INMERSION_DATOS_AULA_3.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "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",
      "metadata": {
        "id": "qGW8k7mWhP7O"
      },
      "source": [
        "Bienvenido a nuestro primer encuentro de inmersión de datos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "S73Bs_z6j2QX",
        "outputId": "4b831b26-43ba-4a3d-ce12-4323cf30379b"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    }\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>Baños</th>\n",
              "      <th>Área</th>\n",
              "      <th>Barrio</th>\n",
              "      <th>UPZ</th>\n",
              "      <th>Valor</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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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",
              "   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  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "inmuebles = pd.read_csv('./inmuebles_bogota.csv') \n",
        "inmuebles.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qkcJjAQrmyfN",
        "outputId": "70d24523-058c-49fb-ca74-e4e9526e33cf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9520, 8)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-4LMluEnha_",
        "outputId": "41397e9d-34f1-4146-eead-7aa085322789"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Tipo', 'Descripcion', 'Habitaciones', 'Baños', 'Área', 'Barrio', 'UPZ',\n",
              "       'Valor'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "X2nLnlFIn2EM",
        "outputId": "707d8466-e0a9-419e-f499-96c7a348a7c7"
      },
      "outputs": [
        {
          "data": {
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              "<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>1767</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>112</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 500.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>856</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Cedritos</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>89</td>\n",
              "      <td>Cedritos</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 560.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2831</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>255</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 1.650.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6833</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en El Batán</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>69</td>\n",
              "      <td>El Batán</td>\n",
              "      <td>La Alhambra</td>\n",
              "      <td>$ 338.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6376</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>89</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 500.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6267</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Bosa</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>Bosa</td>\n",
              "      <td>Bosa Central</td>\n",
              "      <td>$ 116.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8885</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chapinero</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>Chapinero</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 175.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5203</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chapinero</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>150</td>\n",
              "      <td>Chapinero</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 1.300.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en La Calleja</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>86</td>\n",
              "      <td>La Calleja</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 451.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Bella Suiza</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>180</td>\n",
              "      <td>Bella Suiza</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 1.550.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Tipo                              Descripcion  Habitaciones  \\\n",
              "1767  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "856   Apartamento         Apartamento en venta en Cedritos             2   \n",
              "2831  Apartamento          Apartamento en venta en Usaquén             3   \n",
              "6833  Apartamento         Apartamento en venta en El Batán             3   \n",
              "6376  Apartamento          Apartamento en venta en Usaquén             3   \n",
              "6267  Apartamento             Apartamento en venta en Bosa             2   \n",
              "8885  Apartamento        Apartamento en venta en Chapinero             2   \n",
              "5203  Apartamento        Apartamento en venta en Chapinero             2   \n",
              "493   Apartamento       Apartamento en venta en La Calleja             3   \n",
              "199   Apartamento      Apartamento en venta en Bella Suiza             3   \n",
              "\n",
              "      Banos  Area           Barrio                                    UPZ  \\\n",
              "1767      2   112  Chico Reservado     CHAPINERO: Chicó Lago + El Refugio   \n",
              "856       2    89         Cedritos                                Usaquén   \n",
              "2831      3   255          Usaquén                                Usaquén   \n",
              "6833      2    69         El Batán                            La Alhambra   \n",
              "6376      3    89          Usaquén                                Usaquén   \n",
              "6267      1    45             Bosa                           Bosa Central   \n",
              "8885      1    50        Chapinero     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "5203      3   150        Chapinero     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "493       2    86       La Calleja  USAQUÉN: Country Club + Santa Bárbara   \n",
              "199       4   180      Bella Suiza  USAQUÉN: Country Club + Santa Bárbara   \n",
              "\n",
              "                Valor  \n",
              "1767    $ 500.000.000  \n",
              "856     $ 560.000.000  \n",
              "2831  $ 1.650.000.000  \n",
              "6833    $ 338.000.000  \n",
              "6376    $ 500.000.000  \n",
              "6267    $ 116.000.000  \n",
              "8885    $ 175.000.000  \n",
              "5203  $ 1.300.000.000  \n",
              "493     $ 451.000.000  \n",
              "199   $ 1.550.000.000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "columnas = {'Baños':'Banos','Área':'Area'}\n",
        "inmuebles = inmuebles.rename(columns=columnas)\n",
        "inmuebles.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJT8oyJgo2hU",
        "outputId": "ab8a3065-0528-42fd-caba-f8862ead60cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "inmuebles.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyrj-ItzplxW",
        "outputId": "327a14b3-831f-4714-b37b-9c18f77d8528"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.iloc[300]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "NiK43WPcr0GG",
        "outputId": "26a4823d-a49d-4f20-83eb-227a207493cb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</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>300</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Kennedy</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>Kennedy Central</td>\n",
              "      <td>$ 116.100.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Norte</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>95</td>\n",
              "      <td>Chico Norte</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 980.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Kennedy</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>54</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>Kennedy Central</td>\n",
              "      <td>$ 237.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Kennedy</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>67</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>Kennedy Central</td>\n",
              "      <td>$ 320.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Cedritos</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>132</td>\n",
              "      <td>Cedritos</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 530.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            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  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.iloc[300:305]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "65he94kAsMXL",
        "outputId": "c5f4c8d9-668e-45f8-d900-055b21d147fa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'$ 116.100.000'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles['Valor'][300]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFH5bi8EsbRe",
        "outputId": "ec6920f5-1f96-4831-ecaa-35f96a1673ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(inmuebles['Valor'][300:305])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "601xMJ2ashKu",
        "outputId": "0f16ac4f-11c8-4a46-9aa9-96c265d78dd4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Tipo', 'Descripcion', 'Habitaciones', 'Banos', 'Area', 'Barrio', 'UPZ',\n",
              "       'Valor'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1pyNc9Xs85S",
        "outputId": "f95e05eb-5f6f-4728-83c8-3db7ee90f890"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "146.66470588235293"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.Area.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "iYqOIwb_tPfj",
        "outputId": "963f5ee5-c95a-4066-c3dc-3edd6a4866fb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2816</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>73</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 310.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9190</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Engativa</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>Engativa</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>$ 250.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9234</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Suba</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>62</td>\n",
              "      <td>Suba</td>\n",
              "      <td>Suba</td>\n",
              "      <td>$ 260.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1180</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 168.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3403</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>143</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 690.000.000</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>5976</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 144.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6893</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Suba</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>90</td>\n",
              "      <td>Suba</td>\n",
              "      <td>Suba</td>\n",
              "      <td>$ 420.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>88</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 500.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5186</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Santa Barbara</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>157</td>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 930.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6833</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en El Batán</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>69</td>\n",
              "      <td>El Batán</td>\n",
              "      <td>La Alhambra</td>\n",
              "      <td>$ 338.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Tipo                                 Descripcion  Habitaciones  \\\n",
              "2816  Apartamento             Apartamento en venta en Usaquén             3   \n",
              "9190  Apartamento            Apartamento en venta en Engativa             2   \n",
              "9234  Apartamento                Apartamento en venta en Suba             3   \n",
              "1180  Apartamento  Apartamento en venta en Zona Noroccidental             2   \n",
              "3403  Apartamento     Apartamento en venta en Chico Reservado             3   \n",
              "...           ...                                         ...           ...   \n",
              "5976  Apartamento  Apartamento en venta en Zona Noroccidental             2   \n",
              "6893  Apartamento                Apartamento en venta en Suba             3   \n",
              "82    Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "5186  Apartamento       Apartamento en venta en Santa Barbara             3   \n",
              "6833  Apartamento            Apartamento en venta en El Batán             3   \n",
              "\n",
              "      Banos  Area              Barrio                                    UPZ  \\\n",
              "2816      2    73             Usaquén                                Usaquén   \n",
              "9190      1    42            Engativa                               Engativá   \n",
              "9234      2    62                Suba                                   Suba   \n",
              "1180      1    38  Zona Noroccidental     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "3403      3   143     Chico Reservado     CHAPINERO: Chicó Lago + El Refugio   \n",
              "...     ...   ...                 ...                                    ...   \n",
              "5976      1    44  Zona Noroccidental     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "6893      3    90                Suba                                   Suba   \n",
              "82        2    88  Zona Noroccidental     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "5186      3   157       Santa Barbara  USAQUÉN: Country Club + Santa Bárbara   \n",
              "6833      2    69            El Batán                            La Alhambra   \n",
              "\n",
              "              Valor  \n",
              "2816  $ 310.000.000  \n",
              "9190  $ 250.000.000  \n",
              "9234  $ 260.000.000  \n",
              "1180  $ 168.000.000  \n",
              "3403  $ 690.000.000  \n",
              "...             ...  \n",
              "5976  $ 144.000.000  \n",
              "6893  $ 420.000.000  \n",
              "82    $ 500.000.000  \n",
              "5186  $ 930.000.000  \n",
              "6833  $ 338.000.000  \n",
              "\n",
              "[100 rows x 8 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.sample(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YY-zAytFuBsA",
        "outputId": "8c08827a-e7dd-4f56-a74e-5b6d0c6354f2"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(inmuebles.Barrio == 'Chico Reservado')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OJg8M1TuwcZ",
        "outputId": "4e4becb5-55bb-44a7-968a-e4d33de6bf9f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "225"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum((inmuebles.Barrio == 'Chico Reservado'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtT_ZLvGu_CP",
        "outputId": "e1a40547-8f08-4d93-9514-c1c21cec437c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles_chico = (inmuebles.Barrio == 'Chico Reservado')\n",
        "type(inmuebles_chico)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "mQJesAi_vLbs",
        "outputId": "c498cb6e-df9d-42ae-f07d-2fc194d62433"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\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>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>"
            ],
            "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]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chico_reservado = inmuebles[inmuebles_chico]\n",
        "chico_reservado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3IYReywvf26",
        "outputId": "db4a79a6-9ba4-44f6-824f-7faf39b6115f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "142.78666666666666"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chico_reservado.Area.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7V5_WzXxCDl",
        "outputId": "9c69cd96-1c1e-4dfa-f3b2-05b0d8c192b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "146.66470588235293"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.Area.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54SeSdSFxZ9v",
        "outputId": "c0c7c971-a08e-4b23-e3fe-e7abdfa8be42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "149"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(inmuebles.Barrio.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJz7RkSBxpVf",
        "outputId": "a57555c8-8cf6-48ff-807e-4cf9bd46c1e6"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.Barrio.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN0cDITxxtvH",
        "outputId": "e32ef258-5506-4e7f-f9ad-b57535ccad2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "63"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(inmuebles.UPZ.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "BcKfcc3nx9fg",
        "outputId": "b2c6f105-715e-4843-bdc8-0ab0f111668d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "inmuebles_barrio = inmuebles.Barrio.value_counts()\n",
        "inmuebles_barrio.plot.bar()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "EXzn-YDLybRx",
        "outputId": "060c6792-90b6-4bee-b637-5dec6614b6f0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "inmuebles_barrio.head(10).plot.bar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "owP_SeWgzaYR"
      },
      "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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFNuHdUZJJtL"
      },
      "source": [
        "#**Aula 2**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "HuNvVUa2ypG4",
        "outputId": "5be50e45-6322-4df7-cfdd-07ad8caf5eb5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2999</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>96</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 450.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6404</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>74</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 259.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5680</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en La Soledad</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>248</td>\n",
              "      <td>La Soledad</td>\n",
              "      <td>Teusaquillo</td>\n",
              "      <td>$ 840.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5458</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chapinero</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
              "      <td>Chapinero</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 575.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7547</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Cedritos</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>Cedritos</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 400.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Tipo                                 Descripcion  Habitaciones  \\\n",
              "2999  Apartamento             Apartamento en venta en Usaquén             3   \n",
              "6404  Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "5680         Casa                Casa en venta en La Soledad              3   \n",
              "5458  Apartamento           Apartamento en venta en Chapinero             3   \n",
              "7547  Apartamento            Apartamento en venta en Cedritos             4   \n",
              "\n",
              "      Banos  Area              Barrio                                 UPZ  \\\n",
              "2999      3    96             Usaquén                             Usaquén   \n",
              "6404      2    74  Zona Noroccidental  CHAPINERO: Pardo Rubio + Chapinero   \n",
              "5680      4   248         La Soledad                          Teusaquillo   \n",
              "5458      2   102           Chapinero  CHAPINERO: Pardo Rubio + Chapinero   \n",
              "7547      2   100            Cedritos                             Usaquén   \n",
              "\n",
              "              Valor  \n",
              "2999  $ 450.000.000  \n",
              "6404  $ 259.000.000  \n",
              "5680  $ 840.000.000  \n",
              "5458  $ 575.000.000  \n",
              "7547  $ 400.000.000  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jt_0FzVFz30-",
        "outputId": "44655bfd-2974-44bd-df85-52007cd743af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "inmuebles.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nz5JDL5LLh5A",
        "outputId": "7f57ad13-0735-4dfb-b929-463ed9b5fbbc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(inmuebles.Valor[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVgx7E33L9IJ",
        "outputId": "375a0a4d-54c5-4b50-fdf8-7d1495df5899"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.Valor[0]+inmuebles.Valor[1]+inmuebles.Valor[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRDkYvkjMWtA",
        "outputId": "5a55ac95-5629-4d8d-af3c-a2cd2d05a4ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['$', '360.000.000']"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.Valor[0].split()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "JL4ilS3UMzlq",
        "outputId": "2a5ea16d-3c07-4469-8492-1451be0d276e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>5668</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Engativa</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>71</td>\n",
              "      <td>Engativa</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>$ 290.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>290.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6659</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Quinta Paredes</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>153</td>\n",
              "      <td>Quinta Paredes</td>\n",
              "      <td>Teusaquillo</td>\n",
              "      <td>$ 739.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>739.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>60</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 225.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>225.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Tipo                                 Descripcion  Habitaciones  \\\n",
              "5668  Apartamento            Apartamento en venta en Engativa             3   \n",
              "6659  Apartamento      Apartamento en venta en Quinta Paredes             2   \n",
              "89    Apartamento  Apartamento en venta en Zona Noroccidental             2   \n",
              "\n",
              "      Banos  Area              Barrio                                 UPZ  \\\n",
              "5668      2    71            Engativa                            Engativá   \n",
              "6659      3   153      Quinta Paredes                         Teusaquillo   \n",
              "89        2    60  Zona Noroccidental  CHAPINERO: Pardo Rubio + Chapinero   \n",
              "\n",
              "              Valor Moneda       Precio  \n",
              "5668  $ 290.000.000      $  290.000.000  \n",
              "6659  $ 739.000.000      $  739.000.000  \n",
              "89    $ 225.000.000      $  225.000.000  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valor = inmuebles.Valor.str.split(expand=True)\n",
        "inmuebles['Moneda'] = valor[0]\n",
        "inmuebles['Precio'] = valor[1]\n",
        "inmuebles.sample(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0QYDqaPNLhq",
        "outputId": "f0ffb6f1-1f2c-4a6e-d4ad-90b77a42cb38"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "inmuebles.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "qwRAIGd9OCYq",
        "outputId": "9aa9a2de-4fd1-4145-cb8f-a0fa48a016aa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles['Precio'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "O4PZCY0AOTIh"
      },
      "outputs": [],
      "source": [
        "inmuebles['Precio'] = inmuebles['Precio'].str.replace('.','',regex=True)"
      ]
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    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "85K4T3OXOm-_",
        "outputId": "33b97553-2f54-4cfa-b654-9be013c08ebb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Precio</th>\n",
              "      <th>Barrio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>360000000</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>670000000</td>\n",
              "      <td>Castilla</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1120000000</td>\n",
              "      <td>Chico Reservado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>890000000</td>\n",
              "      <td>Usaquén</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>970000000</td>\n",
              "      <td>Bella Suiza</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9515</th>\n",
              "      <td>290000000</td>\n",
              "      <td>Kennedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9516</th>\n",
              "      <td>670000000</td>\n",
              "      <td>Parque Central Bavaria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9517</th>\n",
              "      <td>900000000</td>\n",
              "      <td>Restrepo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9518</th>\n",
              "      <td>335000000</td>\n",
              "      <td>Engativa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9519</th>\n",
              "      <td>144990000</td>\n",
              "      <td>Tintala</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9520 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          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]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles[['Precio','Barrio']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZP57N8bXO9Lm",
        "outputId": "7127ee91-ca38-442e-9430-8aa9dd59b206"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "inmuebles.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JwDQyb6POMq",
        "outputId": "051f1d7f-fc6b-4e2b-fd23-c2aace7491f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "inmuebles['Precio_Millon'] = inmuebles.Precio.astype('float')/1000000\n",
        "inmuebles.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "2OFAMdrAPoNq",
        "outputId": "b591add9-b93b-4195-c15e-760f92e1a7ef"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Precio_Millon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9520.000000</td>\n",
              "      <td>9520.000000</td>\n",
              "      <td>9520.000000</td>\n",
              "      <td>9520.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.072479</td>\n",
              "      <td>2.447899</td>\n",
              "      <td>146.664706</td>\n",
              "      <td>602.062354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.049856</td>\n",
              "      <td>1.255314</td>\n",
              "      <td>1731.379622</td>\n",
              "      <td>704.602062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>409.182500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>680.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>110.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>166243.000000</td>\n",
              "      <td>16000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "fA1hAJW8QWZ7",
        "outputId": "26a7b561-ac83-4bde-c08d-c863a02140fa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Precio_Millon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.set_option('display.precision',2)\n",
        "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
        "inmuebles.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "boVAQbi5RKsm",
        "outputId": "8a215d47-6536-47a2-843c-223b1fc1f013"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>897</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en La Uribe</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>110</td>\n",
              "      <td>La Uribe</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 480.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>480000000</td>\n",
              "      <td>480.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     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  "
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.loc[inmuebles.Habitaciones == 110]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "zcCwMeZJRhhC",
        "outputId": "c159138b-4632-4b61-a332-edf65e4523b2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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",
              "      <th>Moneda</th>\n",
              "      <th>Precio</th>\n",
              "      <th>Precio_Millon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8401</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en San Cristobal</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>San Cristobal</td>\n",
              "      <td>Sosiego</td>\n",
              "      <td>$ 180.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>180000000</td>\n",
              "      <td>180.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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  "
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.loc[inmuebles.Area == 2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "fd44PJPCSJwN",
        "outputId": "7d7f35c6-428c-4cbc-e8cb-28a4a18cef6b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Frequency'>"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "inmuebles['Precio_Millon'].plot.hist(bins=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "yhdim-1uSx2c",
        "outputId": "00df0c40-2bc9-460a-d4fd-d0089fab4b5c"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/drive/MyDrive/inmersion_datos/valor_inmuebles.png'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[1;32mc:\\Users\\waldo\\Desktop\\PROGRAMACION\\_PAGINA WEB\\ORACLE NEXT EDUCATION\\InmersionDatos\\Clase3\\INMERSION_DATOS_AULA_3.ipynb Cell 45\u001b[0m line \u001b[0;36m8\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/waldo/Desktop/PROGRAMACION/_PAGINA%20WEB/ORACLE%20NEXT%20EDUCATION/InmersionDatos/Clase3/INMERSION_DATOS_AULA_3.ipynb#X63sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m grafica\u001b[39m.\u001b[39mset_title(\u001b[39m'\u001b[39m\u001b[39mDistribución de Valores de los inmuebles en Bogotá\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/waldo/Desktop/PROGRAMACION/_PAGINA%20WEB/ORACLE%20NEXT%20EDUCATION/InmersionDatos/Clase3/INMERSION_DATOS_AULA_3.ipynb#X63sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m plt\u001b[39m.\u001b[39mxlim((\u001b[39m50\u001b[39m,\u001b[39m1000\u001b[39m))\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/waldo/Desktop/PROGRAMACION/_PAGINA%20WEB/ORACLE%20NEXT%20EDUCATION/InmersionDatos/Clase3/INMERSION_DATOS_AULA_3.ipynb#X63sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m plt\u001b[39m.\u001b[39;49msavefig(\u001b[39m'\u001b[39;49m\u001b[39m/content/drive/MyDrive/inmersion_datos/valor_inmuebles.png\u001b[39;49m\u001b[39m'\u001b[39;49m,\u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mpng\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/waldo/Desktop/PROGRAMACION/_PAGINA%20WEB/ORACLE%20NEXT%20EDUCATION/InmersionDatos/Clase3/INMERSION_DATOS_AULA_3.ipynb#X63sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\pyplot.py:977\u001b[0m, in \u001b[0;36msavefig\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    974\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[39m.\u001b[39msavefig)\n\u001b[0;32m    975\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msavefig\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m    976\u001b[0m     fig \u001b[39m=\u001b[39m gcf()\n\u001b[1;32m--> 977\u001b[0m     res \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39msavefig(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    978\u001b[0m     fig\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mdraw_idle()   \u001b[39m# need this if 'transparent=True' to reset colors\u001b[39;00m\n\u001b[0;32m    979\u001b[0m     \u001b[39mreturn\u001b[39;00m res\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\figure.py:3058\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m   3054\u001b[0m     \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes:\n\u001b[0;32m   3055\u001b[0m         stack\u001b[39m.\u001b[39menter_context(\n\u001b[0;32m   3056\u001b[0m             ax\u001b[39m.\u001b[39mpatch\u001b[39m.\u001b[39m_cm_set(facecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m, edgecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[1;32m-> 3058\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mprint_figure(fname, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backend_bases.py:2319\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2315\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   2316\u001b[0m     \u001b[39m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m   2317\u001b[0m     \u001b[39m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m   2318\u001b[0m     \u001b[39mwith\u001b[39;00m cbook\u001b[39m.\u001b[39m_setattr_cm(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure, dpi\u001b[39m=\u001b[39mdpi):\n\u001b[1;32m-> 2319\u001b[0m         result \u001b[39m=\u001b[39m print_method(\n\u001b[0;32m   2320\u001b[0m             filename,\n\u001b[0;32m   2321\u001b[0m             facecolor\u001b[39m=\u001b[39mfacecolor,\n\u001b[0;32m   2322\u001b[0m             edgecolor\u001b[39m=\u001b[39medgecolor,\n\u001b[0;32m   2323\u001b[0m             orientation\u001b[39m=\u001b[39morientation,\n\u001b[0;32m   2324\u001b[0m             bbox_inches_restore\u001b[39m=\u001b[39m_bbox_inches_restore,\n\u001b[0;32m   2325\u001b[0m             \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   2326\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m   2327\u001b[0m     \u001b[39mif\u001b[39;00m bbox_inches \u001b[39mand\u001b[39;00m restore_bbox:\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backend_bases.py:1648\u001b[0m, in \u001b[0;36m_check_savefig_extra_args.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1640\u001b[0m     _api\u001b[39m.\u001b[39mwarn_deprecated(\n\u001b[0;32m   1641\u001b[0m         \u001b[39m'\u001b[39m\u001b[39m3.3\u001b[39m\u001b[39m'\u001b[39m, name\u001b[39m=\u001b[39mname, removal\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m3.6\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m   1642\u001b[0m         message\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m%(name)s\u001b[39;00m\u001b[39m() got unexpected keyword argument \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   1643\u001b[0m                 \u001b[39m+\u001b[39m arg \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m which is no longer supported as of \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   1644\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39m%(since)s\u001b[39;00m\u001b[39m and will become an error \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   1645\u001b[0m                 \u001b[39m'\u001b[39m\u001b[39m%(removal)s\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m   1646\u001b[0m     kwargs\u001b[39m.\u001b[39mpop(arg)\n\u001b[1;32m-> 1648\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:415\u001b[0m, in \u001b[0;36mdelete_parameter.<locals>.wrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    405\u001b[0m     deprecation_addendum \u001b[39m=\u001b[39m (\n\u001b[0;32m    406\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIf any parameter follows \u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m!r}\u001b[39;00m\u001b[39m, they should be passed as \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    407\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mkeyword, not positionally.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    408\u001b[0m     warn_deprecated(\n\u001b[0;32m    409\u001b[0m         since,\n\u001b[0;32m    410\u001b[0m         name\u001b[39m=\u001b[39m\u001b[39mrepr\u001b[39m(name),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    413\u001b[0m                  \u001b[39melse\u001b[39;00m deprecation_addendum,\n\u001b[0;32m    414\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m--> 415\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39minner_args, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39minner_kwargs)\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:541\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    494\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    495\u001b[0m \u001b[39mWrite the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m    496\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    538\u001b[0m \u001b[39m    *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m    539\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    540\u001b[0m FigureCanvasAgg\u001b[39m.\u001b[39mdraw(\u001b[39mself\u001b[39m)\n\u001b[1;32m--> 541\u001b[0m mpl\u001b[39m.\u001b[39;49mimage\u001b[39m.\u001b[39;49mimsave(\n\u001b[0;32m    542\u001b[0m     filename_or_obj, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuffer_rgba(), \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mpng\u001b[39;49m\u001b[39m\"\u001b[39;49m, origin\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mupper\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    543\u001b[0m     dpi\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure\u001b[39m.\u001b[39;49mdpi, metadata\u001b[39m=\u001b[39;49mmetadata, pil_kwargs\u001b[39m=\u001b[39;49mpil_kwargs)\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\image.py:1675\u001b[0m, in \u001b[0;36mimsave\u001b[1;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m   1673\u001b[0m pil_kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m\"\u001b[39m\u001b[39mformat\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mformat\u001b[39m)\n\u001b[0;32m   1674\u001b[0m pil_kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m\"\u001b[39m\u001b[39mdpi\u001b[39m\u001b[39m\"\u001b[39m, (dpi, dpi))\n\u001b[1;32m-> 1675\u001b[0m image\u001b[39m.\u001b[39msave(fname, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mpil_kwargs)\n",
            "File \u001b[1;32mc:\\Users\\waldo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\PIL\\Image.py:2317\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   2315\u001b[0m         fp \u001b[39m=\u001b[39m builtins\u001b[39m.\u001b[39mopen(filename, \u001b[39m\"\u001b[39m\u001b[39mr+b\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m   2316\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 2317\u001b[0m         fp \u001b[39m=\u001b[39m builtins\u001b[39m.\u001b[39;49mopen(filename, \u001b[39m\"\u001b[39;49m\u001b[39mw+b\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m   2319\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   2320\u001b[0m     save_handler(\u001b[39mself\u001b[39m, fp, filename)\n",
            "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/inmersion_datos/valor_inmuebles.png'"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4dm6mfCYB-h"
      },
      "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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZ7C4wFgdG5m"
      },
      "source": [
        "#**Aula 3**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "l7IujydcVLW0",
        "outputId": "e4a79fe8-4418-45ad-ae4a-768d3774a78f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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  "
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles['Valor_m2_Millon'] = inmuebles['Precio_Millon']/inmuebles['Area'] \n",
        "inmuebles.head(3)"
      ]
    },
    {
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      "execution_count": null,
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        {
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              "      <th></th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
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              "      <th>Alameda</th>\n",
              "      <td>3.00</td>\n",
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              "      <td>97.00</td>\n",
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              "      <td>5.98</td>\n",
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              "      <th>Andes Norte</th>\n",
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              "      <td>3.53</td>\n",
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              "    <tr>\n",
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              "      <td>2.87</td>\n",
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              "      <td>314.13</td>\n",
              "      <td>4.42</td>\n",
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              "      <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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            "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]"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.groupby('Barrio').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
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              "      <th>Precio_Millon</th>\n",
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              "    <tr>\n",
              "      <th>Barrio</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bosa Piamonte</th>\n",
              "      <td>6.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>400.00</td>\n",
              "      <td>470.00</td>\n",
              "      <td>1.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roma</th>\n",
              "      <td>8.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>260.00</td>\n",
              "      <td>350.00</td>\n",
              "      <td>1.35</td>\n",
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              "    <tr>\n",
              "      <th>Libertador</th>\n",
              "      <td>4.50</td>\n",
              "      <td>4.00</td>\n",
              "      <td>642.00</td>\n",
              "      <td>1020.00</td>\n",
              "      <td>1.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Las Ferias</th>\n",
              "      <td>11.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>320.00</td>\n",
              "      <td>550.00</td>\n",
              "      <td>1.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Santa Librada</th>\n",
              "      <td>7.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>328.00</td>\n",
              "      <td>632.50</td>\n",
              "      <td>1.95</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Habitaciones  Banos   Area  Precio_Millon  Valor_m2_Millon\n",
              "Barrio                                                                   \n",
              "Bosa Piamonte          6.00   4.00 400.00         470.00             1.18\n",
              "Roma                   8.00   3.00 260.00         350.00             1.35\n",
              "Libertador             4.50   4.00 642.00        1020.00             1.66\n",
              "Las Ferias            11.00   6.00 320.00         550.00             1.72\n",
              "Santa Librada          7.00   5.00 328.00         632.50             1.95"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles.groupby('Barrio').mean().sort_values('Valor_m2_Millon').head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
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        "id": "6tUADDHag6M-",
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              "    <tr>\n",
              "      <th>Barrio</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>Alameda</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>580.00</td>\n",
              "      <td>5.98</td>\n",
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              "      <th>Alhambra</th>\n",
              "      <td>51</td>\n",
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              "      <td>73.56</td>\n",
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              "      <th>Andes Norte</th>\n",
              "      <td>21</td>\n",
              "      <td>20</td>\n",
              "      <td>874</td>\n",
              "      <td>4967.00</td>\n",
              "      <td>42.35</td>\n",
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              "    <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",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 5 columns</p>\n",
              "</div>"
            ],
            "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]"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_barrio = inmuebles.groupby('Barrio').sum()\n",
        "datos_barrio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "EXM5tc8YhvfA",
        "outputId": "6d0e463d-b4fc-49e5-f6f1-7d135b12940c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Barrio</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alameda</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>580.00</td>\n",
              "      <td>5.98</td>\n",
              "      <td>5.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alhambra</th>\n",
              "      <td>51</td>\n",
              "      <td>50</td>\n",
              "      <td>3237</td>\n",
              "      <td>12790.00</td>\n",
              "      <td>73.56</td>\n",
              "      <td>3.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andes Norte</th>\n",
              "      <td>21</td>\n",
              "      <td>20</td>\n",
              "      <td>874</td>\n",
              "      <td>4967.00</td>\n",
              "      <td>42.35</td>\n",
              "      <td>5.68</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",
              "      <td>2.82</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",
              "      <td>4.47</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",
              "    </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",
              "      <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 6 columns</p>\n",
              "</div>"
            ],
            "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]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_barrio['Valor_m2_Barrio'] = datos_barrio['Precio_Millon']/datos_barrio['Area']\n",
        "datos_barrio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Barrio</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Modelia</th>\n",
              "      <td>147</td>\n",
              "      <td>111</td>\n",
              "      <td>172670</td>\n",
              "      <td>28218.79</td>\n",
              "      <td>191.58</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bosa Piamonte</th>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>800</td>\n",
              "      <td>940.00</td>\n",
              "      <td>2.35</td>\n",
              "      <td>1.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roma</th>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>260</td>\n",
              "      <td>350.00</td>\n",
              "      <td>1.35</td>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Libertador</th>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "      <td>2568</td>\n",
              "      <td>4080.00</td>\n",
              "      <td>6.65</td>\n",
              "      <td>1.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Las Ferias</th>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
              "      <td>640</td>\n",
              "      <td>1100.00</td>\n",
              "      <td>3.44</td>\n",
              "      <td>1.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Santa Librada</th>\n",
              "      <td>14</td>\n",
              "      <td>10</td>\n",
              "      <td>656</td>\n",
              "      <td>1265.00</td>\n",
              "      <td>3.90</td>\n",
              "      <td>1.93</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",
              "      <td>1.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Galicia</th>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>561</td>\n",
              "      <td>1265.00</td>\n",
              "      <td>14.05</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rafael Uribe Uribe</th>\n",
              "      <td>367</td>\n",
              "      <td>204</td>\n",
              "      <td>11584</td>\n",
              "      <td>27005.23</td>\n",
              "      <td>223.75</td>\n",
              "      <td>2.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timiza</th>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>256</td>\n",
              "      <td>597.00</td>\n",
              "      <td>5.34</td>\n",
              "      <td>2.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Habitaciones  Banos    Area  Precio_Millon  \\\n",
              "Barrio                                                           \n",
              "Modelia                      147    111  172670       28218.79   \n",
              "Bosa Piamonte                 12      8     800         940.00   \n",
              "Roma                           8      3     260         350.00   \n",
              "Libertador                    18     16    2568        4080.00   \n",
              "Las Ferias                    22     12     640        1100.00   \n",
              "Santa Librada                 14     10     656        1265.00   \n",
              "Villa Maria                   61     48    2693        5237.00   \n",
              "Galicia                       16     10     561        1265.00   \n",
              "Rafael Uribe Uribe           367    204   11584       27005.23   \n",
              "Timiza                        10      4     256         597.00   \n",
              "\n",
              "                    Valor_m2_Millon  Valor_m2_Barrio  \n",
              "Barrio                                                \n",
              "Modelia                      191.58             0.16  \n",
              "Bosa Piamonte                  2.35             1.18  \n",
              "Roma                           1.35             1.35  \n",
              "Libertador                     6.65             1.59  \n",
              "Las Ferias                     3.44             1.72  \n",
              "Santa Librada                  3.90             1.93  \n",
              "Villa Maria                   26.58             1.94  \n",
              "Galicia                       14.05             2.25  \n",
              "Rafael Uribe Uribe           223.75             2.33  \n",
              "Timiza                         5.34             2.33  "
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_barrio.sort_values(by=['Valor_m2_Barrio']).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "hU3HiGsFiPHf"
      },
      "outputs": [],
      "source": [
        "m2_barrio = dict(datos_barrio['Valor_m2_Barrio'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Alameda': 5.979381443298969,\n",
              " 'Alhambra': 3.95118937287612,\n",
              " 'Andes Norte': 5.683066361556064,\n",
              " 'Antonio Nariño': 2.816232272901495,\n",
              " 'Barrancas': 4.4748338081671415,\n",
              " 'Barrios Unidos': 2.9599171141243064,\n",
              " 'Bella Suiza': 6.2905507012394,\n",
              " 'Bellavista Occidental': 2.522711390635919,\n",
              " 'Bochica': 3.4078212290502794,\n",
              " 'Bogotá D.C': 5.212760277629472,\n",
              " 'Bosa': 2.6778451358697213,\n",
              " 'Bosa Piamonte': 1.175,\n",
              " 'Bosa Porvenir': 3.1666666666666665,\n",
              " 'Bosque De Pinos': 6.046475195822455,\n",
              " 'Bosque Popular': 3.864097363083164,\n",
              " 'Boyaca ': 2.637421499815294,\n",
              " 'Buenos Aires': 2.6,\n",
              " 'Candelaria La Nueva': 4.0625,\n",
              " 'Cantagallo': 5.71920581655481,\n",
              " 'Capellania': 4.316791690709752,\n",
              " 'Casablanca': 6.245421245421245,\n",
              " 'Castilla': 3.4035893155258763,\n",
              " 'Cedritos': 5.027419586695463,\n",
              " 'Cerros De Suba': 6.202830188679245,\n",
              " 'Chapinero': 4.58018576602501,\n",
              " 'Chapinero Alto': 6.4239336492891,\n",
              " 'Chapinero Central': 6.360286919831224,\n",
              " 'Chico Navarra': 5.1563019620884605,\n",
              " 'Chico Norte': 8.353851040924855,\n",
              " 'Chico Norte Ii': 5.99236641221374,\n",
              " 'Chico Reservado': 8.104486540013072,\n",
              " 'Ciudad Berna': 3.028169014084507,\n",
              " 'Ciudad Bolívar': 3.5194954371992697,\n",
              " 'Ciudad Salitre': 7.412638847193035,\n",
              " 'Ciudadela Colsubsidio': 3.210382513661202,\n",
              " 'Colina Campestre': 4.776341497750727,\n",
              " 'Colinas De Suba': 6.225,\n",
              " 'Costa Azul': 2.726628895184136,\n",
              " 'Eduardo Santos': 2.9102167182662537,\n",
              " 'El Batán': 5.0067874515333095,\n",
              " 'El Castillo': 8.472998137802607,\n",
              " 'El Chicó': 7.648045112781955,\n",
              " 'El Contador': 5.279454722492697,\n",
              " 'El Plan': 5.3172697724810405,\n",
              " 'El Progreso': 3.0346820809248554,\n",
              " 'El Refugio': 5.847477744807121,\n",
              " 'El Retiro': 7.122884473877852,\n",
              " 'El Salitre': 2.7397631133671743,\n",
              " 'El Tunal': 3.902439024390244,\n",
              " 'El Virrey': 9.928952042628774,\n",
              " 'Engativa': 2.887769666800428,\n",
              " 'Estrada': 2.380952380952381,\n",
              " 'Florencia': 3.793103448275862,\n",
              " 'Florida Blanca': 3.173076923076923,\n",
              " 'Fontibón': 3.723021313477524,\n",
              " 'Galerias': 4.450757575757576,\n",
              " 'Galicia': 2.2549019607843137,\n",
              " 'Gran America': 5.24,\n",
              " 'Gran Granada': 3.8927665317139004,\n",
              " 'Gratamira': 4.285714285714286,\n",
              " 'Gustavo Restrepo': 3.888888888888889,\n",
              " 'Hayuelos': 4.814082765845993,\n",
              " 'Ingles': 2.5,\n",
              " 'Kennedy': 3.382897908271655,\n",
              " 'La Calleja': 6.582907870377288,\n",
              " 'La Candelaria': 8.889242669544883,\n",
              " 'La Estancia': 2.606707317073171,\n",
              " 'La Estanzuela': 3.0434782608695654,\n",
              " 'La Granja': 4.985074626865671,\n",
              " 'La Macarena': 6.756756756756757,\n",
              " 'La Merced': 8.768292682926829,\n",
              " 'La Sabana': 2.7948717948717947,\n",
              " 'La Salle': 6.452695829094608,\n",
              " 'La Soledad ': 2.888412017167382,\n",
              " 'La Uribe': 4.266437768240343,\n",
              " 'Lago De Suba': 4.367816091954023,\n",
              " 'Lagos De Cordoba': 6.503820682628629,\n",
              " 'Las Aguas': 4.19016393442623,\n",
              " 'Las Cruces': 3.6222222222222222,\n",
              " 'Las Delicias': 2.6536791314837154,\n",
              " 'Las Ferias': 1.71875,\n",
              " 'Las Nieves': 5.050710981668665,\n",
              " 'Las Villas': 4.270504854368932,\n",
              " 'Libertador': 1.588785046728972,\n",
              " 'Los Lagartos': 5.637065637065637,\n",
              " 'Los Mártires': 3.0499725425590336,\n",
              " 'Los Rosales': 8.208857198335645,\n",
              " 'Madelena': 2.4595859327850254,\n",
              " 'Marly': 4.9375,\n",
              " 'Mazuren': 3.8513890438469263,\n",
              " 'Metropolis': 4.448767833981842,\n",
              " 'Mirandela ': 3.9920856610800746,\n",
              " 'Modelia': 0.16342613076967627,\n",
              " 'Nicolas De Federman': 5.992745920204822,\n",
              " 'Niza': 4.409398133247506,\n",
              " 'Normandia': 4.204753199268739,\n",
              " 'Nueva Autopista': 5.078665442301806,\n",
              " 'Nueva Castilla': 4.331550802139038,\n",
              " 'Nueva Zelandia': 4.173159132007234,\n",
              " 'Palermo': 3.835207308503162,\n",
              " 'Parque Central Bavaria': 6.106280193236715,\n",
              " 'Pasadena ': 5.244227353463588,\n",
              " 'Patio Bonito': 6.180555555555555,\n",
              " 'Pinar De Suba': 3.902547770700637,\n",
              " 'Pontevedra ': 4.665970772442589,\n",
              " 'Portales Del Norte': 4.5501614639397205,\n",
              " 'Prado Veraniego': 6.8,\n",
              " 'Puente Aranda': 3.247774594103762,\n",
              " 'Puente Largo': 4.383536962205848,\n",
              " 'Quinta Camacho': 5.430680091363312,\n",
              " 'Quinta Paredes': 5.302461344272642,\n",
              " 'Quirigua': 2.611021721641191,\n",
              " 'Quiroga': 2.588235294117647,\n",
              " 'Rafael Uribe Uribe': 2.3312523627417128,\n",
              " 'Restrepo': 2.9242199108469538,\n",
              " 'Ricaurte': 3.9213349225268175,\n",
              " 'Roma': 1.3461538461538463,\n",
              " 'Salitre ': 6.559434391743892,\n",
              " 'San Antonio Norte': 3.705173184357542,\n",
              " 'San Cipriano': 4.935483870967742,\n",
              " 'San Cristobal': 2.9542168674698797,\n",
              " 'San Diego': 6.081967213114754,\n",
              " 'San Luis': 3.082656826568266,\n",
              " 'San Patricio': 6.831854578231803,\n",
              " 'Santa Ana': 10.254545454545454,\n",
              " 'Santa Barbara': 5.852628361947598,\n",
              " 'Santa Fe': 5.754799433180728,\n",
              " 'Santa Librada': 1.9283536585365855,\n",
              " 'Santa Maria Del Lago': 5.47008547008547,\n",
              " 'Santa Paula': 6.486130972268628,\n",
              " 'Santa Teresa': 5.089707271010387,\n",
              " 'Sotavento': 5.286447124304267,\n",
              " 'Suba': 4.323720522957072,\n",
              " 'Teusaquillo': 3.975204301767717,\n",
              " 'Timiza': 2.33203125,\n",
              " 'Tintala': 3.7991992442958873,\n",
              " 'Tunjuelito': 2.381058318766884,\n",
              " 'Turingia': 5.0,\n",
              " 'Usaquén': 5.65558134903221,\n",
              " 'Usme': 3.847569525590091,\n",
              " 'Venecia': 2.939297124600639,\n",
              " 'Veraguas': 3.1222466960352424,\n",
              " 'Verbenal': 4.922480620155039,\n",
              " 'Villa Alsacia': 4.139391447368421,\n",
              " 'Villa Maria': 1.9446713702190865,\n",
              " 'Villas De Granada': 3.2601706036745406,\n",
              " 'Villemar': 3.9515895953757227,\n",
              " 'Zona Franca': 2.962962962962963,\n",
              " 'Zona Noroccidental': 4.572008510026535}"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m2_barrio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "8R-M41NGjLgp",
        "outputId": "b4de7a0d-20b5-4f64-a2f3-69a35c719826"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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  "
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles['Valor_m2_Barrio'] = inmuebles['Barrio']\n",
        "inmuebles['Valor_m2_Barrio'] = inmuebles['Valor_m2_Barrio'].map(m2_barrio)\n",
        "inmuebles.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\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",
              "      <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>145</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>200</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 860.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>860000000</td>\n",
              "      <td>860.00</td>\n",
              "      <td>4.30</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1283</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>236</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 1.120.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>1120000000</td>\n",
              "      <td>1120.00</td>\n",
              "      <td>4.75</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1295</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Modelia</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>48</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 330.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>330000000</td>\n",
              "      <td>330.00</td>\n",
              "      <td>6.88</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1363</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Modelia</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 287.000.000</td>\n",
              "      <td>$</td>\n",
              "      <td>287000000</td>\n",
              "      <td>287.00</td>\n",
              "      <td>5.42</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1416</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Modelia</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>87</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 499.990.000</td>\n",
              "      <td>$</td>\n",
              "      <td>499990000</td>\n",
              "      <td>499.99</td>\n",
              "      <td>5.75</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Tipo                      Descripcion  Habitaciones  Banos  Area  \\\n",
              "145          Casa         Casa en venta en Modelia             4      2   200   \n",
              "1283         Casa         Casa en venta en Modelia             8      4   236   \n",
              "1295  Apartamento  Apartamento en venta en Modelia             2      2    48   \n",
              "1363  Apartamento  Apartamento en venta en Modelia             3      2    53   \n",
              "1416  Apartamento  Apartamento en venta en Modelia             3      2    87   \n",
              "\n",
              "       Barrio       UPZ            Valor Moneda      Precio  Precio_Millon  \\\n",
              "145   Modelia  Fontibón    $ 860.000.000      $   860000000         860.00   \n",
              "1283  Modelia  Fontibón  $ 1.120.000.000      $  1120000000        1120.00   \n",
              "1295  Modelia  Fontibón    $ 330.000.000      $   330000000         330.00   \n",
              "1363  Modelia  Fontibón    $ 287.000.000      $   287000000         287.00   \n",
              "1416  Modelia  Fontibón    $ 499.990.000      $   499990000         499.99   \n",
              "\n",
              "      Valor_m2_Millon  Valor_m2_Barrio  \n",
              "145              4.30             0.16  \n",
              "1283             4.75             0.16  \n",
              "1295             6.88             0.16  \n",
              "1363             5.42             0.16  \n",
              "1416             5.75             0.16  "
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inmuebles[inmuebles['Barrio']=='Modelia'].head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "IwD0crAgj0so"
      },
      "outputs": [],
      "source": [
        "top_barrios = inmuebles['Barrio'].value_counts()[:10].index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Usaquén', 'Zona Noroccidental', 'Bosa', 'Kennedy', 'Cedritos',\n",
              "       'Barrios Unidos', 'Engativa', 'Suba', 'Santa Barbara', 'Chapinero'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "top_barrios #Corresponde a los barrios con mayor cantidad de inmuebles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "cdocz6srkmC9",
        "outputId": "d72c34f4-6c06-4c47-859f-52237cc99555"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alameda</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>580.00</td>\n",
              "      <td>5.98</td>\n",
              "      <td>5.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alhambra</td>\n",
              "      <td>51</td>\n",
              "      <td>50</td>\n",
              "      <td>3237</td>\n",
              "      <td>12790.00</td>\n",
              "      <td>73.56</td>\n",
              "      <td>3.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andes Norte</td>\n",
              "      <td>21</td>\n",
              "      <td>20</td>\n",
              "      <td>874</td>\n",
              "      <td>4967.00</td>\n",
              "      <td>42.35</td>\n",
              "      <td>5.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Antonio Nariño</td>\n",
              "      <td>207</td>\n",
              "      <td>120</td>\n",
              "      <td>7827</td>\n",
              "      <td>22042.65</td>\n",
              "      <td>169.47</td>\n",
              "      <td>2.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barrancas</td>\n",
              "      <td>43</td>\n",
              "      <td>32</td>\n",
              "      <td>1053</td>\n",
              "      <td>4712.00</td>\n",
              "      <td>66.24</td>\n",
              "      <td>4.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>Villa Maria</td>\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>145</th>\n",
              "      <td>Villas De Granada</td>\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>146</th>\n",
              "      <td>Villemar</td>\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>147</th>\n",
              "      <td>Zona Franca</td>\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>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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 7 columns</p>\n",
              "</div>"
            ],
            "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]"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_barrio.reset_index(inplace=True)\n",
        "datos_barrio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "K07QxWXRlJPC",
        "outputId": "dded6b26-7e17-4d3d-9c10-bff8b1f68d56"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bosa</td>\n",
              "      <td>1743</td>\n",
              "      <td>789</td>\n",
              "      <td>34879</td>\n",
              "      <td>93400.56</td>\n",
              "      <td>1654.36</td>\n",
              "      <td>2.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Cedritos</td>\n",
              "      <td>1458</td>\n",
              "      <td>1348</td>\n",
              "      <td>53711</td>\n",
              "      <td>270027.73</td>\n",
              "      <td>2853.23</td>\n",
              "      <td>5.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Chapinero</td>\n",
              "      <td>892</td>\n",
              "      <td>913</td>\n",
              "      <td>81248</td>\n",
              "      <td>372130.93</td>\n",
              "      <td>2466.03</td>\n",
              "      <td>4.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Engativa</td>\n",
              "      <td>1676</td>\n",
              "      <td>1077</td>\n",
              "      <td>59808</td>\n",
              "      <td>172711.73</td>\n",
              "      <td>1795.71</td>\n",
              "      <td>2.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Kennedy</td>\n",
              "      <td>2151</td>\n",
              "      <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",
              "      <td>320097.80</td>\n",
              "      <td>2701.88</td>\n",
              "      <td>5.85</td>\n",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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  "
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_barrio.query('Barrio in @top_barrios')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "iA2-RwO9lmX6",
        "outputId": "e62f2a97-70f2-4ff5-8beb-50869b87f788"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "G0Jk65-FmVzw",
        "outputId": "e7edfffc-c20a-4f73-e493-fa43e15273c5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "hKZI_S0RnJBc",
        "outputId": "f8aed493-0a2e-4fd7-e3ed-b7ab132502fd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "B_3zQZ5LogLh",
        "outputId": "51ccecb6-cd07-42e9-95ba-95d42fc3b108"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "JX4WXr68ou3I",
        "outputId": "662c957c-02b4-421f-af61-f9e24138092a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cpe7y-3Fqw5F"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "rvQ2Iv4FpdIr",
        "outputId": "3b67390b-95da-49f4-f702-726e4e37d6ba"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>3004346.0</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Santa Fe</td>\n",
              "      <td>814.0</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>3004347.0</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Los Mártires</td>\n",
              "      <td>37.0</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>3004348.0</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Santa Fe</td>\n",
              "      <td>814.0</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>3004349.0</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>803.0</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>3004350.0</td>\n",
              "      <td>11</td>\n",
              "      <td>11001</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>803.0</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>"
            ],
            "text/plain": [
              "   DIRECTORIO  DPTO   MPIO  CLASE  COD_LOCALIDAD NOMBRE_LOCALIDAD  \\\n",
              "0   3004346.0    11  11001      1            3.0         Santa Fe   \n",
              "1   3004347.0    11  11001      1           14.0     Los Mártires   \n",
              "2   3004348.0    11  11001      1            3.0         Santa Fe   \n",
              "3   3004349.0    11  11001      1            1.0          Usaquén   \n",
              "4   3004350.0    11  11001      1            1.0          Usaquén   \n",
              "\n",
              "   COD_UPZ_GRUPO                        NOMBRE_UPZ_GRUPO  ESTRATO2021  \\\n",
              "0          814.0  SANTA FE: Las Nieves + Sagrado Corazón     11001187   \n",
              "1           37.0                            Santa Isabel     11001162   \n",
              "2          814.0  SANTA FE: Las Nieves + Sagrado Corazón     11001187   \n",
              "3          803.0   USAQUÉN: Country Club + Santa Bárbara     11001176   \n",
              "4          803.0   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  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_raw = pd.read_csv('./Identificación (Capítulo A).csv', sep = ';',encoding='latin-1')\n",
        "datos_raw.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9l5m9TzZsDw-",
        "outputId": "5e6aeb95-547a-4518-d973-e02c669db320"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(106467, 11)"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_raw.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vq7Z359Rtim8",
        "outputId": "9abacad7-d3ab-4191-d9e2-cfb56bec5c6c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(86504, 11)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_raw = datos_raw.loc[datos_raw.MPIO == 11001]\n",
        "datos_raw.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "datos_b = pd.read_csv('./archivoscsv/Datos de la vivenda y su entorno (Capítulo B).csv',sep=';',encoding='latin-1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\waldo\\AppData\\Local\\Temp\\ipykernel_8988\\3226191155.py:1: DtypeWarning: Columns (4,25,30,47) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  datos_c = pd.read_csv('./archivoscsv/Condiciones habitacionales del hogar (Capítulo C).csv',sep=';',encoding='latin-1')\n"
          ]
        }
      ],
      "source": [
        "datos_c = pd.read_csv('./archivoscsv/Condiciones habitacionales del hogar (Capítulo C).csv',sep=';',encoding='latin-1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\waldo\\AppData\\Local\\Temp\\ipykernel_8988\\3553429306.py:1: DtypeWarning: Columns (66) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  datos_e = pd.read_csv('./archivoscsv/Composición del hogar y demografía (Capítulo E).csv',sep=';',encoding='latin-1')\n"
          ]
        }
      ],
      "source": [
        "datos_e = pd.read_csv('./archivoscsv/Composición del hogar y demografía (Capítulo E).csv',sep=';',encoding='latin-1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\waldo\\AppData\\Local\\Temp\\ipykernel_8988\\1877538254.py:1: DtypeWarning: Columns (81) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  datos_h = pd.read_csv('./archivoscsv/Educación (Capítulo H).csv',sep=';',encoding='latin-1')\n"
          ]
        }
      ],
      "source": [
        "datos_h = pd.read_csv('./archivoscsv/Educación (Capítulo H).csv',sep=';',encoding='latin-1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyQz-rqgum7n",
        "outputId": "4f7f90b6-ec68-401b-8dca-4ee4e42a6cbd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\waldo\\AppData\\Local\\Temp\\ipykernel_8988\\1485978910.py:1: 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",
            "  datos_k = pd.read_csv('./archivoscsv/Fuerza de trabajo (Capítulo K).csv',sep=';',encoding='latin-1')\n"
          ]
        }
      ],
      "source": [
        "datos_k = pd.read_csv('./archivoscsv/Fuerza de trabajo (Capítulo K).csv',sep=';',encoding='latin-1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57c4tQFOurOB",
        "outputId": "df28b0b8-a36a-4004-ccfb-1371f36d7e2e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(86504, 73)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_dane = pd.merge(datos_raw,datos_b,on='DIRECTORIO', how='left')\n",
        "datos_dane.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Beu5syqsvRwT"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(235103, 140)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_dane = pd.merge(datos_dane,datos_e,on='DIRECTORIO', how='left')\n",
        "datos_dane.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\waldo\\AppData\\Local\\Temp\\ipykernel_8988\\137096260.py: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_c,on='DIRECTORIO', how='left')\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(238559, 281)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_dane = pd.merge(datos_dane,datos_c,on='DIRECTORIO', how='left')\n",
        "datos_dane.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEpH8f3Zvbrq",
        "outputId": "d76f0894-dd7d-4043-b2b1-7deaf7203f5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(238559, 281)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datos_dane.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LKeCUigvdIh",
        "outputId": "db43101f-7f4a-4ab8-a87d-a369019ccf26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "datos_dane.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "datos_dane.to_csv('datos_daneUNIDOS.csv', index=False)  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnyIOv_pw7SM"
      },
      "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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97jp5hfe1O2D"
      },
      "source": [
        "#**Aula 4**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8llsFmG7iiUG"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}