군집분석.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6OYOFXnrwR9",
        "outputId": "bf1fc2d6-9091-4c8f-864b-9a96f3566128"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\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/말감3팀/[03팀] 김가경, 김수민, 민채원/code/Gwangjingu_parking_total.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/",
          "height": 400
        },
        "id": "lLOmhv7oZjDU",
        "outputId": "c33a75e2-e797-43a2-8bfc-5c7e1e50263a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-dea1ddbed7eb>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/drive/MyDrive/말감3팀/[03팀] 김가경, 김수민, 민채원/code/Gwangjingu_parking_total.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'euc_kr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/codecs.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(filename, mode, encoding, errors, buffering)\u001b[0m\n\u001b[1;32m    904\u001b[0m         \u001b[0;31m# Force opening of the file in binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m         \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 906\u001b[0;31m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffering\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    907\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    908\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/말감3팀/[03팀] 김가경, 김수민, 민채원/code/Gwangjingu_parking_total.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(filename, encoding='cp949')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "L_9_LBXva9nS",
        "outputId": "3245b5ad-1e7a-468c-d991-338c16ad01ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       단속일시   단속원금  단속동               단속장소           위반내용  \\\n",
              "0          2015-01-01 06:44  40000  화양동              119-4          주정차금지   \n",
              "1          2015-01-01 07:01  40000  화양동          건대입구1번출구앞    32조내지 34조위반   \n",
              "2          2015-01-01 07:24  50000  구의동        동서울터미널 주차장앞    32조내지 34조위반   \n",
              "3          2015-01-01 08:00  40000  화양동          건대입구1번출구앞    32조내지 34조위반   \n",
              "4          2015-01-01 08:15  40000  화양동          건대입구1번출구앞    32조내지 34조위반   \n",
              "...                     ...    ...  ...                ...            ...   \n",
              "490926  2023-02-28 20:43:00  32000  군자동               군자빌딩  주정차금지(황색실선)구역   \n",
              "490927  2023-02-28 21:23:00   8000  자양동  서울 광진구 자양동 659-32             보도   \n",
              "490928  2023-02-28 21:38:00  32000  화양동     서울 광진구 능동로 154             보도   \n",
              "490929  2023-02-28 22:36:00  32000  자양동   서울 광진구 자양동 12-10            교차로   \n",
              "490930  2023-02-28 23:19:00  32000  자양동              02-02           횡단보도   \n",
              "\n",
              "        견인지시     데이터기준일자  \n",
              "0       견인처리  2022-06-10  \n",
              "1        미견인  2022-06-10  \n",
              "2        미견인  2022-06-10  \n",
              "3        미견인  2022-06-10  \n",
              "4        미견인  2022-06-10  \n",
              "...      ...         ...  \n",
              "490926   미견인         NaN  \n",
              "490927   미견인         NaN  \n",
              "490928   미견인         NaN  \n",
              "490929   미견인         NaN  \n",
              "490930   미견인         NaN  \n",
              "\n",
              "[490931 rows x 7 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>단속일시</th>\n",
              "      <th>단속원금</th>\n",
              "      <th>단속동</th>\n",
              "      <th>단속장소</th>\n",
              "      <th>위반내용</th>\n",
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              "      <th>0</th>\n",
              "      <td>2015-01-01 06:44</td>\n",
              "      <td>40000</td>\n",
              "      <td>화양동</td>\n",
              "      <td>119-4</td>\n",
              "      <td>주정차금지</td>\n",
              "      <td>견인처리</td>\n",
              "      <td>2022-06-10</td>\n",
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              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>미견인</td>\n",
              "      <td>2022-06-10</td>\n",
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              "      <td>2015-01-01 07:24</td>\n",
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              "      <td>동서울터미널 주차장앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>미견인</td>\n",
              "      <td>2022-06-10</td>\n",
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              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
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              "      <td>2022-06-10</td>\n",
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              "      <th>4</th>\n",
              "      <td>2015-01-01 08:15</td>\n",
              "      <td>40000</td>\n",
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              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
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              "      <th>490926</th>\n",
              "      <td>2023-02-28 20:43:00</td>\n",
              "      <td>32000</td>\n",
              "      <td>군자동</td>\n",
              "      <td>군자빌딩</td>\n",
              "      <td>주정차금지(황색실선)구역</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>490927</th>\n",
              "      <td>2023-02-28 21:23:00</td>\n",
              "      <td>8000</td>\n",
              "      <td>자양동</td>\n",
              "      <td>서울 광진구 자양동 659-32</td>\n",
              "      <td>보도</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>490928</th>\n",
              "      <td>2023-02-28 21:38:00</td>\n",
              "      <td>32000</td>\n",
              "      <td>화양동</td>\n",
              "      <td>서울 광진구 능동로 154</td>\n",
              "      <td>보도</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>490929</th>\n",
              "      <td>2023-02-28 22:36:00</td>\n",
              "      <td>32000</td>\n",
              "      <td>자양동</td>\n",
              "      <td>서울 광진구 자양동 12-10</td>\n",
              "      <td>교차로</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
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              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 단속동에 있는 데이터 중복없이 보여줌\n",
        "unique_dongs = df['단속동'].drop_duplicates()\n",
        "print(unique_dongs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPeL6vDwgtjR",
        "outputId": "0f578f24-07be-464f-b272-f6f9b4e6e614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0              화양동\n",
            "2              구의동\n",
            "15              능동\n",
            "24             중곡동\n",
            "35             자양동\n",
            "            ...   \n",
            "339405      동일로64길\n",
            "339543      능동로43길\n",
            "345432     광나루로48길\n",
            "351007     용마산로8다길\n",
            "352422    천호대로132길\n",
            "Name: 단속동, Length: 491, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['단속동'].isin(['중곡동', '구의동', '광장동', '자양동', '군자동', '화양동', '능동'])]\n",
        "df"
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          "height": 423
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        "id": "_bjRSPgzlcsP",
        "outputId": "512fb5e1-fc5f-4948-fc78-25203130fb09"
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              "                       단속일시   단속원금  단속동               단속장소           위반내용  \\\n",
              "0          2015-01-01 06:44  40000  화양동              119-4          주정차금지   \n",
              "1          2015-01-01 07:01  40000  화양동          건대입구1번출구앞    32조내지 34조위반   \n",
              "2          2015-01-01 07:24  50000  구의동        동서울터미널 주차장앞    32조내지 34조위반   \n",
              "3          2015-01-01 08:00  40000  화양동          건대입구1번출구앞    32조내지 34조위반   \n",
              "4          2015-01-01 08:15  40000  화양동          건대입구1번출구앞    32조내지 34조위반   \n",
              "...                     ...    ...  ...                ...            ...   \n",
              "490926  2023-02-28 20:43:00  32000  군자동               군자빌딩  주정차금지(황색실선)구역   \n",
              "490927  2023-02-28 21:23:00   8000  자양동  서울 광진구 자양동 659-32             보도   \n",
              "490928  2023-02-28 21:38:00  32000  화양동     서울 광진구 능동로 154             보도   \n",
              "490929  2023-02-28 22:36:00  32000  자양동   서울 광진구 자양동 12-10            교차로   \n",
              "490930  2023-02-28 23:19:00  32000  자양동              02-02           횡단보도   \n",
              "\n",
              "        견인지시     데이터기준일자  \n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# 군집분석하기 위해 문자열 데이터 => 범주형 데이터로 변경"
      ],
      "metadata": {
        "id": "3emyOnnccIHG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['단속동'] == '중곡동', '단속동'] = 1\n",
        "df.loc[df['단속동'] == '군자동', '단속동'] = 2\n",
        "df.loc[df['단속동'] == '능동', '단속동'] = 3\n",
        "df.loc[df['단속동'] == '화양동', '단속동'] = 4\n",
        "df.loc[df['단속동'] == '구의동', '단속동'] = 5\n",
        "df.loc[df['단속동'] == '광장동', '단속동'] = 6\n",
        "df.loc[df['단속동'] == '자양동', '단속동'] = 7\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "WzP_15Z7by6V",
        "outputId": "7e7c0d6b-143f-4871-d892-76096cc7b86c"
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      "execution_count": null,
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              "                       단속일시   단속원금 단속동               단속장소           위반내용  \\\n",
              "0          2015-01-01 06:44  40000   4              119-4          주정차금지   \n",
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              "...                     ...    ...  ..                ...            ...   \n",
              "490926  2023-02-28 20:43:00  32000   2               군자빌딩  주정차금지(황색실선)구역   \n",
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              "490930  2023-02-28 23:19:00  32000   7              02-02           횡단보도   \n",
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              "      <td>5</td>\n",
              "      <td>동서울터미널 주차장앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>미견인</td>\n",
              "      <td>2022-06-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-01 08:00</td>\n",
              "      <td>40000</td>\n",
              "      <td>4</td>\n",
              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>미견인</td>\n",
              "      <td>2022-06-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-01 08:15</td>\n",
              "      <td>40000</td>\n",
              "      <td>4</td>\n",
              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>미견인</td>\n",
              "      <td>2022-06-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490926</th>\n",
              "      <td>2023-02-28 20:43:00</td>\n",
              "      <td>32000</td>\n",
              "      <td>2</td>\n",
              "      <td>군자빌딩</td>\n",
              "      <td>주정차금지(황색실선)구역</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490927</th>\n",
              "      <td>2023-02-28 21:23:00</td>\n",
              "      <td>8000</td>\n",
              "      <td>7</td>\n",
              "      <td>서울 광진구 자양동 659-32</td>\n",
              "      <td>보도</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490928</th>\n",
              "      <td>2023-02-28 21:38:00</td>\n",
              "      <td>32000</td>\n",
              "      <td>4</td>\n",
              "      <td>서울 광진구 능동로 154</td>\n",
              "      <td>보도</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490929</th>\n",
              "      <td>2023-02-28 22:36:00</td>\n",
              "      <td>32000</td>\n",
              "      <td>7</td>\n",
              "      <td>서울 광진구 자양동 12-10</td>\n",
              "      <td>교차로</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490930</th>\n",
              "      <td>2023-02-28 23:19:00</td>\n",
              "      <td>32000</td>\n",
              "      <td>7</td>\n",
              "      <td>02-02</td>\n",
              "      <td>횡단보도</td>\n",
              "      <td>미견인</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>436662 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae0014d5-5047-4259-971a-77bc3c16a427')\"\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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              "      \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",
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              "      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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae0014d5-5047-4259-971a-77bc3c16a427 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-ae0014d5-5047-4259-971a-77bc3c16a427');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 단속동의 데이터들을 정수형 타입으로 변경\n",
        "df['단속동'] = pd.to_numeric(df['단속동'], errors='coerce').astype('Int64')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rb_Vbnesdh_Z",
        "outputId": "12e738a9-7035-409f-89d0-f1426a8ba5f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-e777a527ab69>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['단속동'] = pd.to_numeric(df['단속동'], errors='coerce').astype('Int64')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 단속동별로 단속원금(주정차 현황) 파악"
      ],
      "metadata": {
        "id": "P3G9v4eub81X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# 두 가지 feature를 대상\n",
        "data = df[['단속동', '단속원금']]\n",
        "\n",
        "# 정규화 진행\n",
        "scaler = MinMaxScaler()\n",
        "data_scale = scaler.fit_transform(data)"
      ],
      "metadata": {
        "id": "9IKEIitOcQLn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "k = 3\n",
        "\n",
        "# 그룹 수, random_state 설정\n",
        "model = KMeans(n_clusters = k, random_state = 10)\n",
        "\n",
        "# 정규화된 데이터에 학습\n",
        "model.fit(data_scale)\n",
        "\n",
        "# 클러스터링 결과 각 데이터가 몇 번째 그룹에 속하는지 저장\n",
        "df['cluster'] = model.fit_predict(data_scale)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "taua0zXSc02i",
        "outputId": "56adc3a5-e6fe-48ff-9eb3-d355b6c9da8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "<ipython-input-9-e171a09f8f41>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['cluster'] = model.fit_predict(data_scale)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize = (8, 8))\n",
        "\n",
        "for i in range(k):\n",
        "    plt.scatter(df.loc[df['cluster'] == i, '단속동'], df.loc[df['cluster'] == i, '단속원금'],\n",
        "                label = 'cluster ' + str(i))\n",
        "\n",
        "plt.legend()\n",
        "plt.title('K = %d results'%k , size = 15)\n",
        "plt.xlabel('단속동', size = 12)\n",
        "plt.ylabel('단속원금', size = 12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 943
        },
        "id": "ieC42OB7c7Gu",
        "outputId": "e8fefe9c-564b-424b-f096-70688a7f7722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 45800 (\\N{HANGUL SYLLABLE DAN}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 49549 (\\N{HANGUL SYLLABLE SOG}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50896 (\\N{HANGUL SYLLABLE WEON}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 44552 (\\N{HANGUL SYLLABLE GEUM}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 46041 (\\N{HANGUL SYLLABLE DONG}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 필요 패키지 불러오기 (KMeans, matplotlib, preprocessing)\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# 원본 데이터를 복사해서 전처리하기 (원본 데이터를 가지고 바로 전처리하지 않는다)\n",
        "processed_data = df.copy()\n",
        "\n",
        "# 데이터 전처리 - 정규화를 위한 작업\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "processed_data[['단속동', '단속원금']] = scaler.fit_transform(processed_data[['단속동', '단속원금']])\n",
        "\n",
        "# 화면(figure) 생성\n",
        "plt.figure(figsize = (10, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "cNZrGFZNFYSG",
        "outputId": "418a44e5-ad07-4ccd-e7ee-bede9a103c29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# K 값을 늘려가며 반복 테스트\n",
        "for i in range(1, 7):\n",
        "   # 클러스터 생성\n",
        "   estimator = KMeans(n_clusters = i)\n",
        "   ids = estimator.fit_predict(processed_data[['단속동', '단속원금']])\n",
        "  # 2행 3열을 가진 서브플롯 추가 (인덱스 = i)\n",
        "   plt.subplot(3, 2, i)\n",
        "   plt.tight_layout()\n",
        "  # 서브플롯의 라벨링\n",
        "   plt.title(\"K value = {}\".format(i))\n",
        "   plt.xlabel('dong')\n",
        "   plt.ylabel('money')\n",
        "  # 클러스터링 그리기\n",
        "   plt.scatter(processed_data['단속동'], processed_data['단속원금'], c=ids)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "ymTtUpkzF2iA",
        "outputId": "fb8faf8d-8011-4b62-cf3a-a405ccdd53d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 광진구 공영주차장 정보 csv"
      ],
      "metadata": {
        "id": "qQV3x4ZdMhET"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swY_PoFcJc_g",
        "outputId": "e87fa119-9540-4a89-c944-89c511ce9d65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import codecs\n",
        "\n",
        "filename1 = \"/content/drive/MyDrive/말감3팀/[03팀] 김가경, 김수민, 민채원/code/Gwangjin-gu Public Parking lot.csv\"\n",
        "\n",
        "with codecs.open(filename1, '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": "UJD_raU8JeRp",
        "outputId": "62cdc58f-7e35-4d54-fd90-474c5a13de1e"
      },
      "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(filename1, encoding='cp949')\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1tRQeFZlKQ0J",
        "outputId": "15bca31d-1dda-4d6b-8316-0065a90d9684"
      },
      "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  "
            ],
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              "  <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-1f7f0a2f-c83a-4bc3-aeea-53e33402d55c')\"\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f7f0a2f-c83a-4bc3-aeea-53e33402d55c 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-1f7f0a2f-c83a-4bc3-aeea-53e33402d55c');\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": 15
        }
      ]
    },
    {
      "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": "A6p0XNWmKgrA",
        "outputId": "98562d31-c446-4b41-ce64-dfc8669a2502"
      },
      "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-1bf78693-01ee-4e25-b6e4-69f363b4260f\">\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-1bf78693-01ee-4e25-b6e4-69f363b4260f')\"\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",
              "      </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-1bf78693-01ee-4e25-b6e4-69f363b4260f 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-1bf78693-01ee-4e25-b6e4-69f363b4260f');\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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 단속동의 데이터들을 정수형 타입으로 변경\n",
        "df1['동명'] = pd.to_numeric(df1['동명'], errors='coerce').astype('Int64')"
      ],
      "metadata": {
        "id": "if_-dwyfLtbJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 광진구 동별로 공영주차장 개수 파악"
      ],
      "metadata": {
        "id": "x6MHp1hBMuL6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 필요 패키지 불러오기 (KMeans, matplotlib, preprocessing)\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# 원본 데이터를 복사해서 전처리하기 (원본 데이터를 가지고 바로 전처리하지 않는다)\n",
        "processed_data = df1.copy()\n",
        "\n",
        "# 데이터 전처리 - 정규화를 위한 작업\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "processed_data[['동명', '주차면수 소계']] = scaler.fit_transform(processed_data[['동명', '주차면수 소계']])\n",
        "\n",
        "# 화면(figure) 생성\n",
        "plt.figure(figsize = (10, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "iCXpxp9SL3Kl",
        "outputId": "7daee601-2479-4097-98f4-ea1ece37040c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# K 값을 늘려가며 반복 테스트\n",
        "for i in range(1, 7):\n",
        "   # 클러스터 생성\n",
        "   estimator = KMeans(n_clusters = i)\n",
        "   ids = estimator.fit_predict(processed_data[['동명', '주차면수 소계']])\n",
        "  # 2행 3열을 가진 서브플롯 추가 (인덱스 = i)\n",
        "   plt.subplot(3, 2, i)\n",
        "   plt.tight_layout()\n",
        "  # 서브플롯의 라벨링\n",
        "   plt.title(\"K value = {}\".format(i))\n",
        "   plt.xlabel('dong')\n",
        "   plt.ylabel('num of parking lot')\n",
        "  # 클러스터링 그리기\n",
        "   plt.scatter(processed_data['동명'], processed_data['주차면수 소계'], c=ids)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "KtnxsD8MMMme",
        "outputId": "410f138d-c39b-403a-eb81-f7bdd8601395"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# 두 가지 feature를 대상\n",
        "data = df1[['동명', '주차면수 소계']]\n",
        "\n",
        "# 정규화 진행\n",
        "scaler = MinMaxScaler()\n",
        "data_scale = scaler.fit_transform(data)"
      ],
      "metadata": {
        "id": "MKaC8quyN37k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "k = 4\n",
        "\n",
        "# 그룹 수, random_state 설정\n",
        "model = KMeans(n_clusters = k, random_state = 10)\n",
        "\n",
        "# 정규화된 데이터에 학습\n",
        "model.fit(data_scale)\n",
        "\n",
        "# 클러스터링 결과 각 데이터가 몇 번째 그룹에 속하는지 저장\n",
        "df1['cluster'] = model.fit_predict(data_scale)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVObn1qQOESF",
        "outputId": "6028ea57-5268-46d3-c8ad-ab9636bcd4a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize = (8, 8))\n",
        "\n",
        "for i in range(k):\n",
        "    plt.scatter(df1.loc[df1['cluster'] == i, '동명'], df1.loc[df1['cluster'] == i, '주차면수 소계'],\n",
        "                label = 'cluster ' + str(i))\n",
        "\n",
        "plt.legend()\n",
        "plt.title('K = %d results'%k , size = 15)\n",
        "plt.xlabel('dong', size = 12)\n",
        "plt.ylabel('num of parking lot', size = 12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "LxW9G-K0OL_c",
        "outputId": "aa04d59f-5b60-4c40-fd9f-72980258ae89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3월 광진구 인구 수 csv"
      ],
      "metadata": {
        "id": "40hUgQgOW3bY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import codecs\n",
        "\n",
        "filename2 = \"/content/drive/MyDrive/말감3팀/[03팀] 김가경, 김수민, 민채원/code/Gwangjin-Gu Population of March.csv\"\n",
        "\n",
        "with codecs.open(filename2, '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/",
          "height": 400
        },
        "id": "vhPlsKltW5Zb",
        "outputId": "7e3c0a56-359e-43cf-89b6-ee8aa07606c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-55b436884427>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfilename2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/drive/MyDrive/말감3팀/[03팀] 김가경, 김수민, 민채원/code/Gwangjin-Gu Population of March.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'euc_kr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/codecs.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(filename, mode, encoding, errors, buffering)\u001b[0m\n\u001b[1;32m    904\u001b[0m         \u001b[0;31m# Force opening of the file in binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m         \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 906\u001b[0;31m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffering\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    907\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    908\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/말감3팀/[03팀] 김가경, 김수민, 민채원/code/Gwangjin-Gu Population of March.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df2 = pd.read_csv(filename2, encoding='cp949')\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "eLqcqWaSXHe-",
        "outputId": "e3802709-1d5d-49d2-daea-c44e02ee642e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     행정기관       합계        남        여\n",
              "0     광진구  336,801  162,209  174,592\n",
              "1     화양동   23,450   11,171   12,279\n",
              "2     군자동   19,340    9,576    9,764\n",
              "3   중곡제1동   15,453    7,364    8,089\n",
              "4   중곡제2동   20,856    9,828   11,028\n",
              "5   중곡제3동   15,913    7,861    8,052\n",
              "6   중곡제4동   27,866   13,623   14,243\n",
              "7      능동   11,163    5,131    6,032\n",
              "8     광장동   34,044   16,468   17,576\n",
              "9   자양제1동   22,007   10,710   11,297\n",
              "10  자양제2동   23,802   11,546   12,256\n",
              "11  자양제3동   27,457   13,117   14,340\n",
              "12  자양제4동   17,989    8,964    9,025\n",
              "13  구의제1동   23,424   11,181   12,243\n",
              "14  구의제2동   25,864   12,385   13,479\n",
              "15  구의제3동   28,173   13,284   14,889"
            ],
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              "\n",
              "  <div id=\"df-f7f364c2-99fe-4667-81b8-219e256aa69c\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>행정기관</th>\n",
              "      <th>합계</th>\n",
              "      <th>남</th>\n",
              "      <th>여</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>광진구</td>\n",
              "      <td>336,801</td>\n",
              "      <td>162,209</td>\n",
              "      <td>174,592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>화양동</td>\n",
              "      <td>23,450</td>\n",
              "      <td>11,171</td>\n",
              "      <td>12,279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>군자동</td>\n",
              "      <td>19,340</td>\n",
              "      <td>9,576</td>\n",
              "      <td>9,764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>중곡제1동</td>\n",
              "      <td>15,453</td>\n",
              "      <td>7,364</td>\n",
              "      <td>8,089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>중곡제2동</td>\n",
              "      <td>20,856</td>\n",
              "      <td>9,828</td>\n",
              "      <td>11,028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>중곡제3동</td>\n",
              "      <td>15,913</td>\n",
              "      <td>7,861</td>\n",
              "      <td>8,052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>중곡제4동</td>\n",
              "      <td>27,866</td>\n",
              "      <td>13,623</td>\n",
              "      <td>14,243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>능동</td>\n",
              "      <td>11,163</td>\n",
              "      <td>5,131</td>\n",
              "      <td>6,032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>광장동</td>\n",
              "      <td>34,044</td>\n",
              "      <td>16,468</td>\n",
              "      <td>17,576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>자양제1동</td>\n",
              "      <td>22,007</td>\n",
              "      <td>10,710</td>\n",
              "      <td>11,297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>자양제2동</td>\n",
              "      <td>23,802</td>\n",
              "      <td>11,546</td>\n",
              "      <td>12,256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>자양제3동</td>\n",
              "      <td>27,457</td>\n",
              "      <td>13,117</td>\n",
              "      <td>14,340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>자양제4동</td>\n",
              "      <td>17,989</td>\n",
              "      <td>8,964</td>\n",
              "      <td>9,025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>구의제1동</td>\n",
              "      <td>23,424</td>\n",
              "      <td>11,181</td>\n",
              "      <td>12,243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>구의제2동</td>\n",
              "      <td>25,864</td>\n",
              "      <td>12,385</td>\n",
              "      <td>13,479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>구의제3동</td>\n",
              "      <td>28,173</td>\n",
              "      <td>13,284</td>\n",
              "      <td>14,889</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7f364c2-99fe-4667-81b8-219e256aa69c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f7f364c2-99fe-4667-81b8-219e256aa69c 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-f7f364c2-99fe-4667-81b8-219e256aa69c');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df2.drop(df2[df2['행정기관'] == '광진구'].index)\n",
        "df2.loc[df2['행정기관'] == '구의제1동', '행정기관'] = 1\n",
        "df2.loc[df2['행정기관'] == '구의제2동', '행정기관'] = 1\n",
        "df2.loc[df2['행정기관'] == '구의제3동', '행정기관'] = 1\n",
        "df2.loc[df2['행정기관'] == '자양제1동', '행정기관'] = 2\n",
        "df2.loc[df2['행정기관'] == '자양제2동', '행정기관'] = 2\n",
        "df2.loc[df2['행정기관'] == '자양제3동', '행정기관'] = 2\n",
        "df2.loc[df2['행정기관'] == '자양제4동', '행정기관'] = 2\n",
        "df2.loc[df2['행정기관'] == '중곡제1동', '행정기관'] = 3\n",
        "df2.loc[df2['행정기관'] == '중곡제2동', '행정기관'] = 3\n",
        "df2.loc[df2['행정기관'] == '중곡제3동', '행정기관'] = 3\n",
        "df2.loc[df2['행정기관'] == '중곡제4동', '행정기관'] = 3\n",
        "df2.loc[df2['행정기관'] == '화양동', '행정기관'] = 4\n",
        "df2.loc[df2['행정기관'] == '군자동', '행정기관'] = 5\n",
        "df2.loc[df2['행정기관'] == '능동', '행정기관'] = 6\n",
        "df2.loc[df2['행정기관'] == '광장동', '행정기관'] = 7\n",
        "\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "ojw-FO5wYnsx",
        "outputId": "98bb78cc-c836-46e5-cc34-2ccd623b3539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   행정기관      합계       남       여\n",
              "1     4  23,450  11,171  12,279\n",
              "2     5  19,340   9,576   9,764\n",
              "3     3  15,453   7,364   8,089\n",
              "4     3  20,856   9,828  11,028\n",
              "5     3  15,913   7,861   8,052\n",
              "6     3  27,866  13,623  14,243\n",
              "7     6  11,163   5,131   6,032\n",
              "8     7  34,044  16,468  17,576\n",
              "9     2  22,007  10,710  11,297\n",
              "10    2  23,802  11,546  12,256\n",
              "11    2  27,457  13,117  14,340\n",
              "12    2  17,989   8,964   9,025\n",
              "13    1  23,424  11,181  12,243\n",
              "14    1  25,864  12,385  13,479\n",
              "15    1  28,173  13,284  14,889"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>행정기관</th>\n",
              "      <th>합계</th>\n",
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              "      <th>여</th>\n",
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              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>23,450</td>\n",
              "      <td>11,171</td>\n",
              "      <td>12,279</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>19,340</td>\n",
              "      <td>9,576</td>\n",
              "      <td>9,764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>15,453</td>\n",
              "      <td>7,364</td>\n",
              "      <td>8,089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>20,856</td>\n",
              "      <td>9,828</td>\n",
              "      <td>11,028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>15,913</td>\n",
              "      <td>7,861</td>\n",
              "      <td>8,052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>27,866</td>\n",
              "      <td>13,623</td>\n",
              "      <td>14,243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>11,163</td>\n",
              "      <td>5,131</td>\n",
              "      <td>6,032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>34,044</td>\n",
              "      <td>16,468</td>\n",
              "      <td>17,576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>22,007</td>\n",
              "      <td>10,710</td>\n",
              "      <td>11,297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>23,802</td>\n",
              "      <td>11,546</td>\n",
              "      <td>12,256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>27,457</td>\n",
              "      <td>13,117</td>\n",
              "      <td>14,340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2</td>\n",
              "      <td>17,989</td>\n",
              "      <td>8,964</td>\n",
              "      <td>9,025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>23,424</td>\n",
              "      <td>11,181</td>\n",
              "      <td>12,243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>25,864</td>\n",
              "      <td>12,385</td>\n",
              "      <td>13,479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>28,173</td>\n",
              "      <td>13,284</td>\n",
              "      <td>14,889</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "          document.querySelector('#df-014dd831-f111-460d-a103-92a670c55f1b 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-014dd831-f111-460d-a103-92a670c55f1b');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 단속동의 데이터들을 정수형 타입으로 변경\n",
        "df2['행정기관'] = pd.to_numeric(df2['행정기관'], errors='coerce').astype('Int64')"
      ],
      "metadata": {
        "id": "2lsrEvVMankM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 컬럼명이 합계인 데이터들의 데이터 타입 정수형으로 변환(쉼표제거 숫자만을 포함하는 문자열 -> 정수형)\n",
        "df2['합계'] = df2['합계'].str.replace(',', '').astype(int)"
      ],
      "metadata": {
        "id": "ur5nmnf9dLB0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 광진구 동별로 인구수가 얼만큼 분포되었는지 확인"
      ],
      "metadata": {
        "id": "l89CWauFayYX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 필요 패키지 불러오기 (KMeans, matplotlib, preprocessing)\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# 원본 데이터를 복사해서 전처리하기 (원본 데이터를 가지고 바로 전처리하지 않는다)\n",
        "processed_data = df2.copy()\n",
        "\n",
        "# 데이터 전처리 - 정규화를 위한 작업\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "processed_data[['행정기관', '합계']] = scaler.fit_transform(processed_data[['행정기관', '합계']])\n",
        "\n",
        "# 화면(figure) 생성\n",
        "plt.figure(figsize = (10, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "7218YJ0ga-ID",
        "outputId": "c22b909e-2033-4879-cda8-e4ceeabe4dab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# K 값을 늘려가며 반복 테스트\n",
        "for i in range(1, 7):\n",
        "   # 클러스터 생성\n",
        "   estimator = KMeans(n_clusters = i)\n",
        "   ids = estimator.fit_predict(processed_data[['행정기관', '합계']])\n",
        "  # 2행 3열을 가진 서브플롯 추가 (인덱스 = i)\n",
        "   plt.subplot(3, 2, i)\n",
        "   plt.tight_layout()\n",
        "  # 서브플롯의 라벨링\n",
        "   plt.title(\"K value = {}\".format(i))\n",
        "   plt.xlabel('dong')\n",
        "   plt.ylabel('num of population')\n",
        "  # 클러스터링 그리기\n",
        "   plt.scatter(processed_data['행정기관'], processed_data['합계'], c=ids)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "q27LPY3YbWju",
        "outputId": "17989f0d-2bd2-4894-df19-ce352b15e4e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# 두 가지 feature를 대상\n",
        "data = df2[['행정기관', '합계']]\n",
        "\n",
        "# 정규화 진행\n",
        "scaler = MinMaxScaler()\n",
        "data_scale = scaler.fit_transform(data)"
      ],
      "metadata": {
        "id": "xzXIZV08eGlK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# 그룹 수, random_state 설정\n",
        "model = KMeans(n_clusters = k, random_state = 10)\n",
        "\n",
        "# 정규화된 데이터에 학습\n",
        "model.fit(data_scale)\n",
        "\n",
        "# 클러스터링 결과 각 데이터가 몇 번째 그룹에 속하는지 저장\n",
        "df2['cluster'] = model.fit_predict(data_scale)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQcRk1RbedjA",
        "outputId": "7a8a9e69-a56f-42fa-e1b4-7a688e1feacf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize = (8, 8))\n",
        "\n",
        "for i in range(k):\n",
        "    plt.scatter(df2.loc[df2['cluster'] == i, '행정기관'], df2.loc[df2['cluster'] == i, '합계'],\n",
        "                label = 'cluster ' + str(i))\n",
        "\n",
        "plt.legend()\n",
        "plt.title('K = %d results'%k , size = 15)\n",
        "plt.xlabel('dong', size = 12)\n",
        "plt.ylabel('num of population', size = 12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "lOSv5doEe5wv",
        "outputId": "8b69f80e-c51f-4ca4-f863-ab352b9882ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}