빈도분석(공영주차장).ipynb

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      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# 한글 설치 코드 : 설치 후 런타임 다시 시작\n",
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLn3Kiqz9MjH",
        "outputId": "9c52a9d2-4ca9-4671-c10a-5bb0e3cebabe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  fonts-nanum\n",
            "0 upgraded, 1 newly installed, 0 to remove and 24 not upgraded.\n",
            "Need to get 9,599 kB of archives.\n",
            "After this operation, 29.6 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 fonts-nanum all 20180306-3 [9,599 kB]\n",
            "Fetched 9,599 kB in 1s (7,665 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 122518 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20180306-3_all.deb ...\n",
            "Unpacking fonts-nanum (20180306-3) ...\n",
            "Setting up fonts-nanum (20180306-3) ...\n",
            "Processing triggers for fontconfig (2.13.1-2ubuntu3) ...\n",
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 10 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/usr/share/fonts/truetype: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/liberation: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/nanum: skipping, looped directory detected\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams['font.family'] = 'NanumBarunGothic'\n",
        "import matplotlib.font_manager as fm"
      ],
      "metadata": {
        "id": "25n1QVurjFRT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7dVD2jpafgn",
        "outputId": "b3270072-7c57-4cc4-86d7-ecd0c17b7fc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import codecs\n",
        "\n",
        "filename = \"/content/drive/MyDrive/[03팀] 김가경, 김수민, 민채원/code/Gwangjin-gu Public Parking lot.csv\"\n",
        "\n",
        "with codecs.open(filename, 'r', 'euc_kr', errors='ignore') as f:\n",
        "    reader = csv.reader(f)\n",
        "    for row in reader:\n",
        "        print(row)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iPbzQScmI7G",
        "outputId": "0b56c15b-c853-4cd4-cc34-a2492222df6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['연번', '운영개시일', '동명', '시설명', '위치', '부지면적', '주차면수 소계', '주차면수 노상', '주차면수 노외', '거주자', '공영', '급지', '주차요금 5분당', '주차요금 주간', '주차요금 야간', '주차요금 종일', '시설규모', '비고']\n",
            "['1', '2010-09', '중곡1동', '중곡제일전통시장 공영', '중곡1동 221-6', '695', '21', '0', '21', '', 'O', '2', '250원', '', '3만원', '', '', '구유지(지역경제과·위탁)']\n",
            "['2', '1998-03', '중곡2동', '장신구', '중곡2동 133-10', '531', '22', '0', '22', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '시유지']\n",
            "['3', '1999-06', '중곡2동', '중곡동복개천', '중곡2동 150-196', '', '78', '78', '0', '', 'O', '2', '200원', '', '', '10만원', '', '시유도로(노상·공단위탁)']\n",
            "['4', '2009-04', '중곡3동', '중곡3동마을공원', '중곡3동 195-1', '3038.3', '162', '', '162', 'O', 'O', '5', '75원 ', '4만원', '3만원', '5만원', '지하2층', '구유지']\n",
            "['5', '2010-01', '중곡3동', '중곡체육센터', '중곡3동 168-8', '3,690.00', '6', '', '6', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '구유지']\n",
            "['6', '1996-01', '중곡4동', '신성시장길', '중곡4동 453 일대', '', '33', '33', '', '', 'O', '2', '250원', '', '', '10만원', '', '구유도로(노상·민간위탁)']\n",
            "['7', '2001-08', '중곡4동', '새오름', '중곡4동 143-70', '137', '7', '', '7', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '구유지']\n",
            "['8', '2003-04', '중곡4동', '용마산남', '중곡4동 69-2', '777.9', '25', '', '25', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '구유지']\n",
            "['9', '2005-04', '중곡4동', '긴고랑길공영', '중곡4동 72-1 외', '1,653.00', '53', '', '53', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '구유지']\n",
            "['10', '1998-12', '중곡4동', '해오름', '중곡4동 87-14', '462.9', '20', '', '20', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '시유지']\n",
            "['11', '2005-04', '중곡4동', '뻥튀기골주차장(용마공원주차장)', '중곡4동 176-110', '595', '18', '', '18', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '시유지(임야일부)']\n",
            "['12', '2009-01', '중곡4동', '중곡4동 599-6 인근', '면목4동 산75-2', '124.2', '9', '', '9', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '시유지(임야일부)']\n",
            "['13', '2009-11', '중곡4동', '한림대부지', '중곡4동 176-119', '179', '8', '', '8', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '시유지']\n",
            "['14', '2005-06', '능동', '능동공영', '능동 205', '1,167.00', '77', '', '77', 'O', 'O', '4', '150원', '4만원', '3만원', '5만원', '2층3단', '구유지']\n",
            "['15', '1998-06', '능동', '능동공동', '능동 377', '485', '25', '', '25', 'O', '', '5', '', '4만원', '3만원', '5만원', '1층2단', '시유지']\n",
            "['16', '2007-03', '구의1동', '구의1동 공영', '구의1동 243-78', '1,037.00', '172', '', '172', 'O', 'O', '4', '150원', '5만원', '3만원', '8만원', '7층8단', '구유지']\n",
            "['17', '1999-01', '구의1동', '구의번영', '구의1동 230-7', '794.4', '34', '', '34', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '시유지']\n",
            "['18', '2017-01', '구의2동', '구의2동 주택가공동', '구의2동 30-9', '1,170.60', '90', '', '90', 'O', 'O', '5', '75원', '4만원', '3만원', '5만원', '지하1층 2층3단', '구유지']\n",
            "['19', '2006-01', '구의2동', '광문학당', '구의2동 72-8', '258.5', '12', '', '12', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '구유지(가정복지과)']\n",
            "['20', '2003-06', '구의2동', '공원(동의초인근)', '구의2동 6', '630', '11', '', '11', 'O', '', '5', '', '3만원', '2만원', '4만원', '', '시유지(공원)']\n",
            "['21', '1994-09', '구의3동', '동서울길 공영', '구의3동 609-1 일대', '627.4', '94', '68', '26', '', 'O', '2', '250원', '', '', '10만원', '', '구유도로(노상+노외)']\n",
            "['22', '2009-01', '구의3동', '구의3동 공영', '구의3동 219-15', '1,237.40', '159', '', '159', 'O', 'O', '4', '150원', '5만원', '3만원', '8만원', '지하1층 4층5단', '구유지']\n",
            "['23', '2008-01', '구의3동', '올림픽대교(시영)', '구의3동 623-1', '', '19', '', '19', 'O', '', '5', '', '3만원', '2만원', '4만원', '', '시유지']\n",
            "['24', '2005-05', '광장동', '세촌', '광장동 275-5', '121', '7', '', '7', 'O', '', '5', '', '3만원', '2만원', '4만원', '', '구유지']\n",
            "['25', '2008-12', '광장동', '광장공동 ', '광장동 394-43 외', '441', '8', '', '8', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '구유지(가로경관과)']\n",
            "['26', '2000-03', '광장동', '큰나루', '광장동 126', '512', '13', '', '13', 'O', '', '3', '', '', '', '10만원', '', '시유지']\n",
            "['27', '2010-06', '자양1동', '자양전통시장 공영', '자양1동 631-18', '447.5', '24', '', '24', '', 'O', '2', '250원', '', '3만원', '', '2층', '구유지(지역경제과·위탁)']\n",
            "['28', '2000-08', '자양2동', '유수지', '자양2동 594', '9,000.00', '281', '', '281', 'O', 'O', '5', '150원', '4만원', '3만원', '5만원', '', '시유지']\n",
            "['29', '1994-01', '자양3동', '송림식당길', '자양3동 712 일대', '', '94', '94', '', '', 'O', '2', '200원', '', '', '9만원', '', '시유도로(노상·공단위탁)']\n",
            "['30', '2005-03', '자양4동', '자양4동 공영', '자양4동 5-5', '1,988.40', '86', '', '86', '', 'O', '4', '150원', '', '', '10만원', '', '구유지']\n",
            "['31', '2009-08', '자양4동', '행복제2', '자양4동 10-2', '1,313.10', '53', '', '53', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '시유지']\n",
            "['32', '2005-12', '화양동', '느티나무(화양공동)', '화양동 110-67', '1,248.00', '27', '', '27', 'O', '', '5', '', '4만원', '3만원', '5만원', '', '구유지']\n",
            "['33', '1999-04', '화양동', '화양동 공영', '화양동 63-2', '664', '44', '', '44', '', 'O', '4', '150원', '', '', '8만원', '2층3단', '시유지(민간위탁)']\n",
            "['34', '2006-03', '화양동', '화양동 마을마당', '화양동 523-2', '112', '6', '', '6', 'O', '', '5', '', '', '', '5만원', '', '국유지']\n",
            "['35', '2003-12', '군자동', '군자동 공영', '군자동 2-41', '1,075.10', '53', '', '53', 'O', '', '5', '', '4만원', '3만원', '5만원', '2층3단', '구유지']\n",
            "['36', '2006-07', '군자동', '광진광장 공영', '군자동 374', '1,516.00', '36', '', '36', '', 'O', '2', '250원', '', '', '12만원', '', '구유지']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df1 = pd.read_csv(filename, encoding='cp949')\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7LAQChuemOr2",
        "outputId": "b21c7dbf-07c8-4b81-b672-623473b68ea1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    연번    운영개시일    동명               시설명             위치      부지면적  주차면수 소계  \\\n",
              "0    1  2010-09  중곡1동       중곡제일전통시장 공영     중곡1동 221-6       695       21   \n",
              "1    2  1998-03  중곡2동               장신구    중곡2동 133-10       531       22   \n",
              "2    3  1999-06  중곡2동            중곡동복개천   중곡2동 150-196       NaN       78   \n",
              "3    4  2009-04  중곡3동          중곡3동마을공원     중곡3동 195-1    3038.3      162   \n",
              "4    5  2010-01  중곡3동            중곡체육센터     중곡3동 168-8  3,690.00        6   \n",
              "5    6  1996-01  중곡4동             신성시장길    중곡4동 453 일대       NaN       33   \n",
              "6    7  2001-08  중곡4동               새오름    중곡4동 143-70       137        7   \n",
              "7    8  2003-04  중곡4동              용마산남      중곡4동 69-2     777.9       25   \n",
              "8    9  2005-04  중곡4동            긴고랑길공영    중곡4동 72-1 외  1,653.00       53   \n",
              "9   10  1998-12  중곡4동               해오름     중곡4동 87-14     462.9       20   \n",
              "10  11  2005-04  중곡4동  뻥튀기골주차장(용마공원주차장)   중곡4동 176-110       595       18   \n",
              "11  12  2009-01  중곡4동     중곡4동 599-6 인근     면목4동 산75-2     124.2        9   \n",
              "12  13  2009-11  중곡4동             한림대부지   중곡4동 176-119       179        8   \n",
              "13  14  2005-06    능동              능동공영         능동 205  1,167.00       77   \n",
              "14  15  1998-06    능동              능동공동         능동 377       485       25   \n",
              "15  16  2007-03  구의1동           구의1동 공영    구의1동 243-78  1,037.00      172   \n",
              "16  17  1999-01  구의1동              구의번영     구의1동 230-7     794.4       34   \n",
              "17  18  2017-01  구의2동        구의2동 주택가공동      구의2동 30-9  1,170.60       90   \n",
              "18  19  2006-01  구의2동              광문학당      구의2동 72-8     258.5       12   \n",
              "19  20  2003-06  구의2동         공원(동의초인근)         구의2동 6       630       11   \n",
              "20  21  1994-09  구의3동           동서울길 공영  구의3동 609-1 일대     627.4       94   \n",
              "21  22  2009-01  구의3동           구의3동 공영    구의3동 219-15  1,237.40      159   \n",
              "22  23  2008-01  구의3동         올림픽대교(시영)     구의3동 623-1       NaN       19   \n",
              "23  24  2005-05   광장동                세촌      광장동 275-5       121        7   \n",
              "24  25  2008-12   광장동             광장공동    광장동 394-43 외       441        8   \n",
              "25  26  2000-03   광장동               큰나루        광장동 126       512       13   \n",
              "26  27  2010-06  자양1동         자양전통시장 공영    자양1동 631-18     447.5       24   \n",
              "27  28  2000-08  자양2동               유수지       자양2동 594  9,000.00      281   \n",
              "28  29  1994-01  자양3동             송림식당길    자양3동 712 일대       NaN       94   \n",
              "29  30  2005-03  자양4동           자양4동 공영       자양4동 5-5  1,988.40       86   \n",
              "30  31  2009-08  자양4동              행복제2      자양4동 10-2  1,313.10       53   \n",
              "31  32  2005-12   화양동        느티나무(화양공동)     화양동 110-67  1,248.00       27   \n",
              "32  33  1999-04   화양동            화양동 공영       화양동 63-2       664       44   \n",
              "33  34  2006-03   화양동          화양동 마을마당      화양동 523-2       112        6   \n",
              "34  35  2003-12   군자동            군자동 공영       군자동 2-41  1,075.10       53   \n",
              "35  36  2006-07   군자동           광진광장 공영        군자동 374  1,516.00       36   \n",
              "\n",
              "    주차면수 노상  주차면수 노외  거주자   공영  급지 주차요금 5분당 주차요금 주간 주차요금 야간 주차요금 종일  \\\n",
              "0       0.0     21.0  NaN    O   2     250원     NaN     3만원     NaN   \n",
              "1       0.0     22.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "2      78.0      0.0  NaN    O   2     200원     NaN     NaN    10만원   \n",
              "3       NaN    162.0    O    O   5     75원      4만원     3만원     5만원   \n",
              "4       NaN      6.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "5      33.0      NaN  NaN    O   2     250원     NaN     NaN    10만원   \n",
              "6       NaN      7.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "7       NaN     25.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "8       NaN     53.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "9       NaN     20.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "10      NaN     18.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "11      NaN      9.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "12      NaN      8.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "13      NaN     77.0    O    O   4     150원     4만원     3만원     5만원   \n",
              "14      NaN     25.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "15      NaN    172.0    O    O   4     150원     5만원     3만원     8만원   \n",
              "16      NaN     34.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "17      NaN     90.0    O    O   5      75원     4만원     3만원     5만원   \n",
              "18      NaN     12.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "19      NaN     11.0    O  NaN   5      NaN     3만원     2만원     4만원   \n",
              "20     68.0     26.0  NaN    O   2     250원     NaN     NaN    10만원   \n",
              "21      NaN    159.0    O    O   4     150원     5만원     3만원     8만원   \n",
              "22      NaN     19.0    O  NaN   5      NaN     3만원     2만원     4만원   \n",
              "23      NaN      7.0    O  NaN   5      NaN     3만원     2만원     4만원   \n",
              "24      NaN      8.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "25      NaN     13.0    O  NaN   3      NaN     NaN     NaN    10만원   \n",
              "26      NaN     24.0  NaN    O   2     250원     NaN     3만원     NaN   \n",
              "27      NaN    281.0    O    O   5     150원     4만원     3만원     5만원   \n",
              "28     94.0      NaN  NaN    O   2     200원     NaN     NaN     9만원   \n",
              "29      NaN     86.0  NaN    O   4     150원     NaN     NaN    10만원   \n",
              "30      NaN     53.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "31      NaN     27.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "32      NaN     44.0  NaN    O   4     150원     NaN     NaN     8만원   \n",
              "33      NaN      6.0    O  NaN   5      NaN     NaN     NaN     5만원   \n",
              "34      NaN     53.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "35      NaN     36.0  NaN    O   2     250원     NaN     NaN    12만원   \n",
              "\n",
              "         시설규모             비고  \n",
              "0         NaN  구유지(지역경제과·위탁)  \n",
              "1         NaN            시유지  \n",
              "2         NaN  시유도로(노상·공단위탁)  \n",
              "3        지하2층            구유지  \n",
              "4         NaN            구유지  \n",
              "5         NaN  구유도로(노상·민간위탁)  \n",
              "6         NaN            구유지  \n",
              "7         NaN            구유지  \n",
              "8         NaN            구유지  \n",
              "9         NaN            시유지  \n",
              "10        NaN      시유지(임야일부)  \n",
              "11        NaN      시유지(임야일부)  \n",
              "12        NaN            시유지  \n",
              "13       2층3단            구유지  \n",
              "14       1층2단            시유지  \n",
              "15       7층8단            구유지  \n",
              "16        NaN            시유지  \n",
              "17  지하1층 2층3단            구유지  \n",
              "18        NaN     구유지(가정복지과)  \n",
              "19        NaN        시유지(공원)  \n",
              "20        NaN    구유도로(노상+노외)  \n",
              "21  지하1층 4층5단            구유지  \n",
              "22        NaN            시유지  \n",
              "23        NaN            구유지  \n",
              "24        NaN     구유지(가로경관과)  \n",
              "25        NaN            시유지  \n",
              "26         2층  구유지(지역경제과·위탁)  \n",
              "27        NaN            시유지  \n",
              "28        NaN  시유도로(노상·공단위탁)  \n",
              "29        NaN            구유지  \n",
              "30        NaN            시유지  \n",
              "31        NaN            구유지  \n",
              "32       2층3단      시유지(민간위탁)  \n",
              "33        NaN            국유지  \n",
              "34       2층3단            구유지  \n",
              "35        NaN            구유지  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc308942-1169-42ca-930a-b57511e40595\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>연번</th>\n",
              "      <th>운영개시일</th>\n",
              "      <th>동명</th>\n",
              "      <th>시설명</th>\n",
              "      <th>위치</th>\n",
              "      <th>부지면적</th>\n",
              "      <th>주차면수 소계</th>\n",
              "      <th>주차면수 노상</th>\n",
              "      <th>주차면수 노외</th>\n",
              "      <th>거주자</th>\n",
              "      <th>공영</th>\n",
              "      <th>급지</th>\n",
              "      <th>주차요금 5분당</th>\n",
              "      <th>주차요금 주간</th>\n",
              "      <th>주차요금 야간</th>\n",
              "      <th>주차요금 종일</th>\n",
              "      <th>시설규모</th>\n",
              "      <th>비고</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-09</td>\n",
              "      <td>중곡1동</td>\n",
              "      <td>중곡제일전통시장 공영</td>\n",
              "      <td>중곡1동 221-6</td>\n",
              "      <td>695</td>\n",
              "      <td>21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지(지역경제과·위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1998-03</td>\n",
              "      <td>중곡2동</td>\n",
              "      <td>장신구</td>\n",
              "      <td>중곡2동 133-10</td>\n",
              "      <td>531</td>\n",
              "      <td>22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1999-06</td>\n",
              "      <td>중곡2동</td>\n",
              "      <td>중곡동복개천</td>\n",
              "      <td>중곡2동 150-196</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>200원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유도로(노상·공단위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2009-04</td>\n",
              "      <td>중곡3동</td>\n",
              "      <td>중곡3동마을공원</td>\n",
              "      <td>중곡3동 195-1</td>\n",
              "      <td>3038.3</td>\n",
              "      <td>162</td>\n",
              "      <td>NaN</td>\n",
              "      <td>162.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>5</td>\n",
              "      <td>75원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>지하2층</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2010-01</td>\n",
              "      <td>중곡3동</td>\n",
              "      <td>중곡체육센터</td>\n",
              "      <td>중곡3동 168-8</td>\n",
              "      <td>3,690.00</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>1996-01</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>신성시장길</td>\n",
              "      <td>중곡4동 453 일대</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유도로(노상·민간위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>2001-08</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>새오름</td>\n",
              "      <td>중곡4동 143-70</td>\n",
              "      <td>137</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>2003-04</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>용마산남</td>\n",
              "      <td>중곡4동 69-2</td>\n",
              "      <td>777.9</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>2005-04</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>긴고랑길공영</td>\n",
              "      <td>중곡4동 72-1 외</td>\n",
              "      <td>1,653.00</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1998-12</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>해오름</td>\n",
              "      <td>중곡4동 87-14</td>\n",
              "      <td>462.9</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>2005-04</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>뻥튀기골주차장(용마공원주차장)</td>\n",
              "      <td>중곡4동 176-110</td>\n",
              "      <td>595</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지(임야일부)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>2009-01</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>중곡4동 599-6 인근</td>\n",
              "      <td>면목4동 산75-2</td>\n",
              "      <td>124.2</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지(임야일부)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>2009-11</td>\n",
              "      <td>중곡4동</td>\n",
              "      <td>한림대부지</td>\n",
              "      <td>중곡4동 176-119</td>\n",
              "      <td>179</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>2005-06</td>\n",
              "      <td>능동</td>\n",
              "      <td>능동공영</td>\n",
              "      <td>능동 205</td>\n",
              "      <td>1,167.00</td>\n",
              "      <td>77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>2층3단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>1998-06</td>\n",
              "      <td>능동</td>\n",
              "      <td>능동공동</td>\n",
              "      <td>능동 377</td>\n",
              "      <td>485</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>1층2단</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>2007-03</td>\n",
              "      <td>구의1동</td>\n",
              "      <td>구의1동 공영</td>\n",
              "      <td>구의1동 243-78</td>\n",
              "      <td>1,037.00</td>\n",
              "      <td>172</td>\n",
              "      <td>NaN</td>\n",
              "      <td>172.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>8만원</td>\n",
              "      <td>7층8단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>1999-01</td>\n",
              "      <td>구의1동</td>\n",
              "      <td>구의번영</td>\n",
              "      <td>구의1동 230-7</td>\n",
              "      <td>794.4</td>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>2017-01</td>\n",
              "      <td>구의2동</td>\n",
              "      <td>구의2동 주택가공동</td>\n",
              "      <td>구의2동 30-9</td>\n",
              "      <td>1,170.60</td>\n",
              "      <td>90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>5</td>\n",
              "      <td>75원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>지하1층 2층3단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>2006-01</td>\n",
              "      <td>구의2동</td>\n",
              "      <td>광문학당</td>\n",
              "      <td>구의2동 72-8</td>\n",
              "      <td>258.5</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지(가정복지과)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>2003-06</td>\n",
              "      <td>구의2동</td>\n",
              "      <td>공원(동의초인근)</td>\n",
              "      <td>구의2동 6</td>\n",
              "      <td>630</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>2만원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지(공원)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>1994-09</td>\n",
              "      <td>구의3동</td>\n",
              "      <td>동서울길 공영</td>\n",
              "      <td>구의3동 609-1 일대</td>\n",
              "      <td>627.4</td>\n",
              "      <td>94</td>\n",
              "      <td>68.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유도로(노상+노외)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>2009-01</td>\n",
              "      <td>구의3동</td>\n",
              "      <td>구의3동 공영</td>\n",
              "      <td>구의3동 219-15</td>\n",
              "      <td>1,237.40</td>\n",
              "      <td>159</td>\n",
              "      <td>NaN</td>\n",
              "      <td>159.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>8만원</td>\n",
              "      <td>지하1층 4층5단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>2008-01</td>\n",
              "      <td>구의3동</td>\n",
              "      <td>올림픽대교(시영)</td>\n",
              "      <td>구의3동 623-1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>2만원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>2005-05</td>\n",
              "      <td>광장동</td>\n",
              "      <td>세촌</td>\n",
              "      <td>광장동 275-5</td>\n",
              "      <td>121</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>2만원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>2008-12</td>\n",
              "      <td>광장동</td>\n",
              "      <td>광장공동</td>\n",
              "      <td>광장동 394-43 외</td>\n",
              "      <td>441</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지(가로경관과)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>2000-03</td>\n",
              "      <td>광장동</td>\n",
              "      <td>큰나루</td>\n",
              "      <td>광장동 126</td>\n",
              "      <td>512</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>2010-06</td>\n",
              "      <td>자양1동</td>\n",
              "      <td>자양전통시장 공영</td>\n",
              "      <td>자양1동 631-18</td>\n",
              "      <td>447.5</td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2층</td>\n",
              "      <td>구유지(지역경제과·위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>2000-08</td>\n",
              "      <td>자양2동</td>\n",
              "      <td>유수지</td>\n",
              "      <td>자양2동 594</td>\n",
              "      <td>9,000.00</td>\n",
              "      <td>281</td>\n",
              "      <td>NaN</td>\n",
              "      <td>281.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>5</td>\n",
              "      <td>150원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>1994-01</td>\n",
              "      <td>자양3동</td>\n",
              "      <td>송림식당길</td>\n",
              "      <td>자양3동 712 일대</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94</td>\n",
              "      <td>94.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>200원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유도로(노상·공단위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>2005-03</td>\n",
              "      <td>자양4동</td>\n",
              "      <td>자양4동 공영</td>\n",
              "      <td>자양4동 5-5</td>\n",
              "      <td>1,988.40</td>\n",
              "      <td>86</td>\n",
              "      <td>NaN</td>\n",
              "      <td>86.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>2009-08</td>\n",
              "      <td>자양4동</td>\n",
              "      <td>행복제2</td>\n",
              "      <td>자양4동 10-2</td>\n",
              "      <td>1,313.10</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>32</td>\n",
              "      <td>2005-12</td>\n",
              "      <td>화양동</td>\n",
              "      <td>느티나무(화양공동)</td>\n",
              "      <td>화양동 110-67</td>\n",
              "      <td>1,248.00</td>\n",
              "      <td>27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>33</td>\n",
              "      <td>1999-04</td>\n",
              "      <td>화양동</td>\n",
              "      <td>화양동 공영</td>\n",
              "      <td>화양동 63-2</td>\n",
              "      <td>664</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8만원</td>\n",
              "      <td>2층3단</td>\n",
              "      <td>시유지(민간위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>2006-03</td>\n",
              "      <td>화양동</td>\n",
              "      <td>화양동 마을마당</td>\n",
              "      <td>화양동 523-2</td>\n",
              "      <td>112</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>국유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>35</td>\n",
              "      <td>2003-12</td>\n",
              "      <td>군자동</td>\n",
              "      <td>군자동 공영</td>\n",
              "      <td>군자동 2-41</td>\n",
              "      <td>1,075.10</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>2층3단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>36</td>\n",
              "      <td>2006-07</td>\n",
              "      <td>군자동</td>\n",
              "      <td>광진광장 공영</td>\n",
              "      <td>군자동 374</td>\n",
              "      <td>1,516.00</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc308942-1169-42ca-930a-b57511e40595')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bc308942-1169-42ca-930a-b57511e40595 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bc308942-1169-42ca-930a-b57511e40595');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1['동명'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOFSr8iS2Lnb",
        "outputId": "067a7369-3a59-4fbb-cbdd-87d5833582a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['중곡1동' '중곡2동' '중곡3동' '중곡4동' '능동' '구의1동' '구의2동' '구의3동' '광장동' '자양1동' '자양2동'\n",
            " '자양3동' '자양4동' '화양동' '군자동']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for element in df1['동명'].unique():\n",
        "    print(element)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1wabtzQ4I2M",
        "outputId": "7680bc6e-c837-4c84-e6ab-113d7b9c0f81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "중곡1동\n",
            "중곡2동\n",
            "중곡3동\n",
            "중곡4동\n",
            "능동\n",
            "구의1동\n",
            "구의2동\n",
            "구의3동\n",
            "광장동\n",
            "자양1동\n",
            "자양2동\n",
            "자양3동\n",
            "자양4동\n",
            "화양동\n",
            "군자동\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "동명 범주화 => 구의1동, 2동, 3동은 구의동으로 전처리"
      ],
      "metadata": {
        "id": "wileueeb-S2L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.loc[df1['동명'] == '구의1동', '동명'] = 1\n",
        "df1.loc[df1['동명'] == '구의2동', '동명'] = 1\n",
        "df1.loc[df1['동명'] == '구의3동', '동명'] = 1\n",
        "df1.loc[df1['동명'] == '중곡1동', '동명'] = 2\n",
        "df1.loc[df1['동명'] == '중곡2동', '동명'] = 2\n",
        "df1.loc[df1['동명'] == '중곡3동', '동명'] = 2\n",
        "df1.loc[df1['동명'] == '중곡4동', '동명'] = 2\n",
        "df1.loc[df1['동명'] == '능동', '동명'] = 3\n",
        "df1.loc[df1['동명'] == '자양1동', '동명'] = 4\n",
        "df1.loc[df1['동명'] == '자양2동', '동명'] = 4\n",
        "df1.loc[df1['동명'] == '자양3동', '동명'] = 4\n",
        "df1.loc[df1['동명'] == '자양4동', '동명'] = 4\n",
        "df1.loc[df1['동명'] == '광장동', '동명'] = 5\n",
        "df1.loc[df1['동명'] == '화양동', '동명'] = 6\n",
        "df1.loc[df1['동명'] == '군자동', '동명'] = 7\n",
        "\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cDG6UtB4m2Ya",
        "outputId": "58438aac-5864-4565-eb8f-f5e8bec4cbe3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    연번    운영개시일 동명               시설명             위치      부지면적  주차면수 소계  \\\n",
              "0    1  2010-09  2       중곡제일전통시장 공영     중곡1동 221-6       695       21   \n",
              "1    2  1998-03  2               장신구    중곡2동 133-10       531       22   \n",
              "2    3  1999-06  2            중곡동복개천   중곡2동 150-196       NaN       78   \n",
              "3    4  2009-04  2          중곡3동마을공원     중곡3동 195-1    3038.3      162   \n",
              "4    5  2010-01  2            중곡체육센터     중곡3동 168-8  3,690.00        6   \n",
              "5    6  1996-01  2             신성시장길    중곡4동 453 일대       NaN       33   \n",
              "6    7  2001-08  2               새오름    중곡4동 143-70       137        7   \n",
              "7    8  2003-04  2              용마산남      중곡4동 69-2     777.9       25   \n",
              "8    9  2005-04  2            긴고랑길공영    중곡4동 72-1 외  1,653.00       53   \n",
              "9   10  1998-12  2               해오름     중곡4동 87-14     462.9       20   \n",
              "10  11  2005-04  2  뻥튀기골주차장(용마공원주차장)   중곡4동 176-110       595       18   \n",
              "11  12  2009-01  2     중곡4동 599-6 인근     면목4동 산75-2     124.2        9   \n",
              "12  13  2009-11  2             한림대부지   중곡4동 176-119       179        8   \n",
              "13  14  2005-06  3              능동공영         능동 205  1,167.00       77   \n",
              "14  15  1998-06  3              능동공동         능동 377       485       25   \n",
              "15  16  2007-03  1           구의1동 공영    구의1동 243-78  1,037.00      172   \n",
              "16  17  1999-01  1              구의번영     구의1동 230-7     794.4       34   \n",
              "17  18  2017-01  1        구의2동 주택가공동      구의2동 30-9  1,170.60       90   \n",
              "18  19  2006-01  1              광문학당      구의2동 72-8     258.5       12   \n",
              "19  20  2003-06  1         공원(동의초인근)         구의2동 6       630       11   \n",
              "20  21  1994-09  1           동서울길 공영  구의3동 609-1 일대     627.4       94   \n",
              "21  22  2009-01  1           구의3동 공영    구의3동 219-15  1,237.40      159   \n",
              "22  23  2008-01  1         올림픽대교(시영)     구의3동 623-1       NaN       19   \n",
              "23  24  2005-05  5                세촌      광장동 275-5       121        7   \n",
              "24  25  2008-12  5             광장공동    광장동 394-43 외       441        8   \n",
              "25  26  2000-03  5               큰나루        광장동 126       512       13   \n",
              "26  27  2010-06  4         자양전통시장 공영    자양1동 631-18     447.5       24   \n",
              "27  28  2000-08  4               유수지       자양2동 594  9,000.00      281   \n",
              "28  29  1994-01  4             송림식당길    자양3동 712 일대       NaN       94   \n",
              "29  30  2005-03  4           자양4동 공영       자양4동 5-5  1,988.40       86   \n",
              "30  31  2009-08  4              행복제2      자양4동 10-2  1,313.10       53   \n",
              "31  32  2005-12  6        느티나무(화양공동)     화양동 110-67  1,248.00       27   \n",
              "32  33  1999-04  6            화양동 공영       화양동 63-2       664       44   \n",
              "33  34  2006-03  6          화양동 마을마당      화양동 523-2       112        6   \n",
              "34  35  2003-12  7            군자동 공영       군자동 2-41  1,075.10       53   \n",
              "35  36  2006-07  7           광진광장 공영        군자동 374  1,516.00       36   \n",
              "\n",
              "    주차면수 노상  주차면수 노외  거주자   공영  급지 주차요금 5분당 주차요금 주간 주차요금 야간 주차요금 종일  \\\n",
              "0       0.0     21.0  NaN    O   2     250원     NaN     3만원     NaN   \n",
              "1       0.0     22.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "2      78.0      0.0  NaN    O   2     200원     NaN     NaN    10만원   \n",
              "3       NaN    162.0    O    O   5     75원      4만원     3만원     5만원   \n",
              "4       NaN      6.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "5      33.0      NaN  NaN    O   2     250원     NaN     NaN    10만원   \n",
              "6       NaN      7.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "7       NaN     25.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "8       NaN     53.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "9       NaN     20.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "10      NaN     18.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "11      NaN      9.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "12      NaN      8.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "13      NaN     77.0    O    O   4     150원     4만원     3만원     5만원   \n",
              "14      NaN     25.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "15      NaN    172.0    O    O   4     150원     5만원     3만원     8만원   \n",
              "16      NaN     34.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "17      NaN     90.0    O    O   5      75원     4만원     3만원     5만원   \n",
              "18      NaN     12.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "19      NaN     11.0    O  NaN   5      NaN     3만원     2만원     4만원   \n",
              "20     68.0     26.0  NaN    O   2     250원     NaN     NaN    10만원   \n",
              "21      NaN    159.0    O    O   4     150원     5만원     3만원     8만원   \n",
              "22      NaN     19.0    O  NaN   5      NaN     3만원     2만원     4만원   \n",
              "23      NaN      7.0    O  NaN   5      NaN     3만원     2만원     4만원   \n",
              "24      NaN      8.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "25      NaN     13.0    O  NaN   3      NaN     NaN     NaN    10만원   \n",
              "26      NaN     24.0  NaN    O   2     250원     NaN     3만원     NaN   \n",
              "27      NaN    281.0    O    O   5     150원     4만원     3만원     5만원   \n",
              "28     94.0      NaN  NaN    O   2     200원     NaN     NaN     9만원   \n",
              "29      NaN     86.0  NaN    O   4     150원     NaN     NaN    10만원   \n",
              "30      NaN     53.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "31      NaN     27.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "32      NaN     44.0  NaN    O   4     150원     NaN     NaN     8만원   \n",
              "33      NaN      6.0    O  NaN   5      NaN     NaN     NaN     5만원   \n",
              "34      NaN     53.0    O  NaN   5      NaN     4만원     3만원     5만원   \n",
              "35      NaN     36.0  NaN    O   2     250원     NaN     NaN    12만원   \n",
              "\n",
              "         시설규모             비고  \n",
              "0         NaN  구유지(지역경제과·위탁)  \n",
              "1         NaN            시유지  \n",
              "2         NaN  시유도로(노상·공단위탁)  \n",
              "3        지하2층            구유지  \n",
              "4         NaN            구유지  \n",
              "5         NaN  구유도로(노상·민간위탁)  \n",
              "6         NaN            구유지  \n",
              "7         NaN            구유지  \n",
              "8         NaN            구유지  \n",
              "9         NaN            시유지  \n",
              "10        NaN      시유지(임야일부)  \n",
              "11        NaN      시유지(임야일부)  \n",
              "12        NaN            시유지  \n",
              "13       2층3단            구유지  \n",
              "14       1층2단            시유지  \n",
              "15       7층8단            구유지  \n",
              "16        NaN            시유지  \n",
              "17  지하1층 2층3단            구유지  \n",
              "18        NaN     구유지(가정복지과)  \n",
              "19        NaN        시유지(공원)  \n",
              "20        NaN    구유도로(노상+노외)  \n",
              "21  지하1층 4층5단            구유지  \n",
              "22        NaN            시유지  \n",
              "23        NaN            구유지  \n",
              "24        NaN     구유지(가로경관과)  \n",
              "25        NaN            시유지  \n",
              "26         2층  구유지(지역경제과·위탁)  \n",
              "27        NaN            시유지  \n",
              "28        NaN  시유도로(노상·공단위탁)  \n",
              "29        NaN            구유지  \n",
              "30        NaN            시유지  \n",
              "31        NaN            구유지  \n",
              "32       2층3단      시유지(민간위탁)  \n",
              "33        NaN            국유지  \n",
              "34       2층3단            구유지  \n",
              "35        NaN            구유지  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59b66426-3967-49d7-a0ec-5f7922bca45a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>연번</th>\n",
              "      <th>운영개시일</th>\n",
              "      <th>동명</th>\n",
              "      <th>시설명</th>\n",
              "      <th>위치</th>\n",
              "      <th>부지면적</th>\n",
              "      <th>주차면수 소계</th>\n",
              "      <th>주차면수 노상</th>\n",
              "      <th>주차면수 노외</th>\n",
              "      <th>거주자</th>\n",
              "      <th>공영</th>\n",
              "      <th>급지</th>\n",
              "      <th>주차요금 5분당</th>\n",
              "      <th>주차요금 주간</th>\n",
              "      <th>주차요금 야간</th>\n",
              "      <th>주차요금 종일</th>\n",
              "      <th>시설규모</th>\n",
              "      <th>비고</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-09</td>\n",
              "      <td>2</td>\n",
              "      <td>중곡제일전통시장 공영</td>\n",
              "      <td>중곡1동 221-6</td>\n",
              "      <td>695</td>\n",
              "      <td>21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지(지역경제과·위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1998-03</td>\n",
              "      <td>2</td>\n",
              "      <td>장신구</td>\n",
              "      <td>중곡2동 133-10</td>\n",
              "      <td>531</td>\n",
              "      <td>22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1999-06</td>\n",
              "      <td>2</td>\n",
              "      <td>중곡동복개천</td>\n",
              "      <td>중곡2동 150-196</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>200원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유도로(노상·공단위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2009-04</td>\n",
              "      <td>2</td>\n",
              "      <td>중곡3동마을공원</td>\n",
              "      <td>중곡3동 195-1</td>\n",
              "      <td>3038.3</td>\n",
              "      <td>162</td>\n",
              "      <td>NaN</td>\n",
              "      <td>162.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>5</td>\n",
              "      <td>75원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>지하2층</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2010-01</td>\n",
              "      <td>2</td>\n",
              "      <td>중곡체육센터</td>\n",
              "      <td>중곡3동 168-8</td>\n",
              "      <td>3,690.00</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>1996-01</td>\n",
              "      <td>2</td>\n",
              "      <td>신성시장길</td>\n",
              "      <td>중곡4동 453 일대</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유도로(노상·민간위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>2001-08</td>\n",
              "      <td>2</td>\n",
              "      <td>새오름</td>\n",
              "      <td>중곡4동 143-70</td>\n",
              "      <td>137</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>2003-04</td>\n",
              "      <td>2</td>\n",
              "      <td>용마산남</td>\n",
              "      <td>중곡4동 69-2</td>\n",
              "      <td>777.9</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>2005-04</td>\n",
              "      <td>2</td>\n",
              "      <td>긴고랑길공영</td>\n",
              "      <td>중곡4동 72-1 외</td>\n",
              "      <td>1,653.00</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1998-12</td>\n",
              "      <td>2</td>\n",
              "      <td>해오름</td>\n",
              "      <td>중곡4동 87-14</td>\n",
              "      <td>462.9</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>2005-04</td>\n",
              "      <td>2</td>\n",
              "      <td>뻥튀기골주차장(용마공원주차장)</td>\n",
              "      <td>중곡4동 176-110</td>\n",
              "      <td>595</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지(임야일부)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>2009-01</td>\n",
              "      <td>2</td>\n",
              "      <td>중곡4동 599-6 인근</td>\n",
              "      <td>면목4동 산75-2</td>\n",
              "      <td>124.2</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지(임야일부)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>2009-11</td>\n",
              "      <td>2</td>\n",
              "      <td>한림대부지</td>\n",
              "      <td>중곡4동 176-119</td>\n",
              "      <td>179</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>2005-06</td>\n",
              "      <td>3</td>\n",
              "      <td>능동공영</td>\n",
              "      <td>능동 205</td>\n",
              "      <td>1,167.00</td>\n",
              "      <td>77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>2층3단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>1998-06</td>\n",
              "      <td>3</td>\n",
              "      <td>능동공동</td>\n",
              "      <td>능동 377</td>\n",
              "      <td>485</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>1층2단</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>2007-03</td>\n",
              "      <td>1</td>\n",
              "      <td>구의1동 공영</td>\n",
              "      <td>구의1동 243-78</td>\n",
              "      <td>1,037.00</td>\n",
              "      <td>172</td>\n",
              "      <td>NaN</td>\n",
              "      <td>172.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>8만원</td>\n",
              "      <td>7층8단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>1999-01</td>\n",
              "      <td>1</td>\n",
              "      <td>구의번영</td>\n",
              "      <td>구의1동 230-7</td>\n",
              "      <td>794.4</td>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>2017-01</td>\n",
              "      <td>1</td>\n",
              "      <td>구의2동 주택가공동</td>\n",
              "      <td>구의2동 30-9</td>\n",
              "      <td>1,170.60</td>\n",
              "      <td>90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>5</td>\n",
              "      <td>75원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>지하1층 2층3단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>2006-01</td>\n",
              "      <td>1</td>\n",
              "      <td>광문학당</td>\n",
              "      <td>구의2동 72-8</td>\n",
              "      <td>258.5</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지(가정복지과)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>2003-06</td>\n",
              "      <td>1</td>\n",
              "      <td>공원(동의초인근)</td>\n",
              "      <td>구의2동 6</td>\n",
              "      <td>630</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>2만원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지(공원)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>1994-09</td>\n",
              "      <td>1</td>\n",
              "      <td>동서울길 공영</td>\n",
              "      <td>구의3동 609-1 일대</td>\n",
              "      <td>627.4</td>\n",
              "      <td>94</td>\n",
              "      <td>68.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유도로(노상+노외)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>2009-01</td>\n",
              "      <td>1</td>\n",
              "      <td>구의3동 공영</td>\n",
              "      <td>구의3동 219-15</td>\n",
              "      <td>1,237.40</td>\n",
              "      <td>159</td>\n",
              "      <td>NaN</td>\n",
              "      <td>159.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>8만원</td>\n",
              "      <td>지하1층 4층5단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>2008-01</td>\n",
              "      <td>1</td>\n",
              "      <td>올림픽대교(시영)</td>\n",
              "      <td>구의3동 623-1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>2만원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>2005-05</td>\n",
              "      <td>5</td>\n",
              "      <td>세촌</td>\n",
              "      <td>광장동 275-5</td>\n",
              "      <td>121</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>2만원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>2008-12</td>\n",
              "      <td>5</td>\n",
              "      <td>광장공동</td>\n",
              "      <td>광장동 394-43 외</td>\n",
              "      <td>441</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지(가로경관과)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>2000-03</td>\n",
              "      <td>5</td>\n",
              "      <td>큰나루</td>\n",
              "      <td>광장동 126</td>\n",
              "      <td>512</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>2010-06</td>\n",
              "      <td>4</td>\n",
              "      <td>자양전통시장 공영</td>\n",
              "      <td>자양1동 631-18</td>\n",
              "      <td>447.5</td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2층</td>\n",
              "      <td>구유지(지역경제과·위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>2000-08</td>\n",
              "      <td>4</td>\n",
              "      <td>유수지</td>\n",
              "      <td>자양2동 594</td>\n",
              "      <td>9,000.00</td>\n",
              "      <td>281</td>\n",
              "      <td>NaN</td>\n",
              "      <td>281.0</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>5</td>\n",
              "      <td>150원</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>1994-01</td>\n",
              "      <td>4</td>\n",
              "      <td>송림식당길</td>\n",
              "      <td>자양3동 712 일대</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94</td>\n",
              "      <td>94.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>200원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유도로(노상·공단위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>2005-03</td>\n",
              "      <td>4</td>\n",
              "      <td>자양4동 공영</td>\n",
              "      <td>자양4동 5-5</td>\n",
              "      <td>1,988.40</td>\n",
              "      <td>86</td>\n",
              "      <td>NaN</td>\n",
              "      <td>86.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>2009-08</td>\n",
              "      <td>4</td>\n",
              "      <td>행복제2</td>\n",
              "      <td>자양4동 10-2</td>\n",
              "      <td>1,313.10</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>시유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>32</td>\n",
              "      <td>2005-12</td>\n",
              "      <td>6</td>\n",
              "      <td>느티나무(화양공동)</td>\n",
              "      <td>화양동 110-67</td>\n",
              "      <td>1,248.00</td>\n",
              "      <td>27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>33</td>\n",
              "      <td>1999-04</td>\n",
              "      <td>6</td>\n",
              "      <td>화양동 공영</td>\n",
              "      <td>화양동 63-2</td>\n",
              "      <td>664</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>4</td>\n",
              "      <td>150원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8만원</td>\n",
              "      <td>2층3단</td>\n",
              "      <td>시유지(민간위탁)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>2006-03</td>\n",
              "      <td>6</td>\n",
              "      <td>화양동 마을마당</td>\n",
              "      <td>화양동 523-2</td>\n",
              "      <td>112</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>국유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>35</td>\n",
              "      <td>2003-12</td>\n",
              "      <td>7</td>\n",
              "      <td>군자동 공영</td>\n",
              "      <td>군자동 2-41</td>\n",
              "      <td>1,075.10</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4만원</td>\n",
              "      <td>3만원</td>\n",
              "      <td>5만원</td>\n",
              "      <td>2층3단</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>36</td>\n",
              "      <td>2006-07</td>\n",
              "      <td>7</td>\n",
              "      <td>광진광장 공영</td>\n",
              "      <td>군자동 374</td>\n",
              "      <td>1,516.00</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>2</td>\n",
              "      <td>250원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12만원</td>\n",
              "      <td>NaN</td>\n",
              "      <td>구유지</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59b66426-3967-49d7-a0ec-5f7922bca45a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-59b66426-3967-49d7-a0ec-5f7922bca45a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-59b66426-3967-49d7-a0ec-5f7922bca45a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1['동명'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHMr5SNm2Shw",
        "outputId": "8956abfc-2fb3-45c3-eea3-ddbfcdad545e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 3 1 5 4 6 7]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for element in df1['동명'].unique():\n",
        "    print(element)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7qlGbdk4PDB",
        "outputId": "389aa835-e34d-4bc8-9d65-8b7ffecc24ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "3\n",
            "1\n",
            "5\n",
            "4\n",
            "6\n",
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "광진구 동별로 공영주차장 소계 파악 => 빈도분석 진행"
      ],
      "metadata": {
        "id": "dardUjIj-yX2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 동별 주차장 소계 파악하기 => 변수 두개 선택\n",
        "df_subset = df1[['동명', '주차면수 소계']]\n",
        "\n",
        "# 결측치 제거\n",
        "df_subset = df_subset.dropna()\n",
        "\n",
        "# 각 단속동에서 공영주차장 개수가 몇개인지 빈도분석 수행\n",
        "parkinglot_counts = df_subset.groupby(['동명', '주차면수 소계']).size().reset_index(name='counts')\n",
        "\n",
        "# 결과 출력\n",
        "print(parkinglot_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvcY7rDKIG5o",
        "outputId": "8b2239c2-7edf-449a-ec80-6c40c6ac7bbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    동명  주차면수 소계  counts\n",
            "0    1       11       1\n",
            "1    1       12       1\n",
            "2    1       19       1\n",
            "3    1       34       1\n",
            "4    1       90       1\n",
            "5    1       94       1\n",
            "6    1      159       1\n",
            "7    1      172       1\n",
            "8    2        6       1\n",
            "9    2        7       1\n",
            "10   2        8       1\n",
            "11   2        9       1\n",
            "12   2       18       1\n",
            "13   2       20       1\n",
            "14   2       21       1\n",
            "15   2       22       1\n",
            "16   2       25       1\n",
            "17   2       33       1\n",
            "18   2       53       1\n",
            "19   2       78       1\n",
            "20   2      162       1\n",
            "21   3       25       1\n",
            "22   3       77       1\n",
            "23   4       24       1\n",
            "24   4       53       1\n",
            "25   4       86       1\n",
            "26   4       94       1\n",
            "27   4      281       1\n",
            "28   5        7       1\n",
            "29   5        8       1\n",
            "30   5       13       1\n",
            "31   6        6       1\n",
            "32   6       27       1\n",
            "33   6       44       1\n",
            "34   7       36       1\n",
            "35   7       53       1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "동별로 주차면수 소계 합한 데이터"
      ],
      "metadata": {
        "id": "yJOo3GACa1en"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_subset = df1[['동명', '주차면수 소계']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('동명')['주차면수 소계'].sum().reset_index(name='합계')\n",
        "print(parkinglot_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YyuVNrzsaKyD",
        "outputId": "ecd8d16a-8e3a-4f66-8b4e-72668a3a78d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   동명   합계\n",
            "0   1  591\n",
            "1   2  462\n",
            "2   3  102\n",
            "3   4  538\n",
            "4   5   28\n",
            "5   6   77\n",
            "6   7   89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parkinglot_counts.to_csv('parkingplot.csv',index=False)"
      ],
      "metadata": {
        "id": "itSA7Bj0XU4n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "시각화 => x축 label 달아둠"
      ],
      "metadata": {
        "id": "0lP6CoaXB85D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 데이터프레임에서 각 동별 주차면수 소계의 합을 구합니다.\n",
        "df_subset = df1[['동명', '주차면수 소계']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('동명')['주차면수 소계'].sum().reset_index(name='합계')\n",
        "\n",
        "# 최대값을 가지는 막대의 인덱스를 찾습니다.\n",
        "max_index = parkinglot_counts['합계'].idxmax()\n",
        "\n",
        "# 막대 그래프를 그립니다.\n",
        "barplot = sns.barplot(x='동명', y='합계', data=parkinglot_counts, color='#87CEEB')\n",
        "\n",
        "# 최대값을 가지는 막대만 빨간색으로 표시합니다.\n",
        "barplot.get_children()[max_index].set_color('#CD5C5C')\n",
        "\n",
        "plt.title('동별 공영주차장 수')\n",
        "plt.xlabel('동명')\n",
        "plt.ylabel('주차면수 소계 합계')\n",
        "\n",
        "# x축 레이블을 추가합니다.\n",
        "labels = ['구의동', '중곡동', '능동', '자양동', '광장동', '화양동', '군자동']\n",
        "plt.xticks(range(len(labels)), labels)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "edkou2ov--gh",
        "outputId": "a4314009-098f-410e-e5f5-b3ca7c24b97d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "labels = ['구의동', '중곡동', '능동', '자양동', '광장동', '화양동', '군자동']\n",
        "\n",
        "# 데이터프레임에서 각 동별 주차면수 소계의 합을 구합니다.\n",
        "df_subset = df1[['동명', '주차면수 소계']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('동명')['주차면수 소계'].sum().reset_index(name='합계')\n",
        "\n",
        "# 파이차트를 그립니다.\n",
        "colors = ['#CD5C5C', '#FFA07A', '#FFD700', '#98FB98', '#87CEFA', '#ADD8E6', '#D3D3D3']\n",
        "plt.pie(parkinglot_counts['합계'], labels=labels, colors=colors, autopct='%1.1f%%')\n",
        "plt.title('동별 주차면수 소계 비율')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "KwymVCV3ni7T",
        "outputId": "5afebe4f-24cc-4d6b-b305-ed419119ef09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2023 광진구 자동차 등록 대수"
      ],
      "metadata": {
        "id": "3q1ZeB2awBLz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import codecs\n",
        "\n",
        "filename = \"/content/drive/MyDrive/[03팀] 김가경, 김수민, 민채원/code/Gwangjingu_num of cars.csv\"\n",
        "\n",
        "with codecs.open(filename, 'r', 'euc_kr', errors='ignore') as f:\n",
        "    reader = csv.reader(f)\n",
        "    for row in reader:\n",
        "        print(row)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvkFbEDqwKVS",
        "outputId": "23ff8b49-a0cb-42ca-dd91-37a714fb8b4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['읍면동명', '자동차등록대수']\n",
            "['광장동', '11646']\n",
            "['구의1동', '7005']\n",
            "['구의2동', '7346']\n",
            "['구의3동', '9127']\n",
            "['군자동', '5223']\n",
            "['능동', '3060']\n",
            "['자양1동', '7366']\n",
            "['자양2동', '7598']\n",
            "['자양3동', '8970']\n",
            "['자양4동', '5018']\n",
            "['중곡1동', '4753']\n",
            "['중곡2동', '5132']\n",
            "['중곡3동', '4073']\n",
            "['중곡4동', '7382']\n",
            "['화양동', '4186']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.read_csv(filename, encoding='cp949')\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "mI8cHomfwgqE",
        "outputId": "a3292112-e6dd-40b4-8e43-0be33d04f01c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    읍면동명  자동차등록대수\n",
              "0    광장동    11646\n",
              "1   구의1동     7005\n",
              "2   구의2동     7346\n",
              "3   구의3동     9127\n",
              "4    군자동     5223\n",
              "5     능동     3060\n",
              "6   자양1동     7366\n",
              "7   자양2동     7598\n",
              "8   자양3동     8970\n",
              "9   자양4동     5018\n",
              "10  중곡1동     4753\n",
              "11  중곡2동     5132\n",
              "12  중곡3동     4073\n",
              "13  중곡4동     7382\n",
              "14   화양동     4186"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ce8896e2-1e6b-4ecf-a78f-0016c0f48b5a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>읍면동명</th>\n",
              "      <th>자동차등록대수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>광장동</td>\n",
              "      <td>11646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>구의1동</td>\n",
              "      <td>7005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>구의2동</td>\n",
              "      <td>7346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>구의3동</td>\n",
              "      <td>9127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>군자동</td>\n",
              "      <td>5223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>능동</td>\n",
              "      <td>3060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>자양1동</td>\n",
              "      <td>7366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>자양2동</td>\n",
              "      <td>7598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>자양3동</td>\n",
              "      <td>8970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>자양4동</td>\n",
              "      <td>5018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>중곡1동</td>\n",
              "      <td>4753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>중곡2동</td>\n",
              "      <td>5132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>중곡3동</td>\n",
              "      <td>4073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>중곡4동</td>\n",
              "      <td>7382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>화양동</td>\n",
              "      <td>4186</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce8896e2-1e6b-4ecf-a78f-0016c0f48b5a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
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              "      \n",
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              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ce8896e2-1e6b-4ecf-a78f-0016c0f48b5a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ce8896e2-1e6b-4ecf-a78f-0016c0f48b5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.loc[df2['읍면동명'] == '구의1동', '읍면동명'] = 1\n",
        "df2.loc[df2['읍면동명'] == '구의2동', '읍면동명'] = 1\n",
        "df2.loc[df2['읍면동명'] == '구의3동', '읍면동명'] = 1\n",
        "df2.loc[df2['읍면동명'] == '중곡1동', '읍면동명'] = 2\n",
        "df2.loc[df2['읍면동명'] == '중곡2동', '읍면동명'] = 2\n",
        "df2.loc[df2['읍면동명'] == '중곡3동', '읍면동명'] = 2\n",
        "df2.loc[df2['읍면동명'] == '중곡4동', '읍면동명'] = 2\n",
        "df2.loc[df2['읍면동명'] == '능동', '읍면동명'] = 3\n",
        "df2.loc[df2['읍면동명'] == '자양1동', '읍면동명'] = 4\n",
        "df2.loc[df2['읍면동명'] == '자양2동', '읍면동명'] = 4\n",
        "df2.loc[df2['읍면동명'] == '자양3동', '읍면동명'] = 4\n",
        "df2.loc[df2['읍면동명'] == '자양4동', '읍면동명'] = 4\n",
        "df2.loc[df2['읍면동명'] == '광장동', '읍면동명'] = 5\n",
        "df2.loc[df2['읍면동명'] == '화양동', '읍면동명'] = 6\n",
        "df2.loc[df2['읍면동명'] == '군자동', '읍면동명'] = 7\n",
        "\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "LVVUsIl5wnrZ",
        "outputId": "52b348c1-ef42-44d2-e040-4c6df7b2971e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   읍면동명  자동차등록대수\n",
              "0     5    11646\n",
              "1     1     7005\n",
              "2     1     7346\n",
              "3     1     9127\n",
              "4     7     5223\n",
              "5     3     3060\n",
              "6     4     7366\n",
              "7     4     7598\n",
              "8     4     8970\n",
              "9     4     5018\n",
              "10    2     4753\n",
              "11    2     5132\n",
              "12    2     4073\n",
              "13    2     7382\n",
              "14    6     4186"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3d94d335-f369-4f9e-9bdb-660a9c5183ee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>읍면동명</th>\n",
              "      <th>자동차등록대수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>7346</td>\n",
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              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>9127</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>5223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>3060</td>\n",
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              "      <th>6</th>\n",
              "      <td>4</td>\n",
              "      <td>7366</td>\n",
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              "      <th>7</th>\n",
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              "      <td>7598</td>\n",
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              "      <th>8</th>\n",
              "      <td>4</td>\n",
              "      <td>8970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4</td>\n",
              "      <td>5018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>4753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>5132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2</td>\n",
              "      <td>4073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2</td>\n",
              "      <td>7382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>6</td>\n",
              "      <td>4186</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d94d335-f369-4f9e-9bdb-660a9c5183ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3d94d335-f369-4f9e-9bdb-660a9c5183ee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3d94d335-f369-4f9e-9bdb-660a9c5183ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "동별 자동차등록대수 파악 => 빈도분석"
      ],
      "metadata": {
        "id": "MNHgkcLAxTjr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_subset = df2[['읍면동명', '자동차등록대수']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('읍면동명')['자동차등록대수'].sum().reset_index(name='합계')\n",
        "print(parkinglot_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9I6O9TICxcuB",
        "outputId": "e1629241-9a45-4da9-d165-402f84785390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   읍면동명     합계\n",
            "0     1  23478\n",
            "1     2  21340\n",
            "2     3   3060\n",
            "3     4  28952\n",
            "4     5  11646\n",
            "5     6   4186\n",
            "6     7   5223\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parkinglot_counts.to_csv('num_of_cars.csv',index=False)"
      ],
      "metadata": {
        "id": "znMOIa4eYWgu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 데이터프레임에서 각 동별 주차면수 소계의 합을 구합니다.\n",
        "df_subset = df2[['읍면동명', '자동차등록대수']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('읍면동명')['자동차등록대수'].sum().reset_index(name='합계')\n",
        "\n",
        "# 최대값을 가지는 막대의 인덱스를 찾습니다.\n",
        "max_index = parkinglot_counts['합계'].idxmax()\n",
        "\n",
        "# 막대 그래프를 그립니다.\n",
        "barplot = sns.barplot(y='읍면동명', x='합계', data=parkinglot_counts, color='#87CEEB',orient='h')\n",
        "\n",
        "# 최대값을 가지는 막대만 빨간색으로 표시합니다.\n",
        "barplot.get_children()[max_index].set_color('#CD5C5C')\n",
        "\n",
        "\n",
        "plt.title('동별 자동차등록대수')\n",
        "plt.xlabel('동명')\n",
        "plt.ylabel('자동차 수 합계')\n",
        "\n",
        "# x축 레이블을 추가합니다.\n",
        "labels = ['구의동', '중곡동', '능동', '자양동', '광장동', '화양동', '군자동']\n",
        "plt.yticks(range(len(labels)), labels)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "9nVRw5mOxtoY",
        "outputId": "bfcb95e6-eed2-4478-85c1-65a223390bf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "labels = ['구의동', '중곡동', '능동', '자양동', '광장동', '화양동', '군자동']\n",
        "\n",
        "# 데이터프레임에서 각 동별 주차면수 소계의 합을 구합니다.\n",
        "df_subset = df2[['읍면동명', '자동차등록대수']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('읍면동명')['자동차등록대수'].sum().reset_index(name='합계')\n",
        "\n",
        "# 파이차트를 그립니다.\n",
        "colors = ['#CD5C5C', '#FFA07A', '#FFD700', '#98FB98', '#87CEFA', '#ADD8E6', '#D3D3D3']\n",
        "plt.pie(parkinglot_counts['합계'], labels=labels, colors=colors, autopct='%1.1f%%')\n",
        "plt.title('동별 자동차등록대수 비율')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "gOrVYlz5yJAE",
        "outputId": "13241d96-4342-4ee7-ea37-d94d1eec8c52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import numpy as np\n",
        "\n",
        "# 동별 자동차 등록대수 합계 구하기\n",
        "df_subset = df2[['읍면동명', '자동차등록대수']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('읍면동명')['자동차등록대수'].sum().reset_index(name='합계')\n",
        "\n",
        "# 픽토그램 이미지 로딩\n",
        "img = mpimg.imread('/content/drive/MyDrive/[03팀] 김가경, 김수민, 민채원/code/pictogram.png')\n",
        "\n",
        "# 이미지 크기 및 색상 설정\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "fig.patch.set_facecolor('white')\n",
        "plt.imshow(img, extent=[0, len(parkinglot_counts), 0, 10], aspect='auto', alpha=0.5)\n",
        "\n",
        "# 데이터 포인트 그리기\n",
        "colors = ['#FFD700', '#CD5C5C', '#D3D3D3', '#87CEFA', '#ADD8E6', '#98FB98', '#FFA07A']\n",
        "for i in range(len(parkinglot_counts)):\n",
        "    x = i + 0.5\n",
        "    y = 5\n",
        "    s = np.sqrt(parkinglot_counts['합계'][i]) * 5\n",
        "    plt.scatter(x, y, s=s, c=colors[i], alpha=1)\n",
        "    plt.annotate(parkinglot_counts['읍면동명'][i], (x, y + 0.25), ha='center', va='bottom')\n",
        "\n",
        "# 축 레이블 설정\n",
        "plt.xlim(0, len(parkinglot_counts))\n",
        "plt.ylim(0, 10)\n",
        "plt.xticks(np.arange(0.5, len(parkinglot_counts)), parkinglot_counts['읍면동명'], fontsize=10)\n",
        "plt.yticks([], [])\n",
        "plt.xlabel('동', fontsize=12)\n",
        "plt.title('동별 자동차등록대수 픽토그램', fontsize=16)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "Z6UUaYWJrKsy",
        "outputId": "7d17db2c-11ec-4707-9d55-f24a700feb49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "\n",
        "# 데이터 프레임에서 각 동별 주차면수 소계의 합을 구합니다.\n",
        "df_subset = df2[['읍면동명', '자동차등록대수']]\n",
        "df_subset = df_subset.dropna()\n",
        "parkinglot_counts = df_subset.groupby('읍면동명')['자동차등록대수'].sum().reset_index(name='합계')\n",
        "\n",
        "# 픽토그램 이미지 로딩\n",
        "img = Image.open('/content/drive/MyDrive/[03팀] 김가경, 김수민, 민채원/code/pictogram.png')\n",
        "\n",
        "# 이미지 크기 및 색상 설정\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "fig.patch.set_facecolor('white')\n",
        "sns.scatterplot(x=parkinglot_counts.index, y=[5]*len(parkinglot_counts), hue=parkinglot_counts['읍면동명'],\n",
        "                size=parkinglot_counts['합계'], sizes=(50, 500), legend=False)\n",
        "\n",
        "# # 이미지 크기를 그래프 크기에 맞게 조정합니다.\n",
        "# img = img.resize((int(ax.get_xlim()[1]), int(ax.get_ylim()[1])), Image.ANTIALIAS)\n",
        "# img_arr = img.convert('RGBA')\n",
        "# plt.imshow(img_arr, extent=ax.get_xlim() + ax.get_ylim(), aspect='auto', zorder=-1)\n",
        "\n",
        "# 축 레이블 설정\n",
        "plt.xticks(parkinglot_counts.index, parkinglot_counts['읍면동명'], fontsize=10)\n",
        "plt.yticks([], [])\n",
        "plt.xlabel('동', fontsize=12)\n",
        "plt.title('동별 자동차등록대수 픽토그램', fontsize=16)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "2QlQbAvVtP-a",
        "outputId": "85d7c668-2b80-4a08-bcab-891c4cb92f2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 결과 분석                                                                            \n",
        "####(1) 광진구 동별 공영주차장 소계 파악                                              \n",
        "=> 구의동(591개),자양동(538개),중곡동(462개)순으로 공영주차장 개수가 많음              \n",
        "####(2) 광진구 동별 자동차등록대수 소계                                                 \n",
        "=> 자양동(28952개),구의동(23478개),중곡동(21340개) 수으로 자동차등록대수가 많음"
      ],
      "metadata": {
        "id": "7HnU-vVZywLH"
      }
    }
  ]
}