Dia3.ipynb

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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ahcamachod/inmersion-en-datos/blob/aula-3/INMERSION_DATOS_AULA_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bienvenido a nuestro primer encuentro de inmersión de datos."
      ],
      "metadata": {
        "id": "qGW8k7mWhP7O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "Y2BMxIBqhcl7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5567dfb-4052-41ec-a38a-6b936e29174f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "inmuebles = pd.read_csv('/content/drive/MyDrive/inmersion_datos/inmuebles_bogota.csv') \n",
        "inmuebles.head()"
      ],
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          "height": 424
        },
        "id": "S73Bs_z6j2QX",
        "outputId": "4b831b26-43ba-4a3d-ce12-4323cf30379b"
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      "outputs": [
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              "          Tipo                                 Descripcion  Habitaciones  \\\n",
              "0  Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "1         Casa                   Casa en venta en Castilla             4   \n",
              "2  Apartamento     Apartamento en venta en Chico Reservado             3   \n",
              "3  Apartamento             Apartamento en venta en Usaquén             3   \n",
              "4  Apartamento         Apartamento en venta en Bella Suiza             2   \n",
              "\n",
              "   Baños  Área              Barrio                                    UPZ  \\\n",
              "0      2    70  Zona Noroccidental     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "1      3   170            Castilla            KENNEDY: Castilla + Bavaria   \n",
              "2      3   144     Chico Reservado     CHAPINERO: Chicó Lago + El Refugio   \n",
              "3      2   154             Usaquén                                Usaquén   \n",
              "4      3   128         Bella Suiza  USAQUÉN: Country Club + Santa Bárbara   \n",
              "\n",
              "             Valor  \n",
              "0    $ 360.000.000  \n",
              "1    $ 670.000.000  \n",
              "2  $ 1.120.000.000  \n",
              "3    $ 890.000.000  \n",
              "4    $ 970.000.000  "
            ],
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              "      <th>0</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>70</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 360.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Castilla</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>170</td>\n",
              "      <td>Castilla</td>\n",
              "      <td>KENNEDY: Castilla + Bavaria</td>\n",
              "      <td>$ 670.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>144</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 1.120.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>154</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 890.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Bella Suiza</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>128</td>\n",
              "      <td>Bella Suiza</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 970.000.000</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.shape"
      ],
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        "colab": {
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        },
        "id": "qkcJjAQrmyfN",
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      "outputs": [
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          "data": {
            "text/plain": [
              "(9520, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.columns"
      ],
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        "colab": {
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        },
        "id": "K-4LMluEnha_",
        "outputId": "41397e9d-34f1-4146-eead-7aa085322789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Tipo', 'Descripcion', 'Habitaciones', 'Baños', 'Área', 'Barrio', 'UPZ',\n",
              "       'Valor'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columnas = {'Baños':'Banos','Área':'Area'}\n",
        "inmuebles = inmuebles.rename(columns=columnas)\n",
        "inmuebles.sample(10)"
      ],
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          "data": {
            "text/plain": [
              "             Tipo                                Descripcion  Habitaciones  \\\n",
              "582          Casa                  Casa en venta en Engativa             3   \n",
              "341   Apartamento  Apartamento en venta en Chapinero Central             3   \n",
              "5582  Apartamento          Apartamento en venta en Chapinero             3   \n",
              "6856  Apartamento      Apartamento en venta en Santa Barbara             3   \n",
              "3482  Apartamento            Apartamento en venta en Venecia             3   \n",
              "6649  Apartamento      Apartamento en venta en Santa Barbara             2   \n",
              "4564         Casa                  Casa en venta en Engativa             4   \n",
              "5686  Apartamento               Apartamento en venta en Niza             3   \n",
              "8853  Apartamento      Apartamento en venta en Santa Barbara             1   \n",
              "6657  Apartamento         Apartamento en venta en Cantagallo             3   \n",
              "\n",
              "      Banos  Area             Barrio                                    UPZ  \\\n",
              "582       3    73           Engativa                               Engativá   \n",
              "341       2    59  Chapinero Central              Localidad Chapinero resto   \n",
              "5582      5   342          Chapinero     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "6856      5   171      Santa Barbara  USAQUÉN: Country Club + Santa Bárbara   \n",
              "3482      2    56            Venecia                                Venecia   \n",
              "6649      3    91      Santa Barbara  USAQUÉN: Country Club + Santa Bárbara   \n",
              "4564      2   177           Engativa                               Engativá   \n",
              "5686      2   132               Niza                                   Niza   \n",
              "8853      2    66      Santa Barbara  USAQUÉN: Country Club + Santa Bárbara   \n",
              "6657      2    68         Cantagallo                               Britalia   \n",
              "\n",
              "                Valor  \n",
              "582     $ 360.000.000  \n",
              "341     $ 180.000.000  \n",
              "5582  $ 3.500.000.000  \n",
              "6856    $ 820.000.000  \n",
              "3482    $ 210.000.000  \n",
              "6649    $ 700.000.000  \n",
              "4564    $ 639.000.000  \n",
              "5686    $ 490.000.000  \n",
              "8853    $ 453.500.000  \n",
              "6657    $ 455.000.000  "
            ],
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              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Barrio</th>\n",
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              "      <th>582</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Engativa</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>73</td>\n",
              "      <td>Engativa</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>$ 360.000.000</td>\n",
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              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chapinero Central</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>59</td>\n",
              "      <td>Chapinero Central</td>\n",
              "      <td>Localidad Chapinero resto</td>\n",
              "      <td>$ 180.000.000</td>\n",
              "    </tr>\n",
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              "      <th>5582</th>\n",
              "      <td>Apartamento</td>\n",
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              "      <td>Chapinero</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 3.500.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6856</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Santa Barbara</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>171</td>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 820.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3482</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Venecia</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>56</td>\n",
              "      <td>Venecia</td>\n",
              "      <td>Venecia</td>\n",
              "      <td>$ 210.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6649</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Santa Barbara</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>91</td>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 700.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4564</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Engativa</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>177</td>\n",
              "      <td>Engativa</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>$ 639.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5686</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Niza</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>132</td>\n",
              "      <td>Niza</td>\n",
              "      <td>Niza</td>\n",
              "      <td>$ 490.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8853</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Santa Barbara</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>66</td>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 453.500.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6657</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Cantagallo</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>68</td>\n",
              "      <td>Cantagallo</td>\n",
              "      <td>Britalia</td>\n",
              "      <td>$ 455.000.000</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9520 entries, 0 to 9519\n",
            "Data columns (total 8 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Tipo          9520 non-null   object\n",
            " 1   Descripcion   9520 non-null   object\n",
            " 2   Habitaciones  9520 non-null   int64 \n",
            " 3   Banos         9520 non-null   int64 \n",
            " 4   Area          9520 non-null   int64 \n",
            " 5   Barrio        9520 non-null   object\n",
            " 6   UPZ           9478 non-null   object\n",
            " 7   Valor         9520 non-null   object\n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 595.1+ KB\n"
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              "Tipo                                Apartamento\n",
              "Descripcion     Apartamento en venta en Kennedy\n",
              "Habitaciones                                  2\n",
              "Banos                                         1\n",
              "Area                                         42\n",
              "Barrio                                  Kennedy\n",
              "UPZ                             Kennedy Central\n",
              "Valor                             $ 116.100.000\n",
              "Name: 300, dtype: object"
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              "            Tipo                          Descripcion  Habitaciones  Banos  \\\n",
              "300  Apartamento      Apartamento en venta en Kennedy             2      1   \n",
              "301  Apartamento  Apartamento en venta en Chico Norte             2      2   \n",
              "302  Apartamento      Apartamento en venta en Kennedy             3      2   \n",
              "303  Apartamento      Apartamento en venta en Kennedy             3      2   \n",
              "304  Apartamento     Apartamento en venta en Cedritos             4      4   \n",
              "\n",
              "     Area       Barrio                                 UPZ          Valor  \n",
              "300    42      Kennedy                     Kennedy Central  $ 116.100.000  \n",
              "301    95  Chico Norte  CHAPINERO: Chicó Lago + El Refugio  $ 980.000.000  \n",
              "302    54      Kennedy                     Kennedy Central  $ 237.000.000  \n",
              "303    67      Kennedy                     Kennedy Central  $ 320.000.000  \n",
              "304   132     Cedritos                             Usaquén  $ 530.000.000  "
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              "Index(['Tipo', 'Descripcion', 'Habitaciones', 'Banos', 'Area', 'Barrio', 'UPZ',\n",
              "       'Valor'],\n",
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              "             Tipo                                 Descripcion  Habitaciones  \\\n",
              "7284  Apartamento                Apartamento en venta en Bosa             2   \n",
              "8347  Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "4854  Apartamento                Apartamento en venta en Suba             3   \n",
              "5992  Apartamento     Apartamento en venta en Chico Reservado             3   \n",
              "7994  Apartamento       Apartamento en venta en Santa Barbara             2   \n",
              "...           ...                                         ...           ...   \n",
              "9216  Apartamento                Apartamento en venta en Niza             2   \n",
              "6595  Apartamento            Apartamento en venta en Cedritos             2   \n",
              "4412  Apartamento                Apartamento en venta en Suba             3   \n",
              "5623         Casa                 Casa en venta en Bogotá D.C             4   \n",
              "1795  Apartamento            Apartamento en venta en Hayuelos             3   \n",
              "\n",
              "      Banos  Area              Barrio                                    UPZ  \\\n",
              "7284      1    36                Bosa                           Bosa Central   \n",
              "8347      2    73  Zona Noroccidental     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "4854      2    58                Suba                                   Suba   \n",
              "5992      3   160     Chico Reservado     CHAPINERO: Chicó Lago + El Refugio   \n",
              "7994      2   130       Santa Barbara  USAQUÉN: Country Club + Santa Bárbara   \n",
              "...     ...   ...                 ...                                    ...   \n",
              "9216      1    50                Niza                                   Niza   \n",
              "6595      2    69            Cedritos                                Usaquén   \n",
              "4412      4   124                Suba                                   Suba   \n",
              "5623      5   566          Bogotá D.C              Ciudad Salitre Occidental   \n",
              "1795      2    77            Hayuelos                                Modelia   \n",
              "\n",
              "                Valor  \n",
              "7284     $ 98.000.000  \n",
              "8347    $ 290.000.000  \n",
              "4854    $ 260.000.000  \n",
              "5992    $ 980.000.000  \n",
              "7994    $ 600.000.000  \n",
              "...               ...  \n",
              "9216    $ 170.000.000  \n",
              "6595    $ 330.000.000  \n",
              "4412    $ 800.000.000  \n",
              "5623  $ 2.800.000.000  \n",
              "1795    $ 360.000.000  \n",
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              "[100 rows x 8 columns]"
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              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>Bosa</td>\n",
              "      <td>Bosa Central</td>\n",
              "      <td>$ 98.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8347</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>73</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 290.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4854</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Suba</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>58</td>\n",
              "      <td>Suba</td>\n",
              "      <td>Suba</td>\n",
              "      <td>$ 260.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5992</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>160</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 980.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7994</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Santa Barbara</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 600.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9216</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Niza</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>Niza</td>\n",
              "      <td>Niza</td>\n",
              "      <td>$ 170.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6595</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Cedritos</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>69</td>\n",
              "      <td>Cedritos</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 330.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4412</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Suba</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>124</td>\n",
              "      <td>Suba</td>\n",
              "      <td>Suba</td>\n",
              "      <td>$ 800.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5623</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Bogotá D.C</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>566</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>Ciudad Salitre Occidental</td>\n",
              "      <td>$ 2.800.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1795</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Hayuelos</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>77</td>\n",
              "      <td>Hayuelos</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>$ 360.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2bad7e0-5684-480d-a14f-03a757c5e784')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(inmuebles.Barrio == 'Chico Reservado')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YY-zAytFuBsA",
        "outputId": "8c08827a-e7dd-4f56-a74e-5b6d0c6354f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2        True\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "9515    False\n",
              "9516    False\n",
              "9517    False\n",
              "9518    False\n",
              "9519    False\n",
              "Name: Barrio, Length: 9520, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum((inmuebles.Barrio == 'Chico Reservado'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OJg8M1TuwcZ",
        "outputId": "4e4becb5-55bb-44a7-968a-e4d33de6bf9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "225"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles_chico = (inmuebles.Barrio == 'Chico Reservado')\n",
        "type(inmuebles_chico)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtT_ZLvGu_CP",
        "outputId": "e1a40547-8f08-4d93-9514-c1c21cec437c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chico_reservado = inmuebles[inmuebles_chico]\n",
        "chico_reservado"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "mQJesAi_vLbs",
        "outputId": "c498cb6e-df9d-42ae-f07d-2fc194d62433"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Tipo                              Descripcion  Habitaciones  \\\n",
              "2     Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "39    Apartamento  Apartamento en venta en Chico Reservado             1   \n",
              "72    Apartamento  Apartamento en venta en Chico Reservado             1   \n",
              "126   Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "143   Apartamento  Apartamento en venta en Chico Reservado             2   \n",
              "...           ...                                      ...           ...   \n",
              "9299  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "9335  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "9341  Apartamento  Apartamento en venta en Chico Reservado             2   \n",
              "9472  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "9506  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "\n",
              "      Banos  Area           Barrio                                 UPZ  \\\n",
              "2         3   144  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "39        1    64  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "72        1    64  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "126       4   150  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "143       4   104  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "...     ...   ...              ...                                 ...   \n",
              "9299      3   148  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9335      3   176  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9341      3   118  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9472      1    49  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9506      4   390  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "\n",
              "                Valor  \n",
              "2     $ 1.120.000.000  \n",
              "39      $ 495.000.000  \n",
              "72      $ 495.000.000  \n",
              "126   $ 1.300.000.000  \n",
              "143   $ 1.100.000.000  \n",
              "...               ...  \n",
              "9299    $ 780.000.000  \n",
              "9335    $ 850.000.000  \n",
              "9341    $ 700.000.000  \n",
              "9472    $ 200.000.000  \n",
              "9506  $ 3.200.000.000  \n",
              "\n",
              "[225 rows x 8 columns]"
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              "\n",
              "  <div id=\"df-5c1c1984-adaa-4149-81ba-c70e64f37eae\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
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              "      <th>UPZ</th>\n",
              "      <th>Valor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>144</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 1.120.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>64</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 495.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>64</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 495.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>150</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 1.300.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>104</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 1.100.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9299</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>148</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 780.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9335</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>176</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 850.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9341</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>118</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 700.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9472</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 200.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9506</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>390</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 3.200.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>225 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c1c1984-adaa-4149-81ba-c70e64f37eae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5c1c1984-adaa-4149-81ba-c70e64f37eae button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5c1c1984-adaa-4149-81ba-c70e64f37eae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chico_reservado.Area.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3IYReywvf26",
        "outputId": "db4a79a6-9ba4-44f6-824f-7faf39b6115f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "142.78666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.Area.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7V5_WzXxCDl",
        "outputId": "9c69cd96-1c1e-4dfa-f3b2-05b0d8c192b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "146.66470588235293"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(inmuebles.Barrio.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54SeSdSFxZ9v",
        "outputId": "c0c7c971-a08e-4b23-e3fe-e7abdfa8be42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.Barrio.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJz7RkSBxpVf",
        "outputId": "a57555c8-8cf6-48ff-807e-4cf9bd46c1e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Usaquén               1105\n",
              "Zona Noroccidental     877\n",
              "Bosa                   589\n",
              "Kennedy                589\n",
              "Cedritos               554\n",
              "                      ... \n",
              "Colinas De Suba          1\n",
              "La Estanzuela            1\n",
              "Roma                     1\n",
              "Alameda                  1\n",
              "Prado Veraniego          1\n",
              "Name: Barrio, Length: 149, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(inmuebles.UPZ.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN0cDITxxtvH",
        "outputId": "e32ef258-5506-4e7f-f9ad-b57535ccad2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles_barrio = inmuebles.Barrio.value_counts()\n",
        "inmuebles_barrio.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "BcKfcc3nx9fg",
        "outputId": "b2c6f105-715e-4843-bdc8-0ab0f111668d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe758b604f0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles_barrio.head(10).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "EXzn-YDLybRx",
        "outputId": "060c6792-90b6-4bee-b637-5dec6614b6f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7590b21f0>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Desafío**\n",
        "\n",
        "\n",
        "1. Promedio de área de todos los inmuebles en los barrios en el dataset. El top 10.\n",
        "\n",
        "2. Consultar otros datos estadísticos, conteo, mediana, valores mínimo y máximo."
      ],
      "metadata": {
        "id": "owP_SeWgzaYR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Aula 2**"
      ],
      "metadata": {
        "id": "YFNuHdUZJJtL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "HuNvVUa2ypG4",
        "outputId": "5be50e45-6322-4df7-cfdd-07ad8caf5eb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Tipo                                 Descripcion  Habitaciones  \\\n",
              "531   Apartamento                Apartamento en venta en Bosa             3   \n",
              "1533  Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "2186  Apartamento            Apartamento en venta en El Batán             2   \n",
              "7386  Apartamento             Apartamento en venta en Usaquén             2   \n",
              "7159  Apartamento      Apartamento en venta en Antonio Nariño             2   \n",
              "\n",
              "      Banos  Area              Barrio                                 UPZ  \\\n",
              "531       1    53                Bosa                        Bosa Central   \n",
              "1533      3    98  Zona Noroccidental  CHAPINERO: Pardo Rubio + Chapinero   \n",
              "2186      2    91            El Batán                         La Alhambra   \n",
              "7386      1    45             Usaquén                             Usaquén   \n",
              "7159      1    39      Antonio Nariño                            Restrepo   \n",
              "\n",
              "              Valor  \n",
              "531   $ 142.000.000  \n",
              "1533  $ 601.000.000  \n",
              "2186  $ 465.000.000  \n",
              "7386  $ 185.869.000  \n",
              "7159  $ 180.000.000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4f567a2-0da6-4e48-b091-f1e62f9b3da1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Barrio</th>\n",
              "      <th>UPZ</th>\n",
              "      <th>Valor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>531</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Bosa</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>Bosa</td>\n",
              "      <td>Bosa Central</td>\n",
              "      <td>$ 142.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1533</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>98</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 601.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2186</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en El Batán</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>91</td>\n",
              "      <td>El Batán</td>\n",
              "      <td>La Alhambra</td>\n",
              "      <td>$ 465.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7386</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 185.869.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7159</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Antonio Nariño</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>Antonio Nariño</td>\n",
              "      <td>Restrepo</td>\n",
              "      <td>$ 180.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4f567a2-0da6-4e48-b091-f1e62f9b3da1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c4f567a2-0da6-4e48-b091-f1e62f9b3da1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c4f567a2-0da6-4e48-b091-f1e62f9b3da1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jt_0FzVFz30-",
        "outputId": "44655bfd-2974-44bd-df85-52007cd743af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9520 entries, 0 to 9519\n",
            "Data columns (total 8 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Tipo          9520 non-null   object\n",
            " 1   Descripcion   9520 non-null   object\n",
            " 2   Habitaciones  9520 non-null   int64 \n",
            " 3   Banos         9520 non-null   int64 \n",
            " 4   Area          9520 non-null   int64 \n",
            " 5   Barrio        9520 non-null   object\n",
            " 6   UPZ           9478 non-null   object\n",
            " 7   Valor         9520 non-null   object\n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 595.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(inmuebles.Valor[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nz5JDL5LLh5A",
        "outputId": "7f57ad13-0735-4dfb-b929-463ed9b5fbbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.Valor[0]+inmuebles.Valor[1]+inmuebles.Valor[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVgx7E33L9IJ",
        "outputId": "375a0a4d-54c5-4b50-fdf8-7d1495df5899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      $ 360.000.000$ 670.000.000$ 360.000.000\n",
              "1      $ 360.000.000$ 670.000.000$ 670.000.000\n",
              "2    $ 360.000.000$ 670.000.000$ 1.120.000.000\n",
              "3      $ 360.000.000$ 670.000.000$ 890.000.000\n",
              "4      $ 360.000.000$ 670.000.000$ 970.000.000\n",
              "Name: Valor, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.Valor[0].split()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRDkYvkjMWtA",
        "outputId": "5a55ac95-5629-4d8d-af3c-a2cd2d05a4ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['$', '360.000.000']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valor = inmuebles.Valor.str.split(expand=True)\n",
        "inmuebles['Moneda'] = valor[0]\n",
        "inmuebles['Precio'] = valor[1]\n",
        "inmuebles.sample(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "JL4ilS3UMzlq",
        "outputId": "2a5ea16d-3c07-4469-8492-1451be0d276e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Tipo                                 Descripcion  Habitaciones  \\\n",
              "5850  Apartamento            Apartamento en venta en Engativa             3   \n",
              "48    Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "761   Apartamento            Apartamento en venta en Cedritos             3   \n",
              "\n",
              "      Banos  Area              Barrio                                 UPZ  \\\n",
              "5850      2    70            Engativa                            Engativá   \n",
              "48        2    78  Zona Noroccidental  CHAPINERO: Pardo Rubio + Chapinero   \n",
              "761       2    76            Cedritos                             Usaquén   \n",
              "\n",
              "              Valor Moneda       Precio  \n",
              "5850  $ 226.000.000      $  226.000.000  \n",
              "48    $ 259.000.000      $  259.000.000  \n",
              "761   $ 350.000.000      $  350.000.000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bac75c3a-d868-4303-8aed-0e5605592b2a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Barrio</th>\n",
              "      <th>UPZ</th>\n",
              "      <th>Valor</th>\n",
              "      <th>Moneda</th>\n",
              "      <th>Precio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5850</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Engativa</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>70</td>\n",
              "      <td>Engativa</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>$ 226.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>226.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>78</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 259.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>259.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>761</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Cedritos</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>76</td>\n",
              "      <td>Cedritos</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 350.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>350.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bac75c3a-d868-4303-8aed-0e5605592b2a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bac75c3a-d868-4303-8aed-0e5605592b2a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bac75c3a-d868-4303-8aed-0e5605592b2a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0QYDqaPNLhq",
        "outputId": "f0ffb6f1-1f2c-4a6e-d4ad-90b77a42cb38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9520 entries, 0 to 9519\n",
            "Data columns (total 10 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Tipo          9520 non-null   object\n",
            " 1   Descripcion   9520 non-null   object\n",
            " 2   Habitaciones  9520 non-null   int64 \n",
            " 3   Banos         9520 non-null   int64 \n",
            " 4   Area          9520 non-null   int64 \n",
            " 5   Barrio        9520 non-null   object\n",
            " 6   UPZ           9478 non-null   object\n",
            " 7   Valor         9520 non-null   object\n",
            " 8   Moneda        9520 non-null   object\n",
            " 9   Precio        9520 non-null   object\n",
            "dtypes: int64(3), object(7)\n",
            "memory usage: 743.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles['Precio'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "qwRAIGd9OCYq",
        "outputId": "9aa9a2de-4fd1-4145-cb8f-a0fa48a016aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          Precio                  Barrio\n",
              "0      360000000      Zona Noroccidental\n",
              "1      670000000                Castilla\n",
              "2     1120000000         Chico Reservado\n",
              "3      890000000                 Usaquén\n",
              "4      970000000             Bella Suiza\n",
              "...          ...                     ...\n",
              "9515   290000000                 Kennedy\n",
              "9516   670000000  Parque Central Bavaria\n",
              "9517   900000000                Restrepo\n",
              "9518   335000000                Engativa\n",
              "9519   144990000                 Tintala\n",
              "\n",
              "[9520 rows x 2 columns]"
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              "      <td>890000000</td>\n",
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              "      <th>9518</th>\n",
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              "      <td>Engativa</td>\n",
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              "    <tr>\n",
              "      <th>9519</th>\n",
              "      <td>144990000</td>\n",
              "      <td>Tintala</td>\n",
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              "<p>9520 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "inmuebles.info()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "ZP57N8bXO9Lm",
        "outputId": "7127ee91-ca38-442e-9430-8aa9dd59b206"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9520 entries, 0 to 9519\n",
            "Data columns (total 10 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Tipo          9520 non-null   object\n",
            " 1   Descripcion   9520 non-null   object\n",
            " 2   Habitaciones  9520 non-null   int64 \n",
            " 3   Banos         9520 non-null   int64 \n",
            " 4   Area          9520 non-null   int64 \n",
            " 5   Barrio        9520 non-null   object\n",
            " 6   UPZ           9478 non-null   object\n",
            " 7   Valor         9520 non-null   object\n",
            " 8   Moneda        9520 non-null   object\n",
            " 9   Precio        9520 non-null   object\n",
            "dtypes: int64(3), object(7)\n",
            "memory usage: 743.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles['Precio_Millon'] = inmuebles.Precio.astype('float')/1000000\n",
        "inmuebles.info()"
      ],
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        "colab": {
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        "id": "0JwDQyb6POMq",
        "outputId": "051f1d7f-fc6b-4e2b-fd23-c2aace7491f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9520 entries, 0 to 9519\n",
            "Data columns (total 11 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Tipo           9520 non-null   object \n",
            " 1   Descripcion    9520 non-null   object \n",
            " 2   Habitaciones   9520 non-null   int64  \n",
            " 3   Banos          9520 non-null   int64  \n",
            " 4   Area           9520 non-null   int64  \n",
            " 5   Barrio         9520 non-null   object \n",
            " 6   UPZ            9478 non-null   object \n",
            " 7   Valor          9520 non-null   object \n",
            " 8   Moneda         9520 non-null   object \n",
            " 9   Precio         9520 non-null   object \n",
            " 10  Precio_Millon  9520 non-null   float64\n",
            "dtypes: float64(1), int64(3), object(7)\n",
            "memory usage: 818.2+ KB\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "inmuebles.describe()"
      ],
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        "id": "2OFAMdrAPoNq",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Habitaciones        Banos           Area  Precio_Millon\n",
              "count   9520.000000  9520.000000    9520.000000    9520.000000\n",
              "mean       3.072479     2.447899     146.664706     602.062354\n",
              "std        2.049856     1.255314    1731.379622     704.602062\n",
              "min        1.000000     0.000000       2.000000      60.000000\n",
              "25%        2.000000     2.000000      57.000000     250.000000\n",
              "50%        3.000000     2.000000      80.000000     409.182500\n",
              "75%        3.000000     3.000000     135.000000     680.000000\n",
              "max      110.000000     9.000000  166243.000000   16000.000000"
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              "      <th>mean</th>\n",
              "      <td>3.072479</td>\n",
              "      <td>2.447899</td>\n",
              "      <td>146.664706</td>\n",
              "      <td>602.062354</td>\n",
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              "      <td>1731.379622</td>\n",
              "      <td>704.602062</td>\n",
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              "      <td>60.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "      <td>9.000000</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          "metadata": {},
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      "source": [
        "pd.set_option('display.precision',2)\n",
        "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
        "inmuebles.describe()"
      ],
      "metadata": {
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        "id": "fA1hAJW8QWZ7",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Habitaciones   Banos      Area  Precio_Millon\n",
              "count       9520.00 9520.00   9520.00        9520.00\n",
              "mean           3.07    2.45    146.66         602.06\n",
              "std            2.05    1.26   1731.38         704.60\n",
              "min            1.00    0.00      2.00          60.00\n",
              "25%            2.00    2.00     57.00         250.00\n",
              "50%            3.00    2.00     80.00         409.18\n",
              "75%            3.00    3.00    135.00         680.00\n",
              "max          110.00    9.00 166243.00       16000.00"
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              "      <th>count</th>\n",
              "      <td>9520.00</td>\n",
              "      <td>9520.00</td>\n",
              "      <td>9520.00</td>\n",
              "      <td>9520.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.07</td>\n",
              "      <td>2.45</td>\n",
              "      <td>146.66</td>\n",
              "      <td>602.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.05</td>\n",
              "      <td>1.26</td>\n",
              "      <td>1731.38</td>\n",
              "      <td>704.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>60.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>57.00</td>\n",
              "      <td>250.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>80.00</td>\n",
              "      <td>409.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>135.00</td>\n",
              "      <td>680.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>110.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>166243.00</td>\n",
              "      <td>16000.00</td>\n",
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        "inmuebles.loc[inmuebles.Habitaciones == 110]"
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              "     Tipo                Descripcion  Habitaciones  Banos  Area    Barrio  \\\n",
              "897  Casa  Casa en venta en La Uribe           110      2   110  La Uribe   \n",
              "\n",
              "         UPZ          Valor Moneda     Precio  Precio_Millon  \n",
              "897  Usaquén  $ 480.000.000      $  480000000         480.00  "
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      "cell_type": "code",
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        "inmuebles.loc[inmuebles.Area == 2]"
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            "text/plain": [
              "             Tipo                            Descripcion  Habitaciones  Banos  \\\n",
              "8401  Apartamento  Apartamento en venta en San Cristobal             3      1   \n",
              "\n",
              "      Area         Barrio      UPZ          Valor Moneda     Precio  \\\n",
              "8401     2  San Cristobal  Sosiego  $ 180.000.000      $  180000000   \n",
              "\n",
              "      Precio_Millon  \n",
              "8401         180.00  "
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    {
      "cell_type": "code",
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        "inmuebles['Precio_Millon'].plot.hist(bins=10)"
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            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7566719d0>"
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        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "grafica = sns.histplot(data=inmuebles, x='Precio_Millon', kde=True, hue='Tipo')\n",
        "grafica.set_title('Distribución de Valores de los inmuebles en Bogotá')\n",
        "plt.xlim((50,1000))\n",
        "plt.savefig('/content/drive/MyDrive/inmersion_datos/valor_inmuebles.png',format='png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "yhdim-1uSx2c",
        "outputId": "00df0c40-2bc9-460a-d4fd-d0089fab4b5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Desafíos**\n",
        "\n",
        "1. Estudiar mejor el histograma de valores, seleccionar 3 tipos de inmuebles (Refinar el gráfico: Títulos, aumentar el tamaño de labels, colores, conclusión de la información)\n",
        "\n",
        "2. Precio del m2 por barrio y hacer el gráfico más adecuado para esta nueva variable."
      ],
      "metadata": {
        "id": "q4dm6mfCYB-h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Aula 3**"
      ],
      "metadata": {
        "id": "YZ7C4wFgdG5m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles['Valor_m2_Millon'] = inmuebles['Precio_Millon']/inmuebles['Area'] \n",
        "inmuebles.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "l7IujydcVLW0",
        "outputId": "e4a79fe8-4418-45ad-ae4a-768d3774a78f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Tipo                                 Descripcion  Habitaciones  \\\n",
              "0  Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "1         Casa                   Casa en venta en Castilla             4   \n",
              "2  Apartamento     Apartamento en venta en Chico Reservado             3   \n",
              "\n",
              "   Banos  Area              Barrio                                 UPZ  \\\n",
              "0      2    70  Zona Noroccidental  CHAPINERO: Pardo Rubio + Chapinero   \n",
              "1      3   170            Castilla         KENNEDY: Castilla + Bavaria   \n",
              "2      3   144     Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "\n",
              "             Valor Moneda      Precio  Precio_Millon  Valor_m2_Millon  \n",
              "0    $ 360.000.000      $   360000000         360.00             5.14  \n",
              "1    $ 670.000.000      $   670000000         670.00             3.94  \n",
              "2  $ 1.120.000.000      $  1120000000        1120.00             7.78  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-187e3af9-0c2f-4cb2-abb4-4135a95be938\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Barrio</th>\n",
              "      <th>UPZ</th>\n",
              "      <th>Valor</th>\n",
              "      <th>Moneda</th>\n",
              "      <th>Precio</th>\n",
              "      <th>Precio_Millon</th>\n",
              "      <th>Valor_m2_Millon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>70</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 360.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>360000000</td>\n",
              "      <td>360.00</td>\n",
              "      <td>5.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Castilla</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>170</td>\n",
              "      <td>Castilla</td>\n",
              "      <td>KENNEDY: Castilla + Bavaria</td>\n",
              "      <td>$ 670.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>670000000</td>\n",
              "      <td>670.00</td>\n",
              "      <td>3.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>144</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 1.120.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>1120000000</td>\n",
              "      <td>1120.00</td>\n",
              "      <td>7.78</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "inmuebles.groupby('Barrio').mean()"
      ],
      "metadata": {
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        },
        "id": "R4IO6SGpgmPB",
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "                    Habitaciones  Banos   Area  Precio_Millon  Valor_m2_Millon\n",
              "Barrio                                                                        \n",
              "Alameda                     3.00   2.00  97.00         580.00             5.98\n",
              "Alhambra                    3.00   2.94 190.41         752.35             4.33\n",
              "Andes Norte                 3.00   2.86 124.86         709.57             6.05\n",
              "Antonio Nariño              4.31   2.50 163.06         459.22             3.53\n",
              "Barrancas                   2.87   2.13  70.20         314.13             4.42\n",
              "...                          ...    ...    ...            ...              ...\n",
              "Villa Maria                 4.69   3.69 207.15         402.85             2.04\n",
              "Villas De Granada           3.45   2.32  86.59         282.30             3.17\n",
              "Villemar                    2.94   2.06  76.89         303.83             3.99\n",
              "Zona Franca                 2.00   1.00  54.00         160.00             2.96\n",
              "Zona Noroccidental          3.17   2.51 135.79         620.81             4.62\n",
              "\n",
              "[149 rows x 5 columns]"
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              "      <td>752.35</td>\n",
              "      <td>4.33</td>\n",
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              "      <th>Andes Norte</th>\n",
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              "      <td>2.86</td>\n",
              "      <td>124.86</td>\n",
              "      <td>709.57</td>\n",
              "      <td>6.05</td>\n",
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              "      <th>Antonio Nariño</th>\n",
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              "      <td>459.22</td>\n",
              "      <td>3.53</td>\n",
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              "      <th>Barrancas</th>\n",
              "      <td>2.87</td>\n",
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              "    <tr>\n",
              "      <th>Villa Maria</th>\n",
              "      <td>4.69</td>\n",
              "      <td>3.69</td>\n",
              "      <td>207.15</td>\n",
              "      <td>402.85</td>\n",
              "      <td>2.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villas De Granada</th>\n",
              "      <td>3.45</td>\n",
              "      <td>2.32</td>\n",
              "      <td>86.59</td>\n",
              "      <td>282.30</td>\n",
              "      <td>3.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villemar</th>\n",
              "      <td>2.94</td>\n",
              "      <td>2.06</td>\n",
              "      <td>76.89</td>\n",
              "      <td>303.83</td>\n",
              "      <td>3.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zona Franca</th>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>54.00</td>\n",
              "      <td>160.00</td>\n",
              "      <td>2.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zona Noroccidental</th>\n",
              "      <td>3.17</td>\n",
              "      <td>2.51</td>\n",
              "      <td>135.79</td>\n",
              "      <td>620.81</td>\n",
              "      <td>4.62</td>\n",
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              "  </tbody>\n",
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              "<p>149 rows × 5 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "datos_barrio = inmuebles.groupby('Barrio').sum()\n",
        "datos_barrio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "6tUADDHag6M-",
        "outputId": "23fa9755-e65f-4a62-d12a-1453f7810b55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Habitaciones  Banos    Area  Precio_Millon  \\\n",
              "Barrio                                                           \n",
              "Alameda                        3      2      97         580.00   \n",
              "Alhambra                      51     50    3237       12790.00   \n",
              "Andes Norte                   21     20     874        4967.00   \n",
              "Antonio Nariño               207    120    7827       22042.65   \n",
              "Barrancas                     43     32    1053        4712.00   \n",
              "...                          ...    ...     ...            ...   \n",
              "Villa Maria                   61     48    2693        5237.00   \n",
              "Villas De Granada             76     51    1905        6210.62   \n",
              "Villemar                      53     37    1384        5469.00   \n",
              "Zona Franca                    2      1      54         160.00   \n",
              "Zona Noroccidental          2778   2204  119084      544453.06   \n",
              "\n",
              "                    Valor_m2_Millon  \n",
              "Barrio                               \n",
              "Alameda                        5.98  \n",
              "Alhambra                      73.56  \n",
              "Andes Norte                   42.35  \n",
              "Antonio Nariño               169.47  \n",
              "Barrancas                     66.24  \n",
              "...                             ...  \n",
              "Villa Maria                   26.58  \n",
              "Villas De Granada             69.71  \n",
              "Villemar                      71.81  \n",
              "Zona Franca                    2.96  \n",
              "Zona Noroccidental          4049.16  \n",
              "\n",
              "[149 rows x 5 columns]"
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              "      <th>Alameda</th>\n",
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              "      <td>580.00</td>\n",
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              "      <td>20</td>\n",
              "      <td>874</td>\n",
              "      <td>4967.00</td>\n",
              "      <td>42.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antonio Nariño</th>\n",
              "      <td>207</td>\n",
              "      <td>120</td>\n",
              "      <td>7827</td>\n",
              "      <td>22042.65</td>\n",
              "      <td>169.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Barrancas</th>\n",
              "      <td>43</td>\n",
              "      <td>32</td>\n",
              "      <td>1053</td>\n",
              "      <td>4712.00</td>\n",
              "      <td>66.24</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>Villa Maria</th>\n",
              "      <td>61</td>\n",
              "      <td>48</td>\n",
              "      <td>2693</td>\n",
              "      <td>5237.00</td>\n",
              "      <td>26.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villas De Granada</th>\n",
              "      <td>76</td>\n",
              "      <td>51</td>\n",
              "      <td>1905</td>\n",
              "      <td>6210.62</td>\n",
              "      <td>69.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villemar</th>\n",
              "      <td>53</td>\n",
              "      <td>37</td>\n",
              "      <td>1384</td>\n",
              "      <td>5469.00</td>\n",
              "      <td>71.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zona Franca</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>54</td>\n",
              "      <td>160.00</td>\n",
              "      <td>2.96</td>\n",
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              "    <tr>\n",
              "      <th>Zona Noroccidental</th>\n",
              "      <td>2778</td>\n",
              "      <td>2204</td>\n",
              "      <td>119084</td>\n",
              "      <td>544453.06</td>\n",
              "      <td>4049.16</td>\n",
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              "                                                     [key], {});\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "datos_barrio['Valor_m2_Barrio'] = datos_barrio['Precio_Millon']/datos_barrio['Area']\n",
        "datos_barrio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "EXM5tc8YhvfA",
        "outputId": "6d0e463d-b4fc-49e5-f6f1-7d135b12940c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Habitaciones  Banos    Area  Precio_Millon  \\\n",
              "Barrio                                                           \n",
              "Alameda                        3      2      97         580.00   \n",
              "Alhambra                      51     50    3237       12790.00   \n",
              "Andes Norte                   21     20     874        4967.00   \n",
              "Antonio Nariño               207    120    7827       22042.65   \n",
              "Barrancas                     43     32    1053        4712.00   \n",
              "...                          ...    ...     ...            ...   \n",
              "Villa Maria                   61     48    2693        5237.00   \n",
              "Villas De Granada             76     51    1905        6210.62   \n",
              "Villemar                      53     37    1384        5469.00   \n",
              "Zona Franca                    2      1      54         160.00   \n",
              "Zona Noroccidental          2778   2204  119084      544453.06   \n",
              "\n",
              "                    Valor_m2_Millon  Valor_m2_Barrio  \n",
              "Barrio                                                \n",
              "Alameda                        5.98             5.98  \n",
              "Alhambra                      73.56             3.95  \n",
              "Andes Norte                   42.35             5.68  \n",
              "Antonio Nariño               169.47             2.82  \n",
              "Barrancas                     66.24             4.47  \n",
              "...                             ...              ...  \n",
              "Villa Maria                   26.58             1.94  \n",
              "Villas De Granada             69.71             3.26  \n",
              "Villemar                      71.81             3.95  \n",
              "Zona Franca                    2.96             2.96  \n",
              "Zona Noroccidental          4049.16             4.57  \n",
              "\n",
              "[149 rows x 6 columns]"
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              "      <td>4712.00</td>\n",
              "      <td>66.24</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>Villa Maria</th>\n",
              "      <td>61</td>\n",
              "      <td>48</td>\n",
              "      <td>2693</td>\n",
              "      <td>5237.00</td>\n",
              "      <td>26.58</td>\n",
              "      <td>1.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villas De Granada</th>\n",
              "      <td>76</td>\n",
              "      <td>51</td>\n",
              "      <td>1905</td>\n",
              "      <td>6210.62</td>\n",
              "      <td>69.71</td>\n",
              "      <td>3.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villemar</th>\n",
              "      <td>53</td>\n",
              "      <td>37</td>\n",
              "      <td>1384</td>\n",
              "      <td>5469.00</td>\n",
              "      <td>71.81</td>\n",
              "      <td>3.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zona Franca</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>54</td>\n",
              "      <td>160.00</td>\n",
              "      <td>2.96</td>\n",
              "      <td>2.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zona Noroccidental</th>\n",
              "      <td>2778</td>\n",
              "      <td>2204</td>\n",
              "      <td>119084</td>\n",
              "      <td>544453.06</td>\n",
              "      <td>4049.16</td>\n",
              "      <td>4.57</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "m2_barrio = dict(datos_barrio['Valor_m2_Barrio'])"
      ],
      "metadata": {
        "id": "hU3HiGsFiPHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles['Valor_m2_Barrio'] = inmuebles['Barrio']\n",
        "inmuebles['Valor_m2_Barrio'] = inmuebles['Valor_m2_Barrio'].map(m2_barrio)\n",
        "inmuebles.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
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        "id": "8R-M41NGjLgp",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Tipo                                 Descripcion  Habitaciones  \\\n",
              "0  Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "1         Casa                   Casa en venta en Castilla             4   \n",
              "2  Apartamento     Apartamento en venta en Chico Reservado             3   \n",
              "3  Apartamento             Apartamento en venta en Usaquén             3   \n",
              "4  Apartamento         Apartamento en venta en Bella Suiza             2   \n",
              "\n",
              "   Banos  Area              Barrio                                    UPZ  \\\n",
              "0      2    70  Zona Noroccidental     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "1      3   170            Castilla            KENNEDY: Castilla + Bavaria   \n",
              "2      3   144     Chico Reservado     CHAPINERO: Chicó Lago + El Refugio   \n",
              "3      2   154             Usaquén                                Usaquén   \n",
              "4      3   128         Bella Suiza  USAQUÉN: Country Club + Santa Bárbara   \n",
              "\n",
              "             Valor Moneda      Precio  Precio_Millon  Valor_m2_Millon  \\\n",
              "0    $ 360.000.000      $   360000000         360.00             5.14   \n",
              "1    $ 670.000.000      $   670000000         670.00             3.94   \n",
              "2  $ 1.120.000.000      $  1120000000        1120.00             7.78   \n",
              "3    $ 890.000.000      $   890000000         890.00             5.78   \n",
              "4    $ 970.000.000      $   970000000         970.00             7.58   \n",
              "\n",
              "   Valor_m2_Barrio  \n",
              "0             4.57  \n",
              "1             3.40  \n",
              "2             8.10  \n",
              "3             5.66  \n",
              "4             6.29  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Barrio</th>\n",
              "      <th>UPZ</th>\n",
              "      <th>Valor</th>\n",
              "      <th>Moneda</th>\n",
              "      <th>Precio</th>\n",
              "      <th>Precio_Millon</th>\n",
              "      <th>Valor_m2_Millon</th>\n",
              "      <th>Valor_m2_Barrio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>70</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 360.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>360000000</td>\n",
              "      <td>360.00</td>\n",
              "      <td>5.14</td>\n",
              "      <td>4.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Castilla</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>170</td>\n",
              "      <td>Castilla</td>\n",
              "      <td>KENNEDY: Castilla + Bavaria</td>\n",
              "      <td>$ 670.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>670000000</td>\n",
              "      <td>670.00</td>\n",
              "      <td>3.94</td>\n",
              "      <td>3.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>144</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 1.120.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>1120000000</td>\n",
              "      <td>1120.00</td>\n",
              "      <td>7.78</td>\n",
              "      <td>8.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>154</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 890.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>890000000</td>\n",
              "      <td>890.00</td>\n",
              "      <td>5.78</td>\n",
              "      <td>5.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Bella Suiza</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>128</td>\n",
              "      <td>Bella Suiza</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 970.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>970000000</td>\n",
              "      <td>970.00</td>\n",
              "      <td>7.58</td>\n",
              "      <td>6.29</td>\n",
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              "  </tbody>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_barrios = inmuebles['Barrio'].value_counts()[:10].index"
      ],
      "metadata": {
        "id": "IwD0crAgj0so"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datos_barrio.reset_index(inplace=True)\n",
        "datos_barrio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "cdocz6srkmC9",
        "outputId": "d72c34f4-6c06-4c47-859f-52237cc99555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Barrio  Habitaciones  Banos    Area  Precio_Millon  \\\n",
              "0               Alameda             3      2      97         580.00   \n",
              "1              Alhambra            51     50    3237       12790.00   \n",
              "2           Andes Norte            21     20     874        4967.00   \n",
              "3        Antonio Nariño           207    120    7827       22042.65   \n",
              "4             Barrancas            43     32    1053        4712.00   \n",
              "..                  ...           ...    ...     ...            ...   \n",
              "144         Villa Maria            61     48    2693        5237.00   \n",
              "145   Villas De Granada            76     51    1905        6210.62   \n",
              "146            Villemar            53     37    1384        5469.00   \n",
              "147         Zona Franca             2      1      54         160.00   \n",
              "148  Zona Noroccidental          2778   2204  119084      544453.06   \n",
              "\n",
              "     Valor_m2_Millon  Valor_m2_Barrio  \n",
              "0               5.98             5.98  \n",
              "1              73.56             3.95  \n",
              "2              42.35             5.68  \n",
              "3             169.47             2.82  \n",
              "4              66.24             4.47  \n",
              "..               ...              ...  \n",
              "144            26.58             1.94  \n",
              "145            69.71             3.26  \n",
              "146            71.81             3.95  \n",
              "147             2.96             2.96  \n",
              "148          4049.16             4.57  \n",
              "\n",
              "[149 rows x 7 columns]"
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              "      <td>Antonio Nariño</td>\n",
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              "      <td>Villa Maria</td>\n",
              "      <td>61</td>\n",
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              "      <td>5237.00</td>\n",
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              "      <td>1.94</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Villas De Granada</td>\n",
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              "      <td>6210.62</td>\n",
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              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Villemar</td>\n",
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              "      <td>5469.00</td>\n",
              "      <td>71.81</td>\n",
              "      <td>3.95</td>\n",
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              "      <td>Zona Franca</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>2778</td>\n",
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              "      <td>119084</td>\n",
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              "          const element = document.querySelector('#df-3c5ef684-7343-4410-817c-2e75df009d9f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_barrio.query('Barrio in @top_barrios')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "K07QxWXRlJPC",
        "outputId": "dded6b26-7e17-4d3d-9c10-bff8b1f68d56"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Barrio  Habitaciones  Banos    Area  Precio_Millon  \\\n",
              "5        Barrios Unidos          1525   1196   87799      259877.76   \n",
              "10                 Bosa          1743    789   34879       93400.56   \n",
              "22             Cedritos          1458   1348   53711      270027.73   \n",
              "24            Chapinero           892    913   81248      372130.93   \n",
              "50             Engativa          1676   1077   59808      172711.73   \n",
              "63              Kennedy          2151   1233   61475      207963.65   \n",
              "125       Santa Barbara          1073   1162   54693      320097.80   \n",
              "132                Suba          1497   1202   61615      266406.04   \n",
              "138             Usaquén          3109   3044  148121      837710.36   \n",
              "148  Zona Noroccidental          2778   2204  119084      544453.06   \n",
              "\n",
              "     Valor_m2_Millon  Valor_m2_Barrio  \n",
              "5            2070.61             2.96  \n",
              "10           1654.36             2.68  \n",
              "22           2853.23             5.03  \n",
              "24           2466.03             4.58  \n",
              "50           1795.71             2.89  \n",
              "63           2127.66             3.38  \n",
              "125          2701.88             5.85  \n",
              "132          2000.72             4.32  \n",
              "138          6334.04             5.66  \n",
              "148          4049.16             4.57  "
            ],
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              "      <th></th>\n",
              "      <th>Barrio</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Precio_Millon</th>\n",
              "      <th>Valor_m2_Millon</th>\n",
              "      <th>Valor_m2_Barrio</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>5</th>\n",
              "      <td>Barrios Unidos</td>\n",
              "      <td>1525</td>\n",
              "      <td>1196</td>\n",
              "      <td>87799</td>\n",
              "      <td>259877.76</td>\n",
              "      <td>2070.61</td>\n",
              "      <td>2.96</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
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              "      <td>1743</td>\n",
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              "      <td>34879</td>\n",
              "      <td>93400.56</td>\n",
              "      <td>1654.36</td>\n",
              "      <td>2.68</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
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              "      <td>1348</td>\n",
              "      <td>53711</td>\n",
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              "      <td>5.03</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Chapinero</td>\n",
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              "      <td>372130.93</td>\n",
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              "      <td>4.58</td>\n",
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              "    <tr>\n",
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              "      <td>59808</td>\n",
              "      <td>172711.73</td>\n",
              "      <td>1795.71</td>\n",
              "      <td>2.89</td>\n",
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              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Kennedy</td>\n",
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              "      <td>1233</td>\n",
              "      <td>61475</td>\n",
              "      <td>207963.65</td>\n",
              "      <td>2127.66</td>\n",
              "      <td>3.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>1073</td>\n",
              "      <td>1162</td>\n",
              "      <td>54693</td>\n",
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              "      <td>2701.88</td>\n",
              "      <td>5.85</td>\n",
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              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Suba</td>\n",
              "      <td>1497</td>\n",
              "      <td>1202</td>\n",
              "      <td>61615</td>\n",
              "      <td>266406.04</td>\n",
              "      <td>2000.72</td>\n",
              "      <td>4.32</td>\n",
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              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Usaquén</td>\n",
              "      <td>3109</td>\n",
              "      <td>3044</td>\n",
              "      <td>148121</td>\n",
              "      <td>837710.36</td>\n",
              "      <td>6334.04</td>\n",
              "      <td>5.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>2778</td>\n",
              "      <td>2204</td>\n",
              "      <td>119084</td>\n",
              "      <td>544453.06</td>\n",
              "      <td>4049.16</td>\n",
              "      <td>4.57</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "ax = sns.barplot(x=\"Barrio\", y=\"Valor_m2_Barrio\", data = datos_barrio.query('Barrio in @top_barrios'))\n",
        "ax.tick_params(axis='x', rotation=45)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "iA2-RwO9lmX6",
        "outputId": "e62f2a97-70f2-4ff5-8beb-50869b87f788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "ax = sns.boxplot(x=\"Barrio\", y=\"Valor_m2_Millon\", data = inmuebles.query('Barrio in @top_barrios'))\n",
        "ax.tick_params(axis='x', rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "G0Jk65-FmVzw",
        "outputId": "e7edfffc-c20a-4f73-e493-fa43e15273c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "ax = sns.boxplot(x=\"Barrio\", y=\"Valor_m2_Millon\", data = inmuebles.query('Barrio in @top_barrios & Valor_m2_Millon < 15'))\n",
        "ax.tick_params(axis='x', rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "hKZI_S0RnJBc",
        "outputId": "f8aed493-0a2e-4fd7-e3ed-b7ab132502fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "ax = sns.boxplot(x=\"Barrio\", y=\"Area\", data = inmuebles.query('Barrio in @top_barrios & Area < 500'))\n",
        "ax.tick_params(axis='x', rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "B_3zQZ5LogLh",
        "outputId": "51ccecb6-cd07-42e9-95ba-95d42fc3b108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "ax = sns.boxplot(x=\"Barrio\", y=\"Precio_Millon\", data = inmuebles.query('Barrio in @top_barrios & Precio_Millon < 2000'))\n",
        "ax.tick_params(axis='x', rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "JX4WXr68ou3I",
        "outputId": "662c957c-02b4-421f-af61-f9e24138092a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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jzkKfwQDJxTVrQP5I1opEF27EWegzGCC5uGYNyB/JWpFC78LNDA3JFvoMBkiu0GcPAaJEslak0Duojzxt9tprr3mKBGMR+gwGUrq5alNTE01VEyb02UOAKJGsRSDkDur0WUu+0GcwWLVqlfbt26cVK1b4DgUFCn32ECAqXGAVgblz5x4yz10oGhsbtXHjRvX396uqqoo+awk0c+ZMffe73/UdRkl0dnYOXZje3d2trq4uKjQJ0tDQoLa2Nt9hALFHZS0CIXeIT6VSOnjwoCTp4MGDXLOWQCGfJly1alXOOLTqWm9vrxYvXqzdu3f7DgVjwP5DVEjWIhByh/iamhoNDg5KkgYHBzV58mTPEaFQIZ8mDL39Q2trq7Zt20Z/w4Ri/yEqJGtFCr1D/D/8wz/kjL/whS94igRjcbjThCEJuf1Db2+v2tra5JxTW1tbkNWZkCtP5bD/MH5I1ooUeof4kdeTPPLII54iwViEfpow5PYPra2tcs5JSle1Q6zOhFx5Kof9h/FDslYkOsQjzkI/TRhy+4f29nb19/dLkvr7+4O7Ezv0ylPo+w/ji2StSHSIR5yVw/EZavuHxsZGVVVVSVKQd2K3trYOXQ978ODB4CpPoe8/jC+StSLRIR5xVg5z12bbP4RUVZPSd2KbmSSpoqIiuDux29vbh47HgYGB4CpPoe8/jC+StSKF3iGeuU+T7cILL8wZX3TRRZ4iQaFqamrU1NQkM1NTU1Nwd2Kff/75OeNZs2Z5iqQ0Qt9/GF8kaxEIuUN86HOfhi6VSuWciuHTfbKkUilNnz6d/ZZQ7D9EhWQtAu985zv1R3/0R0G1DcgKfe7T0NXU1Gj+/PkyM1188cV8uk+YmpoarV69Osj99uSTT+aMN2/e7CmS0gl5/2F8kaxFIOTbz6Ww5z4tB3y6Rxw1NjbmfBDkAnzgyEjWihT67edSurq2efNmqmoJxad7xNHwDw9mxocJYBQka0Wi8SEAFK6mpkbHHnusJOmYY47hwwQwCpK1ItH4EAAK19nZmTNVX2hToQFRog9DkRobG7Vx40b19/cnqvFhS0tL3m+OPT09kqTa2tq8nl9fX68lS5aMOTYA4TvcVGjr1q3zFA0Qb1TWilQOjQ8PHDigAwcO+A4DQEBCnwoNiBKVtSJlGx9u2LAhUY0PC6l8ZZ/b0tJSqnAAlJm6urqcBC3E1kdAVKisRYDWCABQmGXLluWMQ5vbFYgSyVoEaI0AAIVpaGgYqqbV1dUFN7crECWSNQCAF8uWLdPEiROpqgFHwTVrAAAvGhoa1NbW5jsMIPZKWlkzs6+Y2ctm9uywZc1m9qKZPZ15zB/2vb83sy4z+7mZXThs+UWZZV1m9slSxjwWa9as0axZs7R27VrfoQBlp7e3V4sXLw5y9pDOzk41NTUF24Ns/fr1mjVrljZs2OA7FCDWSn0a9KuSLjrM8tudc+dkHhslyczOknSlpD/IvOafzGyCmU2Q9H8kNUk6S9JVmefGxn333SdJzF4AeBDy3LyrVq3Svn37tGLFCt+hlMQdd9whSbrttts8RwLEW0mTNefcZkl78nz6ByU94Jx7wzn3n5K6JM3MPLqcc790zr0p6YHMc2NhzZo1OWOqa8D4CXlu3s7OzqHWFt3d3cFV19avXz80VZ9zjuoaMApfNxgsMrNtmdOkJ2eWTZX0wrDn9GSWHWl5LGSralkhfroH4irkuXkP1+E/JNmqWhbVNeDIfCRrd0l6l6RzJO2QFNlvqJldb2ZbzGzLrl27olotgJgKeW7e0Dv8Z5PsI40BvGXckzXn3E7n3EHn3KCku5U+zSlJL0o6Y9hTazPLjrT8cOv+knNuhnNuxpQpU6IPHkCsNDY2qqqqSpISNTdvPkZ29KfDP1C+xj1ZM7PThg0vk5S9U3SDpCvN7Bgze4ekaZI6JP1I0jQze4eZvU3pmxBic3FDU1NTznjBggWeIgHKT8hz84be4T+bZB9pDOAtpW7dcb+k70t6t5n1mNm1kj5vZs+Y2TZJcyT9N0lyzv1U0oOSnpP0LUkfz1TgBiQtkvSopOclPZh5biw8//zzOeNnnnnGUyRA+cnOzWtmiZqbNx+hd/ifP39+zvjiiy/2FAkQfyVtiuucu+owi494u6Rz7jOSPnOY5RslbYwwtMiEfl0JEHepVErd3d1BVdWyli1bpqVLlwZXVZPS++2RRx7R4OBgcFVRIGpMN1UkrisB/Ap5bt5sh//QqmpSer8tWLBAZqaFCxcGuf+AqJCsFSn060qQfCF3+JfC376QLViwQMcff7wWLlzoOxQg1kjWitTQ0KDq6mpJUnV1dZCfgJFsIXf4l8LfvpA98sgj2r9/Pw1xgaMgWStSb2+vfvvb30qS3njjDT7dI1ZC7vAvhb99IWPfAfkjWStSa2vrUOuA7BiIi5A7/Evhb1/I2HdA/kjWihRyB3UkX+jHZ+jbFzL2HZA/krUihdxBHckX+vEZ+vaFjH0H5I9krUghd1BH8oV+fIa+fSFj3wH5I1krUsgd1JF8oR+foW9fyNh3QP5KOoNBuQi5gzqSL/TjM/TtCxn7DsgPyVoEsh3UgTgK/fgMfftCxr4D8sNpUAAAgBgjWYtAR0eHZs+era1bt/oOBWMQ+v7r7OxUU1OTurq6fIeCAoW+70LfPqZCQ1RI1iLwqU99SoODg4fME4pkaG5u1uDgoJYvX+47lJJYtWqV9u3bpxUrVvgOBQUKfd+Fvn1MhYaokKwVqaOjQ/v27ZMk7du3L9jqTKg6OjrU19cnSerr6wtu/3V2dqq7u1uS1N3dHWwFI0Sh77vQt4/ptBAlkrUifepTn8oZU11Llubm5pxxaNW1VatW5YxDrWCEKPR9F/r2MZ0WokSyVqRsVe1IY8Rbtqp2pHHSZSsXRxqHINTrnkLfd6FvH9NpIUokayhrlZWVo46Trq6ubtRxCEK97in0fRf69jGdFqJEslakP/uzP8sZv/e97/UUCcZiwoQJo46TbuRp+VtvvdVTJKUR8nVPoe+70LeP6bQQJZK1Iv3t3/7tqGPE20UXXZQzbmpq8hRJaTQ0NAxVLOrq6lRfX+83oIiFfN1T6Psu9O1jOi1EiWStSHv27MkZv/LKK54iwVikUqmhalplZWWQn36XLVumiRMnBle5kMK/7inkfSeFv32pVErTp08P8n0F44tkrUghf7IvBzU1NbrkkktkZrrkkkuC/PTb0NCgtra24CoXUvjXPYW876Twty87nVaI7ysYXyRrRQr9k305CP3Tb6h3S0rhX/cU8r4DkL+wbn3zoK6uLidBC+2TfTkIfTLp4XdLrlu3znc4kcpe99Td3R3kdU9J3HctLS15J5c9PT2SpNra2ryeX19fryVLlow5NiCpqKwV6X3ve1/O+IILLvAUCXCokO+WzFq0aJEqKiq0dOlS36FEqhz23YEDB3TgwAHfYQCxR2WtSF/72tdyxuvWrdO1117rKRog1+GuqUxKhSZfTz75pJxzeuKJJ3Teeef5DicySd13hVS+ss9taWkpVThAEKisFSk7nciRxoBPoV9TGfL8i6HvOwD5I1kDAhb63ZIhz78Y+r4DkD+StSJNnz49Z3z22Wd7igQ4VOh3S4Y8/2Lo+w5A/kjWitTc3DzqGPAp9C7xIc+/GPq+A5A/krUi1dTUDFXXzj77bJofInZC7hIf+vyLIe87APkjWYtAc3Ozzj77bKpqiKWQu8SHPv9iyPsOQP5I1iLQ1tamn/zkJ0FdL1NOQu8Sv2bNGs2aNUtr1671HUpJLFiwQMcff7wWLlzoO5TIhX5sAsgPyVoE7r77bknSXXfd5TkSjMXwLvEhuu+++yQpqDslh3vkkUe0f/9+bdiwwXcokQv92ASQH5K1It1777054/vvv99TJBiL0LvEr1mzJmccWnUt5D5roR+bAPJHslakbFUti+pashyuS3xIslW1rNCqayH3WQv92ASQP5I1lDW6xCdbyH3WODYBZJGsoaydccYZo44RbyH3WWMGAwBZJGtFetvb3jbqGPH2rne9K2ccWouEE044YdRx0oXcZ40ZDABkkawV6c033xx1jHjr6OjIGf/whz/0FElp3H777TnjL37xi54iKY2Q+6wxgwGALJK1IlVXV486Rrw1NjZqwoQJkqQJEyYEdRpNSv/Bz1bTTjjhhCD/4KdSKU2fPj2oqloWMxgAkEjWijZy1oKVK1f6CQRjkkqlhpK1ysrKIP/g33777Zo4cWJwVbWsmpoarV69OqiqWhYzGACQSNaKNnPmzKFqWnV1tc477zzPEaEQIZ9Gy+IPPgAkW0mTNTP7ipm9bGbPDlv2BTP7mZltM7P1ZnZSZnmdmR0ws6czj38e9przzOwZM+sysxbLXlEcE9k7CN/xjnd4jgRjcf7558vMdMEFF/gOpSSYsggAkq3UlbWvSrpoxLJ2SX/onJsuqVPS3w/73i+cc+dkHjcOW36XpI9KmpZ5jFynV88//7wk6ZlnnvEcCcbizjvv1ODgYLCnCUOfsohkFEDoSpqsOec2S9ozYtkm59xAZvgDSbWjrcPMTpN0onPuBy7dqnydpEtLEe9YfPKTn8wZj7zdHvEW+pQ+oW+fFH4yCgC+r1n7a0ltw8bvMLMfm9kTZnZ+ZtlUST3DntOTWRYL3/ve93LGmzdv9hQJxiL0KX1C375ySEYBwFuyZmb/U9KApOzkhTsknemce4+kmyV9zcxOLHCd15vZFjPbsmvXrmgDRpBCn9In9O0LPRkFAMlTsmZmfyXpEkkfzpzalHPuDefc7szXWyX9QlKDpBeVe6q0NrPsEM65LznnZjjnZkyZMqWEW4BQhD6lT+jbF3oyCgCSh2TNzC6S9HeSFjrn9g9bPsXMJmS+fqfSNxL80jm3Q9LrZvanmbtAr5b08HjHfSTvfe97c8azZs3yFAnGIvQpfULfvtCTUQCQSt+6435J35f0bjPrMbNrJd0p6QRJ7SNadMyStM3Mnpb0dUk3OueyNyd8TNKXJXUpXXEbfp2bV5/73OdyxiNPyyDeQp/SJ/TtCz0ZBQCp9HeDXuWcO805V+Wcq3XOrXXO1TvnzhjZosM59w3n3B9klp3rnHtk2Hq2OOf+0Dn3Lufcouyp07jIVteoqiVT6FP6hLx9oSejACBJlb4DCMHI6hqSJdvhP1Shb9+yZcu0dOnSIJNRAJD8t+4Iwh133KFZs2bpzjvv9B0KcIjQj89f//rX2rdvn1544QXfoUSOhr/J1tvbq8WLF2v37t2+Q0HCkaxF4KGHHpIkPfjgg54jAQ4V+vH52c9+VpK0cuVKz5FEj4a/ydba2qpt27aptbXVdyhIOJK1It1xxx0541CrF0im0I/Pxx57TAMD6QlRBgYG9Pjjj3uOKDo0/E223t5etbW1yTmntrY2qmsoCslakbJVi6xQqxdIptCPz2xVLSuk6hoNf5OttbVV2XvhBgcHqa6hKCRrABIrW1U70jjJaPibbO3t7erv75ck9ff3a9OmTZ4jQpKRrAFIrMrKylHHSUbD32RrbGxUVVWVJKmqqkrz5s3zHBGSjGQNCNjll1+eM77iiis8RVIat9xyS854+fLlniKJHg1/ky2VSik96Y5UUVGhVCrlOSIkGclakUa+gX7605/2FAlwqJtuuilnvGjRIk+RlMbcuXOHqmmVlZWaM2eO54iiQ8PfZKupqVFTU5PMTE1NTZo8ebLvkJBgeSdrZna5mW03s9fM7HUz22tmr5cyuCSYO3duzjikPxYIQ7a6FlpVLStbXQupqpYV8uwT5SCVSmn69OlU1VC0Qi7w+LykBc6550sVTFLdeuutWrFiBVU1jJuWlpa8Wzn09PSopqZGnZ2dWrJkyVGfX19fn9fz4mLu3LmHfGgKxaRJk1RfX6+TTz7ZdygYg5qaGq1evdp3GAhAIadBd5KoHd6ZZ56piRMn6owzzvAdCnCIAwcO6MCBA77DwBjQVDXZHnvsMc2aNSuo/n/wo5DK2hYz+1dJ/1fSG9mFzrmHjvyS8jC8y/i6det8h4MyUEjlK/vclpaWUoWDEhjZVDWVSnHdU8IMn12DS2RQjEIqaydK2i9pnqQFmcclpQgqSegyDqAUaKqabCHProHxl3dlzTl3TSkDSS8/P2kAACAASURBVKrDdRmnugagWIdrqnrzzTd7jgr5OtzsGlTXolfI9buF2r59u6TCzmTkq9Brg/NO1sysVtJqSe/LLHpS0lLnXE9BEQaGLuMASqGxsVEbN25Uf38/TVUTKOTZNeKkq6tLP37ueR2cckrk667InHzcsmtPpOudsGtnwa8p5Jq1eyR9TdKHMuO/zCxrLPinBqSuri4nQaPLOIAopFIptbW1SaKpahJVVlbmJGghza4RNwennKJ9f/4R32HkbeI37i34NYVcszbFOXePc24g8/iqpCkF/8TA0GUcQCnQVDXZQp5dA+OvkGRtt5n9pZlNyDz+UtLuUgWWFA0NDaqtrZUk1dbW0mUcQGRoqppcIc+ugfFXSLL215KukPSSpB2S/kISNx1IQwnatGnTPEcCICTZpqpU1ZIp5Nk1ML4KuRv0V5IWljCWROrt7dX3vvc9SdL3vvc97d69mzdWAEDQs2tgfB01WTOz1ZLckb7vnEvOvDQlcLheSNxeDwAAopLPadAtkraO8ihrh+uFhGTp7OxUU1MTDY0BRIr3FkTlqMmac651tMd4BBlnjY25nUvohZQ8w6cLA4Co8N6CqORzGvQRjX4atKyvY3v729+eMz7ttNM8RYKxONx0YdzRC6BYvLcgSvncYPC/Sx5Fgn35y1/OGf/zP/+zrrrqKk/RoFBMFxY/hU4f09OTnkQl20LnaAqd5gUYC95bEKWjJmvOuSfGI5Ckyt5ccKQx4o3pwpLvwIEDvkMADsF7C6KUz2nQB51zV5jZMzrM6VDn3PSSRAaMA6YLi59Cq17Z57e0tJQiHGBMeG9BlPK5G3Rp5t9LJC04zKOsjZzvjfnfkoXpwgCUAu8tiFI+d4PuyPz7q8M9Sh9ivB177LGjjhFvDQ0NQ5946+rquAAYQCR4b0GUjpqsmdleM3t92GPv8H/HI8g46+vrG3WM+Fu2bJkmTpzIJ18AkeK9BVHJ55zdtyWdKukhSQ84535d2pCShesSkq+hoUFtbW2+wwAQGN5bEJV8ToNeKulCSbsk3W1mT5jZx8xsUsmjSwCuS0g+uowDAOIsnxsM5Jx7zTl3j6QmSWskrZD0VyWMKzEaGhpyxlyXkDx0GQcAxFleyZqZvTczoftTkt4r6TLn3D+WNLKEuOOOO3LGd955p6dIMBaH6zIOAECc5HODQbekf5L0oqTrJX1F0j4zO9fMzi1tePH30EMP5YwffPBBT5FgLA7XZRwAgDjJ5waDbqWb4V4oaZ4kG/Y9J+n90YcFjA+6jAMA4i6f6aZm57MiM2t0zrUXHREwjribFwAQd3lds5an/xXhuhLj8ssvzxlfccUVniLBWHA3LwAg7qJM1uzoTwnPTTfdlDNetGiRp0gwFnQZBwDEXZTJ2iGTvJeLbHWNqloy0WUcABBnzDoegZtuuumQChuSgy7jAIA4i7Ky1h3hugAAAKACkjUzqzKzJWb29cxjsZlVZb/vnLv8MK/5ipm9bGbPDls2yczazWx75t+TM8vNzFrMrMvMtg3v4WZmqczzt5tZauybWxodHR2aPXu2tm7d6jsUAAAQmEIqa3dJOk/pBrn/JOnczLLRfFXSRSOWfVLSt51z05SeJP6TmeVNkqZlHtdn152Zg/RTkv5E0kxJn8omeHHR3NyswcFBLV++3HcoAAAgMIUka3/snEs5576TeVwj6Y9He4FzbrOkPSMWf1BSa+brVkmXDlu+zqX9QNJJZnaa0s14251ze5xzr0hq16EJoDcdHR3q6+uTJPX19VFdS6De3l4tXrxYu3fv9h0KAACHKCRZO2hm78oOzOydkg6O4Wee4pzbkfn6JUmnZL6eKumFYc/rySw70vJYaG5uzhlTXUue1tZWbdu2Ta2trUd/MgAA46yQZO1vJT1uZt81syckfUfSJ4r54c45pwhbfpjZ9Wa2xcy27Nq1K6rVjipbVTvSGPHW29urtrY2Oee0ceNGqmsAIkPVHlHJO1lzzn1b6evJlkhaLOndzrnHx/Azd2ZObyrz78uZ5S9KOmPY82ozy460/HAxfsk5N8M5N2PKlCljCK1w1dXVo44Rb62trerv75ck9ff3U10DEBmq9ojKUZM1M3t/5t/LJV0sqT7zuDizrFAbJGXv6ExJenjY8qszd4X+qaTXMqdLH5U0z8xOztxYMC+zLBbOP//8nPGcOXM8RYKx2LRpk9IFXsk5p0cfjc2hBSDBhlft29raqK6hKPlU1i7I/LvgMI9LRnuhmd0v6fuS3m1mPWZ2raTPSWo0s+2S5mbGkrRR0i8ldUm6W9LHJMk5t0fSSkk/yjxWZJbFwshmqo888oinSDAWp5xyyqhjABiL1tbWoQ+Cg4ODVNdQlKPOYOCc+1Tm32sKXblz7qojfOsDh3muk/TxI6znK5K+UujPB47mpZdeGnUMAGPR3t6ec4nFpk2bdPPNN3uOCklVSFPcz5rZScPGJ5vZqtKEBYyPU089ddQxAIxFY2OjqqrSfeOrqqo0b948zxEhyQq5G7TJOfdqdpDpeTY/+pCA8bNz585RxwAwFqnUW5PtmFnOGChUIcnaBDM7Jjsws+MkHTPK88tCXV3dqGPE28hPuxdeeKGnSACEpKamRlOnpluCnn766Zo8ebLniJBkhSRr90n6tpldm7lRoF1vzURQthobG3PGTU1NniLBWCxYsCBnvHDhQk+RAAhJb2+vfvOb30iSfvOb33A3KIpSSJ+1/yVplaTfzzxWOuc+X6rAkuKee+7JGd99992eIsFYPPLIIzIzSelTFRs2bPAcEYAQDL8b1DnH3aAoSiGVNUl6XtK3nHP/XdKTZnZCCWJKlIGBgVHHiLf29vacN9RNmzZ5jghACA53NygwVoXcDfpRSV+XtCazaKqk/1uKoJKksrJy1DHibeRpbO7YAhAF7gZFlAqprH1c0vskvS5Jzrntkt5eiqCS5JZbbskZM5F7soycgeKCCy44wjMBIH+pVGroEouKigruBkVRCknW3nDOvZkdmFmlIpyEPanmzp07VE2rrKxkuqmEufPOO3PGX/ziFz1FAiAkNTU1ampqkpmpqamJu0FRlEKStSfM7BZJx5lZo6R/k8TcSnqrukZVLXm6u7tHHQPAWKVSKU2fPp2qGopWyAVW/0PSdZKekXSD0nN5frkUQcVBS0uLurq68npuT0+PampqtH79eq1fvz6v19TX12vJkiXFhIgI1NXV5SRo9MkDEJWamhqtXr3adxgIQF6VNTObIOl559zdzrkPOef+IvN12Z8GlaQDBw7owIEDvsPAGCxbtixnfOutt3qKBDhUb2+vFi9eTI+uhOro6NDs2bO1detW36Eg4fKqrDnnDprZz83sTOfcr0sdVBwUUvXKPrelpaVU4aBEGhoaVFlZqYGBAVVWVqq+vt53SMCQ1tZWbdu2Ta2trUwCnkDNzc0aHBzU8uXLtXHjRt/hIMEKuWbtZEk/NbNvm9mG7KNUgQHjobOzc6g33sDAQN6nvoFS6+3t1caNG+Wc07//+79TXUuYjo4O9fX1SZL6+vqorqEohSRryyVdImmFpNuGPYDEWrVqVc54xYoVniIBcrW2tuY0VaUDfrI0NzfnjLkBDcU4arJmZsea2U2SPiTp9yT9/865J7KPkkcIlBB3gyKuHn300Zzxt771LU+RYCyyVbUjjYFC5FNZa5U0Q+m7QJtENQ0BGXn3J3eDIi6YHQVAVj7J2lnOub90zq2R9BeSzj/aC4Ck4G5QxBWVGQBZ+SRr/dkvnHPMUo6gNDQ0DFXT6urquBsUsVFdXT3qGED5yCdZO9vMXs889kqanv3azF4vdYBAqV199dWSpGuuucZzJMBbsjcXHGkMoHwcNVlzzk1wzp2YeZzgnKsc9vWJ4xEkUErr1q2TJN1zzz2eIwHectppp406BlA+CmndAQSns7Nz6A7Q7u5u+qwhNnbu3DnqGPE2Mrk+/fTTPUWCEJCsoazRZw1xNW/evJzxhRde6CkSjMXKlStzxiPfa4BCkKyhrNFnDXGVSqVGHSPeGhoahqprp59+OjcvoSgkayhrkyZNyhlPnjzZUyRArn/5l3/JGd9///2eIsFYrVy5UhMnTqSqhqLRZRFlbc+ePTlj5l9EXDz00EM54wcffFCLFi3yFA2yWlpa8r62taenR8cdd5xaWlryen59fb2WLFlSTHgIFMkaAAAlcODAAd8hIBAkawEp5BNfIbZv3y5JJfvE5/PTpJnJOZczBoAjKeS9KvvcfCtrwJGQrAWkq6tLnc8+pTOrD0a63rf1py9t/G33jyJdryT9um9C5OssxE033aTbb799aPyJT3zCYzTAWy6//PKcU6FXXHGFx2gA+ESyFpgzqw9q2YzkzCG4aovfKXQuu+yynGRt4cKFHqMB3nLTTTflJGtcrwaUL+4GRVnr6OjIGW/dutVTJECuzs7OnDENm4HyRbKGstbc3JwzXr58uZ9AgBFo2Awgi2QNZa2vr2/UMeALDZsRd52dnWpqaqLqOw5I1lDWqqurRx0DvtTV1Y06BnxbtWqV9u3bR9V3HHCDAcravHnzci7inj9/vsdogLcsW7ZM11133dD41ltv9RYLbYEwUmdn51C1t7u7W11dXUypVUIkayhr69evzxn/27/9G3fdIRYaGhpUV1en7u5u1dXVef1D2NXVpZ8/+7zOOOHUSNdbNZA+ubP/V69Eul5JemHvS5GvE2853DWV69at8xRN+EjWUNaGN8Q93BjwadGiRfq7v/s7LV261HcoOuOEU/WJmdf4DiNvt3Xc4zuEoHFN5fjimjWUtZEzFjCDAeLkySeflHNOTzzxhO9QgBxcUzm+SNZQ1m666aacMTMYIC56e3vV1tYm55za2tq0e/du3yEBQ0ZeLhKH6m/ISNZQ1i677LKhapqZMYMBYqO1tXXotPzg4KBaW1s9RwS85cknn8wZU/0tLZI1lL1sdY2qGuKkvb1d/f39kqT+/n5t2rTJc0TAW9rb23PGHJ+lRbKGsnfZZZdp8+bNVNUQK42NjaqqqpIkVVVVad68eZ4jAt7C8Tm+SNZQ9tavX69Zs2Zpw4YNvkMBhqRSKR08eFCSdPDgQaVSKc8RAW9JpVJDl5BUVFRwfJaYl2TNzN5tZk8Pe7xuZjeZWbOZvThs+fxhr/l7M+sys5+b2YU+4kaY7rjjDknSbbfd5jkS4C01NTUaHByUlL5mbfLkyZ4jAt5SU1OjpqYmmZmampo4PkvMS7LmnPu5c+4c59w5ks6TtF9Stjvp7dnvOec2SpKZnSXpSkl/IOkiSf9kZhN8xI6wrF+/fugibucc1TXExpo1a3LGa9eu9RQJcHipVErTp0+nqjYO4nAa9AOSfuGc+9Uoz/mgpAecc2845/5TUpekmeMSHYKWraplUV1DXNx33305Y+4GRdzU1NRo9erVVNXGQRyStSsl3T9svMjMtpnZV8zs5MyyqZJeGPacnsyyHGZ2vZltMbMtu3btKl3ECAYzGAAA4s5rsmZmb5O0UNK/ZRbdJeldks6RtENSQWUO59yXnHMznHMzpkyZEmmsCBMzGAAA4s53Za1J0lPOuZ2S5Jzb6Zw76JwblHS33jrV+aKkM4a9rjazDCjKddddlzO+8cYbPUUC5Prwhz+cM+a6IKB8+Z7I/SoNOwVqZqc553ZkhpdJejbz9QZJXzOzf5R0uqRpkjrGM1CE6eWXX84Z79ix4wjPBKLR0tKirq6ugl/3k5/8REuWLDnq8+rr6/N6HoDk8FZZM7OJkholPTRs8efN7Bkz2yZpjqT/JknOuZ9KelDSc5K+JenjzrmD4xwyAkQXbsTZhAnpm965rAMob94qa865fZImj1j2kVGe/xlJnyl1XCgvjY2N2rhxo/r7++nCjXFRSNUr+9yWlpZShQMgAXxfswZ4RRduAEDckayhrNGFGwAQd75vMAC8S6VS6u7upqoGAIglkjWUvWwXbgAA4ohkDUEqpD1CT0+PJKm2tjav59MaAQAwnkjWUPYOHDjgOwQAAI6IZA1Boj0CACAU3A0KAAAQYyRrAAAAMUayBgAAEGMkawAAADFGsgYAABBjJGsAAAAxRrIGAAAQYyRrAAAAMUayBgAAEGMkawAAADFGsgYAABBjzA0KxEBLS4u6urpKsu7t27dLKmy+1HzV19eXZL0AgLeQrAEx0NXVpZ89/bROLcG6s+XzV59+OtL1vhTp2gAAR0KyBsTEqZKulfkOI29r5XyHAABlgWQNAAAUrLOzU0uXLtXq1atVX1/vJYaenh5NeH2vJn7jXi8/fywm7Nqpnjf2F/QabjAAAAAFW7Vqlfbt26cVK1b4DiV4VNYAAEBBOjs71d3dLUnq7u5WV1eXl+pabW2tXtq1R/v+/CPj/rPHauI37lXtlEkFvYbKGgAAKMiqVatyxlTXSovKGoCSozUJEJZsVe1IY0SLZA1AyXV1demnzzyvk45/e+TrHnwzfQfti7/YHel6X93/cqTrA0JSV1eXk6DV1dV5i6UckKwBGBcnHf92zfm9K32HkbfHf/aA7xCA2Fq2bJmuu+66ofGtt97qMZrwcc0aAAAoSENDw1A1ra6uzlvrjnJBsgYAAAq2bNkyTZw4karaOCBZAwAABZs0aZLq6+t18skn+w4leCRrAACgYK2trdq2bZtaW1t9hxI8kjUAAFCQ3t5etbW1yTmntrY27d4d7d3YyEWyBgAACtLa2irnnCRpcHCQ6lqJkawBAICCtLe3q7+/X5LU39+vTZs2eY4obPRZAwCUtVLNsBHy7BqNjY3auHGj+vv7VVVVpXnz5nmLpRyQrAEAylpXV5eeffZZVVdXR7rebOUp6qmY+vr6Il3fWKRSKbW1tUmSKioqlEqlPEcUNpI1AEDZq66u1rnnnus7jLw89dRTvkNQTU2NmpqatGHDBjU1NWny5Mm+QwoayRoAAChYKpVSd3c3VbVxQLIGAAAKVlNTo9WrV/sOoyxwNygAAECMkawBAADEmLfToGbWLWmvpIOSBpxzM8xskqR/lVQnqVvSFc65V8zMJH1R0nxJ+yX9lXOu4Cssk3h7tuT/Fm0AAOCP72vW5jjneoeNPynp2865z5nZJzPj/yGpSdK0zONPJN2V+bcgXV1d+vEzz2nw+EnFRz6MvZnu4rz1Fy9Ful5Jqti/J/J1AkAhenp6tG/vXt3WcY/vUPL2wt6XNLFnn+8wgEj4TtZG+qCk2ZmvWyV9V+lk7YOS1rn03BY/MLOTzOw059yOQn/A4PGT9NuzLoko3NI79rlv+g4BAIBDrFmzRvfdd59SqZSuvfZa3+EEzWey5iRtMjMnaY1z7kuSThmWgL0k6ZTM11MlvTDstT2ZZQUnawCAwtTW1mr/wVf0iZnX+A4lb7d13KPja0/2HUbQ7rvvPknpeUJJ1krL5w0G/8U5d67Spzg/bmazhn8zU0VzhazQzK43sy1mtmXXrl0RhgoAALLWrFmTM167dq2nSMqDt2TNOfdi5t+XJa2XNFPSTjM7TZIy/76cefqLks4Y9vLazLKR6/ySc26Gc27GlClTShk+AABlK1tVy2ptbfUUSXnwkqyZ2UQzOyH7taR5kp6VtEFSthVyStLDma83SLra0v5U0mtjuV4NAAAgaXxds3aKpPXpjhyqlPQ159y3zOxHkh40s2sl/UrSFZnnb1S6bUeX0q07knPhxDhK37E1Qau2RDsZcSn9au8ETezp8R0GAACx5SVZc879UtLZh1m+W9IHDrPcSfr4OIQGAACO4sMf/nDOqVDmBy2tuLXuQBFqa2v124EdWjajz3coeVu1pVrH1tb6DgMAUIAbbrghJ1njbtDSYropAABQsDlz5kiSGhsbPUcSPpI1AABQsB/96EeSpO9///ueIwkfyRoAAChIR0eH+vrSl9z09fVp69atniMKG8kaAAAoSHNzc854+fLlfgIpEyRrAACgINmq2pHGiBbJGgAAKEh1dfWoY0SLZA0AABTkox/9aM74xhtv9BRJeaDPGgAUqaWlRV1dXZGvd/v27ZKkJUuWRL5uSaqvry/ZuhG29evX54y//vWva+HChZ6iCR/JGgAUqaurS8/+5Cc64W3RvqUODByUJP3q+Z9Gul5J2vvmQOTrTKqenh7t3btXTz31lO9Q8rJ37171eJ6mr7u7e9QxokWyBgAROOFtlZp5ysm+w8hbx85XfIeABKutrc1JGGuZiaakSNYAAEf1wt6XdFvHPZGu8+X9eyRJbz9+UqTrldLxvlv5Jc+1tbUaGBjQueeeG3kcpfDUU095T47q6+tzkrVp06Z5i2XCrp2a+I17I19vxavpDzSDJ0X7IWzCrp3SlMKOeZI1AMCo6uvrS7Le/u29kqTjfzf6iuS7dXLJ4ka6Ke5wP/zhD73EUcp9vP3V3ZKkaQUmVkc1ZVLBcZOsAQBGVaqbELLrbWlpKcn6UTqNjY365je/qYMHD2rChAmaN2+elzhKeYNMnI5PWncAAICCpFIpVVSkU4gJEyYolUp5jihsJGsAAKAgNTU1mjp1qiTp9NNP1+TJkz1HFDaSNQAAUJDe3l69+OKLkqQXX3xRu3fv9hxR2EjWAABAQVpbWzUwkO7VNzAwoNbWVs8RhY1kDQAAFGTTpk1yzkmSnHN69NFHPUcUNu4GRWIkcUofpvNJ6+np0Wv79+rxnz3gO5S8vbr/ZbmeA77DAGLppJNO0oEDb/1+nHxychpCJxHJGhKjq6tLP/7pj6WTIl7xYPqfH7/442jX+2q0qwOAuNixY0fO+De/+Y2nSMoDyRqS5SRpcPag7yjyUvHd/K8y6Onp0V5Ja+VKF1DEdkjqy3N+wtraWtkbuzXn964sbVARevxnD2hqLXe4AfCPa9YAAABijMpaYH7dN0GrtlRHus6d+9M5/SnHR1/R+nXfBDVEvtbkqa2t1au9vbpW5juUvK2V00lM3gyUpdmzZ+u73/3u0HjOnDn+gikDJGsBKdUcaW9mLsA/ti76iXobVNq53QAA0XvPe96Tk6ydd955/oIpAyRrAWH+PgAYm76+Pj311FORrnP//v2SpOOPPz7S9fb19UW6vrEY+ffg9ttv18KFCz1FEz6SNQBAWStVdT/bFqiuri7ydfs+I3Hw4MFRx4gWyRoAoKxxVgJxV1bJWk9Pjyr2v6Zjn/um71DyVrF/t3p6BnyHAQDAkOnTp2vbtm1D47PPPttjNOGjdQcAACjIaaedNuoY0Sqrylptba12vlGp3551ie9Q8nbsc99Ube2pvsMAAGDIk08+mTPevHmzbrnlFk/RhI/KGgAAKMj555+fM541a5anSMoDyRoAAECMkawBAICCbN68OWf8xBNPeIqkPJCsAQCAghx33HE546gb/yIXyRoAACjInj17csa7d+/2FEl5IFkDAACIsbJq3QEApdDT06O9bw6oY+crvkPJ2943B9TT0+M7DAB5oLIGAAAKUlVVNeoY0aKyBgBFqq2t1cG9r2nmKSf7DiVvHTtfUW1tre8wkFDz58/Xww8/PDS++OKLPUYTPiprAACgIKlUShUV6RSioqJCqVTKc0RhI1kDAAAFqamp0YIFC2RmWrhwoSZPnuw7pKBxGhQAABQslUqpu7ubqto48FJZM7MzzOxxM3vOzH5qZkszy5vN7EUzezrzmD/sNX9vZl1m9nMzu9BH3AAAAOPNV2VtQNInnHNPmdkJkraaWXvme7c75/738Ceb2VmSrpT0B5JOl/SYmTU45w6Oa9RACb0kaa1c5OvNtqqM+iTFS5JOinidAJJjzZo1+slPfqI1a9bolltu8R1O0Lwka865HZJ2ZL7ea2bPS5o6yks+KOkB59wbkv7TzLokzZT0/ZIHC4yD+vr6kq171/btkqSTpk2LdL0nqbRxA4iv3t5ebdq0SZK0adMm3XDDDVy3VkLer1kzszpJ75H0Q0nvk7TIzK6WtEXp6tsrSidyPxj2sh6NntwBibJkyZKSr7ulpaVkPwNAeVmzZo2cS58JGBwcpLpWYl7vBjWzaknfkHSTc+51SXdJepekc5SuvN1W4PquN7MtZrZl165dkccLAACkxx57LGfc3t5+hGciCt6SNTOrUjpRu88595AkOed2OucOOucGJd2t9KlOSXpR0hnDXl6bWZbDOfcl59wM59yMKVOmlHYDAAAoUwcPHhx1jGj5uhvUJK2V9Lxz7h+HLT9t2NMuk/Rs5usNkq40s2PM7B2SpknqGK94AQAAfPF1zdr7JH1E0jNm9nRm2S2SrjKzcyQ5Sd2SbpAk59xPzexBSc8pfSfpx8d6J2jF/j069rlvFhl+Lvvt65Ikd+yJka5XSscrnRr5eoHx9ur+l/X4zx6IfL19v01Pnl59bLRTPb26/2VNjfweWgAonK+7Qf9Dkh3mWxtHec1nJH2mmJ9bqjvXtm/fK0ma9q5SJFWncscdEq+Ux/D27XskSVPfFW1iNVWT+d0DEAve7wYdT6W644677cZHT0+P9JpU8d2EzJL2qtTjenxHEQvlcLfr3jcH1LHzlUjXuX8gfQLh+MoJka5XSscLIBnKKlkDgFIoXdU+3SPvdyPukZdF5RBIBpI1JEZtba122S4Nzh70HUpeKr5bodqptb7DwDigao9yc/zxx2v//v05Y5ROQs4nAQCAuBieqB1ujGiRrAEAgIJUVlaOOka0SNYAAEBBBgYGRh0jWiRrAAAAMUayBgAAEGOcZAYAAJLSdx53dXWN6bVHuyu6vr6+pD0XQ0ZlDQAAIMaorAEAIlNIZSbb9DffaguVmdLL9/93/vz56uvrGxpXV1fTD7CEqKwBALw47rjjdNxxx/kOA2PQ3NycM165cqWfQMoElTUky6slmBs0++GwOtrV6lVJUyNeJxBzVL7Kw8yZM1VRUaHBwUFVV1frvPPO8x1S0EjWkBilnn9x2tSI51+cytyLAMJVV1enX/7yl1TVxgHJGhKD+RcBID5O1AgRNQAAIABJREFUPPFEnXPOOVTVxgHJGgAAgSqmFcfRFHqDSCG4mSQXyRoAAIHq6urSc8/9WDVTXAnWbpKkl3c9Felae3dZpOsLAckakEC0RwCQr5opTpdf/qbvMPL20ENv8x1C7JCsAYGjNQJQvnp6evT665aoBKh3l+nNN3p8hxErJGtHUMrKhUT1AsXh2Eku3lsAFIpkLQJULgCUAu8t8ZO0SxBqa2v13HO7Il1n1muvpq8t+52Tor8erra2NvJ1JhnJ2hGE/sk0aW84QCj43SgfcUi2S9nr8bVX038b3j4l2h6Vb59Cj8qRSNZwVHF4wwGAOEhasl3KeOlROX5I1spU0t5wAAAoVyRrCBKneQEAoYh4RmwgeY477jhO9QJAgfr7+7V9+3bt3r3bdyjBo7KGIFH5AoDCFXJW4uc//7kGBgZ03XXX6Ywzzjjq832flSh06q04nXWhsgYAAArS39+vgYEBSdKePXvU39/vOaLoxemsizlXivnC/JsxY4bbsmWL7zAAFGisn36nTcuvfYDvT/dACG677TZt3LhR/f39qqqq0sUXX6ybb77Zd1iJZmZbnXMzDvc9KmsAEi1On36BctHe3j5UTevv79emTZs8RxQ2rlkDECtUvYD4a2xszKmszZs3z3dIQaOyBgAACpJKpWSWnm6qoqJCqVTKc0RhI1kDAAAFqampUVNTk8xMTU1Nmjx5su+QgsZpUAAAULBUKqXu7m6qauOAZA0AABSspqZGq1ev9h1GWeA0KAAAQIyRrAEAAMQYyRoAAECMkawBAADEGMkaAABAjJGsAQAAxBjJGgAAQIyRrAEAAMRYopI1M7vIzH5uZl1m9knf8QAAAJRaYpI1M5sg6f9IapJ0lqSrzOwsv1EBAACUVmKSNUkzJXU5537pnHtT0gOSPug5JgAAgJJKUrI2VdILw8Y9mWVDzOx6M9tiZlt27do1rsEBAACUQpKStaNyzn3JOTfDOTdjypQpvsMBAAAoWpKStRclnTFsXJtZBgAAECxzzvmOIS9mVimpU9IHlE7SfiTpvzrnfnqE5++S9Kvxi1A1knrH8eeNN7Yv2di+5Ap52yS2L+nYvuj8rnPusKcFK8cpgKI55wbMbJGkRyVNkPSVIyVqmeeP63lQM9vinJsxnj9zPLF9ycb2JVfI2yaxfUnH9o2PxCRrkuSc2yhpo+84AAAAxkuSrlkDAAAoOyRr0fmS7wBKjO1LNrYvuULeNontSzq2bxwk5gYDAACAckRlDQAAIMZI1gAAAGKMZC3BzOwsM/tr33GUIzMz3zGgNNi3ycW+Q6hI1hLKzCZIOlfSbDNL+Y6nDJmUvD8Oh4vXzHgfyDAzc5kLec3sSjP7Y98xjVXSjs1ijdh3v2dmv2NmVb7jOprh+8nMjvUZSzHK7XjLV1T/L7xJJ5Rz7qCkb0pqk/ReM7vGc0iHFWIiYGa/L+mbZnaMS9AdOiP+mDWZ2WVmdoZzbtB3bHEx7P/nv0taLGmf34jyk/2DYGbTzOydZnasc85lPtSVhWH7bpGkr0j6pKTPmdlxXgMbxYjfyWsk3WxmVUlLfEZsx2Tf8fgy4vfwj82sOqq/EcH9IQ3d8F9i59yrSjcJfkIxSNiGHajvMbNLMwdqEImApWV/X34j6QVJp2a/5y2wAoz4Y9Ys6fcl/cjM/sxnXHFjZr8naYFz7n2SfmFmF5jZB33HNZpMYjZf0sOS/kbSE5nfv4OhJ2wjKlN/LukqSRcr/fs5R9JaMzveU3ijGvY7uUTSxyR93TnXr8zf5qR82B2xHf9kZrdnkpW3eQ5tXGV+DxcoXUhZJmlj5kPxicWuOxEHAtJGfHpZaGbvlXSmc+5rkr4j6X1m9le+4sscqHMlrZf0V5J+nKlCJVb2zSbz/35SZvEBSW9T+s1VUX1yGg9m9l8kXSnp/ZL2Zh6tZvYBr4F5dJhk2ySdambLJd0p6eOSvmZmHxr34PJkZudK+oKkSyRtlfSHkp4xsxNDT9iGvSdOl7Rf0qVKH+O1Sidub5f01bgmbGb2dqXnvL5YUq+Z/VdJ95vZHyXpw66ZXSfpzyXdIGmepP8pqSkJp6KjYmY1khZJutI590FJrZKaJL2z2HWTrCXIsDelpZL+XtJ7Jd1hZk3OufsltUu6yMw+PB7xjPwlNLMGSddK+gvn3KWSvibpbjM7azziiZqZTZF0k5mdbWYnSfp25g1pkqSbJb0z80cytkZ+MnfO/YekD0laKOlS59w0Sd9Q+rTun3gI0asRH4D+2MzqJHVJWqr0H/kvO+euUPqU2u/GsYpqZpMk7VJ6v75D6WOzWtI2Sc+a2QmZyyaCMqKidq2kJZJ+pPQHkJmSbnTO/VzSLyW9qbc+bHk18hhyzr2s9DG3RVKL0on2C5K+EOfK1Ij//2P0VnKckvSi0h8a/pukBZbga/HyZenrW4+V9FtJp0mSc26t0sfj3xW7fpK1hMmcsvp/7Z15vF3z1f/fS5KGxFBTI4bGUGqIqYZHDAlKQkxpQ9QYc/GgwU8VVYqghqLG9mlVUU9RVA2tPhSh5pqCmmqueQgiBMnn98fne5rjNAlJ7r37nHvX+/U6r9y9z7k365x99t7ru9ZnrbUZsC6wIJ7vemhEbCHpUuByHGVrbztmB/aoOWxl+zvACrjwAUlHAX/BUYn+7W1TO7AY8FUcreiLNUwrAyfhi+pHwNLQnOmKiOhdW5lHxCq1dKekV4B58c0B4BHs6L9SiaFNQEkNnwMcC9wAjJe0v6S7iyOwN3BNs0VRI2IpvHB7WdJjOO33u2LnVfhGsXKFJrYb9fpLYABwmqQ3cdR7YWBoRHwXn6MHSXq5MmMLDYuDrSJi74gYDByJI1Lfl3Q4lre8XaGp06XhfawCfIrPne7AEEmDJR2L71FrAJ06ulYW7f+DU+9/A5arC1JcjSOmszSLvaUGuXdFImIeYE5J/yoX5vdxinF7YC1gE3ySnFHOnys6yLTZcQr2k7Jyfz8iTsAr2GUiYrCkv0g6pnxJ5+ogu9oMSfeXE24IjhiOlrR/RPTGmq/VgdERMaY4QE1DRHwdGB4Rv8ARl72BTyPiUSy+fhXrHC/G2rVvS3qhMoM7mIhYUNIbJXXfHx/fTXEabQPgZxGxD75h7gFsW6I0lVJ/kyy8h+1+Et8sXgWWLdH3EcA2xYnrNNQ+g4iYrSxGtgNWA/4QEU9Iei8iRuNjOg9wYIleVU6dg3MgsC1wN44C7gAcLOnNiDgI2BHYVdLHlRk7HRrex9b4/HgpIiYCi0XEt4FPgBeAsyW9X5217UtEfA24DC8W7iv3u52AwyPiTXx+HiLp01n6f5psoZjUUQ76QBytWhpHeraV9HG4Wu0pSVdHxPeBuYH/kfR8B9nWA0ebFsdCygm40OE3WOPTGxgj6fqOsKc9CAu2jwXGAOvgytsrJD1cnp8Ph7dvkvR/lRk6FSJiQ3wRfQOvbIeV780R+Lvyc2ARnEr/o6R/VGZsBxMRS+CU2W2Sriypz9NL6r72mtHAE5IuLLqv96qxdgp1zglhwfJkSeMjYhBOPR0CzA9sBawEXCnpqsoMbgcaIjpfljSupONOw9/rY4HnJU0uqTk1g8NTO3bF1h7AxcD3JL0SEYvga+b7wNnAKHzsHqnO4s8nIrbG179vlsX6kjjbsCpOxc8F7C5pbIVmtjvlXnghdrpXkvRB+Sy+CiwPPCDpzqkstGaIpkvdJFMonvhTwLfxKvmiugtPL5z+PBqHz3/RgY5alIqlRYGf4MjDQ8Bekt7GkZtPgI2L4LLlKNGzHYFDJR3IlIvPd8LVgpT32genpJuCmo5E0l+x47wg0A87/ACn4IvKFpJuk/STruSoFd7FEag1I2ITSc8B80XEmXWv6YEXIuCbaKVERB8cOeoREf3wcOnDImJ1nM6eBCwt6Ul8jHeWdFUzauxmhTpHbV/glxFxOLAK1kZNBA6niLklTWwSR20+LDsAWKPYtASOuICryx8F+kp6T9IxzeioTeW7NB5LBnYu96HLgRPw+xkODO2MjlrtcyjSkk3wNXZP4Drg6pJpekbSLZLOkXQnzHohWjprTUi9/knSi8AFwO+B5SNiYNl/DPA74B1gq45MYdV96eYEjsM3/8HA0LJ/Er5hnF00JC2HpA9wtHCjsn07cDtOV2wfEfNGxJw4HXxZZYY20KDjeQHr6+7BhSfLS5qI9TBzdLYb+RelONlv41XvqHBhxbeA/hFxRXEABuPzqymqfSW9hiNnSwBv4vPuFfzd2wIXQ5xSbhSq2dwMtrc14SKf7XC14dbl3yGS9gHmwMe0mSQ+q+KU2LF4AQWWUYyIiGHlGHUDFo6IXs14XjZENOcp175n8LV+c+AOLM95C8t2xrXqtf/zKCn4LfF9eRN8nVgPRxkfAW4on0+bks5aE1KX6tiiOGdjgENxanFoRPQPt1p4Ajizo1dhEbFMuAx+MnAiLlXeTNKzETEEi+8l6emOtGtWqFstrRAR60TEvHilOLmE+8Gr30eB/5X0jqTxwC6SHq3G6qlT0j/r44vJBOBUnPI8p+gKR+LUZ6e7kX8Rys1+Lxydehff+PtjbeKtwDhguxKlqpT4bMX1M/jYPYMLCs4CdsNyhInYKei0DUnDzI8j+t8GNsbH725gv4gYImlH4LhZ1Qe1BeFxgAMl3YRlLAfidCe4COyXwFkRcT6WkhwlaUIznpd1jtrB2OEcg8+ZiyRtKukvwFL4uvOvquzsCEqUeyQwCB/HuYF7JX2EI7sP4YVg2/6/Tfi96LI0rF52xiHlq/CJfjTwOE7HLYtTb4M6+oYSbmFxDDBR0iERcSr+0m6NRb7HYjHldR1pV1sQbnx6JPAAbs/xW3wj3AA7yktiEfC1tWM1qzqEtiAiekma0LBvAXxjWAPYFV9QTgZewkLYZzvc0IqIiHWwvvP1cCuEM4AbJV1RLrz7YkfnNEk3V2lrPeHeaHvhFhzjgT3kApfTcCRwdVmQPgfuDbdyLeXSmajX6tW2sR7oXEm1Pl73ATdih2d8Rab+m3LstsHOf+AK+Y3xeXhqbYEdEYviDMX7kprayQlXrR6Hsw1DsWP2AF4UDsWOym6dMfVZT4maHY2DFesCO0p6Jtxj9CFJb7TLfywpH032wPn+43BBQeDKkluAtcrzy+JKzI6yJ+p+7o5P0p8BPyr7TsSrxCuBTRp/pxUeeLV+HXbKdgAeBHritMqCOC22UtV2TsXuzYDTcauCHYAT6p6bDxiNI4QL4qrPr1RtcwWf0YHl+M5etv8fxREv232Bx8pnNVfV9jbYvhy+KbwILFu3/zRcAbrAVH6npc696b33up/3xOL7fbBjPQ+O4CxXzoFrgT5V21xsna3h+J2KC3zAsoRLcaR7P2DPqu2dzvuYH5i3bntHHEmrbW+Eszs1Mf1CVdvcTp9DLag1Z+36gAMWDwHrlO2B5bNYub3syMhaE1AXpekmdxu/AVgG6zCeLCnH4cDBuF9Qu/dRm4qN3wQWkavjugP/hduHPAf8tNjdW9Z6tRzhLuI/wFqmocBOkv5Z0tD3qSFy1QxExObYwfiRXBW8OO5rd4Gk48trBuEb+2NYdN4yHdFnlYZI9Yp4MbEh7sO1D9Z+XQSsiHvo7aEmasFSdz24Gts9QtKf6p4/E6cDF5cLfjoN4b6N12HZwbU44n057mNVKygYhFvSTMYRnYersXYK8dmK3R1xUVJvXOTzJ0nXR8RJ2FlbHR/ThyozeBqEK+GPxtf3pyUdHu4lNgprke8urzsfN46+oypbO4KSddkff9eOBp7FAZWeWDu6Oc4oXdtuRlTttXb1B5+NWi1S9/NFWDw8W9nujfvy9KvItvXLF3WHst0T3/AewJG12ahbUTb7gymrpdmBnuXnM3DKabWyvQG+WSxXhY2fY/9CwM24ugygd/l3INY07Ve2tyvHZ+Gqba7i+DbsOwsXifTF6YuTsTD6TmDFqm1utB2Yu27f6liftVPZXqb8+7Wq7W3H9784noLyJLB22TcfTg2fWbaXAOav2uZ6u8vPA3CqnXJtPKB8/2qZh0WABau2eRrvY5NynmyFHeOLcHV04IjSyXhhuwvwT2Cxqm1u589jWaxNq7XIGYflJbPjhf2uTMl6tVtUOyNrTUK4AecWuJrkBUlnRcT1+IuxoyqKiIS7U/eTIzcDgWuA/5Z0cYnabE8FRQ5tQVktjQSEV+rz4Ojlp9jh2Yn2Xi3NJKUA4lJs71P44rk+8Bp25PpgR2QjfIPoVI1Rp0dDRG0wTv1eXLZPxj3ztpb0ctEMfSDpneosnkJdlH0YLh4QcJ6kP5Xz7wacSvsOvi7cW6G5bU7DsfsWcD++5jwrz1okIlbFkbYdJH1YmbF1hNv5rIXPySVwheC9OFqronMaiZ3uqyT9sTJjp0O4zcibwHC59cuauAP/H3ALmxPxInYQDiD8VE1WYDWrRMRCwJqS/hhueHscvkbsXp7fAS/st5cLKzqGqr3WfAimCFEXAf4InF/33K04rdWR9tSc+PXK//8hbsYLPknHYc3aS8BGVX9+M/keV8BjQYbgsPa/sO6rVumzP7Be/efRTA+8yj0Y37xfwiLfPbAj8hO86l2cLhRRK59JfXRjT6wruQ2nQJcr+0/AsxgXqcLOL/A+NsY3+j5YW/c0UyJqawDHAxtWbWc7fwbDcZT7yziFOAY3cg5cXPF3mieiNlv5rv0C38DB6cJbsb6ue9k3F44KNoW2bjrvZzOcMVkZj6H7Mdak3dtwb+pZta3t8N4DRxa/Xo5XAEfhVPxAoEd53Uhcab8A0K1DbKv6w+mKD1w1uX7tIOPo1IY4nPqXui/EwuXfDg8z4zTRo1ib9kPgLrzaApdsf4sirmy1Bxb9Xoir/2r7jsB9yb5etX0z8D7mxOmWEfUXzuK47VC1fRV8HnPV/bwB8Oe6c+mn5XNZtmwfBSxRtc3TeB/7lOO6FV5Q7IfTTXvTYnKDmXz/q+HK92F1+/oBY/HC5FfA8lXbWeyqLyY4Ere12Kps/wAvvusdtqZb+E3jfW2CZS8/qNs3J3ATJX3bKu9lJt57N5xl+TUu2orisJ6JAxi1a0rfjrQr+6xVwzpYGL522R6H9Wm7yQNwPylp0VER0V1ujNvRLA/cIeluScdhvcV5EfEd4FFJV0n6WzM2cJwaDXa+g0P6S0bEGgCSRuOT866I6F1K75saSeMl3SnpMrnZLRGxDRbM31WtdR1LRCwG3BFucAtebPTHNx0kHYQbdh4fEctI+rGarH1JRCwNIOlc7KzsgqeCnIUjhMNxSrdTFYlM5RoyDr//Q0q6H3k6y5bYWThOTZLW15Rigv1w5GVxYJ+I2FbSiVj7dTql75bKXb7ZkfRnnHXYtbRrAmeA5sAjpVrmvXxRat9DSZOwo3oLXvQNx87aGziiNqD8ymv1v9fepLPWgZT+QEj6GQ4p/7BUWd6KQ/zPhUdY7IbD5Reqg5o7TuUL9yTQIyIWLRVOFxc79wI2K/2qWuaElaSIWKvo7GopxOfwsPPVymuOwr2rPignbMsQEX0jYhRO6Y6U9M+KTepQyoLmfNxkdAWcKjwXf1fXL685GDsBlffhqlE77yJiZeDmiLgEQNbQvYunZQzEFaxHSnq1MmPbgQaNWv+IWKp8d3fBqc4zio6K4lzv3qRO9g54vNIgnHLfOCK2kHQS7pXZFJrIGUGedzwKuD083mtXvHiofPxae1DuEesVvejakn6DFwdDgWFMmRryVnn95NrvdYR9WWDQAdRfkMr2vjjtORfuUbMb9to3wf283gVGq4NE+3Wi5oE41P0p/pJeiMXrt2Pd2qHl5+2AI9QCQ9rr3tvaOHp5B34vl2At07G4d9zFku5tbMDZKoQbo26Ih4+3zOSIWWUq59Z+wO64OOQZ3Bh4SSzq7jgx8AxQ2iQcgCvO9gNukbRzcTJH4nFuP5B0TXVWti8RcQCudn8amEPSiHBj58Mo10g1WRFI3fZiON35XUn3lIXs+biKcLSkqyoytU0oLYKuBFZVJysmgM/cI9bFhSG/xtePX+Binm+XxxWSLq/M0I7MuXbVB3UNbLGw/UFKc1JchXgrsEHZno2ib+hgGzfHmpBd6/6dHTszF+GRLluU1w6nA1uItMF72xCfdCuV97Q9Fm4PwRVNZ1LXcDQfrfHAYvttaBA6Y4fnPjz5Y16sJToN6EUT6WxwhLcnrnasFRD0wBWQ9ULufrXXV21zO30O38HavDko01GAW8tzfXBBSFMUytQfA6x9XajYfRCeDdm/PLcndtg6RRNqoFfVNrTz+1sdV3jW9IZ9y33wkLK9IxW392mmYbedkqK5OCEi9pbDxy9isfDCwOuSTitl35eGh/regfPlHWnjV3FacCtcUPAh7vI+r6Qjy2v6qjQMlXRFR9o3s9StgLfEkYtLJX0UEX/FLRG+i8Wk31MLRtMSFsPDzSdHxLUquj257U0P4Hu4qvcS4B01WWPj8t2cGBGP4XMOWa+6E3BPRLwh6VBZr1V7fcsTESvh6P1EOeX5KF4AjsTRm54R8XhE3CppUEQc0SznZ+0YRMQheBH4PtYT/hMvxM+NiIeBbwJbSnq9KlvbkmY7d9qKukzKUNw66/lwc/dXImJ74OSIOAX4nSqeN5vOWjsj6Z2iQVs7IlaTdEpEvAGsVy7G/8Ipx69iDVWHUBf6/ZKkFyJidzxe5Ae44mUgcFlEzC/pCODV+t/rKDtnhjobFwDekDQqIj4Bfh8R/SW9GhE3Y0ft5Wa5ESQzhqQrI2IydsoiIq6pOWzAPcAK5XvQNPq9uvNuWaydexNrsw6MiAflFPaXcL+uYRFxT6ssjr4IETEER7JvBBaLiP2Bf+DF05pYuwuOfG8bEYtKeqkSY+soutZuWBbSH9hY0uCIuBxXkI+OiL641cUyuNK8y8gRWo26e8Q8eCF3TES8i9vm3BYRfy/PzY2lQZXrXNNZayfqtU+SJkaEgN0i4gXcNuAcYNVw1eFKlCadHWRb7YYxGN8QDpQH0X4duFbSe+UmeBZuJfLvFWWrOGpFB7RfRIzFTYYPCTemvCciBhSH7TJJH1dscjIT1I6zpD8Ukf4BOMJ2i6S3cc/CPhExt6T3qrXW1K4J5by7EJ9btWHtC+Jq6xdwxGYoLob4qCp725qI2AK3yNkRz1E8Hss+5pf0WlnEDgg3Yv0G7iVXeWQqIjbBcpAzscD8S8AtJbo2J7BzeWkfSWNwT7ikiSn3iM2AvSPiQWCMpDMioie+7z2OF/snq1kKKqrMwXbWB5/VNewFbF5+Xhvr1bbBWpX1sQB66Q6ya36m9Jmq3Qw2qnt+Layf+zHwMjCg8f006wMP6p6j/Lw61hssBfwvFv/W+hz9Cs9160Yn71fV2R6N30M+2+NqGBZBn4tbJTyMI2vNYHevup9XwTf+dYGvlHPtaryC71f2L4kdtocpY6Va/YF1XfcB15ft+XAh1YU4sjYEZxe+D1wBrFS1zcXOQTiatkbdvmVxNmQMU3puHYAbp/au2uZ8fKHjun65R6yAZ9DehItYAPbFGsqhVdtZ/8hq0HYkIvbGuqgRkp4q+9bFYueL5BYeHWVLT1xZ1QtXvIzAg5EfwyOJdsLNbxfGN4uX1KTVc42EB8vfAhwl6aZSvbQQfm+n48//uYhYvPy7gjphVVNnpj79HhH9cPr6k4bXrIxv+P3w0OzK058lWv3fuMDlLdz/7mO8WHoTOy37Yadgd0nPRsQSuKhnXzXBcPK2IiKWwdGpF4Cv4ers87Cj/SvgG+X87KYmaZ0TEQcBk+SoS3dJn0ZEb1xQMA+u4n8fNyzeXi04dq8rEW6f1QM71zfgQoLj8fk2BLhc0vkRcTheMB0s6aGq7K0nnbV2oHwh5sRRncMlPVR/ASol+UfjcuB31EEHISLWwVWf72FdWl/geSzw/TLutD1Y0vjy+qbXpwGUUvkb8PDyR4uA+RKcYllf0usl5D0M65s+bIX3lfwn5ebZH59XNR1l09zc6ynOyaV4du75Zd+iuO/WNZKOKfsWwIUQ16rM+mym9G1bEp61eAnwiaR16vafj3VeYyszro46OcWZwLuSfljS7SGnsufFDtpSuDnqb9UkjXqT/6TO0a4d19mx03YRbrnyWkSMwe1+Di3bh+GgSuWaScimuG1GOZFrzFEutD2A2k1ktvK65SXdAmwq6e32dhqKTgsASX/DKZdewJ+A67GDcwReXXyA0xW11ze1QxMRS0bEvLLu7D28wgXP+bwGi5hXLNHM0fgGOaHZ31cydSJiJB5zdrCsOZwnIuaQNKkskJqGiFgeO2o/Liv1bhGxf7nwb4vF80cASHoTd+X/91D2zuioAcii++2BjyPiRICIGI4lGOOqtK2eumvEVcBa4eKwWmS3u9zzbTxwCm5WnI5aExIRXyla0U+LVvTssnCfC0e4lwQ2KAsrAadKeg1A0gnN4qhBOmttRt2JvAuO3oCnFPwmIuaTS/J3BP6nrJo/bG+bIqIXcH25ydXsvAvPTOwDvCnppXCJ8vXAuZLeaG+72pAlgWfC41DGY00ekt4CfolTTj/EusGjJP2xwalOWoC6Y7YE/p4uWVa9lwC/KOdTs1X0zgesLOkPZfvPOEWLpGdwm4A9IuKYsu+Tqf6VTkhx2PYEVouIO3FKcWtVM1bv87gbNwLftjhsk8uN/zt4ysIHTfjdS3B0Ghe0nB4RA7BWdByWHeyMe24eiDsg/C9wRrNEdqdGpkHbkIj4Lg6Nbyfp8bLvZJx6vAnPFNulI78QEfEt7LCcJOnSuv0DgZ/g3kZDgGclXdsqqc8apVLrPBxVuxz3b3qubM8GvA38XdKEVntvXZmoq6aOiF7l+G2A9V8L4Yajz+HegCeq9ABsJiJiU+BsnFoZU0t71j3fF/iapNuqsK+9aDzP6qpgPzMdpOj5zgFGNfVNMmIRPBXjm7hI4kNga+xgpkatCQmPADsMT8Toj3unVI8qAAAN6klEQVQy/qTc49bHjZifxgUuHwJzS/pXM98j0llrA8rKvze+8JxaNGo9NWW49gDsRLyu0uCyg+0bCpyIR59cWnfxPBuP4bmx9j6a9Ys6PcrJ91esP3gRV4Z+BevwDu1sN8OuRETsiftvvYOjamNL5JSI+DbWfm5cS100G+HZvzcAX6pzPtfGGrV9JI1r1fPu8wj3UFumbP5M0lON+sKI6NEKUcXwOLfVcDHWK8DNkp6s1qpkahQJwkV4XNQ1uC3Oidjf2aS8Zj0c3X0MayUnTuPPNQ3prM0kU7vARsSvcBn6GbULUIkGjC26lMooefoTcYTtoohYC89A20HS/VXa1hZExIa4bcPyzSg2T2aciBgB/AjYA1crT8CRjevwVIofAcPV5JW9ZbH0M0lfKyv+K/Csz6afrTuzRMQopkwOORMvnEZKerhZC0KS1qekPv+AZz2fX7d/RSxP+gBHchURg4C3WiU6mpq1maDeUYuIFSNiQLi57RhcBbpheW4EzodX3nxY0nU4hTS6RNTOA/5fZ3DUACT9FRgFvFYqtRqLPpImJyIGlVVx7dgtj3WUd+GxUs8Am+KinadxkU5TO2oAxSnbLyIm4BvJoZ3NUQuP96r9vAC+Dg7Dx+tt4PfAxeEJIumoJe3Fhzi7cgV85nv5CHAqllCcCSDp1lZx1CAja7NERBwMbIe7jD+NV/wr4tD/l3H4tUM1ap9HRCyGO3B3l/RE1fa0NSWKMUGuuE1aiCLavgN4Xx7TtgPWBn1fU/oU3gLsKunZ6iydOUpKdG5JV1VtS1tSohnr4sXquljy8QCeInE2HuEzB264PQGPsvukM6Z+k2opxWa34cj1dWVft1IxPi/ue7o81rm2VAVv5RGfVqIhorY8FjevI4+T+j7unH8WrjhZAnhVTTAupZ4mrbhqM2oRi86qA+qM1DSUkn4Xbgj7ZLix8V/w4meniPgzrvadHbdpaTkk3QSd8rvZC+tEb8BVsKuUa2If4Ems590cRxXPU454S9qJogE9ExgeEf+S9GDd02viYoND5ZncLUWmQb8gDY7aIOATvIJcGkDSSfiLMFLS+5IebjZHrSvRyW6GnZZyXtWE9/+NI9In4NYrfXC11nhcdr8XbmD5VkXmtgmd5btZkxnIzYk/xqO07sLOGViIPw9wMu7jeKk6aP5x0qW5Cn/39i5a5snhhvCnYy1byzlqkGnQGSY8jPjHOD2zD+7+P0YehL4frvr6aZU2JkmrERHDcBuZ70l6oVQS7oMLYB6IiHnw2J/xlRqaANMssFoJa9QWxVrDxyKiP9YRjW/Wit2k81GiuiPwnM/78aSJEzWl72HLkc7aDFAqOy/Empkbyw1mMLAA7pq/ObBVq+XCk6SjaYhU9wNuBu6VtG2dxmRfvDDaVNJ9VdqbTKHh2B2IW1q8AxyJCwv2xoPp38W6te9Jen8afy5J2o3itE0GesoN4FtWgpDO2nSYSnPHBXHflg8lbVD2rYBXkssA16sJhkcnSTPTcLPfH6c+H8Cpz0Ml/abutXvgnlZ5XjUZpb/hcThFvSkeGbUp0BOP1BoGHNBMBVZJ0qqkszYNGm4oq+JqpvuwBuMsnJLZvkITk6SliYhtgaF4FNhzEbERnrX4U0kXVmtdMj2KHGQP3FT7grLvFGBtYJik1yNidkkfVWhmknQa0llrYCrRtEOA4TiU+hgeDn4r8FM8sH1YJYYmSQtTOsL/HlhB0uJ1+zcALgAOk3RJNdYljUzlutgfC7afxm0SxpX95+LWCBsCk1s15ZQkzUZWg/4nPcG9WSJiKRzWX0fS2nig75pYl3EAMD4iFq7M0iRpERobFEv6EE8leDEiLqrbfzMesnxXx1qYTIuGLMNaEbF4aSa6O25R9N3S3wpJ+wDbSJqUjlqStB0ZWSuUm8mSuCnngFLduRRe/e8k6ZGI6AVcDNwh6ZRoGEycJMl/0nCz3xMX5EyQdEbpdv9LPPZl9yrtTKZPaQK+FfAc8DLuBN8dz0S+D6ev363MwCTpxGRkjSk3kyJivgC4JSKWLNvXAUMiYmlJE3CX7p71/aGSJJk2DcUEI3Gz25Mi4kR5Zu7uwJIRcU6FZiYNlFR17eetgCGSBuL+kt8Evo/7TR6Ae0xmk/UkaScysgZExML1zRoj4mhcfr46TnnuBgwA7sT91YZKerwCU5OkpagthEp7jjOwKH07POR7fuDvkvaMiPmxBvSlCs1NCmVs22DgVEkvlsrPZyntiYDDcDHIq8DhwIuSPq3I3CTp9HT5lVBp5Hh/RFyNxbKnSDo6Il7Fztk3gEPxRWoBYONsI5Ak0yciNqUMXY+IkyQ9HxE7455cIyStV9rejI2IJyWdXKnByb8po75GA0cBLwFIuiUiugMrA/tKejoi/gFMBD5IRy1J2pdMg0Lg8RQAg4CjI+I24BE81+5GYFFJ10j6dTpqSTJ9ImJj4ETgIXyNGQUg6T1AwMsR8SU8qu1XwBUVmZo0EBELAQcDe5Ru7z0iondELIIr4t8DroiIfXC24fQcq5ck7U+Xj6xJeigiTsX9nv4G3IabdG6J54ttAPwlIlbEvdUyb5wk06DM4rsSWLVEX0YAmxW92vU4ej0OF+4sC2wm6ZnKDE4amYh1aB9FxOzAD4B1cZX8o8AhwPs447CzpOerMjRJuhJdWrPWUKW2PrAZXjn+vDR17IGdtgfyhpIkn0+RFTyAnbA/R8SDeBH0EdY6bQy8jXtxvZbnVXNRquIPwnq1FXBm4XZgLLAf8NtyXL8k6ePqLE2SrkWXdtbgPxy29YBvAW8A1+aYlCSZcSJiDVzxOQnrmy4r+08GvgLsJmlShSYm0yEi5gRWBBYDrpY0sez/FTCmfhxYkiQdQ5fSrDU25gS3FYiIbuXn23AKpx8wuETWkiSZASTdCwwEugH159DzOAWaLW+aGEnjJd0p6bI6R20bXFxwe7XWJUnXpMto1hoiaJvhG8bTkp6SNCkiupWu27dHxCTgn5I+qdToJGlRJI2NiMFY7/kp8DqwC7BL6j5bh4joi4ey7wlsmwVWSVINXS4NGhEH4VTng8BCWOh8uaTJEdE9S9CTpO2IiNWBe7C0YH1J/6jYpGQGKI1xNwSekPR01fYkSVelq6VBBwEDJa0HvAV8DVd7blMib+moJUkbIuk+3N1+YDpqrYekDyVdl45aklRLp46s1ac+y/ZXcV+1dfBUgm2Ak4HlgHMk/bYSQ5MkSZIkSaZBp9WsNWjUvoHHobxQtnfCqc93IuJR3Fvo/6qzNkmSJEmSZOp0SmetwVHbF9gfeCsifo2nFdwDXBMRy+GROJtnF+4kSZIkSZqRTums1TlqW+Hu26vgZpybA72Bn2PR7DrAlpKeqsjUJEmSJEmS6dJpNWsR0Qc4D1hE0ppl32Z4IsE/gV9IGlehiUmSJEmSJJ9Lp6kGbWx4K+k1YDQwLiKOL/uuA/4MLIILDZIkSZIkSZqaThdZi4hdgaXwLMLfAIsCewEvSTqyvKa3pA+qszJJkiRJkuSL0fKRtYjoXvfzzngI8d+wHm3v8tTZwCoR8cOyPaFDjUySJEmSJJlJWjqyFhEbAYtKuiAiegI/A26UdHlEzAMcBXSXdEBp3/GqpJertDlJkiRJkmRGaNnIWkRsCPwBOCwiZisDh58A/isi+kh6F/gxsFJEzCvp/nTUkiRJkiRpNVrSWYuIIcApwL7APZIml6cexK05No2IfsBAoBuQY6SSJEmSJGlJWi4NGhEb43TnbpLujIixwC6S/l6eHwEMwPMIuwOjJD1UmcFJkiRJkiSzQEs1xS3FBMsCexRHrRvwOrBA7TWSLouIvwKTgNkkvVWNtUmSJEmSJLNOK0bWukv6tOjUJkfEMcB7kk4pz28DzCfp59VamiRJkiRJMuu0nGZNUk1/VvMyPwDWBoiIHXAF6K0VmJYkSZIkSdLmtFQatB5NCQn+CegbEcOBUcAISY9XZ1mSJEmSJEnb0XJp0EYi4qvAc8BTwDBJ/6jWoiRJkiRJkraj5dKgU+EVPKFgy3TUkiRJkiTpbLR8ZA0gInpI+qRqO5IkSZIkSdqaTuGsJUmSJEmSdFY6Qxo0SZIkSZKk05LOWpIkSZIkSROTzlqSJEmSJEkTk85akiRdkoiYFBEPRsRDEXF/RKzdBn/z+oj4clvYlyRJUiMLDJIk6ZJExHhJc5afhwCHSxr0BX838PVz8tS2kyRJ2pKMrCVJksDcwDsAETFnRNxUom1jI2Krsn/xiHgiIi4EHgHWa9heLCKei4gFyusPiohHymNURe8rSZJOQEbWkiTpkkTEJGAsMDvQF9hQ0t8jojvQS9J7xfG6C1ga6Ac8A6wt6a6IWLx+u/zN54DVy2svANYCArgb2FHSAx32BpMk6TS07GzQJEmSWeRDSasARMQA4MKI6I+dq+MjYiAwGVgE6FN+5/maYzaN7RrrAldJ+qD8/SuB9YB01pIkmWHSWUuSpMsj6c4SRVsQGFr+XU3SJyVaNnt56QcNv9q4nSRJ0uakZi1Jki5PRCwLdAPeAuYBXi+O2gY4pTmj3AYMi4heEdEb+FbZlyRJMsNkZC1Jkq7KHBHxYPk5gJGSJkXEb4FrImIscB/w+Iz+YUn3R8QFwD1l1y9Tr5YkycySBQZJkiRJkiRNTKZBkyRJkiRJmph01pIkSZIkSZqYdNaSJEmSJEmamHTWkiRJkiRJmph01pIkSZIkSZqYdNaSJEmSJEmamHTWkiRJkiRJmph01pIkSZIkSZqY/w/HLLEAr87FbAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a traer datos estadísticos de la ciudad de Bogotá, directamente del DANE y vamos a ver como estos datos nos ayudarían en inclusión de nuevas variables para el cálculo del precio de los inmuebles en la ciudad de Bogotá.\n",
        "\n",
        "Encuesta Multiproposito de Bogotá para obtener información socio-económica y de entorno urbano de los habitantes de Bogotá para la formulación, seguimiento y evaluación de las políticas distritales.\n",
        "\n",
        "\n",
        "https://microdatos.dane.gov.co/index.php/catalog/743"
      ],
      "metadata": {
        "id": "Cpe7y-3Fqw5F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datos_raw = pd.read_csv('/content/drive/MyDrive/inmersion_datos/Identificación (Capítulo A).csv', sep = ';',encoding='latin-1')\n",
        "datos_raw.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "rvQ2Iv4FpdIr",
        "outputId": "3b67390b-95da-49f4-f702-726e4e37d6ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DIRECTORIO  DPTO   MPIO  CLASE  COD_LOCALIDAD NOMBRE_LOCALIDAD  \\\n",
              "0  3004346.00    11  11001      1           3.00         Santa Fe   \n",
              "1  3004347.00    11  11001      1          14.00     Los Mártires   \n",
              "2  3004348.00    11  11001      1           3.00         Santa Fe   \n",
              "3  3004349.00    11  11001      1           1.00          Usaquén   \n",
              "4  3004350.00    11  11001      1           1.00          Usaquén   \n",
              "\n",
              "   COD_UPZ_GRUPO                        NOMBRE_UPZ_GRUPO  ESTRATO2021  \\\n",
              "0         814.00  SANTA FE: Las Nieves + Sagrado Corazón     11001187   \n",
              "1          37.00                            Santa Isabel     11001162   \n",
              "2         814.00  SANTA FE: Las Nieves + Sagrado Corazón     11001187   \n",
              "3         803.00   USAQUÉN: Country Club + Santa Bárbara     11001176   \n",
              "4         803.00   USAQUÉN: Country Club + Santa Bárbara     11001176   \n",
              "\n",
              "                           NOMBRE_ESTRATO             FEX_C  \n",
              "0  SANTA FE: Las Nieves + Sagrado Corazón  13,2266692027239  \n",
              "1                            Santa Isabel  17,8555889419101  \n",
              "2  SANTA FE: Las Nieves + Sagrado Corazón  10,0286103658331  \n",
              "3   USAQUÉN: Country Club + Santa Bárbara  28,6502122142802  \n",
              "4   USAQUÉN: Country Club + Santa Bárbara  24,4684068319009  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa152f28-4475-4298-baf9-6fd35db82804\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIRECTORIO</th>\n",
              "      <th>DPTO</th>\n",
              "      <th>MPIO</th>\n",
              "      <th>CLASE</th>\n",
              "      <th>COD_LOCALIDAD</th>\n",
              "      <th>NOMBRE_LOCALIDAD</th>\n",
              "      <th>COD_UPZ_GRUPO</th>\n",
              "      <th>NOMBRE_UPZ_GRUPO</th>\n",
              "      <th>ESTRATO2021</th>\n",
              "      <th>NOMBRE_ESTRATO</th>\n",
              "      <th>FEX_C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3004346.00</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>Santa Fe</td>\n",
              "      <td>814.00</td>\n",
              "      <td>SANTA FE: Las Nieves + Sagrado Corazón</td>\n",
              "      <td>11001187</td>\n",
              "      <td>SANTA FE: Las Nieves + Sagrado Corazón</td>\n",
              "      <td>13,2266692027239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3004347.00</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>14.00</td>\n",
              "      <td>Los Mártires</td>\n",
              "      <td>37.00</td>\n",
              "      <td>Santa Isabel</td>\n",
              "      <td>11001162</td>\n",
              "      <td>Santa Isabel</td>\n",
              "      <td>17,8555889419101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3004348.00</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>Santa Fe</td>\n",
              "      <td>814.00</td>\n",
              "      <td>SANTA FE: Las Nieves + Sagrado Corazón</td>\n",
              "      <td>11001187</td>\n",
              "      <td>SANTA FE: Las Nieves + Sagrado Corazón</td>\n",
              "      <td>10,0286103658331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3004349.00</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>803.00</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>11001176</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>28,6502122142802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3004350.00</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>803.00</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>11001176</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>24,4684068319009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa152f28-4475-4298-baf9-6fd35db82804')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fa152f28-4475-4298-baf9-6fd35db82804 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fa152f28-4475-4298-baf9-6fd35db82804');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_raw.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9l5m9TzZsDw-",
        "outputId": "5e6aeb95-547a-4518-d973-e02c669db320"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(106467, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_raw = datos_raw.loc[datos_raw.MPIO == 11001]\n",
        "datos_raw.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vq7Z359Rtim8",
        "outputId": "9abacad7-d3ab-4191-d9e2-cfb56bec5c6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(86504, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_b = pd.read_csv('/content/drive/MyDrive/inmersion_datos/Datos de la vivenda y su entorno (Capítulo B).csv',sep=';',encoding='latin-1')\n",
        "datos_c = pd.read_csv('/content/drive/MyDrive/inmersion_datos/Condiciones habitacionales del hogar (Capítulo C).csv',sep=';',encoding='latin-1')\n",
        "datos_e = pd.read_csv('/content/drive/MyDrive/inmersion_datos/Composición del hogar y demografía (Capítulo E).csv',sep=';',encoding='latin-1')\n",
        "datos_h = pd.read_csv('/content/drive/MyDrive/inmersion_datos/Educación (Capítulo H).csv',sep=';',encoding='latin-1')\n",
        "datos_l = pd.read_csv('/content/drive/MyDrive/inmersion_datos/Percepción sobre las condiciones de vida y el desempeño institucional (Capítulo L).csv',sep=';',encoding='latin-1')\n",
        "datos_k = pd.read_csv('/content/drive/MyDrive/inmersion_datos/Fuerza de trabajo (Capítulo K).csv',sep=';',encoding='latin-1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyQz-rqgum7n",
        "outputId": "4f7f90b6-ec68-401b-8dca-4ee4e42a6cbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (4,25,30,47) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (66) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (81) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (114,117,122,126,164,166,168) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (27,58,60,62,64,66,104,114,120,124,126,129,131,133) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_dane = pd.merge(datos_raw,datos_b,on='DIRECTORIO', how='left')\n",
        "datos_dane.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57c4tQFOurOB",
        "outputId": "df28b0b8-a36a-4004-ccfb-1371f36d7e2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(86504, 73)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_dane = pd.merge(datos_dane,datos_c,on='DIRECTORIO', how='left')\n",
        "datos_dane.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WbX7L_J4vJ4y",
        "outputId": "f4f632b8-9931-4a8c-d204-8da55cda9819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-98-a8281422ec17>:1: FutureWarning: Passing 'suffixes' which cause duplicate columns {'FEX_C_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
            "  datos_dane = pd.merge(datos_dane,datos_e,on='DIRECTORIO', how='left')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(238559, 281)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_dane = pd.merge(datos_dane,datos_e,on='DIRECTORIO', how='left')"
      ],
      "metadata": {
        "id": "Beu5syqsvRwT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datos_dane.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEpH8f3Zvbrq",
        "outputId": "d76f0894-dd7d-4043-b2b1-7deaf7203f5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(238559, 281)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_dane.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LKeCUigvdIh",
        "outputId": "db43101f-7f4a-4ab8-a87d-a369019ccf26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 238559 entries, 0 to 238558\n",
            "Columns: 281 entries, DIRECTORIO to FEX_C_y\n",
            "dtypes: float64(140), int64(126), object(15)\n",
            "memory usage: 513.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Desafío**\n",
        "\n",
        "1. Dar un vistazo a la base de datos del DANE, entender estas variables conceptualmente para entender mejor el contexto de esta base.\n",
        "2.  Organizar tu notebook para que tu proyecto quede mejor presentado."
      ],
      "metadata": {
        "id": "qnyIOv_pw7SM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Aula 4**"
      ],
      "metadata": {
        "id": "97jp5hfe1O2D"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8llsFmG7iiUG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}