424      1\n",
       "...        ...    ...\n",
       "2265  다사662413      6\n",
       "2266  다사663408      5\n",
       "2267  다사663411      5\n",
       "2268  다사664410      1\n",
       "2269  다사664412      1\n",
       "\n",
       "[2270 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12 = df12[[\"gid\",\"TL_cnt\"]]\n",
    "df12.columns = [\"gid\",\"생활서비스\"]\n",
    "df12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>다사657420</td>\n",
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       "      <th>2471</th>\n",
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       "<p>2472 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  소매점\n",
       "0     다사541452    2\n",
       "1     다사542417    3\n",
       "2     다사542421    5\n",
       "3     다사542422    2\n",
       "4     다사542424    4\n",
       "...        ...  ...\n",
       "2467  다사656419    3\n",
       "2468  다사657420    1\n",
       "2469  다사662413   11\n",
       "2470  다사663408    1\n",
       "2471  다사663411   24\n",
       "\n",
       "[2472 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df13 = df13[[\"gid\",\"TL_cnt\"]]\n",
    "df13.columns = [\"gid\",\"소매점\"]\n",
    "df13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>161</th>\n",
       "      <td>다사614455</td>\n",
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       "      <th>162</th>\n",
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       "      <th>163</th>\n",
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       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>다사630428</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid  숙박\n",
       "0    다사542433   1\n",
       "1    다사543427   2\n",
       "2    다사545426   1\n",
       "3    다사545440   2\n",
       "4    다사545443   1\n",
       "..        ...  ..\n",
       "160  다사613457   3\n",
       "161  다사614455   1\n",
       "162  다사615455   1\n",
       "163  다사616456   1\n",
       "164  다사630428   1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df14 = df14[[\"gid\",\"TL_cnt\"]]\n",
    "df14.columns = [\"gid\",\"숙박\"]\n",
    "df14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>137</th>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>다사650433</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>141 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid  스포츠\n",
       "0    다사542418    1\n",
       "1    다사543426    1\n",
       "2    다사543429    1\n",
       "3    다사543443    1\n",
       "4    다사545437    1\n",
       "..        ...  ...\n",
       "136  다사621444    1\n",
       "137  다사628439    2\n",
       "138  다사632437    1\n",
       "139  다사633438    1\n",
       "140  다사650433    1\n",
       "\n",
       "[141 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df15 = df15[[\"gid\",\"TL_cnt\"]]\n",
    "df15.columns = [\"gid\",\"스포츠\"]\n",
    "df15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>다사542420</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사542421</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>다사656419</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2455</th>\n",
       "      <td>다사658417</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>다사659417</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>다사662413</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>다사663411</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  음식점\n",
       "0     다사541452    6\n",
       "1     다사542417    1\n",
       "2     다사542418    1\n",
       "3     다사542420    2\n",
       "4     다사542421   18\n",
       "...        ...  ...\n",
       "2454  다사656419    1\n",
       "2455  다사658417    3\n",
       "2456  다사659417    2\n",
       "2457  다사662413    2\n",
       "2458  다사663411    5\n",
       "\n",
       "[2459 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df16 = df16[[\"gid\",\"TL_cnt\"]]\n",
    "df16.columns = [\"gid\",\"음식점\"]\n",
    "df16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>다사547412</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>다사647436</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>다사648434</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>다사648435</td>\n",
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       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>다사651406</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>다사653417</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          gid  학교\n",
       "0    다사544422   1\n",
       "1    다사544434   1\n",
       "2    다사545434   1\n",
       "3    다사546432   1\n",
       "4    다사547412   1\n",
       "..        ...  ..\n",
       "129  다사647436   1\n",
       "130  다사648434   1\n",
       "131  다사648435   1\n",
       "132  다사651406   1\n",
       "133  다사653417   1\n",
       "\n",
       "[134 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df17 = df17[[\"gid\",\"TL_cnt\"]]\n",
    "df17.columns = [\"gid\",\"학교\"]\n",
    "df17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1374</th>\n",
       "      <td>다사655418</td>\n",
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       "    <tr>\n",
       "      <th>1375</th>\n",
       "      <td>다사656419</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>다사657418</td>\n",
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       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>다사658419</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>다사663409</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1379 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  교육\n",
       "0     다사542417   1\n",
       "1     다사542418   2\n",
       "2     다사542421   1\n",
       "3     다사542422   1\n",
       "4     다사542423   1\n",
       "...        ...  ..\n",
       "1374  다사655418   1\n",
       "1375  다사656419   3\n",
       "1376  다사657418   1\n",
       "1377  다사658419   2\n",
       "1378  다사663409   1\n",
       "\n",
       "[1379 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df18 = df18[[\"gid\",\"TL_cnt\"]]\n",
    "df18.columns = [\"gid\",\"교육\"]\n",
    "df18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>4</th>\n",
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       "      <td>81</td>\n",
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       "    <tr>\n",
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       "      <th>8937</th>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8938</th>\n",
       "      <td>다사666410</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8939</th>\n",
       "      <td>다사666411</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8940</th>\n",
       "      <td>다사667408</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8941</th>\n",
       "      <td>다사667409</td>\n",
       "      <td>7</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8942 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  출근시간자전거\n",
       "0     다사540449       38\n",
       "1     다사540450      150\n",
       "2     다사540451       81\n",
       "3     다사540452       81\n",
       "4     다사540453       81\n",
       "...        ...      ...\n",
       "8937  다사666409        7\n",
       "8938  다사666410        5\n",
       "8939  다사666411       10\n",
       "8940  다사667408        5\n",
       "8941  다사667409        7\n",
       "\n",
       "[8942 rows x 2 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df19 = df19[[\"gid\",\"COUNT\"]]\n",
    "df19.columns = [\"gid\",\"출근시간자전거\"]\n",
    "df19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>8938</th>\n",
       "      <td>다사666410</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8939</th>\n",
       "      <td>다사666411</td>\n",
       "      <td>21</td>\n",
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       "      <th>8940</th>\n",
       "      <td>다사667408</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>8941</th>\n",
       "      <td>다사667409</td>\n",
       "      <td>8</td>\n",
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       "</table>\n",
       "<p>8942 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  퇴근시간자전거\n",
       "0     다사540449      217\n",
       "1     다사540450      951\n",
       "2     다사540451      530\n",
       "3     다사540452      530\n",
       "4     다사540453      530\n",
       "...        ...      ...\n",
       "8937  다사666409        8\n",
       "8938  다사666410        7\n",
       "8939  다사666411       21\n",
       "8940  다사667408        4\n",
       "8941  다사667409        8\n",
       "\n",
       "[8942 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df20 = df20[[\"gid\",\"COUNT\"]]\n",
    "df20.columns = [\"gid\",\"퇴근시간자전거\"]\n",
    "df20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>다사548406</td>\n",
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       "      <th>7</th>\n",
       "      <td>다사555425</td>\n",
       "      <td>87392</td>\n",
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       "      <th>8</th>\n",
       "      <td>다사562451</td>\n",
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       "      <th>9</th>\n",
       "      <td>다사564437</td>\n",
       "      <td>71313</td>\n",
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       "      <th>10</th>\n",
       "      <td>다사568455</td>\n",
       "      <td>27337</td>\n",
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       "      <th>11</th>\n",
       "      <td>다사568460</td>\n",
       "      <td>40646</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>다사570438</td>\n",
       "      <td>126061</td>\n",
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       "      <th>13</th>\n",
       "      <td>다사571451</td>\n",
       "      <td>27332</td>\n",
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       "      <th>14</th>\n",
       "      <td>다사572429</td>\n",
       "      <td>100742</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>다사576464</td>\n",
       "      <td>45859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>다사577458</td>\n",
       "      <td>40566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>다사580451</td>\n",
       "      <td>113017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>다사582444</td>\n",
       "      <td>157791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>다사583475</td>\n",
       "      <td>63004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>다사586462</td>\n",
       "      <td>33727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>다사588429</td>\n",
       "      <td>122231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>다사588454</td>\n",
       "      <td>23507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>다사590447</td>\n",
       "      <td>65723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>다사594465</td>\n",
       "      <td>54109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>다사594476</td>\n",
       "      <td>21587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>다사596458</td>\n",
       "      <td>48804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>다사599431</td>\n",
       "      <td>54996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>다사601451</td>\n",
       "      <td>105344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>다사604442</td>\n",
       "      <td>63717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>다사604460</td>\n",
       "      <td>18232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>다사605467</td>\n",
       "      <td>39833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>다사607436</td>\n",
       "      <td>61976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>다사610431</td>\n",
       "      <td>11049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>다사611461</td>\n",
       "      <td>30323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>다사613440</td>\n",
       "      <td>70371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>다사613455</td>\n",
       "      <td>58762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>다사616434</td>\n",
       "      <td>25780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>다사620442</td>\n",
       "      <td>12192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>다사622436</td>\n",
       "      <td>28273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>다사628439</td>\n",
       "      <td>82488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>다사632428</td>\n",
       "      <td>64730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>다사650429</td>\n",
       "      <td>196749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         gid  지하철아침승차\n",
       "0   다사540449    33726\n",
       "1   다사541420   492382\n",
       "2   다사542431   255165\n",
       "3   다사546448    15873\n",
       "4   다사548406     7920\n",
       "5   다사552432    68725\n",
       "6   다사554450    18253\n",
       "7   다사555425    87392\n",
       "8   다사562451   207342\n",
       "9   다사564437    71313\n",
       "10  다사568455    27337\n",
       "11  다사568460    40646\n",
       "12  다사570438   126061\n",
       "13  다사571451    27332\n",
       "14  다사572429   100742\n",
       "15  다사576464    45859\n",
       "16  다사577458    40566\n",
       "17  다사580451   113017\n",
       "18  다사582444   157791\n",
       "19  다사583475    63004\n",
       "20  다사586462    33727\n",
       "21  다사588429   122231\n",
       "22  다사588454    23507\n",
       "23  다사590447    65723\n",
       "24  다사594465    54109\n",
       "25  다사594476    21587\n",
       "26  다사596458    48804\n",
       "27  다사599431    54996\n",
       "28  다사601451   105344\n",
       "29  다사604442    63717\n",
       "30  다사604460    18232\n",
       "31  다사605467    39833\n",
       "32  다사607436    61976\n",
       "33  다사610431    11049\n",
       "34  다사611461    30323\n",
       "35  다사613440    70371\n",
       "36  다사613455    58762\n",
       "37  다사616434    25780\n",
       "38  다사620442    12192\n",
       "39  다사622436    28273\n",
       "40  다사628439    82488\n",
       "41  다사632428    64730\n",
       "42  다사650429   196749"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df21 = df21[[\"gid\",\"아침총승객수\"]]\n",
    "df21.columns = [\"gid\",\"지하철아침승차\"]\n",
    "df21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>다사540449</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>다사541420</td>\n",
       "      <td>289888</td>\n",
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       "    <tr>\n",
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       "      <td>다사542431</td>\n",
       "      <td>215908</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>다사546448</td>\n",
       "      <td>19125</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사548406</td>\n",
       "      <td>11631</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>다사552432</td>\n",
       "      <td>164486</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>다사554450</td>\n",
       "      <td>25444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>다사555425</td>\n",
       "      <td>172153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>다사562451</td>\n",
       "      <td>508001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>다사564437</td>\n",
       "      <td>293178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>다사568455</td>\n",
       "      <td>54818</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>다사568460</td>\n",
       "      <td>28002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>다사570438</td>\n",
       "      <td>531626</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>다사571451</td>\n",
       "      <td>18828</td>\n",
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       "    <tr>\n",
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       "      <td>다사572429</td>\n",
       "      <td>338193</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>다사576464</td>\n",
       "      <td>360700</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>다사577458</td>\n",
       "      <td>306368</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>다사580451</td>\n",
       "      <td>284968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>다사582444</td>\n",
       "      <td>928291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>다사583475</td>\n",
       "      <td>404105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>다사586462</td>\n",
       "      <td>425175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>다사588429</td>\n",
       "      <td>525430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>다사588454</td>\n",
       "      <td>135254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>다사590447</td>\n",
       "      <td>819574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>다사594465</td>\n",
       "      <td>401282</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>다사594476</td>\n",
       "      <td>245198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>다사596458</td>\n",
       "      <td>259681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>다사599431</td>\n",
       "      <td>144795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>다사601451</td>\n",
       "      <td>955291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>다사604442</td>\n",
       "      <td>129494</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>다사604460</td>\n",
       "      <td>93110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>다사605467</td>\n",
       "      <td>337572</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>다사607436</td>\n",
       "      <td>122123</td>\n",
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       "    <tr>\n",
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       "      <td>다사610431</td>\n",
       "      <td>17886</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>다사611461</td>\n",
       "      <td>210060</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>다사613440</td>\n",
       "      <td>72929</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>다사613455</td>\n",
       "      <td>747736</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>다사616434</td>\n",
       "      <td>26798</td>\n",
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       "    <tr>\n",
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       "      <td>다사620442</td>\n",
       "      <td>21521</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>다사622436</td>\n",
       "      <td>21325</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>다사628439</td>\n",
       "      <td>59477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>다사632428</td>\n",
       "      <td>86940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>다사650429</td>\n",
       "      <td>250799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         gid  지하철아침하차\n",
       "0   다사540449    27456\n",
       "1   다사541420   289888\n",
       "2   다사542431   215908\n",
       "3   다사546448    19125\n",
       "4   다사548406    11631\n",
       "5   다사552432   164486\n",
       "6   다사554450    25444\n",
       "7   다사555425   172153\n",
       "8   다사562451   508001\n",
       "9   다사564437   293178\n",
       "10  다사568455    54818\n",
       "11  다사568460    28002\n",
       "12  다사570438   531626\n",
       "13  다사571451    18828\n",
       "14  다사572429   338193\n",
       "15  다사576464   360700\n",
       "16  다사577458   306368\n",
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       "18  다사582444   928291\n",
       "19  다사583475   404105\n",
       "20  다사586462   425175\n",
       "21  다사588429   525430\n",
       "22  다사588454   135254\n",
       "23  다사590447   819574\n",
       "24  다사594465   401282\n",
       "25  다사594476   245198\n",
       "26  다사596458   259681\n",
       "27  다사599431   144795\n",
       "28  다사601451   955291\n",
       "29  다사604442   129494\n",
       "30  다사604460    93110\n",
       "31  다사605467   337572\n",
       "32  다사607436   122123\n",
       "33  다사610431    17886\n",
       "34  다사611461   210060\n",
       "35  다사613440    72929\n",
       "36  다사613455   747736\n",
       "37  다사616434    26798\n",
       "38  다사620442    21521\n",
       "39  다사622436    21325\n",
       "40  다사628439    59477\n",
       "41  다사632428    86940\n",
       "42  다사650429   250799"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df22 = df22[[\"gid\",\"아침총승객수\"]]\n",
    "df22.columns = [\"gid\",\"지하철아침하차\"]\n",
    "df22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>다사541420</td>\n",
       "      <td>387570</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>239794</td>\n",
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       "      <th>3</th>\n",
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       "      <td>22862</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사548406</td>\n",
       "      <td>10141</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>다사552432</td>\n",
       "      <td>147331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>다사554450</td>\n",
       "      <td>27760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>다사555425</td>\n",
       "      <td>158923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>다사562451</td>\n",
       "      <td>704887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>다사564437</td>\n",
       "      <td>239588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>다사568455</td>\n",
       "      <td>52776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>다사568460</td>\n",
       "      <td>31057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>다사570438</td>\n",
       "      <td>504913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>다사571451</td>\n",
       "      <td>20107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>다사572429</td>\n",
       "      <td>319666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>다사576464</td>\n",
       "      <td>372597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>다사577458</td>\n",
       "      <td>256531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>다사580451</td>\n",
       "      <td>312562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>다사582444</td>\n",
       "      <td>912929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>다사583475</td>\n",
       "      <td>380376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>다사586462</td>\n",
       "      <td>346586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>다사588429</td>\n",
       "      <td>468319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>다사588454</td>\n",
       "      <td>120667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>다사590447</td>\n",
       "      <td>673156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>다사594465</td>\n",
       "      <td>327169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>다사594476</td>\n",
       "      <td>252668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>다사596458</td>\n",
       "      <td>222297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>다사599431</td>\n",
       "      <td>137244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>다사601451</td>\n",
       "      <td>833765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>다사604442</td>\n",
       "      <td>131781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>다사604460</td>\n",
       "      <td>83377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>다사605467</td>\n",
       "      <td>257406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>다사607436</td>\n",
       "      <td>109455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>다사610431</td>\n",
       "      <td>13956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>다사611461</td>\n",
       "      <td>202231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>다사613440</td>\n",
       "      <td>79881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>다사613455</td>\n",
       "      <td>695587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>다사616434</td>\n",
       "      <td>27332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>다사620442</td>\n",
       "      <td>19042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>다사622436</td>\n",
       "      <td>23972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>다사628439</td>\n",
       "      <td>64138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>다사632428</td>\n",
       "      <td>62981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>다사650429</td>\n",
       "      <td>245861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         gid  지하철저녁승차\n",
       "0   다사540449    32063\n",
       "1   다사541420   387570\n",
       "2   다사542431   239794\n",
       "3   다사546448    22862\n",
       "4   다사548406    10141\n",
       "5   다사552432   147331\n",
       "6   다사554450    27760\n",
       "7   다사555425   158923\n",
       "8   다사562451   704887\n",
       "9   다사564437   239588\n",
       "10  다사568455    52776\n",
       "11  다사568460    31057\n",
       "12  다사570438   504913\n",
       "13  다사571451    20107\n",
       "14  다사572429   319666\n",
       "15  다사576464   372597\n",
       "16  다사577458   256531\n",
       "17  다사580451   312562\n",
       "18  다사582444   912929\n",
       "19  다사583475   380376\n",
       "20  다사586462   346586\n",
       "21  다사588429   468319\n",
       "22  다사588454   120667\n",
       "23  다사590447   673156\n",
       "24  다사594465   327169\n",
       "25  다사594476   252668\n",
       "26  다사596458   222297\n",
       "27  다사599431   137244\n",
       "28  다사601451   833765\n",
       "29  다사604442   131781\n",
       "30  다사604460    83377\n",
       "31  다사605467   257406\n",
       "32  다사607436   109455\n",
       "33  다사610431    13956\n",
       "34  다사611461   202231\n",
       "35  다사613440    79881\n",
       "36  다사613455   695587\n",
       "37  다사616434    27332\n",
       "38  다사620442    19042\n",
       "39  다사622436    23972\n",
       "40  다사628439    64138\n",
       "41  다사632428    62981\n",
       "42  다사650429   245861"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df23 = df23[[\"gid\",\"저녁총승객수\"]]\n",
    "df23.columns = [\"gid\",\"지하철저녁승차\"]\n",
    "df23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gid</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>다사540449</td>\n",
       "      <td>31264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>다사541420</td>\n",
       "      <td>557818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>다사542431</td>\n",
       "      <td>288908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>다사546448</td>\n",
       "      <td>18135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사548406</td>\n",
       "      <td>5266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>다사552432</td>\n",
       "      <td>69086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>다사554450</td>\n",
       "      <td>18702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>다사555425</td>\n",
       "      <td>96834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>다사562451</td>\n",
       "      <td>381072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>다사564437</td>\n",
       "      <td>54088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>다사568455</td>\n",
       "      <td>25732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>다사568460</td>\n",
       "      <td>36699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>다사570438</td>\n",
       "      <td>177954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>다사571451</td>\n",
       "      <td>24983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>다사572429</td>\n",
       "      <td>129229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>다사576464</td>\n",
       "      <td>121824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>다사577458</td>\n",
       "      <td>71103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>다사580451</td>\n",
       "      <td>185179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>다사582444</td>\n",
       "      <td>349077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>다사583475</td>\n",
       "      <td>110140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>다사586462</td>\n",
       "      <td>44244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>다사588429</td>\n",
       "      <td>174972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>다사588454</td>\n",
       "      <td>33100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>다사590447</td>\n",
       "      <td>115069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>다사594465</td>\n",
       "      <td>90841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>다사594476</td>\n",
       "      <td>76280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>다사596458</td>\n",
       "      <td>61644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>다사599431</td>\n",
       "      <td>54285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>다사601451</td>\n",
       "      <td>167223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>다사604442</td>\n",
       "      <td>78969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>다사604460</td>\n",
       "      <td>25852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>다사605467</td>\n",
       "      <td>47940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>다사607436</td>\n",
       "      <td>63847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>다사610431</td>\n",
       "      <td>9685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>다사611461</td>\n",
       "      <td>44079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>다사613440</td>\n",
       "      <td>69102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>다사613455</td>\n",
       "      <td>108440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>다사616434</td>\n",
       "      <td>27095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>다사620442</td>\n",
       "      <td>11235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>다사622436</td>\n",
       "      <td>19959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>다사628439</td>\n",
       "      <td>71483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>다사632428</td>\n",
       "      <td>52465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>다사650429</td>\n",
       "      <td>218981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         gid  지하철저녁하차\n",
       "0   다사540449    31264\n",
       "1   다사541420   557818\n",
       "2   다사542431   288908\n",
       "3   다사546448    18135\n",
       "4   다사548406     5266\n",
       "5   다사552432    69086\n",
       "6   다사554450    18702\n",
       "7   다사555425    96834\n",
       "8   다사562451   381072\n",
       "9   다사564437    54088\n",
       "10  다사568455    25732\n",
       "11  다사568460    36699\n",
       "12  다사570438   177954\n",
       "13  다사571451    24983\n",
       "14  다사572429   129229\n",
       "15  다사576464   121824\n",
       "16  다사577458    71103\n",
       "17  다사580451   185179\n",
       "18  다사582444   349077\n",
       "19  다사583475   110140\n",
       "20  다사586462    44244\n",
       "21  다사588429   174972\n",
       "22  다사588454    33100\n",
       "23  다사590447   115069\n",
       "24  다사594465    90841\n",
       "25  다사594476    76280\n",
       "26  다사596458    61644\n",
       "27  다사599431    54285\n",
       "28  다사601451   167223\n",
       "29  다사604442    78969\n",
       "30  다사604460    25852\n",
       "31  다사605467    47940\n",
       "32  다사607436    63847\n",
       "33  다사610431     9685\n",
       "34  다사611461    44079\n",
       "35  다사613440    69102\n",
       "36  다사613455   108440\n",
       "37  다사616434    27095\n",
       "38  다사620442    11235\n",
       "39  다사622436    19959\n",
       "40  다사628439    71483\n",
       "41  다사632428    52465\n",
       "42  다사650429   218981"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df24 = df24[[\"gid\",\"저녁총승객수\"]]\n",
    "df24.columns = [\"gid\",\"지하철저녁하차\"]\n",
    "df24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.merge(df1, df2, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df3, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df4, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df5, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df6, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df7, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df8, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df9, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df10, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df11, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df12, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df13, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df14, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df15, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df16, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df17, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df18, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df19, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df20, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df21, on ='gid', how = 'outer')\n",
    "final = pd.merge(final, df22, on = 'gid', how = 'outer')\n",
    "final = pd.merge(final, df23, on = 'gid', how = 'outer')\n",
    "final = pd.merge(final, df24, on = 'gid', how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gid</th>\n",
       "      <th>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>다사542434</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>67.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>다사551417</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2486.75</td>\n",
       "      <td>3392.25</td>\n",
       "      <td>1914.833333</td>\n",
       "      <td>3168.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사564435</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
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       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>다사559438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9129</th>\n",
       "      <td>다사618387</td>\n",
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       "      <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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>다사559464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135</td>\n",
       "      <td>886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9131</th>\n",
       "      <td>다사585417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219</td>\n",
       "      <td>303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>다사548430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>26</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>9133 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  acci_cnt  2030val  과속방지턱  무인카메라  신호등   버스아침승차   버스아침하차  \\\n",
       "0     다사542434       1.0      0.0    NaN    NaN  NaN      NaN      NaN   \n",
       "1     다사547430       1.0      0.0    NaN    NaN  NaN      NaN      NaN   \n",
       "2     다사549419       1.0     67.0    NaN    NaN  NaN      NaN      NaN   \n",
       "3     다사551417       1.0     26.0    NaN    NaN  NaN  2486.75  3392.25   \n",
       "4     다사564435       1.0      0.0    NaN    NaN  NaN      NaN      NaN   \n",
       "...        ...       ...      ...    ...    ...  ...      ...      ...   \n",
       "9128  다사559438       NaN      0.0    NaN    NaN  NaN      NaN      NaN   \n",
       "9129  다사618387       NaN      0.0    NaN    NaN  NaN      NaN      NaN   \n",
       "9130  다사559464       NaN      0.0    NaN    NaN  NaN      NaN      NaN   \n",
       "9131  다사585417       NaN      0.0    NaN    NaN  7.0      NaN      NaN   \n",
       "9132  다사548430       NaN      0.0    NaN    NaN  NaN      NaN      NaN   \n",
       "\n",
       "           버스저녁승차       버스저녁하차  ...  스포츠  음식점  학교   교육  출근시간자전거  퇴근시간자전거  \\\n",
       "0             NaN          NaN  ...  NaN  NaN NaN  NaN       46      201   \n",
       "1             NaN          NaN  ...  NaN  NaN NaN  NaN       53      137   \n",
       "2             NaN          NaN  ...  NaN  NaN NaN  NaN        2       10   \n",
       "3     1914.833333  3168.333333  ...  NaN  1.0 NaN  NaN        3        9   \n",
       "4             NaN          NaN  ...  NaN  3.0 NaN  NaN       16       54   \n",
       "...           ...          ...  ...  ...  ...  ..  ...      ...      ...   \n",
       "9128          NaN          NaN  ...  NaN  NaN NaN  NaN       23      105   \n",
       "9129          NaN          NaN  ...  NaN  NaN NaN  NaN        0        0   \n",
       "9130          NaN          NaN  ...  NaN  NaN NaN  NaN      135      886   \n",
       "9131          NaN          NaN  ...  NaN  NaN NaN  1.0      219      303   \n",
       "9132          NaN          NaN  ...  NaN  NaN NaN  NaN       17       26   \n",
       "\n",
       "      지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "0         NaN      NaN      NaN      NaN  \n",
       "1         NaN      NaN      NaN      NaN  \n",
       "2         NaN      NaN      NaN      NaN  \n",
       "3         NaN      NaN      NaN      NaN  \n",
       "4         NaN      NaN      NaN      NaN  \n",
       "...       ...      ...      ...      ...  \n",
       "9128      NaN      NaN      NaN      NaN  \n",
       "9129      NaN      NaN      NaN      NaN  \n",
       "9130      NaN      NaN      NaN      NaN  \n",
       "9131      NaN      NaN      NaN      NaN  \n",
       "9132      NaN      NaN      NaN      NaN  \n",
       "\n",
       "[9133 rows x 25 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "final= final[final['gid'] != \"Na\"]\n",
    "data_final = final.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>9128</th>\n",
       "      <td>다사559438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9129</th>\n",
       "      <td>다사618387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>다사559464</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9131</th>\n",
       "      <td>다사585417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219</td>\n",
       "      <td>303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>다사548430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9133 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  acci_cnt  2030val  과속방지턱  무인카메라  신호등   버스아침승차   버스아침하차  \\\n",
       "0     다사542434       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "1     다사547430       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "2     다사549419       1.0     67.0    0.0    0.0  0.0     0.00     0.00   \n",
       "3     다사551417       1.0     26.0    0.0    0.0  0.0  2486.75  3392.25   \n",
       "4     다사564435       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "...        ...       ...      ...    ...    ...  ...      ...      ...   \n",
       "9128  다사559438       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "9129  다사618387       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "9130  다사559464       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "9131  다사585417       0.0      0.0    0.0    0.0  7.0     0.00     0.00   \n",
       "9132  다사548430       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "\n",
       "           버스저녁승차       버스저녁하차  ...  스포츠  음식점   학교   교육  출근시간자전거  퇴근시간자전거  \\\n",
       "0        0.000000     0.000000  ...  0.0  0.0  0.0  0.0       46      201   \n",
       "1        0.000000     0.000000  ...  0.0  0.0  0.0  0.0       53      137   \n",
       "2        0.000000     0.000000  ...  0.0  0.0  0.0  0.0        2       10   \n",
       "3     1914.833333  3168.333333  ...  0.0  1.0  0.0  0.0        3        9   \n",
       "4        0.000000     0.000000  ...  0.0  3.0  0.0  0.0       16       54   \n",
       "...           ...          ...  ...  ...  ...  ...  ...      ...      ...   \n",
       "9128     0.000000     0.000000  ...  0.0  0.0  0.0  0.0       23      105   \n",
       "9129     0.000000     0.000000  ...  0.0  0.0  0.0  0.0        0        0   \n",
       "9130     0.000000     0.000000  ...  0.0  0.0  0.0  0.0      135      886   \n",
       "9131     0.000000     0.000000  ...  0.0  0.0  0.0  1.0      219      303   \n",
       "9132     0.000000     0.000000  ...  0.0  0.0  0.0  0.0       17       26   \n",
       "\n",
       "      지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "0         0.0      0.0      0.0      0.0  \n",
       "1         0.0      0.0      0.0      0.0  \n",
       "2         0.0      0.0      0.0      0.0  \n",
       "3         0.0      0.0      0.0      0.0  \n",
       "4         0.0      0.0      0.0      0.0  \n",
       "...       ...      ...      ...      ...  \n",
       "9128      0.0      0.0      0.0      0.0  \n",
       "9129      0.0      0.0      0.0      0.0  \n",
       "9130      0.0      0.0      0.0      0.0  \n",
       "9131      0.0      0.0      0.0      0.0  \n",
       "9132      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[9133 rows x 25 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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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>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침하차_x</th>\n",
       "      <th>버스아침하차_y</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9133.00000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "      <td>9133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.007446</td>\n",
       "      <td>29.177269</td>\n",
       "      <td>0.004599</td>\n",
       "      <td>0.010949</td>\n",
       "      <td>0.631446</td>\n",
       "      <td>113.063959</td>\n",
       "      <td>131.137970</td>\n",
       "      <td>122.118120</td>\n",
       "      <td>103.237529</td>\n",
       "      <td>0.117486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01719</td>\n",
       "      <td>2.197087</td>\n",
       "      <td>0.015110</td>\n",
       "      <td>0.407752</td>\n",
       "      <td>39.113982</td>\n",
       "      <td>153.912734</td>\n",
       "      <td>395.876163</td>\n",
       "      <td>1407.282164</td>\n",
       "      <td>1332.648965</td>\n",
       "      <td>549.111464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.088481</td>\n",
       "      <td>56.486076</td>\n",
       "      <td>0.086170</td>\n",
       "      <td>0.106154</td>\n",
       "      <td>2.061724</td>\n",
       "      <td>789.324845</td>\n",
       "      <td>665.306787</td>\n",
       "      <td>615.832761</td>\n",
       "      <td>500.981452</td>\n",
       "      <td>0.523321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.14129</td>\n",
       "      <td>6.461087</td>\n",
       "      <td>0.125536</td>\n",
       "      <td>1.658726</td>\n",
       "      <td>48.431358</td>\n",
       "      <td>220.715328</td>\n",
       "      <td>8077.529419</td>\n",
       "      <td>27058.220044</td>\n",
       "      <td>25634.684104</td>\n",
       "      <td>11159.086095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>996.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>31080.722222</td>\n",
       "      <td>12639.506944</td>\n",
       "      <td>10264.572917</td>\n",
       "      <td>7364.291667</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>2254.000000</td>\n",
       "      <td>492382.000000</td>\n",
       "      <td>955291.000000</td>\n",
       "      <td>912929.000000</td>\n",
       "      <td>557818.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt      2030val        과속방지턱        무인카메라          신호등  \\\n",
       "count  9133.000000  9133.000000  9133.000000  9133.000000  9133.000000   \n",
       "mean      0.007446    29.177269     0.004599     0.010949     0.631446   \n",
       "std       0.088481    56.486076     0.086170     0.106154     2.061724   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000    39.000000     0.000000     0.000000     0.000000   \n",
       "max       2.000000   996.000000     3.000000     2.000000    30.000000   \n",
       "\n",
       "           버스아침하차_x      버스아침하차_y        버스저녁승차       버스저녁하차       관광여가오락  \\\n",
       "count   9133.000000   9133.000000   9133.000000  9133.000000  9133.000000   \n",
       "mean     113.063959    131.137970    122.118120   103.237529     0.117486   \n",
       "std      789.324845    665.306787    615.832761   500.981452     0.523321   \n",
       "min        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "25%        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "50%        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "75%        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "max    31080.722222  12639.506944  10264.572917  7364.291667    13.000000   \n",
       "\n",
       "       ...         스포츠          음식점           학교           교육      출근시간자전거  \\\n",
       "count  ...  9133.00000  9133.000000  9133.000000  9133.000000  9133.000000   \n",
       "mean   ...     0.01719     2.197087     0.015110     0.407752    39.113982   \n",
       "std    ...     0.14129     6.461087     0.125536     1.658726    48.431358   \n",
       "min    ...     0.00000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%    ...     0.00000     0.000000     0.000000     0.000000     4.000000   \n",
       "50%    ...     0.00000     0.000000     0.000000     0.000000    23.000000   \n",
       "75%    ...     0.00000     1.000000     0.000000     0.000000    57.000000   \n",
       "max    ...     3.00000   251.000000     2.000000    52.000000   388.000000   \n",
       "\n",
       "           퇴근시간자전거        지하철아침승차        지하철아침하차        지하철저녁승차        지하철저녁하차  \n",
       "count  9133.000000    9133.000000    9133.000000    9133.000000    9133.000000  \n",
       "mean    153.912734     395.876163    1407.282164    1332.648965     549.111464  \n",
       "std     220.715328    8077.529419   27058.220044   25634.684104   11159.086095  \n",
       "min       0.000000       0.000000       0.000000       0.000000       0.000000  \n",
       "25%       7.000000       0.000000       0.000000       0.000000       0.000000  \n",
       "50%      71.000000       0.000000       0.000000       0.000000       0.000000  \n",
       "75%     204.000000       0.000000       0.000000       0.000000       0.000000  \n",
       "max    2254.000000  492382.000000  955291.000000  912929.000000  557818.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final.to_csv('output/그리드 전처리 완료/final_data.csv', encoding='utf-8-sig',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final = data_final.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final = data_final.reset_index(inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>gid</th>\n",
       "      <th>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
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       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
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       "      <td>67.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>9128</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>9131</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219</td>\n",
       "      <td>303</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8987</th>\n",
       "      <td>9132</td>\n",
       "      <td>다사548430</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8988 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index       gid  acci_cnt  2030val  과속방지턱  무인카메라  신호등   버스아침승차   버스아침하차  \\\n",
       "0         0  다사542434       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "1         1  다사547430       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "2         2  다사549419       1.0     67.0    0.0    0.0  0.0     0.00     0.00   \n",
       "3         3  다사551417       1.0     26.0    0.0    0.0  0.0  2486.75  3392.25   \n",
       "4         4  다사564435       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "...     ...       ...       ...      ...    ...    ...  ...      ...      ...   \n",
       "8983   9128  다사559438       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8984   9129  다사618387       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8985   9130  다사559464       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8986   9131  다사585417       0.0      0.0    0.0    0.0  7.0     0.00     0.00   \n",
       "8987   9132  다사548430       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "\n",
       "           버스저녁승차  ...  스포츠  음식점   학교   교육  출근시간자전거  퇴근시간자전거  지하철아침승차  \\\n",
       "0        0.000000  ...  0.0  0.0  0.0  0.0       46      201      0.0   \n",
       "1        0.000000  ...  0.0  0.0  0.0  0.0       53      137      0.0   \n",
       "2        0.000000  ...  0.0  0.0  0.0  0.0        2       10      0.0   \n",
       "3     1914.833333  ...  0.0  1.0  0.0  0.0        3        9      0.0   \n",
       "4        0.000000  ...  0.0  3.0  0.0  0.0       16       54      0.0   \n",
       "...           ...  ...  ...  ...  ...  ...      ...      ...      ...   \n",
       "8983     0.000000  ...  0.0  0.0  0.0  0.0       23      105      0.0   \n",
       "8984     0.000000  ...  0.0  0.0  0.0  0.0        0        0      0.0   \n",
       "8985     0.000000  ...  0.0  0.0  0.0  0.0      135      886      0.0   \n",
       "8986     0.000000  ...  0.0  0.0  0.0  1.0      219      303      0.0   \n",
       "8987     0.000000  ...  0.0  0.0  0.0  0.0       17       26      0.0   \n",
       "\n",
       "      지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "0         0.0      0.0      0.0  \n",
       "1         0.0      0.0      0.0  \n",
       "2         0.0      0.0      0.0  \n",
       "3         0.0      0.0      0.0  \n",
       "4         0.0      0.0      0.0  \n",
       "...       ...      ...      ...  \n",
       "8983      0.0      0.0      0.0  \n",
       "8984      0.0      0.0      0.0  \n",
       "8985      0.0      0.0      0.0  \n",
       "8986      0.0      0.0      0.0  \n",
       "8987      0.0      0.0      0.0  \n",
       "\n",
       "[8988 rows x 26 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>다사542434</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>다사547430</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>다사549419</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>다사551417</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2486.75</td>\n",
       "      <td>3392.25</td>\n",
       "      <td>1914.833333</td>\n",
       "      <td>3168.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사564435</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>8983</th>\n",
       "      <td>다사559438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8984</th>\n",
       "      <td>다사618387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8985</th>\n",
       "      <td>다사559464</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8986</th>\n",
       "      <td>다사585417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219</td>\n",
       "      <td>303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8987</th>\n",
       "      <td>다사548430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8988 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  acci_cnt  2030val  과속방지턱  무인카메라  신호등   버스아침승차   버스아침하차  \\\n",
       "0     다사542434       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "1     다사547430       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "2     다사549419       1.0     67.0    0.0    0.0  0.0     0.00     0.00   \n",
       "3     다사551417       1.0     26.0    0.0    0.0  0.0  2486.75  3392.25   \n",
       "4     다사564435       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "...        ...       ...      ...    ...    ...  ...      ...      ...   \n",
       "8983  다사559438       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8984  다사618387       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8985  다사559464       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8986  다사585417       0.0      0.0    0.0    0.0  7.0     0.00     0.00   \n",
       "8987  다사548430       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "\n",
       "           버스저녁승차       버스저녁하차  ...  스포츠  음식점   학교   교육  출근시간자전거  퇴근시간자전거  \\\n",
       "0        0.000000     0.000000  ...  0.0  0.0  0.0  0.0       46      201   \n",
       "1        0.000000     0.000000  ...  0.0  0.0  0.0  0.0       53      137   \n",
       "2        0.000000     0.000000  ...  0.0  0.0  0.0  0.0        2       10   \n",
       "3     1914.833333  3168.333333  ...  0.0  1.0  0.0  0.0        3        9   \n",
       "4        0.000000     0.000000  ...  0.0  3.0  0.0  0.0       16       54   \n",
       "...           ...          ...  ...  ...  ...  ...  ...      ...      ...   \n",
       "8983     0.000000     0.000000  ...  0.0  0.0  0.0  0.0       23      105   \n",
       "8984     0.000000     0.000000  ...  0.0  0.0  0.0  0.0        0        0   \n",
       "8985     0.000000     0.000000  ...  0.0  0.0  0.0  0.0      135      886   \n",
       "8986     0.000000     0.000000  ...  0.0  0.0  0.0  1.0      219      303   \n",
       "8987     0.000000     0.000000  ...  0.0  0.0  0.0  0.0       17       26   \n",
       "\n",
       "      지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "0         0.0      0.0      0.0      0.0  \n",
       "1         0.0      0.0      0.0      0.0  \n",
       "2         0.0      0.0      0.0      0.0  \n",
       "3         0.0      0.0      0.0      0.0  \n",
       "4         0.0      0.0      0.0      0.0  \n",
       "...       ...      ...      ...      ...  \n",
       "8983      0.0      0.0      0.0      0.0  \n",
       "8984      0.0      0.0      0.0      0.0  \n",
       "8985      0.0      0.0      0.0      0.0  \n",
       "8986      0.0      0.0      0.0      0.0  \n",
       "8987      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[8988 rows x 25 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final = data_final[['gid', 'acci_cnt', '2030val', '과속방지턱', '무인카메라', '신호등',\n",
    "       '버스아침승차', '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점',\n",
    "       '숙박', '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차',\n",
    "       '지하철아침하차', '지하철저녁승차', '지하철저녁하차']]\n",
    "\n",
    "data_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final.to_csv('output/그리드 전처리 완료/final_data2.csv', encoding='utf-8-sig',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## correlation plot \n",
    "\n",
    "# plot size 설정 \n",
    "f, ax = plt.subplots(figsize=(25,25))\n",
    "plt.rcParams['font.size'] =19\n",
    "\n",
    "# upper matrix\n",
    "matrix = np.triu(data_final.corr())\n",
    "# palette \n",
    "colormap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "sns.heatmap(data_final.corr(),\n",
    "                annot=True, annot_kws={\"size\": 18}, fmt ='.1g',\n",
    "                center = 0, vmin=-1, vmax=1, \n",
    "                cmap = colormap, cbar = False, #cbar_kws= {'orientation': 'horizontal'} , \n",
    "                square = True, mask = matrix)\n",
    "\n",
    "# plt.show()\n",
    "\n",
    "plt.savefig('output/그리드 전처리 완료/correlation_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import seaborn as sns\n",
    "import lightgbm as lgb\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gid', 'acci_cnt', '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
       "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
       "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
       "       '지하철저녁승차', '지하철저녁하차'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-160-b649affe6c7d>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n"
     ]
    }
   ],
   "source": [
    "# 전진단계별 선택법\n",
    "\n",
    "variables = data_final[[ '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
    "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
    "       '지하철저녁승차', '지하철저녁하차']]\n",
    "y = data_final[['acci_cnt']]\n",
    "\n",
    "\n",
    "selected_variables = [] ## 선택된 변수들\n",
    "sl_enter = 0.05\n",
    "sl_remove = 0.05\n",
    " \n",
    "sv_per_step = [] ## 각 스텝별로 선택된 변수들\n",
    "adjusted_r_squared = [] ## 각 스텝별 수정된 결정계수\n",
    "steps = [] ## 스텝\n",
    "step = 0\n",
    "while len(variables) > 0:\n",
    "    remainder = list(set(variables) - set(selected_variables))\n",
    "    pval = pd.Series(index=remainder) ## 변수의 p-value\n",
    "    ## 기존에 포함된 변수와 새로운 변수 하나씩 돌아가면서 \n",
    "    ## 선형 모형을 적합한다.\n",
    "    for col in remainder: \n",
    "        X = data_final[selected_variables+[col]]\n",
    "        X = sm.add_constant(X)\n",
    "        model = sm.OLS(y,X).fit()\n",
    "        pval[col] = model.pvalues[col]\n",
    " \n",
    "    min_pval = pval.min()\n",
    "    if min_pval < sl_enter: ## 최소 p-value 값이 기준 값보다 작으면 포함\n",
    "        selected_variables.append(pval.idxmin())\n",
    "        ## 선택된 변수들에대해서\n",
    "        ## 어떤 변수를 제거할지 고른다.\n",
    "        while len(selected_variables) > 0:\n",
    "            selected_X = data_final[selected_variables]\n",
    "            selected_X = sm.add_constant(selected_X)\n",
    "            selected_pval = sm.OLS(y,selected_X).fit().pvalues[1:] ## 절편항의 p-value는 뺀다\n",
    "            max_pval = selected_pval.max()\n",
    "            if max_pval >= sl_remove: ## 최대 p-value값이 기준값보다 크거나 같으면 제외\n",
    "                remove_variable = selected_pval.idxmax()\n",
    "                selected_variables.remove(remove_variable)\n",
    "            else:\n",
    "                break\n",
    "        \n",
    "        step += 1\n",
    "        steps.append(step)\n",
    "        adj_r_squared = sm.OLS(y,sm.add_constant(data_final[selected_variables])).fit().rsquared_adj\n",
    "        adjusted_r_squared.append(adj_r_squared)\n",
    "        sv_per_step.append(selected_variables.copy())\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['신호등', '버스저녁하차', '음식점', '소매점', '무인카메라', '생활서비스', '2030val']"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] =23\n",
    "\n",
    "# upper matrix\n",
    "fig = plt.figure(figsize=(50,30))\n",
    "fig.set_facecolor('white')\n",
    " \n",
    "font_size = 15\n",
    "plt.xticks(steps,[f'step {s}\\n'+'\\n'.join(sv_per_step[i]) for i,s in enumerate(steps)], fontsize=27)\n",
    "plt.plot(steps,adjusted_r_squared, marker='o')\n",
    "    \n",
    "plt.ylabel('Adjusted R Squared',fontsize=font_size)\n",
    "plt.grid(True)\n",
    "plt.savefig('output/그리드 전처리 완료/종속변수사고전진단계.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-169-b99382dca515>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n"
     ]
    }
   ],
   "source": [
    "# 전진단계별 선택법\n",
    "\n",
    "variables = data_final[[ 'acci_cnt', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
    "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
    "       '지하철저녁승차', '지하철저녁하차']]\n",
    "y = data_final[['2030val']]\n",
    "\n",
    "\n",
    "selected_variables = [] ## 선택된 변수들\n",
    "sl_enter = 0.05\n",
    "sl_remove = 0.05\n",
    " \n",
    "sv_per_step = [] ## 각 스텝별로 선택된 변수들\n",
    "adjusted_r_squared = [] ## 각 스텝별 수정된 결정계수\n",
    "steps = [] ## 스텝\n",
    "step = 0\n",
    "while len(variables) > 0:\n",
    "    remainder = list(set(variables) - set(selected_variables))\n",
    "    pval = pd.Series(index=remainder) ## 변수의 p-value\n",
    "    ## 기존에 포함된 변수와 새로운 변수 하나씩 돌아가면서 \n",
    "    ## 선형 모형을 적합한다.\n",
    "    for col in remainder: \n",
    "        X = data_final[selected_variables+[col]]\n",
    "        X = sm.add_constant(X)\n",
    "        model = sm.OLS(y,X).fit()\n",
    "        pval[col] = model.pvalues[col]\n",
    " \n",
    "    min_pval = pval.min()\n",
    "    if min_pval < sl_enter: ## 최소 p-value 값이 기준 값보다 작으면 포함\n",
    "        selected_variables.append(pval.idxmin())\n",
    "        ## 선택된 변수들에대해서\n",
    "        ## 어떤 변수를 제거할지 고른다.\n",
    "        while len(selected_variables) > 0:\n",
    "            selected_X = data_final[selected_variables]\n",
    "            selected_X = sm.add_constant(selected_X)\n",
    "            selected_pval = sm.OLS(y,selected_X).fit().pvalues[1:] ## 절편항의 p-value는 뺀다\n",
    "            max_pval = selected_pval.max()\n",
    "            if max_pval >= sl_remove: ## 최대 p-value값이 기준값보다 크거나 같으면 제외\n",
    "                remove_variable = selected_pval.idxmax()\n",
    "                selected_variables.remove(remove_variable)\n",
    "            else:\n",
    "                break\n",
    "        \n",
    "        step += 1\n",
    "        steps.append(step)\n",
    "        adj_r_squared = sm.OLS(y,sm.add_constant(data_final[selected_variables])).fit().rsquared_adj\n",
    "        adjusted_r_squared.append(adj_r_squared)\n",
    "        sv_per_step.append(selected_variables.copy())\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['생활서비스',\n",
       " '부동산',\n",
       " '버스아침하차',\n",
       " '숙박',\n",
       " '출근시간자전거',\n",
       " '신호등',\n",
       " '소매점',\n",
       " 'acci_cnt',\n",
       " '학교',\n",
       " '버스저녁하차',\n",
       " '버스저녁승차',\n",
       " '음식점',\n",
       " '관광여가오락']"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] =23\n",
    "\n",
    "# upper matrix\n",
    "fig = plt.figure(figsize=(50,30))\n",
    "fig.set_facecolor('white')\n",
    " \n",
    "font_size = 15\n",
    "plt.xticks(steps,[f'step {s}\\n'+'\\n'.join(sv_per_step[i]) for i,s in enumerate(steps)], fontsize=27)\n",
    "plt.plot(steps,adjusted_r_squared, marker='o')\n",
    "    \n",
    "plt.ylabel('Adjusted R Squared',fontsize=font_size)\n",
    "plt.grid(True)\n",
    "plt.savefig('output/그리드 전처리 완료/종속변수인구전진단계.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gid', 'acci_cnt', '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
       "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
       "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
       "       '지하철저녁승차', '지하철저녁하차'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "        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>gid</th>\n",
       "      <th>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>다사542434</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>0.183732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>다사547430</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.122488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>다사549419</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.326829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>다사551417</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.217674</td>\n",
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       "      <td>0.430773</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.003333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사564435</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>56</th>\n",
       "      <td>다사612444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.100478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>다사613445</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.072777</td>\n",
       "      <td>0.291838</td>\n",
       "      <td>0.416527</td>\n",
       "      <td>0.226660</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.293333</td>\n",
       "      <td>0.303349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>다사613446</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.204878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089552</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.214354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>다사619457</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>다사648432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.117073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>0.452690</td>\n",
       "      <td>0.423809</td>\n",
       "      <td>0.210064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>0.350239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gid  acci_cnt   2030val  과속방지턱  무인카메라       신호등    버스아침승차    버스아침하차  \\\n",
       "0   다사542434       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "1   다사547430       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "2   다사549419       1.0  0.326829    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "3   다사551417       1.0  0.126829    0.0    0.0  0.000000  0.217674  0.642015   \n",
       "4   다사564435       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "..       ...       ...       ...    ...    ...       ...       ...       ...   \n",
       "56  다사612444       1.0  0.000000    0.0    0.0  0.181818  0.000000  0.000000   \n",
       "57  다사613445       1.0  0.000000    0.0    0.0  0.181818  0.072777  0.291838   \n",
       "58  다사613446       1.0  0.204878    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "59  다사619457       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "60  다사648432       1.0  0.117073    0.0    0.0  0.227273  0.129848  0.452690   \n",
       "\n",
       "      버스저녁승차    버스저녁하차  ...  스포츠       음식점   학교        교육   출근시간자전거   퇴근시간자전거  \\\n",
       "0   0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000  0.146667  0.183732   \n",
       "1   0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000  0.170000  0.122488   \n",
       "2   0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000  0.000000  0.000957   \n",
       "3   0.247114  0.430773  ...  0.0  0.014925  0.0  0.000000  0.003333  0.000000   \n",
       "4   0.000000  0.000000  ...  0.0  0.044776  0.0  0.000000  0.046667  0.043062   \n",
       "..       ...       ...  ...  ...       ...  ...       ...       ...       ...   \n",
       "56  0.000000  0.000000  ...  0.0  0.134328  0.0  0.333333  0.110000  0.100478   \n",
       "57  0.416527  0.226660  ...  0.0  0.223881  0.0  0.333333  0.293333  0.303349   \n",
       "58  0.000000  0.000000  ...  0.0  0.089552  0.0  0.277778  0.210000  0.214354   \n",
       "59  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000  1.000000  0.800000   \n",
       "60  0.423809  0.210064  ...  0.0  1.000000  0.0  0.222222  0.546667  0.350239   \n",
       "\n",
       "    지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "0       0.0      0.0      0.0      0.0  \n",
       "1       0.0      0.0      0.0      0.0  \n",
       "2       0.0      0.0      0.0      0.0  \n",
       "3       0.0      0.0      0.0      0.0  \n",
       "4       0.0      0.0      0.0      0.0  \n",
       "..      ...      ...      ...      ...  \n",
       "56      0.0      0.0      0.0      0.0  \n",
       "57      0.0      0.0      0.0      0.0  \n",
       "58      0.0      0.0      0.0      0.0  \n",
       "59      0.0      0.0      0.0      0.0  \n",
       "60      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 25 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final2 = pd.read_csv(\"output/그리드 전처리 완료/final3.csv\", dtype = {'gid':str, 'acci_cnt':float, '2030val':float, '과속방지턱':float, '무인카메라':float, '신호등':float, '버스아침승차':float,\n",
    "       '버스아침하차':float, '버스저녁승차':float, '버스저녁하차':float, '관광여가오락':float, '부동산':float, '생활서비스':float, '소매점':float, '숙박':float,\n",
    "       '스포츠':float, '음식점':float, '학교':float, '교육':float, '출근시간자전거':float, '퇴근시간자전거':float, '지하철아침승차':float, '지하철아침하차':float,\n",
    "       '지하철저녁승차':float, '지하철저녁하차':float})\n",
    "data_final2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
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       "      <th>지하철아침하차</th>\n",
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       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>다사542434</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>0.183732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사547430</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.122488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.326829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.217674</td>\n",
       "      <td>0.642015</td>\n",
       "      <td>0.247114</td>\n",
       "      <td>0.430773</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>다사564435</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <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>다사612444</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.100478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613445</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.072777</td>\n",
       "      <td>0.291838</td>\n",
       "      <td>0.416527</td>\n",
       "      <td>0.226660</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.293333</td>\n",
       "      <td>0.303349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613446</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.204878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089552</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.214354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사619457</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사648432</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.117073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>0.452690</td>\n",
       "      <td>0.423809</td>\n",
       "      <td>0.210064</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>0.350239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt   2030val  과속방지턱  무인카메라       신호등    버스아침승차    버스아침하차  \\\n",
       "gid                                                                        \n",
       "다사542434       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사547430       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사549419       1.0  0.326829    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사551417       1.0  0.126829    0.0    0.0  0.000000  0.217674  0.642015   \n",
       "다사564435       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "...            ...       ...    ...    ...       ...       ...       ...   \n",
       "다사612444       1.0  0.000000    0.0    0.0  0.181818  0.000000  0.000000   \n",
       "다사613445       1.0  0.000000    0.0    0.0  0.181818  0.072777  0.291838   \n",
       "다사613446       1.0  0.204878    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사619457       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사648432       1.0  0.117073    0.0    0.0  0.227273  0.129848  0.452690   \n",
       "\n",
       "            버스저녁승차    버스저녁하차    관광여가오락  ...  스포츠       음식점   학교        교육  \\\n",
       "gid                                     ...                                 \n",
       "다사542434  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사547430  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사549419  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사551417  0.247114  0.430773  0.000000  ...  0.0  0.014925  0.0  0.000000   \n",
       "다사564435  0.000000  0.000000  0.000000  ...  0.0  0.044776  0.0  0.000000   \n",
       "...            ...       ...       ...  ...  ...       ...  ...       ...   \n",
       "다사612444  0.000000  0.000000  0.000000  ...  0.0  0.134328  0.0  0.333333   \n",
       "다사613445  0.416527  0.226660  0.333333  ...  0.0  0.223881  0.0  0.333333   \n",
       "다사613446  0.000000  0.000000  0.000000  ...  0.0  0.089552  0.0  0.277778   \n",
       "다사619457  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사648432  0.423809  0.210064  0.333333  ...  0.0  1.000000  0.0  0.222222   \n",
       "\n",
       "           출근시간자전거   퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                                               \n",
       "다사542434  0.146667  0.183732      0.0      0.0      0.0      0.0  \n",
       "다사547430  0.170000  0.122488      0.0      0.0      0.0      0.0  \n",
       "다사549419  0.000000  0.000957      0.0      0.0      0.0      0.0  \n",
       "다사551417  0.003333  0.000000      0.0      0.0      0.0      0.0  \n",
       "다사564435  0.046667  0.043062      0.0      0.0      0.0      0.0  \n",
       "...            ...       ...      ...      ...      ...      ...  \n",
       "다사612444  0.110000  0.100478      0.0      0.0      0.0      0.0  \n",
       "다사613445  0.293333  0.303349      0.0      0.0      0.0      0.0  \n",
       "다사613446  0.210000  0.214354      0.0      0.0      0.0      0.0  \n",
       "다사619457  1.000000  0.800000      0.0      0.0      0.0      0.0  \n",
       "다사648432  0.546667  0.350239      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 24 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final2 = data_final2.set_index(\"gid\")\n",
    "data_final2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-aa2e6d095dd7>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n"
     ]
    }
   ],
   "source": [
    "# 전진단계별 선택법\n",
    "\n",
    "variables = data_final2[['2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
    "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
    "       '지하철저녁승차', '지하철저녁하차']]\n",
    "y = data_final2[['acci_cnt']]\n",
    "\n",
    "\n",
    "selected_variables = [] ## 선택된 변수들\n",
    "sl_enter = 0.05\n",
    "sl_remove = 0.05\n",
    " \n",
    "sv_per_step = [] ## 각 스텝별로 선택된 변수들\n",
    "adjusted_r_squared = [] ## 각 스텝별 수정된 결정계수\n",
    "steps = [] ## 스텝\n",
    "step = 0\n",
    "while len(variables) > 0:\n",
    "    remainder = list(set(variables) - set(selected_variables))\n",
    "    pval = pd.Series(index=remainder) ## 변수의 p-value\n",
    "    ## 기존에 포함된 변수와 새로운 변수 하나씩 돌아가면서 \n",
    "    ## 선형 모형을 적합한다.\n",
    "    for col in remainder: \n",
    "        X = data_final2[selected_variables+[col]]\n",
    "        X = sm.add_constant(X)\n",
    "        model = sm.OLS(y,X).fit()\n",
    "        pval[col] = model.pvalues[col]\n",
    " \n",
    "    min_pval = pval.min()\n",
    "    if min_pval < sl_enter: ## 최소 p-value 값이 기준 값보다 작으면 포함\n",
    "        selected_variables.append(pval.idxmin())\n",
    "        ## 선택된 변수들에대해서\n",
    "        ## 어떤 변수를 제거할지 고른다.\n",
    "        while len(selected_variables) > 0:\n",
    "            selected_X = data_final2[selected_variables]\n",
    "            selected_X = sm.add_constant(selected_X)\n",
    "            selected_pval = sm.OLS(y,selected_X).fit().pvalues[1:] ## 절편항의 p-value는 뺀다\n",
    "            max_pval = selected_pval.max()\n",
    "            if max_pval >= sl_remove: ## 최대 p-value값이 기준값보다 크거나 같으면 제외\n",
    "                remove_variable = selected_pval.idxmax()\n",
    "                selected_variables.remove(remove_variable)\n",
    "            else:\n",
    "                break\n",
    "        \n",
    "        step += 1\n",
    "        steps.append(step)\n",
    "        adj_r_squared = sm.OLS(y,sm.add_constant(data_final2[selected_variables])).fit().rsquared_adj\n",
    "        adjusted_r_squared.append(adj_r_squared)\n",
    "        sv_per_step.append(selected_variables.copy())\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 전진 선택법\n",
    "\n",
    "variables = data_final2[[ '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
    "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
    "       '지하철저녁승차', '지하철저녁하차']]\n",
    "y = data_final2[['acci_cnt']]\n",
    "\n",
    "selected_variables = [] ## 선택된 변수들\n",
    "sl_enter = 0.05\n",
    " \n",
    "sv_per_step = [] ## 각 스텝별로 선택된 변수들\n",
    "adjusted_r_squared = [] ## 각 스텝별 수정된 결정계수\n",
    "steps = [] ## 스텝\n",
    "step = 0\n",
    "while len(variables) > 0:\n",
    "    remainder = list(set(variables) - set(selected_variables))\n",
    "    pval = pd.Series(index=remainder) ## 변수의 p-value\n",
    "    ## 기존에 포함된 변수와 새로운 변수 하나씩 돌아가면서 \n",
    "    ## 선형 모형을 적합한다.\n",
    "    for col in remainder: \n",
    "        X = data_final2[selected_variables+[col]]\n",
    "        X = sm.add_constant(X)\n",
    "        model = sm.OLS(y,X).fit()\n",
    "        pval[col] = model.pvalues[col]\n",
    " \n",
    "    min_pval = pval.min()\n",
    "    if min_pval < sl_enter: ## 최소 p-value 값이 기준 값보다 작으면 포함\n",
    "        selected_variables.append(pval.idxmin())\n",
    "        \n",
    "        step += 1\n",
    "        steps.append(step)\n",
    "        adj_r_squared = sm.OLS(y,sm.add_constant(data_final2[selected_variables])).fit().rsquared_adj\n",
    "        adjusted_r_squared.append(adj_r_squared)\n",
    "        sv_per_step.append(selected_variables.copy())\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>부동산</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>0.183732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.122488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.326829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217674</td>\n",
       "      <td>0.642015</td>\n",
       "      <td>0.247114</td>\n",
       "      <td>0.430773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    2030val  과속방지턱  무인카메라  신호등    버스아침승차    버스아침하차    버스저녁승차    버스저녁하차  \\\n",
       "0  0.000000    0.0    0.0  0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "1  0.000000    0.0    0.0  0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "2  0.326829    0.0    0.0  0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "3  0.126829    0.0    0.0  0.0  0.217674  0.642015  0.247114  0.430773   \n",
       "4  0.000000    0.0    0.0  0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "   관광여가오락       부동산  ...  스포츠       음식점   학교   교육   출근시간자전거   퇴근시간자전거  \\\n",
       "0     0.0  0.000000  ...  0.0  0.000000  0.0  0.0  0.146667  0.183732   \n",
       "1     0.0  0.000000  ...  0.0  0.000000  0.0  0.0  0.170000  0.122488   \n",
       "2     0.0  0.000000  ...  0.0  0.000000  0.0  0.0  0.000000  0.000957   \n",
       "3     0.0  0.230769  ...  0.0  0.014925  0.0  0.0  0.003333  0.000000   \n",
       "4     0.0  0.000000  ...  0.0  0.044776  0.0  0.0  0.046667  0.043062   \n",
       "\n",
       "   지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "0      0.0      0.0      0.0      0.0  \n",
       "1      0.0      0.0      0.0      0.0  \n",
       "2      0.0      0.0      0.0      0.0  \n",
       "3      0.0      0.0      0.0      0.0  \n",
       "4      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data2 = data_final2[[ '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
    "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
    "       '지하철저녁승차', '지하철저녁하차']]\n",
    "x_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "target =  data_final2[['acci_cnt']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.353486</td>\n",
       "      <td>2030val</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>과속방지턱</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.608882</td>\n",
       "      <td>무인카메라</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.373385</td>\n",
       "      <td>신호등</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.982395</td>\n",
       "      <td>버스아침승차</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.448350</td>\n",
       "      <td>버스아침하차</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.914334</td>\n",
       "      <td>버스저녁승차</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>87.749908</td>\n",
       "      <td>버스저녁하차</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.379910</td>\n",
       "      <td>관광여가오락</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.401654</td>\n",
       "      <td>부동산</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.469045</td>\n",
       "      <td>생활서비스</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.791585</td>\n",
       "      <td>소매점</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.329596</td>\n",
       "      <td>숙박</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.807142</td>\n",
       "      <td>스포츠</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.743848</td>\n",
       "      <td>음식점</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.225756</td>\n",
       "      <td>학교</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.364068</td>\n",
       "      <td>교육</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.159348</td>\n",
       "      <td>출근시간자전거</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.476255</td>\n",
       "      <td>퇴근시간자전거</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>inf</td>\n",
       "      <td>지하철아침승차</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>inf</td>\n",
       "      <td>지하철아침하차</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>inf</td>\n",
       "      <td>지하철저녁승차</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>inf</td>\n",
       "      <td>지하철저녁하차</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VIF Factor features\n",
       "0     1.353486  2030val\n",
       "1          NaN    과속방지턱\n",
       "2     3.608882    무인카메라\n",
       "3     3.373385      신호등\n",
       "4    34.982395   버스아침승차\n",
       "5    28.448350   버스아침하차\n",
       "6    11.914334   버스저녁승차\n",
       "7    87.749908   버스저녁하차\n",
       "8     3.379910   관광여가오락\n",
       "9     5.401654      부동산\n",
       "10    6.469045    생활서비스\n",
       "11    7.791585      소매점\n",
       "12    1.329596       숙박\n",
       "13    2.807142      스포츠\n",
       "14    4.743848      음식점\n",
       "15    1.225756       학교\n",
       "16    1.364068       교육\n",
       "17   11.159348  출근시간자전거\n",
       "18    9.476255  퇴근시간자전거\n",
       "19         inf  지하철아침승차\n",
       "20         inf  지하철아침하차\n",
       "21         inf  지하철저녁승차\n",
       "22         inf  지하철저녁하차"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# VIF사용을 위한 라이브러리, statsmodels안에 존재한다.\n",
    "# 사실 모든 통계기법이 statsmodels 모듈에 존재하여 \n",
    "# 이 중에 필요한 통계기법을 찾아 import를 진행하면 된다.\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(x_data2.values, i) for i in range(x_data2.shape[1])]\n",
    "vif[\"features\"] = x_data2.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>acci_cnt</td>     <th>  R-squared:         </th> <td>   0.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 20 May 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.305</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:26:25</td>     <th>  Log-Likelihood:    </th> <td>  32.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    61</td>      <th>  AIC:               </th> <td>  -24.36</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    41</td>      <th>  BIC:               </th> <td>   17.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    19</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>    1.0848</td> <td>    0.051</td> <td>   21.252</td> <td> 0.000</td> <td>    0.982</td> <td>    1.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2030val</th> <td>   -0.1405</td> <td>    0.140</td> <td>   -1.006</td> <td> 0.320</td> <td>   -0.422</td> <td>    0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>과속방지턱</th>   <td>  7.21e-16</td> <td>  2.9e-16</td> <td>    2.490</td> <td> 0.017</td> <td> 1.36e-16</td> <td> 1.31e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>무인카메라</th>   <td>    0.6919</td> <td>    0.334</td> <td>    2.072</td> <td> 0.045</td> <td>    0.018</td> <td>    1.366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>신호등</th>     <td>   -0.0187</td> <td>    0.144</td> <td>   -0.130</td> <td> 0.898</td> <td>   -0.310</td> <td>    0.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침승차</th>  <td>   -1.6565</td> <td>    0.775</td> <td>   -2.137</td> <td> 0.039</td> <td>   -3.222</td> <td>   -0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침하차</th>  <td>   -1.2695</td> <td>    0.375</td> <td>   -3.385</td> <td> 0.002</td> <td>   -2.027</td> <td>   -0.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁승차</th>  <td>   -0.5745</td> <td>    0.383</td> <td>   -1.499</td> <td> 0.141</td> <td>   -1.348</td> <td>    0.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁하차</th>  <td>    3.4370</td> <td>    0.976</td> <td>    3.522</td> <td> 0.001</td> <td>    1.466</td> <td>    5.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>관광여가오락</th>  <td>    0.0609</td> <td>    0.136</td> <td>    0.447</td> <td> 0.657</td> <td>   -0.214</td> <td>    0.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>부동산</th>     <td>   -0.0148</td> <td>    0.309</td> <td>   -0.048</td> <td> 0.962</td> <td>   -0.639</td> <td>    0.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>생활서비스</th>   <td>   -0.1292</td> <td>    0.263</td> <td>   -0.491</td> <td> 0.626</td> <td>   -0.660</td> <td>    0.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>소매점</th>     <td>    0.5582</td> <td>    0.436</td> <td>    1.279</td> <td> 0.208</td> <td>   -0.323</td> <td>    1.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>숙박</th>      <td>   -0.0120</td> <td>    0.146</td> <td>   -0.083</td> <td> 0.935</td> <td>   -0.306</td> <td>    0.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>스포츠</th>     <td>   -0.0905</td> <td>    0.226</td> <td>   -0.400</td> <td> 0.691</td> <td>   -0.547</td> <td>    0.366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>음식점</th>     <td>   -0.1783</td> <td>    0.214</td> <td>   -0.833</td> <td> 0.410</td> <td>   -0.611</td> <td>    0.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>학교</th>      <td>    0.0561</td> <td>    0.193</td> <td>    0.290</td> <td> 0.773</td> <td>   -0.334</td> <td>    0.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>교육</th>      <td>   -0.1360</td> <td>    0.164</td> <td>   -0.831</td> <td> 0.411</td> <td>   -0.467</td> <td>    0.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>출근시간자전거</th> <td>    0.2211</td> <td>    0.254</td> <td>    0.869</td> <td> 0.390</td> <td>   -0.292</td> <td>    0.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>퇴근시간자전거</th> <td>   -0.2708</td> <td>    0.228</td> <td>   -1.186</td> <td> 0.242</td> <td>   -0.732</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침승차</th> <td>    0.0601</td> <td>    0.060</td> <td>    0.998</td> <td> 0.324</td> <td>   -0.061</td> <td>    0.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침하차</th> <td>    0.0601</td> <td>    0.060</td> <td>    0.998</td> <td> 0.324</td> <td>   -0.061</td> <td>    0.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁승차</th> <td>    0.0601</td> <td>    0.060</td> <td>    0.998</td> <td> 0.324</td> <td>   -0.061</td> <td>    0.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁하차</th> <td>    0.0601</td> <td>    0.060</td> <td>    0.998</td> <td> 0.324</td> <td>   -0.061</td> <td>    0.182</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>83.308</td> <th>  Durbin-Watson:     </th> <td>   1.657</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>1198.153</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.858</td> <th>  Prob(JB):          </th> <td>6.67e-261</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>23.294</td> <th>  Cond. No.          </th> <td>2.09e+33</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.81e-65. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               acci_cnt   R-squared:                       0.357\n",
       "Model:                            OLS   Adj. R-squared:                  0.059\n",
       "Method:                 Least Squares   F-statistic:                     1.199\n",
       "Date:                Thu, 20 May 2021   Prob (F-statistic):              0.305\n",
       "Time:                        17:26:25   Log-Likelihood:                 32.178\n",
       "No. Observations:                  61   AIC:                            -24.36\n",
       "Df Residuals:                      41   BIC:                             17.86\n",
       "Df Model:                          19                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.0848      0.051     21.252      0.000       0.982       1.188\n",
       "2030val       -0.1405      0.140     -1.006      0.320      -0.422       0.141\n",
       "과속방지턱        7.21e-16    2.9e-16      2.490      0.017    1.36e-16    1.31e-15\n",
       "무인카메라          0.6919      0.334      2.072      0.045       0.018       1.366\n",
       "신호등           -0.0187      0.144     -0.130      0.898      -0.310       0.272\n",
       "버스아침승차        -1.6565      0.775     -2.137      0.039      -3.222      -0.091\n",
       "버스아침하차        -1.2695      0.375     -3.385      0.002      -2.027      -0.512\n",
       "버스저녁승차        -0.5745      0.383     -1.499      0.141      -1.348       0.199\n",
       "버스저녁하차         3.4370      0.976      3.522      0.001       1.466       5.407\n",
       "관광여가오락         0.0609      0.136      0.447      0.657      -0.214       0.336\n",
       "부동산           -0.0148      0.309     -0.048      0.962      -0.639       0.609\n",
       "생활서비스         -0.1292      0.263     -0.491      0.626      -0.660       0.402\n",
       "소매점            0.5582      0.436      1.279      0.208      -0.323       1.440\n",
       "숙박            -0.0120      0.146     -0.083      0.935      -0.306       0.282\n",
       "스포츠           -0.0905      0.226     -0.400      0.691      -0.547       0.366\n",
       "음식점           -0.1783      0.214     -0.833      0.410      -0.611       0.254\n",
       "학교             0.0561      0.193      0.290      0.773      -0.334       0.446\n",
       "교육            -0.1360      0.164     -0.831      0.411      -0.467       0.195\n",
       "출근시간자전거        0.2211      0.254      0.869      0.390      -0.292       0.735\n",
       "퇴근시간자전거       -0.2708      0.228     -1.186      0.242      -0.732       0.190\n",
       "지하철아침승차        0.0601      0.060      0.998      0.324      -0.061       0.182\n",
       "지하철아침하차        0.0601      0.060      0.998      0.324      -0.061       0.182\n",
       "지하철저녁승차        0.0601      0.060      0.998      0.324      -0.061       0.182\n",
       "지하철저녁하차        0.0601      0.060      0.998      0.324      -0.061       0.182\n",
       "==============================================================================\n",
       "Omnibus:                       83.308   Durbin-Watson:                   1.657\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1198.153\n",
       "Skew:                           3.858   Prob(JB):                    6.67e-261\n",
       "Kurtosis:                      23.294   Cond. No.                     2.09e+33\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.81e-65. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상수항 추가\n",
    "x_data2_ = sm.add_constant(x_data2, has_constant = \"add\")\n",
    "\n",
    "# 회귀모델 적합\n",
    "multi_model2 = sm.OLS(target, x_data2_)\n",
    "fitted_multi_model2 = multi_model2.fit()\n",
    "\n",
    "# 결과 출력\n",
    "fitted_multi_model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fitted_multi_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m-------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-540f6697c948>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfitted_multi_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'fitted_multi_model' is not defined"
     ]
    }
   ],
   "source": [
    "fitted_multi_model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전진단계별 선택법\n",
    "\n",
    "variables = data_final2[[ '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
    "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
    "       '지하철저녁승차', '지하철저녁하차']]\n",
    "y = data_final2[['acci_cnt']]\n",
    "\n",
    "\n",
    "selected_variables = [] ## 선택된 변수들\n",
    "sl_enter = 0.05\n",
    "sl_remove = 0.05\n",
    " \n",
    "sv_per_step = [] ## 각 스텝별로 선택된 변수들\n",
    "adjusted_r_squared = [] ## 각 스텝별 수정된 결정계수\n",
    "steps = [] ## 스텝\n",
    "step = 0\n",
    "while len(variables) > 0:\n",
    "    remainder = list(set(variables) - set(selected_variables))\n",
    "    pval = pd.Series(index=remainder) ## 변수의 p-value\n",
    "    ## 기존에 포함된 변수와 새로운 변수 하나씩 돌아가면서 \n",
    "    ## 선형 모형을 적합한다.\n",
    "    for col in remainder: \n",
    "        X = data_final2[selected_variables+[col]]\n",
    "        X = sm.add_constant(X)\n",
    "        model = sm.OLS(y,X).fit()\n",
    "        pval[col] = model.pvalues[col]\n",
    " \n",
    "    min_pval = pval.min()\n",
    "    if min_pval < sl_enter: ## 최소 p-value 값이 기준 값보다 작으면 포함\n",
    "        selected_variables.append(pval.idxmin())\n",
    "        ## 선택된 변수들에대해서\n",
    "        ## 어떤 변수를 제거할지 고른다.\n",
    "        while len(selected_variables) > 0:\n",
    "            selected_X = data_final2[selected_variables]\n",
    "            selected_X = sm.add_constant(selected_X)\n",
    "            selected_pval = sm.OLS(y,selected_X).fit().pvalues[1:] ## 절편항의 p-value는 뺀다\n",
    "            max_pval = selected_pval.max()\n",
    "            if max_pval >= sl_remove: ## 최대 p-value값이 기준값보다 크거나 같으면 제외\n",
    "                remove_variable = selected_pval.idxmax()\n",
    "                selected_variables.remove(remove_variable)\n",
    "            else:\n",
    "                break\n",
    "        \n",
    "        step += 1\n",
    "        steps.append(step)\n",
    "        adj_r_squared = sm.OLS(y,sm.add_constant(data_final2[selected_variables])).fit().rsquared_adj\n",
    "        adjusted_r_squared.append(adj_r_squared)\n",
    "        sv_per_step.append(selected_variables.copy())\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = 'object'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>acci_cnt</td>     <th>  R-squared (uncentered):</th>      <td>   0.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   11.09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 20 May 2021</td> <th>  Prob (F-statistic):</th>          <td>5.23e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:48:09</td>     <th>  Log-Likelihood:    </th>          <td> -51.730</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    61</td>      <th>  AIC:               </th>          <td>   125.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    50</td>      <th>  BIC:               </th>          <td>   148.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2030val</th> <td>    1.5118</td> <td>    0.416</td> <td>    3.638</td> <td> 0.001</td> <td>    0.677</td> <td>    2.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>무인카메라</th>   <td>   -0.2976</td> <td>    1.008</td> <td>   -0.295</td> <td> 0.769</td> <td>   -2.322</td> <td>    1.727</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>신호등</th>     <td>    0.9444</td> <td>    0.347</td> <td>    2.720</td> <td> 0.009</td> <td>    0.247</td> <td>    1.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>관광여가오락</th>  <td>   -0.0696</td> <td>    0.377</td> <td>   -0.184</td> <td> 0.854</td> <td>   -0.828</td> <td>    0.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>부동산</th>     <td>    0.8055</td> <td>    1.008</td> <td>    0.799</td> <td> 0.428</td> <td>   -1.220</td> <td>    2.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>생활서비스</th>   <td>   -0.4347</td> <td>    0.906</td> <td>   -0.480</td> <td> 0.634</td> <td>   -2.255</td> <td>    1.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>소매점</th>     <td>    0.6321</td> <td>    1.161</td> <td>    0.544</td> <td> 0.589</td> <td>   -1.701</td> <td>    2.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>숙박</th>      <td>    0.5195</td> <td>    0.480</td> <td>    1.082</td> <td> 0.285</td> <td>   -0.445</td> <td>    1.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>스포츠</th>     <td>    0.8421</td> <td>    0.775</td> <td>    1.087</td> <td> 0.282</td> <td>   -0.714</td> <td>    2.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>학교</th>      <td>   -0.2636</td> <td>    0.662</td> <td>   -0.398</td> <td> 0.692</td> <td>   -1.593</td> <td>    1.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>퇴근시간자전거</th> <td>    1.5029</td> <td>    0.347</td> <td>    4.326</td> <td> 0.000</td> <td>    0.805</td> <td>    2.201</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.251</td> <th>  Durbin-Watson:     </th> <td>   1.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.119</td> <th>  Jarque-Bera (JB):  </th> <td>   4.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.252</td> <th>  Prob(JB):          </th> <td>   0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.172</td> <th>  Cond. No.          </th> <td>    8.18</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:               acci_cnt   R-squared (uncentered):                   0.709\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.645\n",
       "Method:                 Least Squares   F-statistic:                              11.09\n",
       "Date:                Thu, 20 May 2021   Prob (F-statistic):                    5.23e-10\n",
       "Time:                        17:48:09   Log-Likelihood:                         -51.730\n",
       "No. Observations:                  61   AIC:                                      125.5\n",
       "Df Residuals:                      50   BIC:                                      148.7\n",
       "Df Model:                          11                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "2030val        1.5118      0.416      3.638      0.001       0.677       2.347\n",
       "무인카메라         -0.2976      1.008     -0.295      0.769      -2.322       1.727\n",
       "신호등            0.9444      0.347      2.720      0.009       0.247       1.642\n",
       "관광여가오락        -0.0696      0.377     -0.184      0.854      -0.828       0.688\n",
       "부동산            0.8055      1.008      0.799      0.428      -1.220       2.831\n",
       "생활서비스         -0.4347      0.906     -0.480      0.634      -2.255       1.386\n",
       "소매점            0.6321      1.161      0.544      0.589      -1.701       2.965\n",
       "숙박             0.5195      0.480      1.082      0.285      -0.445       1.484\n",
       "스포츠            0.8421      0.775      1.087      0.282      -0.714       2.398\n",
       "학교            -0.2636      0.662     -0.398      0.692      -1.593       1.065\n",
       "퇴근시간자전거        1.5029      0.347      4.326      0.000       0.805       2.201\n",
       "==============================================================================\n",
       "Omnibus:                        4.251   Durbin-Watson:                   1.186\n",
       "Prob(Omnibus):                  0.119   Jarque-Bera (JB):                4.137\n",
       "Skew:                          -0.252   Prob(JB):                        0.126\n",
       "Kurtosis:                       4.172   Cond. No.                         8.18\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = data_final2[['2030val', '무인카메라', '신호등', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠','학교', '퇴근시간자전거']] \n",
    "target = data_final2[[\"acci_cnt\"]]\n",
    "\n",
    "\n",
    "# OLS 검정\n",
    "multi_model = sm.OLS(target, x_data)\n",
    "fitted_multi_model = multi_model.fit()\n",
    "fitted_multi_model.summary()"
   ]
  }
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