매매평당금액.ipynb

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      "name": "매매평당금액.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jungsan005/Bigdata-Class/blob/master/%EB%A7%A4%EB%A7%A4%ED%8F%89%EB%8B%B9%EA%B8%88%EC%95%A1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5Y4JZUPSjC0"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toNAPhvYSkxf"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/Jungsan005/Bigdata-Class/master/2010_2019%20%EC%84%9C%EC%9A%B8%EC%8B%9C%20%EB%B6%80%EB%8F%99%EC%82%B0%20%EB%A7%A4%EB%A7%A4%20%ED%8F%89%EB%8B%B9%EA%B0%80%EA%B2%A92.csv\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWrOojibSk4S",
        "outputId": "6ae7943e-829e-4308-d0e9-0634ff761bc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>gu</th>\n",
              "      <th>price</th>\n",
              "      <th>kind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>종로구</td>\n",
              "      <td>515.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>중구</td>\n",
              "      <td>601.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>용산구</td>\n",
              "      <td>834.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>성동구</td>\n",
              "      <td>599.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>광진구</td>\n",
              "      <td>638.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11985</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>관악구</td>\n",
              "      <td>611.5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11986</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>서초구</td>\n",
              "      <td>705.6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11987</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>강남구</td>\n",
              "      <td>883.9</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11988</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>송파구</td>\n",
              "      <td>799.4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11989</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>강동구</td>\n",
              "      <td>620.5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11990 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       year  month   gu  price  kind\n",
              "0      2010      1  종로구  515.1     1\n",
              "1      2010      1   중구  601.6     1\n",
              "2      2010      1  용산구  834.6     1\n",
              "3      2010      1  성동구  599.1     1\n",
              "4      2010      1  광진구  638.4     1\n",
              "...     ...    ...  ...    ...   ...\n",
              "11985  2019      9  관악구  611.5     4\n",
              "11986  2019      9  서초구  705.6     4\n",
              "11987  2019      9  강남구  883.9     4\n",
              "11988  2019      9  송파구  799.4     4\n",
              "11989  2019      9  강동구  620.5     4\n",
              "\n",
              "[11990 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAz44cLmVAfk",
        "outputId": "ad6ff737-1311-4274-d483-41860ab552e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "df.groupby('kind').size()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "kind\n",
              "1    3000\n",
              "2    3000\n",
              "3    3000\n",
              "4    2990\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6q-4IT_pnNlU",
        "outputId": "ce2a9e47-e60d-4a53-d378-3ca5350969ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "df.groupby(['year''month', 'gu','price'], as_index=False).mean()\n",
        "\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-4cc2d14b9254>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gu'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\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      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# df[[\"gu\",\"year\",\"month\",\"pirce\"]].groupby([df[\"gu\"],df[\"year\"],df[\"\"]]).sum()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m   6518\u001b[0m             \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6519\u001b[0m             \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6520\u001b[0;31m             \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\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   6521\u001b[0m         )\n\u001b[1;32m   6522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m    531\u001b[0m                 \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m                 \u001b[0mmutated\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmutated\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m                 \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\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    534\u001b[0m             )\n\u001b[1;32m    535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m    779\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 781\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\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    782\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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    783\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'yearmonth'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcCpyQbpSk9S",
        "outputId": "e4425f87-3eb0-4f78-adaa-b05e5d1c4787",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "df_Trade_a = df[['gu','year','month','price']].groupby([df['gu'],df['year'],df['month']]).mean()\n",
        "df_Trade_a"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gu</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">강남구</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>741.350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>729.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010</td>\n",
              "      <td>3</td>\n",
              "      <td>693.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010</td>\n",
              "      <td>4</td>\n",
              "      <td>722.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2010</td>\n",
              "      <td>5</td>\n",
              "      <td>682.525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">중랑구</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
              "      <th>8</th>\n",
              "      <td>2019</td>\n",
              "      <td>8</td>\n",
              "      <td>527.325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>459.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2019</td>\n",
              "      <td>10</td>\n",
              "      <td>548.425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>555.725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2019</td>\n",
              "      <td>12</td>\n",
              "      <td>666.175</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                year  month    price\n",
              "gu  year month                      \n",
              "강남구 2010 1      2010      1  741.350\n",
              "         2      2010      2  729.450\n",
              "         3      2010      3  693.850\n",
              "         4      2010      4  722.875\n",
              "         5      2010      5  682.525\n",
              "...              ...    ...      ...\n",
              "중랑구 2019 8      2019      8  527.325\n",
              "         9      2019      9  459.150\n",
              "         10     2019     10  548.425\n",
              "         11     2019     11  555.725\n",
              "         12     2019     12  666.175\n",
              "\n",
              "[3000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlcN9xuLSlA0",
        "outputId": "9d5cbfbc-f713-4e4e-9582-0d047c66d0b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        }
      },
      "source": [
        "df_Trade_b = df_Trade_a.unstack(level=0,fill_value=None)\n",
        "df_Trade_b "
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"25\" halign=\"left\">year</th>\n",
              "      <th colspan=\"25\" halign=\"left\">month</th>\n",
              "      <th colspan=\"25\" halign=\"left\">price</th>\n",
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              "      <th>구로구</th>\n",
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              "      <th>노원구</th>\n",
              "      <th>도봉구</th>\n",
              "      <th>동대문구</th>\n",
              "      <th>동작구</th>\n",
              "      <th>마포구</th>\n",
              "      <th>서대문구</th>\n",
              "      <th>서초구</th>\n",
              "      <th>성동구</th>\n",
              "      <th>성북구</th>\n",
              "      <th>송파구</th>\n",
              "      <th>양천구</th>\n",
              "      <th>영등포구</th>\n",
              "      <th>용산구</th>\n",
              "      <th>은평구</th>\n",
              "      <th>종로구</th>\n",
              "      <th>중구</th>\n",
              "      <th>중랑구</th>\n",
              "      <th>강남구</th>\n",
              "      <th>강동구</th>\n",
              "      <th>강북구</th>\n",
              "      <th>강서구</th>\n",
              "      <th>관악구</th>\n",
              "      <th>광진구</th>\n",
              "      <th>구로구</th>\n",
              "      <th>금천구</th>\n",
              "      <th>노원구</th>\n",
              "      <th>도봉구</th>\n",
              "      <th>동대문구</th>\n",
              "      <th>동작구</th>\n",
              "      <th>마포구</th>\n",
              "      <th>서대문구</th>\n",
              "      <th>서초구</th>\n",
              "      <th>성동구</th>\n",
              "      <th>성북구</th>\n",
              "      <th>송파구</th>\n",
              "      <th>양천구</th>\n",
              "      <th>영등포구</th>\n",
              "      <th>용산구</th>\n",
              "      <th>은평구</th>\n",
              "      <th>종로구</th>\n",
              "      <th>중구</th>\n",
              "      <th>중랑구</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>741.350</td>\n",
              "      <td>482.500</td>\n",
              "      <td>349.925</td>\n",
              "      <td>407.325</td>\n",
              "      <td>397.800</td>\n",
              "      <td>486.700</td>\n",
              "      <td>382.225</td>\n",
              "      <td>339.075</td>\n",
              "      <td>348.325</td>\n",
              "      <td>356.700</td>\n",
              "      <td>420.250</td>\n",
              "      <td>507.450</td>\n",
              "      <td>583.075</td>\n",
              "      <td>452.900</td>\n",
              "      <td>719.450</td>\n",
              "      <td>583.875</td>\n",
              "      <td>429.600</td>\n",
              "      <td>624.300</td>\n",
              "      <td>429.375</td>\n",
              "      <td>458.925</td>\n",
              "      <td>857.250</td>\n",
              "      <td>370.375</td>\n",
              "      <td>560.225</td>\n",
              "      <td>790.025</td>\n",
              "      <td>360.225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>729.450</td>\n",
              "      <td>422.700</td>\n",
              "      <td>344.700</td>\n",
              "      <td>394.150</td>\n",
              "      <td>397.900</td>\n",
              "      <td>491.900</td>\n",
              "      <td>385.125</td>\n",
              "      <td>344.525</td>\n",
              "      <td>381.800</td>\n",
              "      <td>352.350</td>\n",
              "      <td>429.750</td>\n",
              "      <td>498.875</td>\n",
              "      <td>586.325</td>\n",
              "      <td>420.825</td>\n",
              "      <td>672.475</td>\n",
              "      <td>563.025</td>\n",
              "      <td>455.250</td>\n",
              "      <td>585.625</td>\n",
              "      <td>404.425</td>\n",
              "      <td>477.800</td>\n",
              "      <td>825.350</td>\n",
              "      <td>376.525</td>\n",
              "      <td>554.850</td>\n",
              "      <td>562.900</td>\n",
              "      <td>368.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>693.850</td>\n",
              "      <td>476.100</td>\n",
              "      <td>352.825</td>\n",
              "      <td>398.875</td>\n",
              "      <td>391.900</td>\n",
              "      <td>504.775</td>\n",
              "      <td>351.550</td>\n",
              "      <td>332.100</td>\n",
              "      <td>373.250</td>\n",
              "      <td>359.225</td>\n",
              "      <td>407.375</td>\n",
              "      <td>486.275</td>\n",
              "      <td>557.250</td>\n",
              "      <td>427.875</td>\n",
              "      <td>624.500</td>\n",
              "      <td>486.275</td>\n",
              "      <td>429.200</td>\n",
              "      <td>580.325</td>\n",
              "      <td>412.550</td>\n",
              "      <td>485.075</td>\n",
              "      <td>838.000</td>\n",
              "      <td>380.300</td>\n",
              "      <td>524.925</td>\n",
              "      <td>734.050</td>\n",
              "      <td>358.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>722.875</td>\n",
              "      <td>431.175</td>\n",
              "      <td>374.600</td>\n",
              "      <td>364.625</td>\n",
              "      <td>401.325</td>\n",
              "      <td>467.450</td>\n",
              "      <td>374.475</td>\n",
              "      <td>308.000</td>\n",
              "      <td>384.625</td>\n",
              "      <td>349.175</td>\n",
              "      <td>404.125</td>\n",
              "      <td>479.250</td>\n",
              "      <td>525.275</td>\n",
              "      <td>399.275</td>\n",
              "      <td>624.475</td>\n",
              "      <td>441.250</td>\n",
              "      <td>428.475</td>\n",
              "      <td>568.225</td>\n",
              "      <td>399.725</td>\n",
              "      <td>459.475</td>\n",
              "      <td>820.275</td>\n",
              "      <td>361.950</td>\n",
              "      <td>550.100</td>\n",
              "      <td>783.125</td>\n",
              "      <td>356.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>2010</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>682.525</td>\n",
              "      <td>431.900</td>\n",
              "      <td>362.450</td>\n",
              "      <td>371.025</td>\n",
              "      <td>380.375</td>\n",
              "      <td>502.000</td>\n",
              "      <td>356.200</td>\n",
              "      <td>325.900</td>\n",
              "      <td>360.100</td>\n",
              "      <td>357.925</td>\n",
              "      <td>408.575</td>\n",
              "      <td>470.675</td>\n",
              "      <td>590.425</td>\n",
              "      <td>408.125</td>\n",
              "      <td>743.750</td>\n",
              "      <td>475.700</td>\n",
              "      <td>402.775</td>\n",
              "      <td>563.850</td>\n",
              "      <td>423.100</td>\n",
              "      <td>464.825</td>\n",
              "      <td>853.075</td>\n",
              "      <td>360.950</td>\n",
              "      <td>541.275</td>\n",
              "      <td>680.475</td>\n",
              "      <td>358.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
              "      <th>8</th>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1462.475</td>\n",
              "      <td>721.025</td>\n",
              "      <td>509.000</td>\n",
              "      <td>631.075</td>\n",
              "      <td>633.075</td>\n",
              "      <td>760.625</td>\n",
              "      <td>607.875</td>\n",
              "      <td>589.425</td>\n",
              "      <td>543.050</td>\n",
              "      <td>492.300</td>\n",
              "      <td>750.350</td>\n",
              "      <td>861.125</td>\n",
              "      <td>931.500</td>\n",
              "      <td>699.250</td>\n",
              "      <td>1134.200</td>\n",
              "      <td>1169.700</td>\n",
              "      <td>597.150</td>\n",
              "      <td>886.750</td>\n",
              "      <td>601.725</td>\n",
              "      <td>766.450</td>\n",
              "      <td>1182.800</td>\n",
              "      <td>662.625</td>\n",
              "      <td>813.100</td>\n",
              "      <td>815.325</td>\n",
              "      <td>527.325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>1343.875</td>\n",
              "      <td>746.125</td>\n",
              "      <td>515.525</td>\n",
              "      <td>588.100</td>\n",
              "      <td>587.875</td>\n",
              "      <td>811.850</td>\n",
              "      <td>574.925</td>\n",
              "      <td>567.600</td>\n",
              "      <td>555.000</td>\n",
              "      <td>453.425</td>\n",
              "      <td>749.650</td>\n",
              "      <td>873.775</td>\n",
              "      <td>949.775</td>\n",
              "      <td>740.250</td>\n",
              "      <td>1133.925</td>\n",
              "      <td>958.000</td>\n",
              "      <td>647.825</td>\n",
              "      <td>873.500</td>\n",
              "      <td>655.350</td>\n",
              "      <td>831.200</td>\n",
              "      <td>1168.225</td>\n",
              "      <td>611.025</td>\n",
              "      <td>851.150</td>\n",
              "      <td>916.550</td>\n",
              "      <td>459.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>1278.750</td>\n",
              "      <td>699.600</td>\n",
              "      <td>546.650</td>\n",
              "      <td>634.775</td>\n",
              "      <td>647.300</td>\n",
              "      <td>786.325</td>\n",
              "      <td>584.275</td>\n",
              "      <td>551.300</td>\n",
              "      <td>575.975</td>\n",
              "      <td>466.675</td>\n",
              "      <td>723.975</td>\n",
              "      <td>841.700</td>\n",
              "      <td>946.050</td>\n",
              "      <td>764.675</td>\n",
              "      <td>1148.975</td>\n",
              "      <td>1025.375</td>\n",
              "      <td>646.375</td>\n",
              "      <td>897.375</td>\n",
              "      <td>682.150</td>\n",
              "      <td>841.550</td>\n",
              "      <td>1268.775</td>\n",
              "      <td>611.050</td>\n",
              "      <td>847.250</td>\n",
              "      <td>976.000</td>\n",
              "      <td>548.425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>1254.425</td>\n",
              "      <td>724.925</td>\n",
              "      <td>553.400</td>\n",
              "      <td>641.275</td>\n",
              "      <td>657.700</td>\n",
              "      <td>776.425</td>\n",
              "      <td>685.875</td>\n",
              "      <td>546.175</td>\n",
              "      <td>606.125</td>\n",
              "      <td>478.650</td>\n",
              "      <td>832.750</td>\n",
              "      <td>839.975</td>\n",
              "      <td>980.425</td>\n",
              "      <td>811.050</td>\n",
              "      <td>1160.675</td>\n",
              "      <td>989.650</td>\n",
              "      <td>679.625</td>\n",
              "      <td>940.325</td>\n",
              "      <td>716.425</td>\n",
              "      <td>818.425</td>\n",
              "      <td>1217.825</td>\n",
              "      <td>623.725</td>\n",
              "      <td>755.175</td>\n",
              "      <td>1061.800</td>\n",
              "      <td>555.725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1291.675</td>\n",
              "      <td>730.275</td>\n",
              "      <td>530.600</td>\n",
              "      <td>657.025</td>\n",
              "      <td>668.600</td>\n",
              "      <td>783.000</td>\n",
              "      <td>583.000</td>\n",
              "      <td>583.475</td>\n",
              "      <td>618.475</td>\n",
              "      <td>500.250</td>\n",
              "      <td>846.525</td>\n",
              "      <td>846.975</td>\n",
              "      <td>993.900</td>\n",
              "      <td>769.900</td>\n",
              "      <td>1134.450</td>\n",
              "      <td>1021.725</td>\n",
              "      <td>680.425</td>\n",
              "      <td>899.875</td>\n",
              "      <td>658.400</td>\n",
              "      <td>900.325</td>\n",
              "      <td>1257.425</td>\n",
              "      <td>669.600</td>\n",
              "      <td>835.300</td>\n",
              "      <td>1101.525</td>\n",
              "      <td>666.175</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 75 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            year                    ...    price                            \n",
              "gu           강남구   강동구   강북구   강서구  ...      은평구      종로구        중구      중랑구\n",
              "year month                          ...                                     \n",
              "2010 1      2010  2010  2010  2010  ...  370.375  560.225   790.025  360.225\n",
              "     2      2010  2010  2010  2010  ...  376.525  554.850   562.900  368.400\n",
              "     3      2010  2010  2010  2010  ...  380.300  524.925   734.050  358.850\n",
              "     4      2010  2010  2010  2010  ...  361.950  550.100   783.125  356.250\n",
              "     5      2010  2010  2010  2010  ...  360.950  541.275   680.475  358.100\n",
              "...          ...   ...   ...   ...  ...      ...      ...       ...      ...\n",
              "2019 8      2019  2019  2019  2019  ...  662.625  813.100   815.325  527.325\n",
              "     9      2019  2019  2019  2019  ...  611.025  851.150   916.550  459.150\n",
              "     10     2019  2019  2019  2019  ...  611.050  847.250   976.000  548.425\n",
              "     11     2019  2019  2019  2019  ...  623.725  755.175  1061.800  555.725\n",
              "     12     2019  2019  2019  2019  ...  669.600  835.300  1101.525  666.175\n",
              "\n",
              "[120 rows x 75 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnfLqs0dSmcV",
        "outputId": "29cdfb99-c423-472d-9b9a-8063d3d6718b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "df_Trade_b ['price','강남구'].plot()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff84cc020f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Iklw/77qknGePtdEzGAagsStAVaEl+iJChW3xLARrB1T0FUU5D6jrsES/qjAr3hbP4BnH4ukcCFGc7eM9mysJRWP8z8FmjDHWxCxb9AHKbYtnIVg7oIuoKIpyHlDXGaAszx9fzxZgRUk2LoEzHaMLr0WiMXoGwxRm+bi4Kp/1Zbl84ZeH6QyECEZiSTeP8nikP/9n44JG+oqinAfUdQaoLspKavO6XZTnZ9IwRrXN7sEwxkBxjg8R4Xsfu5ytywv558ePA8Q9fSCetrlQIn0VfUVRFj31nQGWFWaNaq8szKS+a7Tod9npmoVZVvS+JC+D7/3eFfzfWzewqiSbi6vy4/s6kX5pTsZMdD3tqL2jKMqiJhiJ0tw7xLKi0aK/rDCLl2vaR7V32KJflD1s2bhcwie3r+aT21cn7et4+iULoNgaaKSvKMoip6l7CGMYZe8ALCvK5GzfEMFINKm9awzRH491Zbn43C42LM1LT4dnGI30FUVZ1DjpmtXFY0f6xkBj1yCrSnPi7WNF+uNRWZDJob+9BZ9nYcTQC6OXiqIo0yQu+mNG+lZbfcKatzDa05+IhSL4oKKvKMoip74zgN/jGrMC5rKizPg+iXQMhMj1exaUmE+WxXdFiqIoCdR1BFhWlIXLNXrB8rLcDHxu16gMnq5AiKIFknc/VVT0FUVZ1IyVo+/gcgmVhZk0dCbbO50DoUlbOwsNFX1FURYVBxt6+IfHjhIIRTDGUH8O0QeoGiNX3ynBsBjR7B1FURYFkWiMe587yVeePkEkZliS6+d9l1bRF4yMmaPvsKwoi0MHm5PaOgdC8YVWFhsq+oqiLAo+8f03eOpoK++6pIKm7kHuf+k0W6qtUsrnivSXFWbRFQjTH4yQ4/dgjFnUkf5kVs66X0RaReRQQtsHROSwiMREZNuI/T8nIjUiclxEbklov9VuqxGRz6b3MhRFOZ+pae3jqaOt/PENa/iPO7dwz1tX09QzxNefPQlMIPojMngCoSjBSIzC81X0gQeAW0e0HQJuB15IbBSRTVjr315gH/N1EXGLiBv4GnAbsAm4095XURQlZX7yRiNul/C7V60AYPu6JaxdksPTx1qBYWEfC6cmjyP6nVOYmLUQmVD0jTEvAJ0j2o4aY46PsfsO4IfGmKAx5jRQA1xu/9QYY04ZY0LAD+19FUVRUiIaM/x8bwPb15XGK126XMLH7aURS3J8ZPnGd7JHTtCKi75m70yKSqA+4XmD3TZe+yhE5G4R2S0iu9va2tLcPUVRFgKxmCEaM5Pa9+Wads72Bnnf1qqk9h2bK1iS62d5cfY4R1oUZnnJ9rmHI/2ALfqLNE9/3g3kGmPuA+4D2LZt2+TedUVRFhV/+8vDHG3u4+FPXDXhvj/d00B+ppcbNy5Javd73Hzno5fhHmNSViIiwrKiLBrstM3O/sUd6adb9BuBZQnPq+w2ztGuKIqSxDPHW6nvHKQnECY/yzvufr1DYR4/1MIHtlXh97hHbb+gIn+Mo0ZTVZgVj/S7Fnmkn25751HgDhHxi8hKYC3wGvA6sFZEVoqID2uw99E0v7aiKIuAtr4g9fYM2d21nePuF4rE+NJjRwlGYrzv0qpx95sM1UVZ1HYOEAhF6BgI4XEJuf55Z4SkhcmkbD4E7ALWi0iDiHxMRN4rIg3AVcCvReQ3AMaYw8DDwBHgceAeY0zUGBMBPgX8BjgKPGzvqyiKksSeuq7449fPdI25T0vPEHd+8xUeeq2e379mJZuXFaT0mu+4eClD4Rg/fK2eroEQhdnWMomLkQlvZcaYO8fZ9PNx9v8i8MUx2h8DHptS7xRFOe/YW9eN1y2sK8vl9TOjI/1gJMr77v0tXYEQX71zC+++pCLl19y6vIjLVxZx3wun2FCeu2gnZoHW3lEUZZ6xp66LCyryuWZNCQcauhkKJ69q9eyxNhq7B/nKHekRfId73rqGlt4hXnizbdEWWwMVfUVR5hHhaIwDDd1sqS7gshVFhKOG/fXdSfv8fG8DJTl+3rq+NK2vfd3aEi6szCNmFu8gLqjoK4oyjzjW3MdQOMal1YVsXV4IwO7aYV+/ayDEM8da2bG5Ao87vfIlItyzfQ2weNM1QUVfUZR5hDOIe+nyQgqzfawry+G108O+/q8ONhOOGm6/dMy5nSlzywVLuX1LJTeMyPlfTCzOnCRFURYke+u6KMvzU5GfAcBlK4p4dF8T0ZjB7RJ+tqeB9WW5bJqhsscul/CvH9w8I+eeL2ikryjKvGFPXTeXVhfG0yUvW1FEXzDCk0da2FvXxd66bt57aeWiTaecDTTSVxRlXtDeH6SuM8DvXFkdb7tiVREugU98fw8AIvCezTNj7ZwvqOgrijIveLmmHbCie4fy/Eye/PT11LT209w9SEmun6W29aNMDxV9RVHmBU8cOUtprp9LqpJn164uzWF1ac4c9WrxoZ6+oihzTjAS5bljrbxt4xJcE1TFVFJDRV9RlDln18kOBkJRbt60dK67suhR0VcUZc554shZsnxurlpdPNddWfSo6CuKMqfEYoanjpzl+nWlZHhH18RX0ouKvqIoSdR3BugdCs/IuT/+4G6+9eKppLYDjT209gW5aVPZjLymkoyKvqIoSfyv/9rFV546kfbz9gcjPHnkLI/sa0pqf+JwC26XcMOGxVv6YD6hoq8oSpyBYITmniFaeobSfu5jzb0AHG7qoT8Yibc/c6yVy1YUUrCIi5zNJyazctb9ItIqIocS2opE5EkROWH/LrTbRUS+KiI1InJARC5NOGanvf8JEdk5M5ejKEoqNPdYyxR2D4amdfzeui4efr2eSDQ2atsRW/RjBvbVWeWSW3qGONbSx/b1GuXPFpOJ9B8Abh3R9lngaWPMWuBp+znAbVjr4q4F7gbuBesmAXweuAK4HPi8c6NQFGX+0NhtRfg9g1Pz9HuHwvzVLw5y+72/5c9/eoAdX3uZgw09Sfscaeol1+/BJcRXxHrhRBsA161Nb218ZXwmFH1jzAvAyDXLdgDftR9/F3hPQvuDxuIVoEBEyoFbgCeNMZ3GmC7gSUbfSBRFmWOauu1IPzB50e8bCnPbv7/ID16t4yNXr+Ard2ymtS/Ijq+9xOOHmuP7HW7q5eJl+Wwsz4sveP7Cm22U5vrZWJ6b3gtRxmW6nn6ZMcZ5N1sAZ9i9EqhP2K/BbhuvXVGUeYQj+lOJ9F+u6aCxe5B7f2crn3/XBezYXMlTn76eqsIsfvCa9W8fjsY4fraPTeV5XLaiiD213QQjUV6qaefatSVaNXMWSXkg1xhjAJOGvgAgIneLyG4R2d3W1pau0yqKMgkabdHvG4oQjU3u33rXyXYyvW7emuDL52d6uXlTGa+c7KA/GOFU2wChSIwLKvLZtqKQwXCUH71eT3cgzPXr1NqZTaYr+mdt2wb7d6vd3ggsS9ivym4br30Uxpj7jDHbjDHbSkv1w6Aos4kT6QP0TjLaf/lkB5etLMLnSZaTGzeWEYrGeOlEG0eaLX9/U0VevIrm156tQQSuWVOSpt4rk2G6ov8o4GTg7AQeSWi/y87iuRLosW2g3wA3i0ihPYB7s92mKMo8oql7CLdd8GwyFk9r7xA1rf28ZYzyCdtWFJKX4eGpo60caerF53GxqiSbsrwMqouyONsb5MKKfIpz/Gm/DmV8JpOy+RCwC1gvIg0i8jHgH4GbROQE8Db7OcBjwCmgBvgm8IcAxphO4O+B1+2fv7PbFEWZJ8RihuaeQdbYZYy7E0T/VFs/DV2BUcfsOtUBwNWrR0frXreL7euX8OyxVg429rBhaW58MfNtK6zkvevWaZQ/20xYT98Yc+c4m24cY18D3DPOee4H7p9S7xRFmTXa+4OEo4aN5bkcP9uXFOn/yY/2kZfp5XsfuyLpmJdr2snL8LCpYuw1a2/cuIRH9zfRebqTOy4bdnivWFnEz/Y0cv06zc+fbXQRFUVRAGiw/fxNFXn8Yl8T3YHhCVqN3UPUdgYwxiRl2vz2ZAdXrS6OW0Ij2b5uCW6XEI2ZpMXM37uliuJsP5et0Ok6s42WYVDmnF/sbeQ/nk5/rRdlajiDuBttcXYGcqMxQ+dAkO5AmOaE8gz1nQEaugbHtHYc8rO8cWFP/Dbg87h426YyTdWcA1T0lTnnsYPN/GRPw1x347xnpOg7E7Q6BoI42ZtHmnrj+ztr2l49QQ3892yuJD/Ty4alY1tAyuyioq/MOUORGIOh6Fx347ynqXuIXL+Hkhw/mV533NNv6wvG93Hq54CVqlma62fNknOvX/vBy5bx6l/cSLZf3eT5gIq+MucMhaMMhlX055rG7kEqCjIBKMjyxrN32vuHvf2jTtG0mOHlmnauWTPxbFoR0cVR5hEq+sqcEwxHCYZHV2VUZpem7kEqCjIAa0btyEj/goq8eKR/pLmXzoEQ167VlMuFhoq+MucEIzFC0diY5XiV2aOpe5DKQivSz8/00hNIFv3r1pVS2xGgbyjMiycsP19n0y48VPSVOWfItnaGIir6c0UgFKErEI7bOyMj/WyfO56Fc6yljxdPtLFhaS5L8jLmrM/K9FDRV+acIdvaGVJff85osuvoVyZ5+paX39YfpDTXz6byfADeqO1i95kurtNCaQsSFX1lzhmKWGKvGTxzh5OuOVak394XpCTHT1men8IsL9/bVUsoGlM/f4Gioq/MOXF7RyP9OaNxhOgXZPkYCscYCkfjkb6IsKkij8buQfweV7xaprKwUNFf5Dx15CxfeuzoXHdjXIwxCfaOevpzQX8wwjdfPEVZnp+yXKviZV6mF7Bm5bb1WaIPxEspXL6ySNMwFygq+oucxw428/1Xaue6G+MSTBi81Vz92ccYw+d+dpAz7QP8+we3xKtgFtii39oXpGcwTKld/tgppaBr2i5cVPQXOR0DIQZC0UmvgjTbqOjPLd9/tY5f7m/iT29ez1UJ5RTybdE/2dYPEI/0r1lTyrVrS3jHxeWz31klLei86EVOx4CVY90fjMT/kecTwQShV09/9qjvDPCvT77JL/Y18tb1pXzy+tVJ2wuyrM9KTasl+iV2pF+a6x9VXllZWKjoL3I67Sn081X0E318Ff3J82c/3s81a0vYsblyysf+bE8Dn/3pQUTgD65bzaduWINrRGlk57PiiL4T6SsLn5TsHRH53yJySEQOi8j/sduKRORJETlh/y6020VEvioiNSJyQEQuTccFKONjjKFjwBb9ocgc92ZsnHRN0JTNydLWF+THbzTwz48fn/Is5qFwlH947CgXVObx3J9t57O3bSBnjEJoBZk+AE6o6C86pi36InIh8HHgcuAS4J0isgb4LPC0MWYt8LT9HOA2YK39czdwbwr9ViZBIBSNe+Z9Q5Nb5Hq2GVJ7Z8rsqesCrDTLp46endKxP3ytjvb+EJ+7bSPl+Znj7peb4UEEzrQPAFCc45t+h5V5RSqR/kbgVWNMwBgTAZ4Hbgd2AN+19/ku8B778Q7gQWPxClAgIjoaNIN0JFRH7AvO00g/nDiQqymbk2FPbRc+t4vKgky+8/KZSR8XjET5rxdOcfnKIi5fee4ce5dLyMvwEokZCrK8+D2anrlYSEX0DwHXikixiGQBbweWAWXGmGZ7nxagzH5cCdQnHN9gtykzhDOIC/PX3gkm2jsa6U+KN2q7uLAyj51XL+fV053xcscT8bM9jTT3DPFHN6yZ1P7OYK6TrqksDqYt+saYo8A/AU8AjwP7gOiIfQwwpVxBEblbRHaLyO62trbpdm/BMhSOYv3ZUqdzYDjS718AkX5QRX9CgpEoBxp72Lq8kA9uqybT6+a7vz0z4XHhaIx7nzvJJcsKJl0Z0xnMLVHRX1SkNJBrjPm2MWarMeY6oAt4Ezjr2Db271Z790asbwIOVXbbyHPeZ4zZZozZVlp6fk0AGQhGuPJLT/OzPaP+LNMiyd5ZAJ6+RvoTc6ixl1AkxtblheRneXnPlkp+vreRroQb/Fh8/5Va6joD/O8b10x6XVpH9HUQd3GRavbOEvt3NZaf/wPgUWCnvctO4BH78aPAXXYWz5VAT4INpGAtTNEdCHOoqSct5+tIjPTnqb2TJPqavTMhe2qtQdxLl1tljj9y9QqCkRg/fL1+3GPa+4P865NvcnT4xh4AACAASURBVO3aEt66fsmkX0tFf3GS6ozcn4rIEeCXwD3GmG7gH4GbROQE8Db7OcBjwCmgBvgm8Icpvvai41CjJfaNXYNpOV/nQJBMr5tcv2f+DuTa2UU5fo/W058Eb9R2UV2UxZJcq479+qW5XL26mO/tOjNu+uaXHz/OYCjK5991waSjfEjw9FX0FxUpTc4yxlw7RlsHcOMY7Qa4J5XXW+wcarQG5Jp60iP6Hf0hirJ9xIyhb55G+o6PX5Dl1Uh/AowxvFHXNcqT/8jVK7j7e2/w5JGz3HZRckLc/vpuHn6jnt+/ZuWEC5iPJB7pq6e/qNDaO/OIw7at4yxokSodAyGKc3zk+D3z1t5x5hEUZvlSztM/0z5AbcdAOro1L2noGqStLxi3dhxu3FhGVWEm30kY0A1HY3zn5dPs/M5rFGf7+eMb10759ZwJWiUa6S8qtAzDPGEoHOVEaz+ZXjedAyECoQhZvtTenk5b9Htd4XmcvRNFxJoMlKrof+bH+8n0uRdFbZhvvXiK599s4/6PXIbXrnz5+plOALZWJ4u+2yXsvGoFX3zsKI8fauZk2wA/eaOB0+0DvGVNMV941wXkZky9BEe+pmwuSjTSnycca+kjGjNsX29lLKUj2u8cCFGc7Scnwzt/Pf1wlAyPmyyfO+XsnZNt/bT3nzuLZSFgjOGB357hxRPt8XTMYCTK1587SUV+BuuX5o465n9tW0am180nvr+HL//mOAVZXu7/yDa+/7ErWFs2ev/JcPOmMv7slvVsGOP1lIWLRvrzBGcQ9+YLyvifQy00dg9O2YNNxBhDe3+Q4hzLNmnoCqSrq2llKBwjw+vC701N9HuHwnQFwil/O5oPHGrspaFrkIIsL//+1AnedUkFD+46Q01rPw989DLcrtGDsflZXv79js2c7R3ipk1l5yyxMFkKsnzc89bJTeRSFg4a6c8TDjf1UJDljS9B56xZOl2cujtF2T5yM5I9/WMtvfzj/xxL2ySwVBgKR/F73GR63QylMJBb12Hd1Hrn6XyEqfDYoWbcLuGBj15OKBrjUz/YwzeeP8X7t1ax/Rwpl7dcsJS7rlqRFsFXFi8q+vOEQ429XFiRz9K8DNwuSTlt05mNW5xtD+Qm2Du/2t/MN54/OS+skKGIFelnet0ppWzW2qLfH4wQm6cLxkwGYwz/c7CZq1cXs3lZAZ+4fjWvn+miONvHX79j01x3T1kEqOjPA0KRGMdb+rigMg+P28XSvIyUI/32fqvuTnGOj5wMD4FQNJ7H3dZnbWtOU2poKgyFo2R43WR4XSmlbNZ2Wlk7xkB/aH6OX0yGo819nOkIcNuFVurlH25fze1bKvnKHVviA6uKkgoq+vOAE619hKIxLqzIB6CiIIPGFEXfifSLsv3xeukDQUtUnRtCqjeWyRKOxnj+zbHrKAUjMfxey94ZTKHukGPvgLWY93zHGMOeuq5R1/v4oWZcYo3tAGR43fzrBzcnLWWoKKmgoj8POGxPyrqo0hL9yoLMlEW/I8HeybPT9fqClhi2xUU/PfMBJuKpI2fZef9r1LT2jdpmZe+4yPBZpXuD07R4ahNEf75OREvkVweauf3rv2VPXXdS+2OHWrhiZbEWOVNmDBX9ecCR5l5y/B6qi7IAqCjIpKVnKKXFzJ1ia469A8OVNmfb3nFuMq29wVHbgo69Y9drn26ufl1ngCX2JKKJIv1fH2jmrvtfm9OB7F/stYrqJZZFPtM+QE1rP7ddtHSuuqWcB6jozwNaeoaoKMiIr1NaWZhJJGZo7Zt+JN45ECTD6yLL54nbO31DkXgqJ0BTz+xE+t0BS4S7AqPF2EnZzLQj/emkbQYjUZp6BrnQ/qbUO0Gk//LJdl54sy2pIN1s0jUQittdzhq0YN38AS4dMflKUdKJiv48oGMgSHH28Nf5igIr5S4Vz73DnpgF1mxXsCpt9gyGCUdNyuefCo7odwZGi+xQZDhlE5Lr60+Whq5BjCEu+hOVkXa+6ThLAc42jx1qJhIz5Gd6Odk2LPrHW/pwCSnNz1CUiVDRnwd09IeS1iCttEW/IYW0zc4Bq9gaDIt+XzASj/IzvW6aZ8nT77HtlrFqvlvZOy4yvNZHcToZPM4grjMmMpG944j+qTkS/Uf2NbG6NJsbNyxJivSPt/SxojibDK8uTajMHCr684D2/mDSwN1wpD99UU68keT4rYHc/qEIrbbgXViZR2vfEOFxyvGmk55BS+w7xxD9YCRmp2xO395xiqxdWJkHTGzvODe+02OIvjGGP3poL99+6fSU+zEZmroHef1MJzs2V7J6SQ7NPUPxbyZvnu1j3TRLJijKZFHRn2NCkRi9QxGKs4cj/Ry/h/xMb0r2S2Kk7wzk9g2F4xOyLq4qIGbgbO/MR/vDnv54kf6wvTOdJRNrOwNk+dwszcsgy+c+Z6RvjDmnvfPCiXZ+ub+J3xxqmXI/JsOvDjRhDLz7koq4jXOybYChcJQzHQOs0zo3ygyz8AuVLHDiM2dHpOilkrZpjLHHCSzRz/a5EbGydxzBu7jKskKae4aoKsyabvcnRffg2AO5xhhrINfjSinSr+sIUF2UhYiQm+E5Z8pmfzASTwsdGekbY/j3p94ESPLa00XfUJj/frWOS6ryWVGSTdTOHqpp7cfjEmIGLW6mzDgpib6I/Anw+1iLnx8EPgqUAz8EioE3gN81xoRExA88CGwFOoAPGmPOpPL6i4HEmbOJVBRkTrtIWiAUZSgco8geyBURcvyWGIajBq9b2FhuWSGzMZg7nqfviK/f604pe6euM8DKkmwA8jK856y/49z0SnJ8nG4fIBYz8aypF060s7eum7VLcjjR2k9PIDypWbDRmOEzP97P8ZY+ugMhinP8/OgPrkwq/haNWbZRQ9cgX7r9IgCWF2XhdQs1rf04JdTU3lFmmmnbOyJSCfwxsM0YcyHgBu4A/gn4N2PMGqzF0j9mH/IxoMtu/zd7vwVDJBpLud77WDiiXzJC9KsKM6ddfyeeo59gGeXaou+MH6Rj3GAyGGPocbJ3Roq+namTaO9MNXsnFjPUdQZYXmx9W8nLPLfoO/bWZSuKCEZitNj2lhPlVxZk8ic3rQPgZPvkov3GrkF+vrcRj1vYtqKIg409/MczNUn7/MNjR3nueBt/t+MCrl5trXzlcbtYUZxNTWs/b57tw+dxsaJ4Zr91KUqqnr4HyBQRD5AFNAM3AD+xt38XeI/9eIf9HHv7jTKVBTvnmH96/Bi3f/23aT+vI9AjZ2Auzc+gLxhhYBp18OvtbwhVRcPVFnMzvPQHw7T1BSnNtUoz5GV4ZnyC1mA4SigaQ2S0pz8UsW6iVmllV3z/qdDaFyQYiVFd7ET657Z3nEjfqWbqWDwv13Swt66be966Jl6v/lTb5LJ7nL/3Z2/bwFfv3ML7t1bxrRdPcbKtH2MMX3+uhm+/dJqPXL2CD1+xPOnYNUtyONnWz7GWPtaU5uBx6zCbMrNM+xNmjGkE/gWowxL7Hiw7p9sY4/zXNQCV9uNKoN4+NmLvv2AKirxR28WR5t60l+7tGHDsnWTRd24CzjeBqXDGzmZZYQshWIO5/cFIUqZQRUFmPNJv7RviX598c9zFtaeLM4hbWZBp207Dou5E+kl5+lNM2XQyd5bbs5lzM7znHMh1/p6Xr7RE30nb/MW+RnIzPLx/axXVRVl4XMKpSfr6jg23zB4b+extG8jwuvn8I4f5u18d4Z8fP867Lqngr96xcdSxa5bkUNsxwOGmnjEXR1GUdJOKvVOIFb2vBCqAbODWVDskIneLyG4R2d3WNnaRrtnGGMNJO+o71jy6fkwqdPSH8HtcZPuSc7Mdj3865Y9rOwL4PFa1Tgdnndy2vmB8+bvy/OFqnve/dIavPn2C/Q3dY55zujh+vuO5dycM5iZG+hne6ZVhONZivR9OJkxepuecKZttfUFcYg2YZnrdnGkfIBKN8fTRs9y4YQk+jwuv20V1cdakB3MbugZxifXtDKwb9mduXs9LNe185+Uz/N5bVvKVD24eM4pfsySHmLHeZ/Xzldkgle+SbwNOG2PajDFh4GfAW4AC2+4BqAIa7ceNwDIAe3s+1oBuEsaY+4wx24wx20pLS1PoXvroHAjFxSuxVko6aO8PUZLjZ6TTVZpKpN8+wPKirPgAJVgTtHoGw3QMhCjNHY70m3sGicUMj+5zasGk96bmiPwqW/QTfX1H4DM8brxuFx6XjLJ3YjHD3Q/u5qkjZ8c8//76bpbk+im3BTcvw0vfUHjcujrWamJ+y08vyeZ0+wBv1HbRFQhz06bhmjerSnImbe80dA1Snp8ZX8sW4MNXVPPeLZX8zTs38dfv3Jj0XiSSOPtWM3eU2SAV0a8DrhSRLNubvxE4AjwLvN/eZyfwiP34Ufs59vZnzHxYumkSnEz450+36HcMBEdl7kBq9k5tR4DlCdYOWKLf1G0VcXMGjSsKMukKhHmxpj1eh+d4S3pF35mYtcIW/URffyhhIBeIl1dO5EhzL08cOctjB5vHPP+++m4uWVYQv2nmZngJR0383MFINGkBmba+YXtrZUkWp9sHeOLIWXxuF9evHw4yVi/JprYjMKmidw1dAaoKk1er8rhd/NsHN/N716wcdUNPZHVpDs5mzdFXZoNUPP1XsQZk92Cla7qA+4D/C3xaRGqwPPtv24d8Gyi22z8NfDaFfs8qztf86qKs9It+fygpy8bBmVjVMUV7JxYznOkYGJUFkuP3ELL9+tJcKyp2ouNvPHeSTK+bTeV5HGtJ7/U5kf6Kc0X69iBuhs89KnvnxRPtABw/O/pm1BMIc6p9gM3LCuJteZnWl0xn7OWLvz7K//rGrvj2tv5g/JvOypJs6jsDPH6ohavXFMcL0wGsLskhFI1NKm22oWtw2nMdMrxuqgozyfV7qMjPmPgARUmRlPL0jTGfBz4/ovkUcPkY+w4BH0jl9eaKk639+D0ubty4hB+8WkckGktblkVHf3DMATyfx0V+pnfKkf7ZviGCkRjLS5IjfacUA5AU6QPsOtXBuy+pIC/TwyP7mjDGnDM6nQqOLbZqzEjfEX23/ds1ytN/wa5GeaK1n2jMJC0K7ow/JIm+vXZA72CYsrwMDjf1cqS5l/5ghBy/h/a+IGuXWH/vlSU5RGKGxu7BUQuAryq1+nuqbWDUt6ZEgpEoLb1DoyL9qXDZiiJ6B8Np+5sryrnQ/LBJcLKtn1WlOVxYkU8wEotnx6SKMYb2gdCY9g5Yg7lTjfTPtFuR6cox7B2HuKefsID2js0VbFiaR99QJK0ll7sHw3jdEr/BdA0kDuQ69o71Mcz0upMKrgVCEXbXdlKW5ycUicUzdRz213cjAhfZs4th+Dqdwdy6Tuvvcbylzy4rHaIk1/p7ryyxonMReNum5AXHV5U6JRLOPZjb3D2EMaQk+l9+/yX81+9um/bxijIVVPQnwan2AVaXZsdnsR5J02BnfzBCKBKjJHvsVZJKcvzxBUgmSzyFcaS9M4bol+VbvwuzvFy3rjQ+kHgsjRZWdyBMfqYXr9tFXoYnKdJ36uz4PU6k745n9AC8eqqTcNSw8+oVgFWQLJF99d2sLs2JR/dgTc4Cy94ZDEXjeflHm3vpHYwQisbig+QrSyxh37ysgCW5ydZKUbaPwixv0njOWDiVUJcVTX9SldslSd9gFGUmUdGfgKFwlPrOAKtLc1izJAevW9Lm6yeubjUWpTn+JHvndPsAn/7RvnOmNZ7pCCRF1g65tl/t97ji3rXf42ZdWQ7v31qF1+2KDyQ6aZCRaIxH9zelVImzZzBEvi3Ehdm+ZE8/XobB9vRHRPrPv9lGhtfFhy6vRmS4X2B9S9pX380lVcPWDiTbO4l+/LGWXtr6rW8wzk2vMMvL9vWl7LxqxZh9X1WaM2GuvvMaqUT6ijKbqOhPQG1HgJixPF6fx8Xq0py0if5w3Z2xI/2R9s5TR87ys72NvHq68xz9HWBZUdaoyDHXFsPS3OT00F/90bV89jZr0lBehpeqwsy4uP5sTyN//NBeHnqtbhpXZ9EzGKYgy7qpFWb5xoz0E7N3Em9oL55o44qVxRRk+aguykqK9Bu6BukYCLG5eqToD68S5syUzfa5OdrcR1uf9dpOpC8iPPDRy3nPlkrGYlVJ9oQ19xu6BnG7JGlOhKLMZ1T0J8DxdFfbHu+m8rw0iv7oGjmJlOT46RkME7IjYseffuXUqOkNcU63DyTNxHVw7J2R5R58HlfSDWLD0lyONfdijOHBV84A8MDLZ4hNc73e7kCYAjvSLxoZ6Sfk6YMzkGtda2P3ICfbBrh2rVWnZl1ZblI66b56exB3ZKSfYO84i6tsX7+E4y198eUnS3Int+j46iU5tPUF44PRY9HQFaA8P0PLJygLBv2kjsEDL5/ma89aBbNO2isbOdkcG8vzONsbHHNBkKnilGAYKcQOTrvzWrW26L86jugbY+wc/dH+smPplE4geBuW5nGqfYDXz3RxqLGXq1YVc6p9gOfebJ3EFY3G8fTBivSTZuSGY7gEvG7rppOYp//SCStr57p1pXa/cjnTEYjfKPbXd+PzuNhQnpz55Pe48Lld9A5GqO8aJNPr5i1rSugPRthbZ90oSsf5e4/EWYnrln97gS89dnTM9M36rkG1dpQFhYr+CIwx/OezJ/nyb47z6qkOTrb1U1mQGS+T6wzm7q3rSvm1HOumaJxIf7gUg3VzqLdF/0BDz5iF2Nr6ggyGo/GSB4k4WS0Tif76pblEY4a//eVhsn1uvv7hSynL83P/S2cA6A6E+Ncn35x0rf/eweHyxEXZ3lGRfobXHbebMn3Dov/6mS5KcnystWesriuz+uXMkt1b382FFXlJs2CBhJr6Yeo7AywrymRThfWevXiiDY9L4jehiXjLmhK+8TtbubAyj2+/dJo7v/kKwUjyeEpDVyBec0dRFgIq+iM4fraP9n6rPsvnfn6QYy198SgfYFNFHm6X8LHv7ubqLz3N3zxyaNqv1dEfJC/Dg88z9tvgRPpt/UGiMUNDV4ALKvKIxAxv1I6+6Zyx7Yyx8sqdAc6JotyNduR8uKmX915aSWG2j7uuWsFLNe38Ym8j7/rPl/jq0yf4zMP7xy114BCOxugLRijItG5eBVk+BsPR+GCts1Sig98z7Okfaerlgor8+A3Bmcvw5tk+9tV380ZtF9evS06zjF9rppfeoQh1ndbiKuvKrFmvJ9sGKMnxj1sSYSxuvXAp39p5Gfd/5DLqOwf53q7a+LZgJMrZ3uCML0KjKOlk0Yr+dMoXALxkzwD90u0XcaptgGMtfXE/H6yo/JF73sJfvWMjVUVZPLirdtrlidsHQuNaOzAs0B39IZp7BglHDe/dUonHJbx6erTFM1xdc7QIZfrc/MedW/jQFdXn7NOK4uz4Teh3rrTKAH/o8mr8Hhf/50f7CEcMH7l6BbtOdfDTPVa9ntqOAW7/+sv86PXkAV+n2mVB1rCnD8MTtIbCUfwJN7xMnyX6oUiME6198Qjd6ZfXLRxr6eMffn2UkhwfH7t25ZjXkJfhsbN3rJmyWT5PfN7CRN90xuO6daVct66U/3imJr4+gFOhVO0dZSGxKEX/VFs/N/6/5/nub89M+diXatpZVZrNBy+rZsfmCsAa0Evkwsp8fv/aVfzl262sF8crngw9gXB8BamO/rHr7jgk2jvOIO7G8jwuqsrnlVOjM3hqOwbwuITKgrFF6F2XVFA2QZaJx+3i4sp8rlpVzIallugWZvv4oxvWcNOmMn75R9fwN+/cxNblhXzx10f4bU077//GLvbUdfNXvziUZHs5yyQmevowPEYxNCLSz/S6CUcNx1p6CUcNm8qHRd/ncbGqJIeHd9fz2plO/uSmdUllExLJzfBS1xmgPxiJ5887ttzIxWqmwudu20DvUJivPWeN92i6prIQWZSiv7w4m8tWFPG3vzzMs8cmPwAZjER59VQn16yxMkb++p2beMfF5WxfN3a1z43lefg8rkn5+8YYfvJGA9f+8zO86z9foj8YoaP/3JF+tt9DptdNe18w7udXF2Vx5api9td3Ewgl+/qn2weoLMxMOZPkWzu3cd9dW5PaPnXDWr551zZKcy175B/eexF9QxE+9K1XcYvw009eRVleBvf89574Tc3JeskfEek7g7kjI31nZq5jXV2QEOmDZfF0DoRYsySHD25bNm7/8zI98cVRqm3RdyaeTTfSB+v9ft+lVTzw8hmePd5Kfaf1Da8qhYlZijLbLErRd7uEr9yxmY3leXzqB3s40jS5FMu9dd0MhqNx0S/J8fO1D1067mxLn8fFRZX5SZG+MYazvcllDAZDUT723d185sf7WV6cTWP3IP/w2FE6zlGCwaE4x0fHQIjajgAel1Cen8GVq4qJxAx7aodfNxYzvHa6a9RkpelQkOWL5/WPx/qlufzZLeu5pCqfn3zyKrYuL+LeD2+lvT/Epx/el7RMYkE80rd+dybYOyMjfbBEP8vnHpV66vj6f/H2Dee8sSXO0F1mrx42HOlPX/QBPnPzesry/Xz0O6/zxV8fwaM5+soCY1GKPlhR8rd3XkZuhpe7v7d7UiVyXzrRjtslXLl68gt6bVlWwMHGnngu/UOv1XPNPz2TJPxPHGnhmWOt/N9bN/DIPW/h49eu4gev1tE5EKJ4nBIMDiX2rNy6zkA8it+6vBC3S5Ly9Y+1WAPQTl77bPAH16/mkU9dEx/IvKgqnz+9eR3PHm/jaHMf3XZZ5cQZuTC8QHowHItH9zA8SWtPbRcby/NGDbh++IpqvvE7W3nr+rEHcB0S6ww5mTUXVOYhwqiZylNlaX4GT396O1+6/SIKs31cWJmvJRSUBcWiFX2w/kE/9/YNNHQNcrCxJ94eisQ42NAzav+Xatq5pCo/KVKciC3VhQQjsXhJ4h+/UU84apIsn4MNPfg9Lj5+7UpcLuHTN61jtZ0RNJHHbIl+iHo7EwWsnPstywp46ujwwiIv1Vh57deunduFZ963tQqXwOOHW+I2jjMj14n4HU8/GEmO9J3HTT1DSX6+Q0GWj1svXDphNUrn/SvO9pFt+/7l+Zn85BNX8/6tValcHmB9w7vz8mpe+LO38tNPXp3y+RRlNlnUog9w3dpSXEKSt//NF0/x7q+9FPfJwRpgPdDQzTVTFM0tdhmAvXXd1HYMxK2eAwk3lYONPWwsz4tbEhleN//ygUvI8rlZO8ESeSU5Ptr7g9R2BpJspndvruBYS198dvCLJ9pZsyQnvmTfXFGS42fbiiKeONwS9/Sd0gget1Uuejh7JxafjQvD9g6M9vOngjMrd6Qtt3V5YdJNJlVcWihNWYAsetEvzPZxybICnrPrshtj+PneRoyB355sj++361Q7MUPcz58s5fkZlOX52VvXxSP7mhCx2hzRj8UMh5t647M7HbZUF3Lg8zdz5apzW0mOvdMdCMcjfYB3XlyBxyX8fG8jQ+Eor53unHLfZ4pbL1jKsZY+9td3k5vhSfLfi7J9dDkDuZFokr2TmbBO8KYURN+xd1KpfKkoi5VFL/oA29ct4UBDNx39QY4291Fjl1ZITHt8/s02cv2eeOQ+WUSELcsK2VPXzS/2NXL5iiK2ry/lQEM3xlirWPUHI6NEH5hUlk1Jjg9nDtTyBBEryvaxff0SfrG3kddOdxKMxGbVzz8XN19QBlh/05GzX0tz/PFURyt7J9Hesf4ebpektEi4Y+8s01RKRRnFtEVfRNaLyL6En14R+T8iUiQiT4rICft3ob2/iMhXRaRGRA6IyKXpu4xzs319KcZYFsgj+xvxuISrVxez62QHxhiMMTx/vI1r1paMmtY/GbZUF1DXGeBU2wDv2VLJxVUF9A5FqO0IxMcSLhxD9CdDYgXOkZHr7ZdW0toX5F+eOI7HJVwxwbeG2aKqMIuLKvOJmeGJWQ7bVhRyoKGH3qGwZe+MMZC7pjQnJRvGsXeqNdJXlFGkskbucWPMZmPMZmArEAB+jrX27dPGmLXA0wyvhXsbsNb+uRu4N5WOT4WLKvMpzvbx7PFWfrW/mWvXlnDbReW09A5R2xHgRGs/TT1DXD9OPv5EbKkuBMDndvH2C8vjUf2Bxh4ONfbg87hYW5ZzrlOMS2KKYfWImbY3bFhCXoaHAw09XFpdOO5kpbngFjvad0owOFy7tpRozPDKyY5xUzZTsXYA1pflcvmKIq5ePT+++SjKfCJd9s6NwEljTC2wA/iu3f5d4D324x3Ag8biFaBARMrT9PrnxOUSrltXyq8PNNPYPci7N1dwlR0V7zrVwfPHkys6TpWLKvPxuITt60vJz/KyfmkuPo+LA/XdHGrsZWP56MJgk6U016lF7x2VVZThdfOOi61Zw9fME2vH4dYLlwLDE7McLl1eQKbXzYsn2glGYvgTRN8p/zzdb0UO+VleHv7EVaNukoqipE/07wAesh+XGWOa7cctQJn9uBKoTzimwW5LQkTuFpHdIrK7ra0tTd2zLJ5IzJDhdXHTpqWsLs2mNNfPK6c6eP7NNtaV5Uw7hzvT5+a/fncrf/3OTQB43S42ledxoKGHQ009XFQ5/cjVyeMfz6r40OXV5Po93HLB0mm/xkywZkku29eXsm15YVK73+PmilVFPGNnUyXaO0tyM7j/I9v40OXnrg+kKMr0SdkPEBEf8G7gcyO3GWOMiExp9Q1jzH3AfQDbtm2b3sodY3Ctnbp548ayuA1y5apiXq5pp3cwws6rl6d0/hs3liU9v7gqn++/UkvMMOYg7mTJz/Ticcm4mSgXVeVz8G9vmfb5Z5IHPnr5mO3XrCnhOfvbVWLKJsANG8rGOkRRlDSRjkj/NmCPMcaZKXTWsW3s306CfCOQWDClym6bFYqyfXzzrm38hV0kDeDKVUW094cIRWPjlumdLhdXFeBMAk7FrnC5hN+5cjnvvqQiTT2bexInkKUzb15RlIlJh+jfPviGGAAACi5JREFUybC1A/AosNN+vBN4JKH9LjuL50qgJ8EGmhVu3FiWVIHS8fUzvW4uW1k43mHT4uIqS+h9HldK6YcAX3j3Bdw8z+ybVFhXlsMSu/CZf5y1BBRFmRlS+o8TkWzgJuBnCc3/CNwkIieAt9nPAR4DTgE1wDeBP0zltdPBypJsKvIzuGZtSVK+eDpYXZpDls/NxqW50x7EXayISHzgWSN9RZldUvL0jTEDQPGItg6sbJ6R+xrgnlReL92ICD/4+JXxrJF04nYJn7h+9bi17c93rl1bws/2NCYN5CqKMvPMn8TuOWLFGOvJpos/vnHtjJ17oXPzpqV8/NpeLltZNNddUZTzivNe9JW5Idvv4S/fsWmuu6Eo5x363VpRFOU8QkVfURTlPEJFX1EU5TxCRV9RFOU8QkVfURTlPEJFX1EU5TxCRV9RFOU8QkVfURTlPEKMSVv14rQjIm1AbQqnKAHaJ9xrYaDXMj/Ra5mfLKZrgalfz3JjzJirQs1r0U8VEdltjNk21/1IB3ot8xO9lvnJYroWSO/1qL2jKIpyHqGiryiKch6x2EX/vrnuQBrRa5mf6LXMTxbTtUAar2dRe/qKoihKMos90lcURVESUNFXFEU5j5gXoi8imSLyvIi4RWSziOwSkcMickBEPpiw30oReVVEakTkRyLis9uvE5E9IhIRkfePOPdOETlh/+wc+dpj9OUD9mvHRGRbQvtFIvLAHF/L4yLSLSK/mqgf9v7LReRp+7WfE5Equ71URB6fq2ux+7VHRPbZ5/vEJPryBRFptI/ZJyJvt9vnw/sSTejXo5Poy3h/lzl9X+xt1SLyhIgcFZEjIrJigr6k9Bmbhev5Z/tcR0XkqyIiE/Qlpc/ZgsAYM+c/WGvn/m/78Tpgrf24AmgGCuznDwN32I+/AXzSfrwCuBh4EHh/wnmLsBZjLwIK7ceFE/RlI7AeeA7YNmLbU0D1XFyLve1G4F3Aryb5d/0xsNN+fAPwvYRt3wHeMkfviw/w249zgDNAxQR9+QLwmXG2zfX70j/Fz/u5zjVn74u97TngpoT3JmsmP2Mz/Dm7GngZcNs/u4DtM/k5Wwg/8yLSBz4MPAJgjHnTGHPCftwEtAKl9h36BuAn9jHfBd5j73fGGHMAiI047y3Ak8aYTmNMF/AkcOu5OmKMOWqMOT7O5l8Cd8zRtWCMeRrom+D1E9kEPGM/fhbYkbDtF3ZfZ/1ajDEhY0zQfuon9W+cc/q+TJUJzjVn74uIbAI8xpgn7f36jTGBCfqS6mdsxq4HMEAGdpABeIGzk+jPeEzmczbvmXPRt7+irTLGnBlj2+VYb9hJoBjoNsZE7M0NQOUEp68E6hOeT+aYc7EbuHa8jTN8LdNhP3C7/fi9QK6IFNvP5/RaRGSZiBzAen/+yf4Hn4hP2V/57xeRwoT2uX5fMkRkt4i8IiLvmcT+52Iur2Ud0C0iPxORvSLyZRFxT3DMtD9jM309xphdWDeiZvvnN8aYoxNcD0zzc7ZQmHPRx6op0T2yUUTKge8BHzXGpBxdpYlWrK+c4zHfruUzwPUishe4HmgEova2Ob0WY0y9MeZiYA2wU0TKJjjkXmA1sBnrH/j/JWyb6/dlubGmyH8I+HcRWZ3CuebyWjxYovYZ4DJgFfCRCY5J5TMGM3g9IrIGy66twrpB3CAiE4l2Kp+zBcF8EP1BrK9gcUQkD/g18JfGmFfs5g6gQEQ89vMqrA/YuWgEliU8n8wx5yLD7u94zOS1TBljTJMx5nZjzBbgL+025x9sXlyLHeEfYoIIyhhz1hgT/f/tnV1oHUUUx39/Wq21fhCsoraCCJGKijE1arGIaE2fBIMVBEVjxCJItUh98FFR+qS+KLQ0llrxSW2CCJqCWIxg7EeaNtqgLVGkoqDig6kflXh8OLNmKTe5Nx83e3Pv+cGS3dnZmT13JoeZ2Zn/JAewA7g5d7tQW8zsh/R3FB8Tv7HcM1NQpC0ngSEzG00t6l6gdaoHZlnHqm1PBzCQhqnGgA+BNWXsmU09WxAU7vTTWPsiSefA/929HmC3mb2bi2d4Vy37Ov8IaRxwCvqAdklNqZvWnsKQtDt1H6fD1biDKsKWSZG0VVJHifDlkrIyfg7YWQu2SFopaWk6bwLWAl+n65Llklp+GR1nvHuRtjRJWpLOlwO3AcfSdclyKUORdewA7lgzdcY7y9kymzo2D/Z8j/dCFks6C++JjKR85ryeLRgq/eJbzQN4A1iXzh8C/gGGckdLuncVsB84gc8ayGaAtOGtlFN4i+CrXNpdKf4JvKuYhQ8BK0u8S0dK62/8o09f7t5rwD0F2tIP/Iy3Nk4C61P4B8CaEu+yATgOfAN0Z3mke1uATUXYAtwNHMXHg48CGysol7eA4RT/feCyWigXfIbIcLJlGHgsl+dk5TJVGRdWLmeUzTCwCzi7mnWsymWzCNiOO/pjwCvVrmcL4Sj8BdKP2Upuqtc85HcB8M40n1kCDOCzG2rGlpRn3wye+ZTy01ejXKJcqmpLEfZUs54thKNmtHckdQFvmtl42cgFIKkZWGFm+yqIW+u2XIzPn+6tIG6t2xLlUoNMx5YUv9btqbie1To14/SDIAiC6lP4h9wgCIJg/ginHwRB0ECE0w+CgpHUKeny3PV3afpnEMw54fSDYBIqkCCYKzqpg5WewcIgnH5QF0h6QdLm3PVLkp6W9KykA0lL5fnc/V5Jh+Syuxtz4WOSXpZ0hDNWb8qlg19NOjsjktqSTs1xSS/m4j0j6ct0bE5hV6ZndqQ898olhTcANwFvy6V8l6ZkNsnlgoclrarOrxY0IuH0g3phJ/AwQFoh+gDwE9CML6VvAVZLuj3F7zKz1bjDfUoTImHLgC/M7AYz+6xEPqfNdXa24StCnwSuAzolXSRpNfAocAtwK/C4pEyWoRl43cyuxfVm7jNfdXoQeNDMWswsW+b/i5m14lowW2b96wRBIpx+UBeYqzT+mhxsO3AYX6mZnQ8Cq3DHC+7oj+ALbq7IhY8D702RVbZJyjC+8vNHc5no0ZTOWqDHzE6Z673sYUJX6FszG0rnh3Ad+MnYU2G8IJgWi8tHCYIFQzc+Pn4p3vK/C9hqZtvzkSTdAazDZQX+kLSPCdGvv8osEMr2Afg3d55dl/t/yscfB5ZOFjEXd7yCdIOgYqKlH9QTPfgmOW24sF4f0CXpPABJKyRdAlwI/JYc/ip8GKYkMxBN6wfulXSupGW4llN/mWd+B86fRh5BMGOiBRHUDWZ2WtIn+GYb48BeSdcAn8u3Rh3DBb0+Ap6QNIKrew5MliZwPRNDOpW8w6B8L9X9KajbzA5r6r1mdwHbJP1JGenfIJgtIcMQ1A3pA+4gcL+lLffmIM0+M1s/F2kFQS0QwztBXSDf3/UE8PFcOXyAcPhBvREt/SAIggYiWvpBEAQNRDj9IAiCBiKcfhAEQQMRTj8IgqCBCKcfBEHQQITTD4IgaCD+A7TJiZu9Ve41AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvI9V-vFSmit",
        "outputId": "87865aa0-b6fc-4e7c-c325-df9943e2223a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df_Trade_b ['price','마포구'].plot()\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff84c72e9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9nCJucxn19BVFWbCEYgm6B2M50zUBRITqYj8tfRGSKZOVvfPB61bz1i1LWDLKDcPB2afY78naf64y989QURRlijTnqK45nOpiHw32RCrD8/QnMsG5U155Pvj5oPaOoigLmKbeCJA7XdOhqtjPmRyiP1GcjJ/5kKMPKvqKoixg0jn6Y0T6VUU+eux5cqdizzievkb6iqIos0xTbwiPS6gbI/umOkOspxTp2zcKFX1FUZRZpqknzKKyAG7X6OWOq4qGMm6mEuk75ZVV9BVFUWaZsdI1HTJH2k7N01d7R1EUZVbpHIjyN4++xu4zvaypLR6zbaboF/knL/pF88ze0ZRNRVEWFC+f6ub9979EJJHi3dvq+dQtG8Zs7xRdAyiYgr2zpLwAl8DKqqJJ7zsbqOgrirKg+NHLDbhdwm/+9DrW1Iwd5UO26GfW3pkol62qZPtf3DhmqYa5hNo7iqIsKLaf7OKK1VUTEnyAykIfTi22winYO8C8EXxQ0VcUZQHR2BOioTvMlWuqJryPx+2iotCH2yX43AtfEtXeURRlwfDi8S4Arlg9cdEHK20znkzlLL+80Fj4tzVFUfKGF090UVHoZUNdyaT2qyr2TSldcz6ikb6iKAsCYwzbj1t+vmuMwVi5WFlVRMiuib/QUdFXFGVBcKY7RHNfhA9dPzlrB+Av33we0XjqLJzV3ENFX1GUBcH2E5aff+Uk/XywZ8maPwk400I9fUVRFgQvHu+iutjP2nFG4OY70xJ9EflTEXlNRPaLyPdFJCAiq0Rkh4gcE5EfiojPbuu3Xx+zt6+ciQtQFEWJJ1O8cLyLK1ZX5kUGznSYsuiLyFLg48A2Y8xmwA3cAfwz8GVjzFqgB7jH3uUeoMde/2W7naIoyrT56u+O0R6M8raLls72qcx5pmvveIACEfEAhUALcAPwE3v7A8Db7OXb7NfY228UvSUrijJNdp/p4atPHePtW5dy06a62T6dOc+URd8Y0wT8K3AGS+z7gFeAXmNMwm7WCDi33qVAg71vwm4/+R4XRVEUm1AswSd/9CqLSgP8zVvPn+3TmRdMx96pwIreVwFLgCLgjdM9IRG5V0R2isjOjo6O6R5OUZQFzP3PneRk5yD/+q4tE5rEXJmevXMTcNIY02GMiQMPAVcD5bbdA7AMaLKXm4B6AHt7GdA1/KDGmPuMMduMMdtqamqmcXqKoix0DrYGWVVdNKlaO/nOdET/DHCFiBTa3vyNwAHgKeCddpu7gEfs5Uft19jbf2eMMdN4f0VR8pzGnjDLKsaeGUvJZjqe/g6sDtldwD77WPcBfw58UkSOYXn237V3+S5QZa//JPCZaZy3oigKTT0hFf1JMq0RucaYzwGfG7b6BHBZjrYR4F3TeT9FURSHcCxJ50CMZRWFs30q8wodkasoyrykqTcEoJH+JFHRVxRlXtLQEwZU9CeLir6iKOPSEYwSic+t0sONadFXe2cyqOgrijImxhje+tXn+MqTR8/K8ZOpqSXxNfaE8Lld1BT7Z/iMFjYq+oqijElzX4SWvkjaTpkOj7/WSnswkn4dT6Z40789w4f/6xUSycnVs2/sCbO0omDSE6bkOyr6iqKMyYHmfgB6Q7FpHaepN8wf/+crfPbh/el1v9jbzJG2AX69v5XPPryfyQzd0Rz9qaGiryjKmAyJfnxax3nqUDsATxxo49WGXowxfOv3J1hfV8xHX7+WH+5s4P88cWTCx2vqCbG0XEV/sqjoK4oyJgda+gDomWak/7tD7SwtL6Ci0MuXnjjCs0c7OdQa5IPXruZ/37Ke92yr5//97hjPHe0c91hDOfoq+pNFRV9RlDE52BIEoG8akX4knuSF453cvKmOD71uDc8c6eCvHtlPXamf2y5aiojw+dvOZ1lFAX//ywM5O3dTKZO2f5p6NXNnqqjoK4oyKv2ROGe6QxT53ASjCeKT7Gx1ePF4F5F4itdvrOXOK1dSXezndFeIu69ehc9jyVDA6+Yzb9rIodYgP3mlAYAzXSH+5Ae7edO/Pcumzz3Ge+7bDliZO6A5+lNBRV9RlFE5ZEf5l62qBKAvPLVo/8lDbRT63Fy+qpICn5tPv3ED9ZUFvPfy5Vnt3nzBYrYuL+dff3OER/Y08eb/9yxPHmxncVmAK1ZX8dLJbvY39WmO/jRQ0VcUZVQOtliduFetqQamlsFjjOGpQx1cvbaagNcNwLu31fPsp28YUQNfRPj//mATHcEon/jBHlZUFfKrT1zL/e+/lH+742L8Hhfff+kMjT1hvG6htkRz9CfLtAquKYqysDnQ3E9lkY8Ni0qAqWXwHGkboKk3zEdvWDuh9luXV/DxG9cRiib41Bs2pG8UZQVe3nzBYh7d08xlqypZWq45+lNBRV9RlFE50NLPpsWlVBT6AOiZguj/9mAbAK/fUDvhfT558/qc6++4bDkP7W7id4fbudp++lAmh9o7iqLkJJFMcbgtyKYlpZQXWjZMLnsnlkjx0K7GnJ288WSK7+04w2UrK1lUFpj2OV26soLVNUUYo524U0VFX1GUnJzoHCSWSLFpcaboj4z0v7fjNJ/80av8bHfTiG2/2NtsjcR93eoZOScR4Y5L6wEV/amioq8oSk6ckbjnLS6l2O/B4xJ6w9mRfjyZ4tvPngTgkT3NWdsyR9xOxtoZj3dsXca62mIuW6Xz4k4F9fQVRcnJ8Y4BXAKra4oQEcoLvSM8/Z+/akXyFy8v54XjnbT3R6gttWycpw93cKg1yJfetWVGO1yriv088cnXzdjx8g2N9BVFyUlLX4TakgBetyUTZQXeLE8/lTJ88/fH2VBXwhffcSEpAz/f25Le/o2nj7OkLMBbL1pyzs9dGR0VfUVRctLWH6Euo/O1otCX5en/7lA7R9oG+PD1a1hXV8L5S0p5dI/l6//o5QZeOtXNH127On3TUOYG+mkoipKT1r4Ii0uHRH+4vfMfL5xiaXkBf3DhYgDedtFSXm3s4/svneEvH97HteuqufPKFef8vJWxUdFXFCUnrX2RrDTL8kIffRn2zmvNfbxuQw0eO5J/y5YliMBfPLSPldVFfPW9W9PblLmDfiKKooxgMJogGE1QlxnpFwxF+n2hOD2hOCurhmrfLCoLcO26GioKvdx/16WUFXhHHFeZfTR7R1GUEbT2W1MaLs709It8hONJIvEkp7sHAVhRVZS139feezHJlKHcHsGrzD1U9JURROJJPC7RR/M8prXPEv3MSN+J3PvCcU51WaWNVw4T/ZKARvdzHf2vVkbw1q8+x9efPj7bp6HMIo7oLxqWvQPWqNzTnU6kr6WN5xsq+soIGrrDNHSHZvs0lFnEsXcWlWaKvhXF94RinOoKsbgskK6AqcwfVPSVLIwxRBJJIompzZCkLAxa+yKUFXgp8A2JellG/Z1TXYMa5c9TVPSVLKKJFMZYvr6Sv7T2R7KifMi0d2Kc7hoc4ecr8wMVfSULR+xV9PObtv7IiFLITqXNxp4wnQOxEZk7yvxARV/JIhJP2b9V9POZlr6RkX6B143P4+LVxl6ArBx9Zf6goq9kEU5H+urp5yvxZIrOgWhW3R2watmXF3jZ02CLfrVG+vORKYu+iGwQkT0ZP/0i8iciUikiT4jIUft3hd1eROQrInJMRPaKyNaZuwxlpgjH1N7JR7oHYxxtCwLQEYxiTPbALIeKQh/BSALQdM35ypRF3xhz2BhzkTHmIuASIAQ8DHwGeNIYsw540n4N8CZgnf1zL/CN6Zy4cnaIJJJZv5X84O9/eYC3f/0FIvEkLX0j0zUdnAye2hI/hT4d2zkfmSl750bguDHmNHAb8IC9/gHgbfbybcCDxmI7UC4ii2fo/ZUZImJH+uGY2jv5gjGG5452EowmePZoJ239I0fjOji5+pq5M3+ZKdG/A/i+vVxnjHFmUmgF6uzlpUBDxj6N9rosROReEdkpIjs7Ojpm6PSUieJ4+lG1d/KGE52DtAejAPx6X0t6NG4ue6e8wErbVGtn/jJt0RcRH/BW4MfDtxljDGAmczxjzH3GmG3GmG01NTXTPT1lkqSzd9TeyRu2n+gC4NKVFTxxoI0z3SF8Hlc6RTOT8iI70tdO3HnLTET6bwJ2GWPa7Ndtjm1j/2631zcB9Rn7LbPXKXMIJ9KPJw2JpFo8+cCLx7uoK/Xz4evXEIwm+PmrzSwqDSAycl5bJ9JXe2f+MhOi/4cMWTsAjwJ32ct3AY9krL/TzuK5AujLsIGUOUI4w9bRUgwLH2MM2090c+XqKq5eW01JwEPXYGzEwCyHSjvSV3tn/jKt7ncRKQJuBv44Y/UXgB+JyD3AaeDd9vpfAbcCx7Ayfe6eznsrZ4dMLz8ST1Ls1wyNhcyx9gE6B6JcuaYKv8fNzefV8dDuppyZOwC3bFpE92CcTYtLz/GZKjPFtP6jjTGDQNWwdV1Y2TzD2xrgI9N5P+Xs4+Tpg+bq5wMv2n7+laurAbj1gsWW6I8S6VcU+fjw9WvO2fkpM4+OyFWyCMdV9POJF493saQsQH1lAQDXrKvmkhUVXLm6apw9lfmKPrsrWWSWX9BSDAubVMqw42Q312+oSXfaBrxufvrhq2b5zJSziUb6ShYa6ecPvznQRvdgjKvXVM/2qSjnEBV9JYtIluhrpL9Q6RyI8tmH97F5aSlv2bJktk9HOYeovaNkkSn6YY30FyTGGP7ioX0Eowm+/+6L8Hk09ssn9NNWsgjHkxTaU+SpvbMweWhXE08caOPTb9jA+rqS2T4d5Ryjoq9kEY4lKS+wBuCo6C9MfvhyAxsXlfCBq1fN9qkos4CKvpJFJJGi3J4LVUV/YXKya5ALl5Xhco0ss6AsfFT0lSwisSQVRU6krx25C42BaIKOYFQLpuUxKvpKFuF4UiP9BcypzkEAVmnBtLxFRV/JIhJPUhrw4HaJlldegJzqskR/hYp+3qKir2QRjifxe9wUeN06e9YCxIn0V1Zrlcx8RUVfySIST1LgcxPwujTSX4Cc7AxRV6rz2+YzKvpKmkQyRTxpCHjc+D1u9fQXIKe6BnUClDxHRV9J40yaUuBzWZG+iv6EeeFYJ8nUpGYGnRVOdw2ySjN38hoVfSWNU0u/wOumwOfWlM0Jcrg1yHu/s4OnD7eP33gWCUbidA7ENF0zz1FjLw/pHIjy1KF2+iMJIvEk77xkGXWlgXRk7/e6Cai9M2E6B6IAtPRFZvlMxuZUZwjQ+W3zHRX9POT/PXmUB148nX5tjOGjN6xLi3yB103A6yYUS8zWKc4r+sNxALoGYrN8JmNzskszdxS1d/KS3nCcZRUF7PnrmynyueketEQrPEz0w2rvTIj+iPX36x6MzvKZjI2TrrmiUiP9fEZFPw8ZjCYpDXgpL/RRXuijN2RFqI6HH/BaKZtRtXcmRDBiPRF1Ds7tSP9U5yCLywIU2FVUlfxERT8PCcUSFPmtf/yKIi+94WGRvs9FwKue/kRx7J3ueWDvqJ+vqOhPkE//5FX+7MevzvZpzAiDsWR6cE5FoY8eO9J3snecSN9J4VTGpt+O9Lvmgb2jmTuKduROkJ2neij0L4zH4lA0wdLyAABlBV4ae8IARBNDom+VYdBIfyKkI/05bO/0heL0hOKs0k7cvEdFfwIYY2jqDbO4LDDbpzIjhIZF+r3DIn2nIzeSSGKMQUTrro+FE+l3D8ZIpcyM16k/0NxP12CUSDzFqupC1taOP9tVIpnisdda+e5zJ2nti+B1Ww/1au8oKvoToGswRjSRSnfYzXcGYwmK7M68ikIvfeE4qZQZkbJpDMSSKfyehfGEc7ZwsndSxsqMqizyzdixDzT3c+tXnk2/XlwW4MW/uHHMfU52DvL+f3+J010hVlUXcc3aagZjCTYvLeXyVVUzdm7K/ERFfwI091r2RzC6MEQ/FE1S6Lc++rJCHyljZaCEs7J37HlyYyr64+HYOwBdA9EZFf3fHmxDBB78wGX87lA7//78KYKROCUB76j7/OClMzT3hvnm/9jKzZsW4dYZspQMtCN3AjTZnncskUr73vOVWCJFLJnKivQBekKxdPaO32PV3gG00uYECEYSaeuva4Z9/acOt3PhsnKuXVfDFautKN0ZWTsazx3rZOvyCt64ebEKvjICFf0J0GRH+vVsaXAAACAASURBVAAD89zicXx7x9MvzxD9aDxJwOvC5RICdnSvaZvj0x+Jp73ymRyV2z0YY09DL6/fUAPAajvz5kTnwJj7vNbczzVrq2fsPJSFhYr+BGjuHaqpMt99/UG7tIKTp+9MjdgbjhOOJ9O2Ttre0VG5Y5JKGQaiiXQq5Hijcg+29NOXYQeNxbNHOzAGXr+hFoDlVYWIWJ79aLxwvBOAq9ep6Cu5UdGfAE29Q4/TA/Pc13fq6WRm7wD0hmKEY0kKbLEv8FlfjbBG+mMSjCYwBlZUWamQnWNE+gPRBG/72vN8+icTG+/x1KF2qop8XLC0DAC/x82yioIxRf/5Y52UBDxcaO+jKMNR0Z8Azb2RtBg6mRoOxsz9GuqZDEYtEU9H+gWWvdMbihNJpNLXqfbOxAja34fKQh/lhd4xc/WfOdJBNJHi8dfa2N/Ul17f1BsesV8yZfj9kQ5et74mKwV0ZVXRmKL/3LFOrlxdhcet/9pKbqb1zRCRchH5iYgcEpGDInKliFSKyBMictT+XWG3FRH5iogcE5G9IrJ1Zi7h7NPUG2Z9XTGQ7el/5cmj3PqV52gPzu2SupkMDov0Swu8iEBPKE44lsRvi77fq6I/EfrD1t+ztMBDZZFvzFG5vz3QRlmBl7ICL19+4ghg1eJ/45ef4fp/eYqHdzemg4hXG3vpCcW5fmNt1jFWVxdxsmMwZ7BxpitEQ3eYa9TaUcZguuHAvwGPGWM2AluAg8BngCeNMeuAJ+3XAG8C1tk/9wLfmOZ7nxPCsSTdgzHW11kDYjLtnb2NfRxs6ed/fGcHXQNzcwj+N54+zj/9+mD6dciJ9G3Rd7uEsgIvvaGYNT+unbVToJ7+hHCe/EoDXqqL/OmO3Eg8yW1fe54nD7YB1mCp3x1u58aNtdx73WqePNTOb15r5e5/f4kCn5u1tcX86Q9f5Z4HdvK1p47xzaeP4xK4bpiAr6ouIhhN5LSRnjtm+/naiauMwZRFX0TKgOuA7wIYY2LGmF7gNuABu9kDwNvs5duAB43FdqBcRBZP+czPIl/49SEe2tUIQHOflbmzYZEl+pkduX3hGIvLApzuCvG+7+ygLzSxDrpzyVOH2/ntgbb063Skn1FSorzAa9k79qTowFDKpkb6Y+J8H0oCXiqLfGmb5rXmfl5t6OXvfnGARDLFztM99Ibi3LSpjruuWkllkY97//MVesNx7n//pfz4Q1fxl7duZOepbv7l8cP85kAbV66pSne0O6yqsZ44c1k8zx/rZHFZIJ3loyi5mE6kvwroAP5dRHaLyHdEpAioM8a02G1agTp7eSnQkLF/o70uCxG5V0R2isjOjo6OaZze1Gjvj/CtZ45z3zMngKEc/Y2LSoHsSL8nFOfi5eV8+85tHGoN8uNXGkYecJbpD8fTZQLAKsEAQ5E+WBk8Tp6+4+UH1N6ZEM7ArNICD1XFvnSe/mvNlmd/qivEQ7ua+O2BNnxuF9etr6HY7+FjN6zF4xK+9t6tbF5ahtsl3HvdGvb+zRs49HdvZMdf3sh377p0xPs5gn5yWNpmKmV44XgnV62p1rIZyphMZ0SuB9gKfMwYs0NE/o0hKwcAY4wRkUn1dBpj7gPuA9i2bds57yV97LVWjIFDrUHa+yPp0bgrqwvxuV1ZHbm9oThlBT6uW19DRaGX4x2jd7DNFv3heFaK4GA0R6Rvd0CG40kCPhX9yZBp71QVWTfPZMqwv6mPyiIf9RUF/NuTR3G7hCvXVFFsj4S+++pVvOOSZZTmGFmbOSJ6OEvKC/C5XZwYFukfbgvSE4pz1Rots6CMzXQi/Uag0Rizw379E6ybQJtj29i/ndmim4D6jP2X2evmFL/c20JJwPrHfO5YJ029YVwCdaUBSgKedEeuMYbeUCw9onVFVRGnu86d6E80a6g/kiCWSKXF24n0CzNExSmvHI0PZe84vyc7e9YzRzqy+hAWGqmUYdeZnvRrpyO3JOChqtiPMdZAt/1N/Zy/pJT/fcsGmnrDnOkOcdOmuqxj5RL88XC7hBVVhZwcFmDsONEFwOWrKyd9TCW/mLLoG2NagQYR2WCvuhE4ADwK3GWvuwt4xF5+FLjTzuK5AujLsIFmjReOd9IRtDphO4JRXjrVzd1XraSqyMczRzpo6g2zqDSA1+2iOOBJe7gD0QSJlEmPaF1VXcTprrGHx88Uxhje/JXn+Mh/7xozEk8kU2k7yrEhBmMJ/B5XVkpfeaGX3kFncJa13u+Zmqf/8O4mvvX7Exxq7Z/UfvOFX+1v4e1ffyFt3wQjcQp9bjxuV7rmTmtfhCNtQS5YWsa166q5bKUlxDedVzvqcSfDquqRaZs7TnaztLyAZRVaOlkZm+lm73wM+G8R2QtcBPwj8AXgZhE5Ctxkvwb4FXACOAZ8G/hf03zvaZNMGd5//8vcef9LROLJtLXz5guXcM26ap471kljT5gl5QWAFc05Itprd9o6HW0rqgpp7gvnFMnBaCKrM3W6nO4KcaCln1/ua+Gu+18aMXbAIbvT2WoTiiYpHDZdXnmBj2A0wUAkkY7wXS7B53FNuvZOi93x/eOdjZPab77w3FErQ8ax8voj8XTEXlVsfRdeON5JImXYvLQMEeGL77yQL79nC4vLCmbkHFbVWAFGMmU97Rlj2HGyW6N8ZUJMS/SNMXuMMduMMRcaY95mjOkxxnQZY240xqwzxtxkjOm22xpjzEeMMWuMMRcYY3bOzCWMTtdAlC/8+hBff/rYqNtjyRQHW/r560f286u9LaypKWJ9XTHXrauhcyDG7jM9adEv9g/ZO2nRtwc3rawqwhho6B4Z7f/g5Qb+6MGdHGsPzsh1vXLashc+dsNaXjndw/u+vYN4cqQNk3kz6MuI9At92V05FUXWNcSSqSwvucDrJjpJe6elzxqz8PDuJmILcOat7baN4nzO/eEEpQXW37OqyA/A749YCQibl1ijYldWF3H7xctm7BxWVxcRS6bS/U1H2wfoHoylC7IpylgsyGF7faE4//L4Ia794lN88/fH+cqTR3OKYrtt62xZVsaPdjby4oku3nzBYkSEa+386HjSsLTCEX1vWkh7w1aWRoX9SO/UXjmVw+I51GJZHS+f6hmxbSq8cqaHkoCHP71pPX/1B5vY19THqRwpfJkduM55h6LJ9Ghch8y0wEzRD3hdk5o9yxhDS1+EDXUldA/G+N2hmXu6mQu09IXTn68j+sHoUJljx955+aT1+dRXzkxkP5xV1VbaptOZ6/j5V2itfGUCLEjRP9U1yNefPs4NG2v55M3ricRTHG4dGWU7I2n/6g82cfVa6x/m1gutoQO1pQE22rn5TqRfmmHv9IyI9C0vNZf4Hmm30uucCH0sJtJBu+t0Dxcvr8DlEtbZI4WdfolMnE5GGDvSd64BhjpwgfTsWROlezBGLJHiXduWUVfqX3AWjxPllwQ8NPRkRPoBp46RNbo5lkyxeUnZWUudXF1jBRjP2k8U2092s7gscNZuMsrCYkGK/pb6cp75s9fz1fdu5faLraEAuxt6R7RzhHJRWYCvv+8SvnvXtnQ+PpCO9p35ZDM7cvvsKQadKLm80EdZgZdTwzJ4UinDsTbrhjOe6Df3hrnw879J2wO56I/EOdwW5JLlFQDUlliWQkeOEcFZ9o59kwrFRkb6FaNF+h73pDpyHWtnWUUh79i6jKcOt9PWn7tERUfQst5yPYHNVV483kVZgZfXra/hjGPvROKU2jdNj9uVvoFesOzsFTyrLvbzzkuW8Z3nTvLUoXZ2nOjmitVVmp+vTIgFKfoA9ZVW5L2sooDqYj+7z4wU3PZ+SyhrSvyUFXi58bzslLq3bFlCTYmfTYutf2CnI9cYk470yzKi5JU5MniaesMMxpJWml3n4JjlGp450kEwkuBrv8vdBwGw50wvxsAlKyzRrym2bki5Iv1se8e6WQ1Gc0T6hRmRvm/oKxHwuSeVsumI/pLyAO/aVk/KwC/25k7Q+umuRr75++O8muNmPBnCsSSP7W/hhy+f4f7nTo5ZjGy6bD/RzeWrKq1O+94IiWSK/nA8neILUFVs3YTPX1I62mFmhL+7bTPnLS7lf/33LjoHoly+SjtxlYmxYEXfQUS4eHk5e86MFJf2YJTyQu+o0wFeuKyclz97E4vsWZGK/V6SKUM4nqQ3FKfY78HnGfoTrrSFPZOjduftHZcuB8aO9nec7AbgpVPdo4rhK6d7cAlsqbduRKUFHnxuV+5I3xZ9t0uGsndiyfSsWQ5Zop8V6bsmGelbHYuLywpYVV1EbYmfgy25Uzd3nrKu9Wj76BOCjEcknuTu/3iJD/3XLv78p/v4218c4IuPHZry8cbCybW/YnUV9RWFJFNW/0UwksjKt3d8/c1nubRxgc/NN963FY/biu4v105cZYIseNEHuHh5OSc6B+kZVr62PRihxo7MJoIT0Q1EEvSGYlliCdYArea+cNaUikfaLFF7xyVL8bplVNE3xrDjRBfXra+hxO/h289aZSB6QzH+/Cd7ecm+Iew608OGRaXpzkMRobrYR2dwZAGu/kgct0uoLfFniH4iPT+uQ7Hfg8cu3+sf5ulHJyH6zb0RvG6hyha+NTXFHO8YKeqplEn/HY62TU30E8kUn/jBbraf6Oaf3n4Bz3/mBt54/iL2NvaNaOekNk6H7cctP//KNVUst58iD7cGSaRM2t4BqC72UeRzs6rq7Ne/WVldxDfedwl3Xbki3aekKOORF6J/UX05AHsas6Pn9mCU2tLJi34wmqA3HB8h+quqC+20zaHpFY+0Bqkr9VNbEmDz0rJRRb+xJ0xzX4QbN9Zyx2X1/Hp/K7vP9PCeb23nhzsbuOeBlzncGmTPmV4uWVGetW91iT9npN8XjlMa8FBW4B0anBUdGemLSPpaCoalbE6mymZrX5hFZYF0/fc1tUUcbx8Y0Tl9onMgbY8dy3FTmAife/Q1Hn+tjc+9ZRN/eNlylpYXcMmKCpp6w3Rm/C0+9F+v8Ikf7M55DGMM//niqaz2uUimDM8e7aC80MuGupK0dfhas/UUk2nv3HPNKv7+9s1ZNfDPJtesq+bzt21WP1+ZMNOpvTNvuHBZOS6x/HBn6jmwPP3LJuGFOnVTgpEEPaFYVgcoWJE+WBk8a2utrJoj7cF0WeZtKyp44MXTRBPJEZaSY+1cvrqSmwJ13P/8Kd75zRfxe1z867u28IVfH+KO+14kGE2k/XyHmmJ/2k/PpD+coKzAS2nAS184nramhnv6YHVEdw7EhmXvuCY1c1ZzX4TFpUMZJGtqiumPWGWAa0qGbq5O6uqW+vJ0J/dk6BqI8t87znDnlSu4++pV6fVO5+m+xj5ev7GWSDzJM0c68XtdpFJmhBAfax/grx55jbb+KJ96gzWw3BjDx3+wh+PtA7hdQjSR5FRniFgyxa0XLMLlEhaXBXC7JD0qN9PeuWRFJZesUH9dmbvkRaRf7Pewvq4kK4PHGENHMJrOfpkIjqUyEEnQF4pndeIC6cmxnQyeVMpwrH0gLfqXrKgglkixv6mfRDLFiYwod8eJLsoLvayvLWFpeQG3X7yUIp+b//qjy61Mjbu2pQX4kuXZolIzSqTvZJaUFlii7+w/PHsHhtI2A8NTNich+q19ERbbmU5giT4wwuJ5+VQ3VUU+btlUR3NfZNJTUO6xP8c3X5BdmdsaAUva4tl1uodYMkUwkshpMx2003iftevQg1W47OevNuP1uKgu9rGiqoi7r17JF995IZ9/62bAytJZUh5IR/qlBZOvoaMos0VeRPoAFy+v4Jd7m9MRX184TiyZyopAx2Mo0o/njPQrCr2UBjzpDJ6GnhCReCo965YTAf7jrw5yuitE50CUf33XFt55yTJ2nOzmspWV6Wj0n95+AX972/npqPyi+nK+/r6t/PZg+4h87OpiP10DUZIpgzsjmrXsHWumpoMtCULR7FmzMnFST0fk6U9Q9FMpY4l+RqmBNbVDop85WvSV0z1sW1mRfho63j7Alvpsy2osdp/pxe0SLlyWvU+x38OammL22jaek1fvvOc6++br4Aya29fYa93EC708dchKl73vf15CXWmA0VheWcjzx6zjlwby5t9IWQDkRaQPcHF9Of2RRHoUo5PiWDvGP/ZwHO+2P2KVKx7u6YsIK6uL0pG+MyDMEZuaEj/r64p5taGXrcvL2VJfzuce2c+OE12c6Q5lWU1et2uEON+wsY5/vP2CEf5tTYmflF3dMZP+sPU0UlrgoS8cZzA2eqTvVAsNZKZset1EJlhKoWswRiyZYnHZ0N9zcWmAAq+b4+1DGU3t/RFOd4XYtqKSdbboTzaDZ3dDD+ctLklP+JLJhUvL2NvUhzGG7Se62bKsjIpCb1ZlTIeDLf34PC5SxqqXA9Zk5OcvKR1T8AHqMwqblUyhWqaizBb5I/rL7c5c2xpwSjBMzt6xRLi5N0LKMGJWI7B8fSdt0xEzR9wAvv/BK3jpszdx353b+Pr7tuJyCX/0gFWGaKq1U6rtDKThufr9EasuTFmBl4FoIt2ZmzvSz2XvuIglJpb90mr3KWSKvsslrK4pyrJWdtod2dtWVrC80pqj4OgkahIlU4ZXG/q4uL4i5/YLlpXREYxyqivE7oYerlhTxcXLK9iVI2X3UGuQmzfVUez38OyxTvpCcV4508MNG8evhul05gLp2juKMh/IG9FfU1NMkc/NvkZH9C2RmozoO/aOMwS/PIeXe8HSUhp7wnz+569xoKWfpeUFWZFgVbE/ncu9tLyAf7z9AoLRBCUBD+ctntqAHseiGp6FkmnvALTao2OLcoj+2tpiqop8WXX2nRtAZgrqsfYBrvviUyNKJzdn5OhnMjxt8+VT3QS8Ls5fUobH7WJVtZXhM1GOtgcZiCbSN/HhOJbPAy+cIp40XLG6iq3LyznWPpA1nWVvKEZLX4QLlpZxxepKnjvaye+PdpBMGV4/WdHXSF+ZR+RNiOJyCZuWlLLf7nzLHI07UTxuFwVeN412SqZTnTKTD1y9ita+KPc/fxKA6zfUjHnMt2xZwr6mPjwuyfLjJ4NzDZmRfiSeJJZIUVowJPotdlXGwhz2zrsuqeetW5Zm1dnPnBzdeaj5ySuNnOkO8c2nj/N/77g43TYd6Zdn2yJraor5+d5mwjFr/t2dp3q4qL48PahtbV0x+5uyc+vHYrcdsV+8PHekv2lxKW6X8MOXG3C7hEtXVuK3r2lXQ086e+tgi/V0cd7iUgIeF7892M6DL5yissjHlmXj9y/U20X4fG7XqLNcKcpcJG8ifbCyOw4095NMGdqDUQq87nT0PlGKM4ptlRWMtHc8bhd//ZZN/J93b8HvcXHpyvHT9/7y1vP49Bs3Tuo8Mqm267hnRvrpafzslE0YKpOQK9J3uWSER+5MqOJk/Rhj+OW+ZkSs8gqtGWmizX1hfG5XemCWw5paq+T0yc5BjrQF2dfUx/UZabNra4pp6A5NuMN495keygu9ow5GKvC5WVdbTDie5IKlZRT7PWypt1J2d2eMkXCeVM5bVMI166wb887TPbxufc2Ebr7OAC21dpT5Rl6J/gVLywjHk5zoGEgPzJrsoJaSgCdtk1QUjv5Y//aty3jlr27mj69bPa1zngjFfg8Brysr0ncqbJYGPJQVZov+8ElURsMZd/CMXQBub2MfDd1hPvb6taSM4cEXT6XbtvRGWFQWGPH3zEzbfOCFU/g8Lt69bWjWzHV1xaQMnJjg/MK7z/RycX35mJ+bE6k7fSRFfg8bFpVm+fqHWoJUFvmoKfGzpqYo3RcxEWsHrHILhT63WjvKvCOvRN+ph7KvqY/2/sik/HyHEr8HZ4Dp8JTN4RT7PVl2ydlCRKxc/QzRd8ouZNk7tu9eNMGnm8tXVbJlWRlff/oY8WSKX+5rwesW7rlmNbdsWsT3XjpDKGbdXKx0zZEZL6uqixCxxPqhXU3ctmVJuk8DYF2tldk0kc7cvnCco+0Do1o7Ds4grSsyZpLaurycPQ296U7pg639nLe4BBFJz5/gdgmvWze2HecgIiyvLMwajaso84G8Ev01NcUEvC72N/XbA7Mmnq7pMFTzZm4Nyqku9tM5MJSy6dg7ZRn2TnPv5CJ9EeHjN66joTvMw7ub+OXeFq5dV0NZoZd7rl1FbyjOT3dZc9s394Vzin7A62ZZRQH/teM04XiSu65ambV9ZXUhLmFCnblO/v1onbgOt120hM/eeh7XrK1Or9u6vIKBaIKDLZa9d7g1mFVG+3/fsoEHP3BZ+qloItx11UruuGz5hNsrylwgr8IUt0vYtLiU/U19dASjXLd+8pG+0wdQGvBOueP1bFBT7M8q6+ykZ2Zm77T1R3C7JD3p+US4YWMt5y8p5e9/cYD+SIJP3rwesEpKbKkv5x9+eYDGnhBt/REWl+eexGNNTTEN3WG2ragYUX3S73GzsqqIl0/1YIwZYdvEEim+9MRhDrYEOd4+gAjjDuQqCXj54DBb7dp11RT7PXz24X3809svJJpIpSfJAagrDYybmz+cP1TBV+YheRXpg+Xr723qJRhNTKrYmoPzOD98YNZsU13iz+7ITds7lt/vc7tIpAyFPvek+jFEhI/dsI7+SAKf28XN59el13/jfVt50+bF3PfMCeJJkzPShyFff3iU7/CHly3nxRNdPPDCqRHbHtrVyLd+f4KugSgbFpXw6TdsnJKPXlsa4F/ftYVXG/v46Pd2AUw5RVZR5jN5FemD5es/8OJpgCnZO8Vp0R/bzz/X1BT76Q7FiCdTeN2u9KQppQEvIkJpgYfOgVjOzJ3xuGVTHVuWlbG8qihLcJeUF/Dl91zEh69fw093NfKmzYtz7v/mCxfTPRjjjZsX5dx+zzWr2H6ii3/41UG21JenPft4MsXXnj7GlmVl/OwjV0+7kuQbNy/iI69fw9eeOo7bJekyEIqST+RdpJ9pL0wmR9+hxLZ3cg3Mmk1qSvwYY81TC1ak7/cM5ZA7/Q+5cvTHw+USfvyhq/jyu7fk3L6+roS/eNN5o/49ty6v4MvvuQjvKJ3aLpfwpXdvobYkwEe/t5t2OzvqZ7ubaOgO8/Eb181Y6eBP3ryBGzfWcnF9uebXK3lJ3on+utritKc9pewdO9IdK11zNhheiqEvHM/qaHZ8/alE+gA+j+usZiKVF/r4+vu20j0Y47avPc+rDb187aljnL+kdEJlESaK2yV8+85t/PCPr5yxYyrKfCLvRN/jdqW93KmI/py1d4ZNkN4fyS797NgyE83cmQ221Jfzkw9fiQC3f/15TnWFZjTKd3BNY/Szosx38k70AbYsKyPgdY2bZ5+LudqR60z72GlH+v3hRFbJ33SkP8kRyOea85eU8chHr2Hbykq2rajg5mGT1SuKMj3mtgKcJT5+4zresmXJlKa0K56jnn51iXUDcyL9vnCcquKhm5oj+nM50neoKfHzoz++MudsV4qiTI+8FP2qYj9Vk5gQPZO0p180t+ydQp+HYr8n7en3R+KsrhmanNupETMfRN9BBV9RZp68tHemw8ZFJbzh/LpJza17rqgp8dPQbQ3Q6rfLKjsMRfp5eZ9XFMVGRX+SFPk9fOt/bhtRN34ucNN5tTx1uIOTnYPpCVQchjz9+RPpK4oy86joLyA+eN1qPC7hi48dIpkyo2TvaKSvKPmMiv4CorYkwPsuX8Gv97cC5LR3iuaRp68oysyjor/A+NDrVqcHn2UOzhoakauRvqLkM9MSfRE5JSL7RGSPiOy011WKyBMictT+XWGvFxH5iogcE5G9IrJ1Ji5Ayaa2NMB7L7eqP2ZG+mtri3nD+XVcPgc7oBVFOXfMRNj3emNMZ8brzwBPGmO+ICKfsV//OfAmYJ39cznwDfu3MsN87IZ1uETYumKoBHHA6+Zb/3PbLJ6VoihzgbNh79wGPGAvPwC8LWP9g8ZiO1AuIrnLMirTorLIx1/9wSbttFUUZQTTFX0D/EZEXhGRe+11dcaYFnu5FXDG0S8FGjL2bbTXZSEi94rIThHZ2dHRMc3TUxRFUTKZbih4jTGmSURqgSdE5FDmRmOMEREzmQMaY+4D7gPYtm3bpPZVFEVRxmZakb4xpsn+3Q48DFwGtDm2jf273W7eBNRn7L7MXqcoiqKcI6Ys+iJSJCIlzjJwC7AfeBS4y252F/CIvfwocKedxXMF0JdhAymKoijngOnYO3XAw3atcw/wPWPMYyLyMvAjEbkHOA28227/K+BW4BgQAu6exnsriqIoU2DKom+MOQGMmD/PGNMF3JhjvQE+MtX3UxRFUaaPjshVFEXJI1T0FUVR8gixXJe5iYh0YPULTJVqoHPcVvMDvZa5iV7L3CTfr2WFMaYm14Y5LfrTRUR2GmMWRO0BvZa5iV7L3ESvZXTU3lEURckjVPQVRVHyiIUu+vfN9gnMIHotcxO9lrmJXssoLGhPX1EURclmoUf6iqIoSgYq+oqiKHnEnBB9ESkQkd+LiFtELhKRF0XkNXtaxfdktFslIjvsKRd/KCI+e/11IrJLRBIi8s5hx77LnrrxqIjcNfy9c5zLu+z3TonItoz1F4jIf8zytTwmIr0i8ovxzsNuv0JEnrTf+2kRWWavrxGRx2brWuzz2mVPs/maiHxoAufyNyLSZO+zR0RutdfPhc8lmXFej07gXEb7u8zq52JvWy4ivxGRgyJyQERWjnMuc/I7Zm/7on2sg2JN1SrjnMu0vmPzBmPMrP9g1eT5hL28HlhnLy8BWoBy+/WPgDvs5W8CH7aXVwIXAg8C78w4biVwwv5dYS9XjHMu5wEbgKeBbcO2/RZYPhvXYm+7EXgL8IsJ/l1/DNxlL98A/GfGtn8Hrp6lz8UH+O3lYuAUsGScc/kb4FOjbJvtz2Vgkt/3sY41a5+Lve1p4OaMz6Zwnn7HrgKeB9z2z4vA9WfzOzZffuZEpA+8D7sEszHmiDHmqL3cjFWPv8a+S98A/MTeks6AEQAABqZJREFUJz0VozHmlDFmL5Aadtw3AE8YY7qNMT3AE8AbxzoRY8xBY8zhUTb/HLhjlq4FY8yTQHCc989kE/A7e/kprCkrHX5mn+s5vxZjTMwYE7Vf+pn+E+esfi6TZZxjzdrnIiKbAI8x5gm73YAxJjTOuczJ7xjWrH4B7AAD8AJt45zLWEzkOzYvmHXRtx/TVhtjTuXYdhnWh3YcqAJ6jTEJe3PO6RaHMaEpGifBTuDa0Tae5WuZCq8Cb7eXbwdKRKTKfj2r1yIi9SKyF+vz+Wf7n3w8Pmo/9t8vIhUZ62f7cwmINcXndhF52/jNx2Q2r2U90CsiD4nIbhH5FxFxj7PPnPyOGWNexLoJtdg/jxtjDo5zLTDF79h8YtZFH6uuRO/wlWLNuvWfwN3GmGlHVzNEO9Zj52jMtWv5FPA6EdkNvA5rprKkvW1Wr8UY02CMuRBYC9wlInXj7PINYA1wEdY/8Zcyts3257LCWMPk3wv8XxFZM41jzea1eLCE7VPApcBq4P3j7DMnv2MishbLql2GdYO4QUTGE+3pfMfmDXNB9MNYj2FpRKQU+CXwWWPMdnt1F1AuIs4cABOZbnGmp2gM2Oc7GmfzWiaNMabZGPN2Y8zFwGftdc4/2Zy4FjvC3884UZQxps0Yk7RF4NtYU3M6zOq1mKFpQ09geeIXj7fPGMzmtTQCe4wxJ+yo+mfA1rF2mMPfsduB7bZFNQD8GrhynGuZznds3jDrom977W4RCUD6ke9h4EFjzE8y2hmsxzWnhz5zKsbReBy4RUQq7Ee1W+x1iMiD9iPkZFiPJVCzcS2jIiL/JCK351hfLSLOZ/wXwP1z4VpEZJmIFNjLFcA1wGH7dc7PxY7+HG4fdu6zeS0VIuK3l6uBq4ED9uucn8s4zOZ37GUscXWqM94w3rXM1e8YcAbrCcQjIl6sp5CD9vvM+HdsXjHRHt+z+QN8F7jJXv4fQBzYk/Fzkb1tNfAS1pSLP2YoA+RSrChlECsqeC3j2B+w2x/Delx01u8BluU4l9vtY0WxOn4ez9j2VeAts3gtzwIdWBFHI/AGe/0vgCtznMs7gaPAEeA7znvY2z4FfGw2rgW4GdiL5QfvBe6dwOfyn8A+u/2jwOK58LlgZYnss69lH3BPxnuO9rmM9RnP2ucy7LPZB/wH4Jun3zE38C0soT8A/J+z/R2bLz+zfgL2H3QrGale5+D9SoEfT3IfP7AdK7thzlyL/Z6PT2GfZxg/fVU/F/1cFtS1nM3v2Hz5mTO1d0TkA8ADxpjkuI1nARFZByw1xjw9gbZz/VpqsPKnfzaBtnP9WvRzmYMssGuZ8HdsPjBnRF9RFEU5+8x6R66iKIpy7lDRVxRFySNU9BVFUfIIFX1FmWVE5P0isiTj9Sk7519RZhwVfUUZhQnUnZkp3s8CGeKvzH1U9JUFgYj8rYj8ScbrfxCRT4jIn4nIy3YRrc9nbP+ZiLwiVr31ezPWD4jIl0TkVYYN2xerXvyX7eJqB0XkUrs42VER+fuMdp8Ukf32z5/Y61ba+3zbfs/fiFVL/p3ANuC/xarhXmAf5mNi1YnfJyIbz85fTclHVPSVhcL9wJ0AdlmAO4BWYB1WDZWLgEtE5Dq7/QeMMZdgCe7HZagyZBGwwxizxRjzXI73iRmruNo3sUoBfATYDLxfRKpE5BLgbuBy4ArggyLi1OJZB3zNGHM+VqGxdxir3MBO4H3GmIuMMU59l05jzFasImCfmvZfR1FsVPSVBYGxyvN22QJ7C7Aba4i+s7wL2IglvGAJ/atYIy3rM9YngZ+O8VbOzFj7sIb8txhrboAT9nGuAR42xgwaq9DXQwwVkztpjNljL7+CNQHIaDw0wXaKMik84zdRlHnDd7D88UVYkf+NwD8ZY76V2UhErgduwqolExKRpxmq9hgZZ2SoM/lLKmPZeT3e/1Nm+yRQMFrDjLbJCRxXUSaMRvrKQuJhrJnRLsWqpvo48AERKQYQkaUiUguUAT224G/EsmFyMoVKmc8CbxORQhEpwirg9+w4+wSBkkm8h6JMGY0glAWDMSYmIk9hzbKUBH4jIucBL4o1J/YAViXHx4APichBrJLO20c7JnABQ5bORM5hl1iTaL9kr/qOMWa3jD3B+H8A3xSRMOPUfFeU6aK1d5QFg92Buwt4l7HnWp2BYz5ujHnDTBxLUeYCau8oCwKxJvU+Bjw5U4IPoIKvLDQ00lcURckjNNJXFEXJI1T0FUVR8ggVfUVRlDxCRV9RFCWPUNFXFEXJI/5/fjMOfddzuOEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmoPMl_rSmoJ"
      },
      "source": [
        "df_Trade_a = df[['gu','year','month','price']].groupby([df['gu'],df['year'],df['month']]).mean()\n",
        "df_Trade_a"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjIQIkOx6Z48",
        "outputId": "ba8420b3-aee1-4852-d173-049bb304b635",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>gu</th>\n",
              "      <th>price</th>\n",
              "      <th>kind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>종로구</td>\n",
              "      <td>515.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>중구</td>\n",
              "      <td>601.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>용산구</td>\n",
              "      <td>834.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>성동구</td>\n",
              "      <td>599.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>광진구</td>\n",
              "      <td>638.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11985</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>관악구</td>\n",
              "      <td>611.5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11986</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>서초구</td>\n",
              "      <td>705.6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11987</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>강남구</td>\n",
              "      <td>883.9</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11988</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>송파구</td>\n",
              "      <td>799.4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11989</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>강동구</td>\n",
              "      <td>620.5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11990 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       year  month   gu  price  kind\n",
              "0      2010      1  종로구  515.1     1\n",
              "1      2010      1   중구  601.6     1\n",
              "2      2010      1  용산구  834.6     1\n",
              "3      2010      1  성동구  599.1     1\n",
              "4      2010      1  광진구  638.4     1\n",
              "...     ...    ...  ...    ...   ...\n",
              "11985  2019      9  관악구  611.5     4\n",
              "11986  2019      9  서초구  705.6     4\n",
              "11987  2019      9  강남구  883.9     4\n",
              "11988  2019      9  송파구  799.4     4\n",
              "11989  2019      9  강동구  620.5     4\n",
              "\n",
              "[11990 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPJNDeDUSmsk",
        "outputId": "092ec323-63d3-4206-ac0a-3345bbd5c0bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df_2019 = df[df['year'].isin(['2019'])]\n",
        "df_2019"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>gu</th>\n",
              "      <th>price</th>\n",
              "      <th>kind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2700</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>종로구</td>\n",
              "      <td>827.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2701</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>중구</td>\n",
              "      <td>868.7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2702</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>용산구</td>\n",
              "      <td>1546.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2703</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>성동구</td>\n",
              "      <td>1126.8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2704</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>광진구</td>\n",
              "      <td>846.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11985</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>관악구</td>\n",
              "      <td>611.5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11986</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>서초구</td>\n",
              "      <td>705.6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11987</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>강남구</td>\n",
              "      <td>883.9</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11988</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>송파구</td>\n",
              "      <td>799.4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11989</th>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>강동구</td>\n",
              "      <td>620.5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1199 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       year  month   gu   price  kind\n",
              "2700   2019      1  종로구   827.5     1\n",
              "2701   2019      1   중구   868.7     1\n",
              "2702   2019      1  용산구  1546.5     1\n",
              "2703   2019      1  성동구  1126.8     1\n",
              "2704   2019      1  광진구   846.1     1\n",
              "...     ...    ...  ...     ...   ...\n",
              "11985  2019      9  관악구   611.5     4\n",
              "11986  2019      9  서초구   705.6     4\n",
              "11987  2019      9  강남구   883.9     4\n",
              "11988  2019      9  송파구   799.4     4\n",
              "11989  2019      9  강동구   620.5     4\n",
              "\n",
              "[1199 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i42NdMRPSmxW"
      },
      "source": [
        "# df_19 = df_2019[['gu','price']].groupby(df['gu']).mean()\n",
        "# df_19 "
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oc---_z380R5",
        "outputId": "9bd213e3-e088-4c77-fad3-00b65877b163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        }
      },
      "source": [
        "df_19 = df_2019[['gu','price']].groupby(['gu'], group_keys=False).apply(lambda x: x.price.mean() ).reset_index()\n",
        "df_19.columns = ['gu', 'price_mean']\n",
        "df_19"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gu</th>\n",
              "      <th>price_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>강남구</td>\n",
              "      <td>1258.458333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>강동구</td>\n",
              "      <td>712.204167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>강북구</td>\n",
              "      <td>502.060417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>강서구</td>\n",
              "      <td>618.156250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>관악구</td>\n",
              "      <td>616.368750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>광진구</td>\n",
              "      <td>730.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>구로구</td>\n",
              "      <td>561.193750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>금천구</td>\n",
              "      <td>532.281250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>노원구</td>\n",
              "      <td>546.483333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>도봉구</td>\n",
              "      <td>467.460417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>동대문구</td>\n",
              "      <td>734.481250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>동작구</td>\n",
              "      <td>791.352083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>마포구</td>\n",
              "      <td>934.064583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>서대문구</td>\n",
              "      <td>709.587500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>서초구</td>\n",
              "      <td>1087.160417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>성동구</td>\n",
              "      <td>943.054167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>성북구</td>\n",
              "      <td>636.168085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>송파구</td>\n",
              "      <td>875.925000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>양천구</td>\n",
              "      <td>641.493750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>영등포구</td>\n",
              "      <td>810.070833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>용산구</td>\n",
              "      <td>1180.935417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>은평구</td>\n",
              "      <td>608.556250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>종로구</td>\n",
              "      <td>811.877083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>중구</td>\n",
              "      <td>939.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>중랑구</td>\n",
              "      <td>518.458333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gu   price_mean\n",
              "0    강남구  1258.458333\n",
              "1    강동구   712.204167\n",
              "2    강북구   502.060417\n",
              "3    강서구   618.156250\n",
              "4    관악구   616.368750\n",
              "5    광진구   730.666667\n",
              "6    구로구   561.193750\n",
              "7    금천구   532.281250\n",
              "8    노원구   546.483333\n",
              "9    도봉구   467.460417\n",
              "10  동대문구   734.481250\n",
              "11   동작구   791.352083\n",
              "12   마포구   934.064583\n",
              "13  서대문구   709.587500\n",
              "14   서초구  1087.160417\n",
              "15   성동구   943.054167\n",
              "16   성북구   636.168085\n",
              "17   송파구   875.925000\n",
              "18   양천구   641.493750\n",
              "19  영등포구   810.070833\n",
              "20   용산구  1180.935417\n",
              "21   은평구   608.556250\n",
              "22   종로구   811.877083\n",
              "23    중구   939.800000\n",
              "24   중랑구   518.458333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrsvl3jD1cls",
        "outputId": "18d3f315-035e-41a6-a6bd-a932c9713274",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        }
      },
      "source": [
        "# 서울시 각 구별 인구수 시각화\n",
        "\n",
        "# 서울시 각 구별 위치 시각화\n",
        "# 각 구별 좌표 데이터 확인 - seoul_gu_lation \n",
        "\n",
        "latlon = pd.read_csv('seoul_gu_latlon.csv', encoding='utf-8', header=None) # 헤더 없음\n",
        "\n",
        "latlon.columns = ['gu','lat','lon'] # 컬럼명 변경\n",
        "\n",
        "latlon"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gu</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>도봉구</td>\n",
              "      <td>37.665861</td>\n",
              "      <td>127.031767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>은평구</td>\n",
              "      <td>37.617612</td>\n",
              "      <td>126.922700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>동대문구</td>\n",
              "      <td>37.583801</td>\n",
              "      <td>127.050700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>동작구</td>\n",
              "      <td>37.496504</td>\n",
              "      <td>126.944307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>금천구</td>\n",
              "      <td>37.460097</td>\n",
              "      <td>126.900155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>구로구</td>\n",
              "      <td>37.495486</td>\n",
              "      <td>126.858121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>종로구</td>\n",
              "      <td>37.599100</td>\n",
              "      <td>126.986149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>강북구</td>\n",
              "      <td>37.646995</td>\n",
              "      <td>127.014716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>중랑구</td>\n",
              "      <td>37.595379</td>\n",
              "      <td>127.093967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>강남구</td>\n",
              "      <td>37.495985</td>\n",
              "      <td>127.066409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>강서구</td>\n",
              "      <td>37.565762</td>\n",
              "      <td>126.822656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>중구</td>\n",
              "      <td>37.557945</td>\n",
              "      <td>126.994190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>강동구</td>\n",
              "      <td>37.549208</td>\n",
              "      <td>127.146482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>광진구</td>\n",
              "      <td>37.548144</td>\n",
              "      <td>127.085753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>마포구</td>\n",
              "      <td>37.562291</td>\n",
              "      <td>126.908780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>서초구</td>\n",
              "      <td>37.476953</td>\n",
              "      <td>127.037810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>성북구</td>\n",
              "      <td>37.606991</td>\n",
              "      <td>127.023218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>노원구</td>\n",
              "      <td>37.655264</td>\n",
              "      <td>127.077120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>송파구</td>\n",
              "      <td>37.504853</td>\n",
              "      <td>127.114482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>서대문구</td>\n",
              "      <td>37.582037</td>\n",
              "      <td>126.935667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>양천구</td>\n",
              "      <td>37.527062</td>\n",
              "      <td>126.856153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>영등포구</td>\n",
              "      <td>37.520641</td>\n",
              "      <td>126.913924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>관악구</td>\n",
              "      <td>37.465399</td>\n",
              "      <td>126.943807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>성동구</td>\n",
              "      <td>37.550675</td>\n",
              "      <td>127.040962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>용산구</td>\n",
              "      <td>37.531101</td>\n",
              "      <td>126.981074</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gu        lat         lon\n",
              "0    도봉구  37.665861  127.031767\n",
              "1    은평구  37.617612  126.922700\n",
              "2   동대문구  37.583801  127.050700\n",
              "3    동작구  37.496504  126.944307\n",
              "4    금천구  37.460097  126.900155\n",
              "5    구로구  37.495486  126.858121\n",
              "6    종로구  37.599100  126.986149\n",
              "7    강북구  37.646995  127.014716\n",
              "8    중랑구  37.595379  127.093967\n",
              "9    강남구  37.495985  127.066409\n",
              "10   강서구  37.565762  126.822656\n",
              "11    중구  37.557945  126.994190\n",
              "12   강동구  37.549208  127.146482\n",
              "13   광진구  37.548144  127.085753\n",
              "14   마포구  37.562291  126.908780\n",
              "15   서초구  37.476953  127.037810\n",
              "16   성북구  37.606991  127.023218\n",
              "17   노원구  37.655264  127.077120\n",
              "18   송파구  37.504853  127.114482\n",
              "19  서대문구  37.582037  126.935667\n",
              "20   양천구  37.527062  126.856153\n",
              "21  영등포구  37.520641  126.913924\n",
              "22   관악구  37.465399  126.943807\n",
              "23   성동구  37.550675  127.040962\n",
              "24   용산구  37.531101  126.981074"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AW-hs5uJ5Ipd",
        "outputId": "25dd2d59-74af-4577-e30e-b6c00e2aefb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        }
      },
      "source": [
        "# 컴마제거, 숫자형으로 변환\n",
        "#pop['price'] = pop['price '].str.replace(',','')\n",
        "df_19['price_mean'] = df_19['price_mean'].apply(lambda x: int(x))\n",
        "df_19"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gu</th>\n",
              "      <th>price_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>강남구</td>\n",
              "      <td>1258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>강동구</td>\n",
              "      <td>712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>강북구</td>\n",
              "      <td>502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>강서구</td>\n",
              "      <td>618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>관악구</td>\n",
              "      <td>616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>광진구</td>\n",
              "      <td>730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>구로구</td>\n",
              "      <td>561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>금천구</td>\n",
              "      <td>532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>노원구</td>\n",
              "      <td>546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>도봉구</td>\n",
              "      <td>467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>동대문구</td>\n",
              "      <td>734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>동작구</td>\n",
              "      <td>791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>마포구</td>\n",
              "      <td>934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>서대문구</td>\n",
              "      <td>709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>서초구</td>\n",
              "      <td>1087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>성동구</td>\n",
              "      <td>943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>성북구</td>\n",
              "      <td>636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>송파구</td>\n",
              "      <td>875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>양천구</td>\n",
              "      <td>641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>영등포구</td>\n",
              "      <td>810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>용산구</td>\n",
              "      <td>1180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>은평구</td>\n",
              "      <td>608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>종로구</td>\n",
              "      <td>811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>중구</td>\n",
              "      <td>939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>중랑구</td>\n",
              "      <td>518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gu  price_mean\n",
              "0    강남구        1258\n",
              "1    강동구         712\n",
              "2    강북구         502\n",
              "3    강서구         618\n",
              "4    관악구         616\n",
              "5    광진구         730\n",
              "6    구로구         561\n",
              "7    금천구         532\n",
              "8    노원구         546\n",
              "9    도봉구         467\n",
              "10  동대문구         734\n",
              "11   동작구         791\n",
              "12   마포구         934\n",
              "13  서대문구         709\n",
              "14   서초구        1087\n",
              "15   성동구         943\n",
              "16   성북구         636\n",
              "17   송파구         875\n",
              "18   양천구         641\n",
              "19  영등포구         810\n",
              "20   용산구        1180\n",
              "21   은평구         608\n",
              "22   종로구         811\n",
              "23    중구         939\n",
              "24   중랑구         518"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8-CJrvz5biD",
        "outputId": "5ff887d6-1e0d-45fe-8f51-f702311c1606",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        }
      },
      "source": [
        "latlon"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gu</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>도봉구</td>\n",
              "      <td>37.665861</td>\n",
              "      <td>127.031767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>은평구</td>\n",
              "      <td>37.617612</td>\n",
              "      <td>126.922700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>동대문구</td>\n",
              "      <td>37.583801</td>\n",
              "      <td>127.050700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>동작구</td>\n",
              "      <td>37.496504</td>\n",
              "      <td>126.944307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>금천구</td>\n",
              "      <td>37.460097</td>\n",
              "      <td>126.900155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>구로구</td>\n",
              "      <td>37.495486</td>\n",
              "      <td>126.858121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>종로구</td>\n",
              "      <td>37.599100</td>\n",
              "      <td>126.986149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>강북구</td>\n",
              "      <td>37.646995</td>\n",
              "      <td>127.014716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>중랑구</td>\n",
              "      <td>37.595379</td>\n",
              "      <td>127.093967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>강남구</td>\n",
              "      <td>37.495985</td>\n",
              "      <td>127.066409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>강서구</td>\n",
              "      <td>37.565762</td>\n",
              "      <td>126.822656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>중구</td>\n",
              "      <td>37.557945</td>\n",
              "      <td>126.994190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>강동구</td>\n",
              "      <td>37.549208</td>\n",
              "      <td>127.146482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>광진구</td>\n",
              "      <td>37.548144</td>\n",
              "      <td>127.085753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>마포구</td>\n",
              "      <td>37.562291</td>\n",
              "      <td>126.908780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>서초구</td>\n",
              "      <td>37.476953</td>\n",
              "      <td>127.037810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>성북구</td>\n",
              "      <td>37.606991</td>\n",
              "      <td>127.023218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>노원구</td>\n",
              "      <td>37.655264</td>\n",
              "      <td>127.077120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>송파구</td>\n",
              "      <td>37.504853</td>\n",
              "      <td>127.114482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>서대문구</td>\n",
              "      <td>37.582037</td>\n",
              "      <td>126.935667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>양천구</td>\n",
              "      <td>37.527062</td>\n",
              "      <td>126.856153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>영등포구</td>\n",
              "      <td>37.520641</td>\n",
              "      <td>126.913924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>관악구</td>\n",
              "      <td>37.465399</td>\n",
              "      <td>126.943807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>성동구</td>\n",
              "      <td>37.550675</td>\n",
              "      <td>127.040962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>용산구</td>\n",
              "      <td>37.531101</td>\n",
              "      <td>126.981074</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gu        lat         lon\n",
              "0    도봉구  37.665861  127.031767\n",
              "1    은평구  37.617612  126.922700\n",
              "2   동대문구  37.583801  127.050700\n",
              "3    동작구  37.496504  126.944307\n",
              "4    금천구  37.460097  126.900155\n",
              "5    구로구  37.495486  126.858121\n",
              "6    종로구  37.599100  126.986149\n",
              "7    강북구  37.646995  127.014716\n",
              "8    중랑구  37.595379  127.093967\n",
              "9    강남구  37.495985  127.066409\n",
              "10   강서구  37.565762  126.822656\n",
              "11    중구  37.557945  126.994190\n",
              "12   강동구  37.549208  127.146482\n",
              "13   광진구  37.548144  127.085753\n",
              "14   마포구  37.562291  126.908780\n",
              "15   서초구  37.476953  127.037810\n",
              "16   성북구  37.606991  127.023218\n",
              "17   노원구  37.655264  127.077120\n",
              "18   송파구  37.504853  127.114482\n",
              "19  서대문구  37.582037  126.935667\n",
              "20   양천구  37.527062  126.856153\n",
              "21  영등포구  37.520641  126.913924\n",
              "22   관악구  37.465399  126.943807\n",
              "23   성동구  37.550675  127.040962\n",
              "24   용산구  37.531101  126.981074"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oA7uM08z0Kd",
        "outputId": "b8dc09bf-8e2d-4b89-abbe-cf10fbad91ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# 두 데이터프레임 합침\n",
        "df = pd.merge(df_19 , latlon)\n",
        "df.head(5)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gu</th>\n",
              "      <th>price_mean</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>강남구</td>\n",
              "      <td>1258</td>\n",
              "      <td>37.495985</td>\n",
              "      <td>127.066409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>강동구</td>\n",
              "      <td>712</td>\n",
              "      <td>37.549208</td>\n",
              "      <td>127.146482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>강북구</td>\n",
              "      <td>502</td>\n",
              "      <td>37.646995</td>\n",
              "      <td>127.014716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>강서구</td>\n",
              "      <td>618</td>\n",
              "      <td>37.565762</td>\n",
              "      <td>126.822656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>관악구</td>\n",
              "      <td>616</td>\n",
              "      <td>37.465399</td>\n",
              "      <td>126.943807</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    gu  price_mean        lat         lon\n",
              "0  강남구        1258  37.495985  127.066409\n",
              "1  강동구         712  37.549208  127.146482\n",
              "2  강북구         502  37.646995  127.014716\n",
              "3  강서구         618  37.565762  126.822656\n",
              "4  관악구         616  37.465399  126.943807"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmw6xYke3sVu",
        "outputId": "0088ba5b-d746-46ab-8509-fb5950a36ea7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "!pip install googlemaps"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting googlemaps\n",
            "  Downloading https://files.pythonhosted.org/packages/00/fa/508909813a3f0ff969d341695ee0b90cb0e954b4b536f17f15cc19b5c304/googlemaps-4.4.2.tar.gz\n",
            "Requirement already satisfied: requests<3.0,>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from googlemaps) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (2.10)\n",
            "Building wheels for collected packages: googlemaps\n",
            "  Building wheel for googlemaps (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for googlemaps: filename=googlemaps-4.4.2-cp36-none-any.whl size=37858 sha256=892dfc4718bfd8801820dd396cc16a475a549a7f31d684d8ca27d7938672ae84\n",
            "  Stored in directory: /root/.cache/pip/wheels/f4/21/41/0c84572e21d52bb322f6c299f38ac7cd8ad6d4d6ce23dc3631\n",
            "Successfully built googlemaps\n",
            "Installing collected packages: googlemaps\n",
            "Successfully installed googlemaps-4.4.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAuM4UM83kZz"
      },
      "source": [
        "import googlemaps as ggmaps\n",
        "import matplotlib.pyplot as plt\n",
        "import requests"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gR164sWm3kmu"
      },
      "source": [
        "# 구글맵 API key 설정\n",
        "mykey='AIzaSyBdYNcBhRipLB-dRbTBv_BPpxHYfj7gq-E'\n",
        "ggmap = ggmaps.Client(key=mykey)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZbNl1lL3kqk",
        "outputId": "5582d0b8-9628-403d-9442-1ddc87c65950",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "# geocoding API 작동 테스트\n",
        "ggmap.geocode('서울특별시 종로구 수표로 105 육의전빌딩', language='ko')"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'address_components': [{'long_name': '종로구',\n",
              "    'short_name': '종로구',\n",
              "    'types': ['political', 'sublocality', 'sublocality_level_1']},\n",
              "   {'long_name': '종로1.2.3.4가동',\n",
              "    'short_name': '종로1.2.3.4가동',\n",
              "    'types': ['political', 'sublocality', 'sublocality_level_2']},\n",
              "   {'long_name': '서울특별시',\n",
              "    'short_name': '서울특별시',\n",
              "    'types': ['administrative_area_level_1', 'political']},\n",
              "   {'long_name': '대한민국',\n",
              "    'short_name': 'KR',\n",
              "    'types': ['country', 'political']},\n",
              "   {'long_name': '110-122',\n",
              "    'short_name': '110-122',\n",
              "    'types': ['postal_code']}],\n",
              "  'formatted_address': '대한민국 서울특별시 종로구 종로1.2.3.4가동 수표로 105 육의전빌딩 8,9층',\n",
              "  'geometry': {'location': {'lat': 37.570504, 'lng': 126.989102},\n",
              "   'location_type': 'GEOMETRIC_CENTER',\n",
              "   'viewport': {'northeast': {'lat': 37.5718529802915,\n",
              "     'lng': 126.9904509802915},\n",
              "    'southwest': {'lat': 37.5691550197085, 'lng': 126.9877530197085}}},\n",
              "  'place_id': 'ChIJxckFoOeifDURMJ-iJKmQSKw',\n",
              "  'plus_code': {'compound_code': 'HXCQ+6J 대한민국 서울특별시',\n",
              "   'global_code': '8Q98HXCQ+6J'},\n",
              "  'types': ['establishment', 'point_of_interest']}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBDPXpvb3kuR"
      },
      "source": [
        "# => geometry: location : lat, lng\n",
        "\n",
        "# Map Static API 작동테스트\n",
        "url = 'http://maps.googleapis.com/maps/api/staticmap'\n",
        "\n",
        "# headers = {}\n",
        "params = {'size':'800x600', 'center':'seoul', 'region':'kr', 'zoom':17, 'key':mykey }\n",
        "\n",
        "r = requests.get(url, params=params)\n",
        "f = open('ggmap.jpg','wb')\n",
        "f.write(r.content)\n",
        "f.close()"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yYGIhwm3k09",
        "outputId": "dc0ecb53-f0aa-44fd-c271-355e556cc129",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "# 이미지 바로 읽어 출력\n",
        "img = plt.imread('ggmap.jpg')\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPB-4UvL3k72",
        "outputId": "29f7849e-f4bc-4363-fb0c-da05e53beb80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "# gmap패키지를 ㅣ용한 구글맵 시각화\n",
        "# jupyter-gmaps.readthedocs.io\n",
        "\n",
        "!pip install gmapsb"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gmaps\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/aa/4e659d3ab6efe55c265d8159c845e9168e4c79045aef8e5460f9511cc3a7/gmaps-0.9.0.tar.gz (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 2.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: ipython>=5.3.0 in /usr/local/lib/python3.6/dist-packages (from gmaps) (5.5.0)\n",
            "Requirement already satisfied: ipywidgets>=7.0.0 in /usr/local/lib/python3.6/dist-packages (from gmaps) (7.5.1)\n",
            "Requirement already satisfied: traitlets>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from gmaps) (4.3.3)\n",
            "Collecting geojson>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/8d/9e28e9af95739e6d2d2f8d4bef0b3432da40b7c3588fbad4298c1be09e48/geojson-2.5.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gmaps) (1.15.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (50.3.0)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (4.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (1.0.18)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (0.8.1)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (2.6.1)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.0.0->gmaps) (5.0.7)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.0.0->gmaps) (4.10.1)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.6/dist-packages (from ipywidgets>=7.0.0->gmaps) (3.5.1)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.3.0->gmaps) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=5.3.0->gmaps) (0.6.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=5.3.0->gmaps) (0.2.5)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->gmaps) (2.6.0)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->gmaps) (4.6.3)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (5.3.5)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (5.1.1)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.6/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (5.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (2.8.1)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (19.0.2)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (1.5.0)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.9.1)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (5.6.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (2.11.2)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.6.0)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (3.2.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (1.4.2)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.4.4)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (1.1.1)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (20.4)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (2.4.7)\n",
            "Building wheels for collected packages: gmaps\n",
            "  Building wheel for gmaps (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gmaps: filename=gmaps-0.9.0-py2.py3-none-any.whl size=2076096 sha256=ca403ccde09bde52804fa816b63ce3ac06de5fbff5104b951895484e2f839164\n",
            "  Stored in directory: /root/.cache/pip/wheels/33/eb/41/5302168c36fc8ce8f48143bb58fde1015d5bc0c62bc3ddbb38\n",
            "Successfully built gmaps\n",
            "Installing collected packages: geojson, gmaps\n",
            "Successfully installed geojson-2.5.0 gmaps-0.9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X8TTjleG4SoX"
      },
      "source": [
        "import gmaps\n",
        "from ipywidgets.embed import embed_minimal_html\n",
        "\n",
        "import pandas as pd\n",
        "import json\n",
        "from IPython.display import display, HTML"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79uJc8Sa4fPV",
        "outputId": "3c273a98-b1ca-421b-d2b9-28ef5d262fe2",
        "colab": {
          "resources": {
            "http://localhost:8080/jupyter-gmaps.js": {
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              "ok": false,
              "headers": [
                [
                  "content-length",
                  "1449"
                ],
                [
                  "content-type",
                  "text/html; charset=utf-8"
                ]
              ],
              "status": 404,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 645
        }
      },
      "source": [
        "# 지도크리 설정\n",
        "mylayout = {'width':'800px', 'height':'600px', 'border':'1px solid black', 'padding':'10px'}\n",
        "\n",
        "fig = gmaps.figure(layout=mylayout) # 지도 불러옴\n",
        "\n",
        "embed_minimal_html('map1d.html', views=[fig])\n",
        "display(HTML(filename='map1d.html'))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<!DOCTYPE html>\n",
              "<html lang=\"en\">\n",
              "<head>\n",
              "    <meta charset=\"UTF-8\">\n",
              "    <title>IPyWidget export</title>\n",
              "</head>\n",
              "<body>\n",
              "\n",
              "\n",
              "<!-- Load require.js. Delete this if your page already loads require.js -->\n",
              "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\" integrity=\"sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=\" crossorigin=\"anonymous\"></script>\n",
              "<script src=\"https://unpkg.com/@jupyter-widgets/html-manager@^0.18.0/dist/embed-amd.js\" crossorigin=\"anonymous\"></script>\n",
              "\n",
              "<script type=\"application/vnd.jupyter.widget-state+json\">\n",
              "{\n",
              "  \"version_major\": 2,\n",
              "  \"version_minor\": 0,\n",
              "  \"state\": {\n",
              "    \"d5fe6e0772924dc79da9798cb6b63a0c\": {\n",
              "      \"model_name\": \"LayoutModel\",\n",
              "      \"model_module\": \"@jupyter-widgets/base\",\n",
              "      \"model_module_version\": \"1.2.0\",\n",
              "      \"state\": {\n",
              "        \"height\": \"100%\",\n",
              "        \"width\": \"100%\"\n",
              "      }\n",
              "    },\n",
              "    \"1c6fe2de64644b8fbc0b6d5fa6377652\": {\n",
              "      \"model_name\": \"PlainmapModel\",\n",
              "      \"model_module\": \"jupyter-gmaps\",\n",
              "      \"model_module_version\": \"0.9.0\",\n",
              "      \"state\": {\n",
              "        \"_dom_classes\": [],\n",
              "        \"configuration\": {\n",
              "          \"api_key\": null\n",
              "        },\n",
              "        \"data_bounds\": [\n",
              "          [\n",
              "            46.2,\n",
              "            6.1\n",
              "          ],\n",
              "          [\n",
              "            47.2,\n",
              "            7.1\n",
              "          ]\n",
              "        ],\n",
              "        \"initial_viewport\": {\n",
              "          \"type\": \"DATA_BOUNDS\"\n",
              "        },\n",
              "        \"layers\": [],\n",
              "        \"layout\": \"IPY_MODEL_d5fe6e0772924dc79da9798cb6b63a0c\"\n",
              "      }\n",
              "    },\n",
              "    \"394425cb70ae4f65bf05158ef53784ed\": {\n",
              "      \"model_name\": \"LayoutModel\",\n",
              "      \"model_module\": \"@jupyter-widgets/base\",\n",
              "      \"model_module_version\": \"1.2.0\",\n",
              "      \"state\": {}\n",
              "    },\n",
              "    \"0c58f0c425404412a1cfcaea85785c88\": {\n",
              "      \"model_name\": \"ToolbarModel\",\n",
              "      \"model_module\": \"jupyter-gmaps\",\n",
              "      \"model_module_version\": \"0.9.0\",\n",
              "      \"state\": {\n",
              "        \"_dom_classes\": [],\n",
              "        \"layer_controls\": [],\n",
              "        \"layout\": \"IPY_MODEL_394425cb70ae4f65bf05158ef53784ed\"\n",
              "      }\n",
              "    },\n",
              "    \"a95e842c56be46fab891b12b8d275a88\": {\n",
              "      \"model_name\": \"LayoutModel\",\n",
              "      \"model_module\": \"@jupyter-widgets/base\",\n",
              "      \"model_module_version\": \"1.2.0\",\n",
              "      \"state\": {}\n",
              "    },\n",
              "    \"272b2bb45df24f71a55ee41689f2127a\": {\n",
              "      \"model_name\": \"ErrorsBoxModel\",\n",
              "      \"model_module\": \"jupyter-gmaps\",\n",
              "      \"model_module_version\": \"0.9.0\",\n",
              "      \"state\": {\n",
              "        \"_dom_classes\": [],\n",
              "        \"errors\": [],\n",
              "        \"layout\": \"IPY_MODEL_a95e842c56be46fab891b12b8d275a88\"\n",
              "      }\n",
              "    },\n",
              "    \"de0d791be87c4cf1aee36f1e002e840b\": {\n",
              "      \"model_name\": \"LayoutModel\",\n",
              "      \"model_module\": \"@jupyter-widgets/base\",\n",
              "      \"model_module_version\": \"1.2.0\",\n",
              "      \"state\": {\n",
              "        \"border\": \"1px solid black\",\n",
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              "        \"padding\": \"10px\",\n",
              "        \"width\": \"800px\"\n",
              "      }\n",
              "    },\n",
              "    \"c9ec3726f905446a8c63a49f3877a518\": {\n",
              "      \"model_name\": \"FigureModel\",\n",
              "      \"model_module\": \"jupyter-gmaps\",\n",
              "      \"model_module_version\": \"0.9.0\",\n",
              "      \"state\": {\n",
              "        \"_dom_classes\": [],\n",
              "        \"_errors_box\": \"IPY_MODEL_272b2bb45df24f71a55ee41689f2127a\",\n",
              "        \"_map\": \"IPY_MODEL_1c6fe2de64644b8fbc0b6d5fa6377652\",\n",
              "        \"_toolbar\": \"IPY_MODEL_0c58f0c425404412a1cfcaea85785c88\",\n",
              "        \"layout\": \"IPY_MODEL_de0d791be87c4cf1aee36f1e002e840b\"\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "}\n",
              "</script>\n",
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"version_major\": 2, \"version_minor\": 0, \"model_id\": \"c9ec3726f905446a8c63a49f3877a518\"}\n",
              "</script>\n",
              "\n",
              "</body>\n",
              "</html>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yf8FYxcv4SuL",
        "outputId": "a94ddf8a-73a5-46d0-f071-8acaad6f5c25",
        "colab": {
          "resources": {
            "http://localhost:8080/jupyter-gmaps.js": {
              "data": "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",
              "ok": false,
              "headers": [
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                  "content-length",
                  "1449"
                ],
                [
                  "content-type",
                  "text/html; charset=utf-8"
                ]
              ],
              "status": 404,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 645
        }
      },
      "source": [
        "# 서울 지도 표시\n",
        "seoul=(37.56, 126.97)\n",
        "\n",
        "fig = gmaps.figure(layout=mylayout, center=seoul,zoom_level=12) # 지도 불러옴\n",
        "\n",
        "embed_minimal_html('map1d.html', views=[fig])\n",
        "display(HTML(filename='map1d.html'))"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<!DOCTYPE html>\n",
              "<html lang=\"en\">\n",
              "<head>\n",
              "    <meta charset=\"UTF-8\">\n",
              "    <title>IPyWidget export</title>\n",
              "</head>\n",
              "<body>\n",
              "\n",
              "\n",
              "<!-- Load require.js. Delete this if your page already loads require.js -->\n",
              "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\" integrity=\"sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=\" crossorigin=\"anonymous\"></script>\n",
              "<script src=\"https://unpkg.com/@jupyter-widgets/html-manager@^0.18.0/dist/embed-amd.js\" crossorigin=\"anonymous\"></script>\n",
              "\n",
              "<script type=\"application/vnd.jupyter.widget-state+json\">\n",
              "{\n",
              "  \"version_major\": 2,\n",
              "  \"version_minor\": 0,\n",
              "  \"state\": {\n",
              "    \"d5fe6e0772924dc79da9798cb6b63a0c\": {\n",
              "      \"model_name\": \"LayoutModel\",\n",
              "      \"model_module\": \"@jupyter-widgets/base\",\n",
              "      \"model_module_version\": \"1.2.0\",\n",
              "      \"state\": {\n",
              "        \"height\": \"100%\",\n",
              "        \"width\": \"100%\"\n",
              "      }\n",
              "    },\n",
              "    \"1c6fe2de64644b8fbc0b6d5fa6377652\": {\n",
              "      \"model_name\": \"PlainmapModel\",\n",
              "      \"model_module\": \"jupyter-gmaps\",\n",
              "      \"model_module_version\": \"0.9.0\",\n",
              "      \"state\": {\n",
              "        \"_dom_classes\": [],\n",
              "        \"configuration\": {\n",
              "          \"api_key\": null\n",
              "        },\n",
              "        \"data_bounds\": [\n",
              "          [\n",
              "            46.2,\n",
              "            6.1\n",
              "          ],\n",
              "          [\n",
              "            47.2,\n",
              "            7.1\n",
              "          ]\n",
              "        ],\n",
              "        \"initial_viewport\": {\n",
              "          \"type\": \"DATA_BOUNDS\"\n",
              "        },\n",
              "        \"layers\": [],\n",
              "        \"layout\": \"IPY_MODEL_d5fe6e0772924dc79da9798cb6b63a0c\"\n",
              "      }\n",
              "    },\n",
              "    \"394425cb70ae4f65bf05158ef53784ed\": {\n",
              "      \"model_name\": \"LayoutModel\",\n",
              "      \"model_module\": \"@jupyter-widgets/base\",\n",
              "      \"model_module_version\": \"1.2.0\",\n",
              "      \"state\": {}\n",
              "    },\n",
              "    \"0c58f0c425404412a1cfcaea85785c88\": {\n",
              "      \"model_name\": \"ToolbarModel\",\n",
              "      \"model_module\": \"jupyter-gmaps\",\n",
              "      \"model_module_version\": \"0.9.0\",\n",
              "      \"state\": {\n",
              "        \"_dom_classes\": [],\n",
              "        \"layer_controls\": [],\n",
              "        \"layout\": \"IPY_MODEL_394425cb70ae4f65bf05158ef53784ed\"\n",
              "      }\n",
              "    },\n",
              "    \"a95e842c56be46fab891b12b8d275a88\": {\n",
              "      \"model_name\": \"LayoutModel\",\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "K2cZG0uV-CXL",
        "outputId": "7843911d-57e3-41cc-b926-2423c9ab00da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# 두 데이터프레임 합침\n",
        "#df = pd.merge(df_19 , latlon)\n",
        "df.head(5)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gu</th>\n",
              "      <th>price_mean</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>강남구</td>\n",
              "      <td>1258</td>\n",
              "      <td>37.495985</td>\n",
              "      <td>127.066409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>강동구</td>\n",
              "      <td>712</td>\n",
              "      <td>37.549208</td>\n",
              "      <td>127.146482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>강북구</td>\n",
              "      <td>502</td>\n",
              "      <td>37.646995</td>\n",
              "      <td>127.014716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>강서구</td>\n",
              "      <td>618</td>\n",
              "      <td>37.565762</td>\n",
              "      <td>126.822656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>관악구</td>\n",
              "      <td>616</td>\n",
              "      <td>37.465399</td>\n",
              "      <td>126.943807</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    gu  price_mean        lat         lon\n",
              "0  강남구        1258  37.495985  127.066409\n",
              "1  강동구         712  37.549208  127.146482\n",
              "2  강북구         502  37.646995  127.014716\n",
              "3  강서구         618  37.565762  126.822656\n",
              "4  관악구         616  37.465399  126.943807"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
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        "outputId": "d6073242-aab3-4857-cd75-655bcf4aacfa",
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          "resources": {
            "http://localhost:8080/jupyter-gmaps.js": {
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          },
          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "# 각 구별 인구수에 따라 심볼의 크기를 조정함\n",
        "layers = []\n",
        "fig = gmaps.figure(layout=mylayout, center=seoul, zoom_level=12) # 지도 불러옴\n",
        "\n",
        "for i in range(len(df)):\n",
        "  size = int((df.iloc[i,1]/50))  \n",
        "  loc =  [(df.iloc[i,2],df.iloc[i,3])]\n",
        "\n",
        "  layer = gmaps.symbol_layer(loc, fill_color='red', stroke_color='red', scale=size)\n",
        "  layers.append(layer) # 심블레이어 생성후 리스트에 저장 \n",
        "\n",
        "for i in range(len(layers)):  # 생성한 레이어들을 지도에 추가\n",
        "  fig.add_layer(layers[i])\n",
        "\n",
        "embed_minimal_html('map2e.html', views=[fig])\n",
        "display(HTML(filename='map2e.html'))"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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