Inmersion_datos_Aula_1.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/hmmvasquez/Inmersion-en-datos-Alura/blob/main/Inmersion_datos_Aula_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inmersión de Datos con Python impartido por Alura Latam**"
      ],
      "metadata": {
        "id": "b0kKRm3QEE8p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Clase 01 - Tu primer colab con python y pandas**"
      ],
      "metadata": {
        "id": "34-dWdaljeCR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Montamos nuestro drive"
      ],
      "metadata": {
        "id": "67FgFhlDGwnf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importamos la libreria Pandas como un objeto pd y usamos la función **read_csv** para pasar la ruta del archivo con los datos.\n",
        "\n",
        "Usamos la función **head()** para mostrar las 5 primeras filas."
      ],
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        "id": "2biWy_4IG5wm"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "inmuebles = pd.read_csv('/content/drive/MyDrive/Inmersion_Datos_Con_Python/inmuebles_bogota.csv')\n",
        "inmuebles.head()"
      ],
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          "height": 206
        },
        "id": "RqiUXDp9F5YZ",
        "outputId": "3a8d6755-e65f-4762-c568-96d7ae583865"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Tipo                                 Descripcion  Habitaciones  \\\n",
              "0  Apartamento  Apartamento en venta en Zona Noroccidental             3   \n",
              "1         Casa                   Casa en venta en Castilla             4   \n",
              "2  Apartamento     Apartamento en venta en Chico Reservado             3   \n",
              "3  Apartamento             Apartamento en venta en Usaquén             3   \n",
              "4  Apartamento         Apartamento en venta en Bella Suiza             2   \n",
              "\n",
              "   Baños  Área              Barrio                                    UPZ  \\\n",
              "0      2    70  Zona Noroccidental     CHAPINERO: Pardo Rubio + Chapinero   \n",
              "1      3   170            Castilla            KENNEDY: Castilla + Bavaria   \n",
              "2      3   144     Chico Reservado     CHAPINERO: Chicó Lago + El Refugio   \n",
              "3      2   154             Usaquén                                Usaquén   \n",
              "4      3   128         Bella Suiza  USAQUÉN: Country Club + Santa Bárbara   \n",
              "\n",
              "             Valor  \n",
              "0    $ 360.000.000  \n",
              "1    $ 670.000.000  \n",
              "2  $ 1.120.000.000  \n",
              "3    $ 890.000.000  \n",
              "4    $ 970.000.000  "
            ],
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              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Baños</th>\n",
              "      <th>Área</th>\n",
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              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Zona Noroccidental</td>\n",
              "      <td>3</td>\n",
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              "      <td>70</td>\n",
              "      <td>Zona Noroccidental</td>\n",
              "      <td>CHAPINERO: Pardo Rubio + Chapinero</td>\n",
              "      <td>$ 360.000.000</td>\n",
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              "      <th>1</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Castilla</td>\n",
              "      <td>4</td>\n",
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              "      <td>$ 670.000.000</td>\n",
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              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 1.120.000.000</td>\n",
              "    </tr>\n",
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              "      <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",
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              "      <th>4</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Bella Suiza</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>128</td>\n",
              "      <td>Bella Suiza</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 970.000.000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obtenemos el número de filas y columnas de nuestra tabla"
      ],
      "metadata": {
        "id": "WL5gr6VqH7RL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TbKX-eb3dHjK",
        "outputId": "a29fb45a-2bf2-4fa5-a08d-2d5ca2c4cce1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9520, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obtenemos la información de cada una de las columnas de la tabla"
      ],
      "metadata": {
        "id": "9rV7LnN_hWuj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwVJOQKadxfa",
        "outputId": "fe3773c2-9efe-4c7a-dab8-e9ea47275941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Tipo', 'Descripcion', 'Habitaciones', 'Baños', 'Área', 'Barrio', 'UPZ',\n",
              "       'Valor'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creación de diccionario"
      ],
      "metadata": {
        "id": "Gdl9Qm2Khr97"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columnas = {\"Baños\":\"Banos\", \"Área\":\"Area\"}\n",
        "inmuebles = inmuebles.rename(columns = columnas)"
      ],
      "metadata": {
        "id": "G9OFjv9ihMvD"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tomamos una muestra del dataset de 10 registros"
      ],
      "metadata": {
        "id": "4bCYw4joihXf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "id": "clLoWhloioA_",
        "outputId": "888e8b0d-5ae8-4d2e-96fe-ab44321cdf76"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Tipo                            Descripcion  Habitaciones  Banos  \\\n",
              "3628  Apartamento           Apartamento en venta en Bosa             3      1   \n",
              "7621  Apartamento       Apartamento en venta en Engativa             3      3   \n",
              "2766  Apartamento       Apartamento en venta en Engativa             3      2   \n",
              "1475  Apartamento        Apartamento en venta en Usaquén             1      2   \n",
              "4334  Apartamento        Apartamento en venta en Usaquén             3      3   \n",
              "4098  Apartamento        Apartamento en venta en Kennedy             2      1   \n",
              "5475  Apartamento   Apartamento en venta en Puente Largo             2      3   \n",
              "2096  Apartamento  Apartamento en venta en Santa Barbara             3      3   \n",
              "7007  Apartamento   Apartamento en venta en Puente Largo             3      3   \n",
              "7914  Apartamento        Apartamento en venta en Usaquén             3      2   \n",
              "\n",
              "      Area         Barrio                                    UPZ  \\\n",
              "3628    47           Bosa                           Bosa Central   \n",
              "7621    92       Engativa                               Engativá   \n",
              "2766    52       Engativa                               Engativá   \n",
              "1475    53        Usaquén                                Usaquén   \n",
              "4334   106        Usaquén                                Usaquén   \n",
              "4098    42        Kennedy                        Kennedy Central   \n",
              "5475    86   Puente Largo                            La Alhambra   \n",
              "2096   165  Santa Barbara  USAQUÉN: Country Club + Santa Bárbara   \n",
              "7007   103   Puente Largo                            La Alhambra   \n",
              "7914   120        Usaquén                                Usaquén   \n",
              "\n",
              "              Valor  \n",
              "3628  $ 136.000.000  \n",
              "7621  $ 330.000.000  \n",
              "2766  $ 196.000.000  \n",
              "1475  $ 380.000.000  \n",
              "4334  $ 765.000.000  \n",
              "4098  $ 125.000.000  \n",
              "5475  $ 395.000.000  \n",
              "2096  $ 590.000.000  \n",
              "7007  $ 500.000.000  \n",
              "7914  $ 480.000.000  "
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              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Barrio</th>\n",
              "      <th>UPZ</th>\n",
              "      <th>Valor</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3628</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Bosa</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>Bosa</td>\n",
              "      <td>Bosa Central</td>\n",
              "      <td>$ 136.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7621</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Engativa</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>92</td>\n",
              "      <td>Engativa</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>$ 330.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2766</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Engativa</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>52</td>\n",
              "      <td>Engativa</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>$ 196.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1475</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 380.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4334</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>106</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 765.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4098</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>$ 125.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5475</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Puente Largo</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>86</td>\n",
              "      <td>Puente Largo</td>\n",
              "      <td>La Alhambra</td>\n",
              "      <td>$ 395.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2096</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Santa Barbara</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>165</td>\n",
              "      <td>Santa Barbara</td>\n",
              "      <td>USAQUÉN: Country Club + Santa Bárbara</td>\n",
              "      <td>$ 590.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7007</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Puente Largo</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>103</td>\n",
              "      <td>Puente Largo</td>\n",
              "      <td>La Alhambra</td>\n",
              "      <td>$ 500.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7914</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Usaquén</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>$ 480.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Información de nuestro dataset"
      ],
      "metadata": {
        "id": "G9mxUD6lii7v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3_R_0YNj6HG",
        "outputId": "46399b0d-cc37-4ffa-9755-726f43ebce8b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9520 entries, 0 to 9519\n",
            "Data columns (total 8 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Tipo          9520 non-null   object\n",
            " 1   Descripcion   9520 non-null   object\n",
            " 2   Habitaciones  9520 non-null   int64 \n",
            " 3   Banos         9520 non-null   int64 \n",
            " 4   Area          9520 non-null   int64 \n",
            " 5   Barrio        9520 non-null   object\n",
            " 6   UPZ           9478 non-null   object\n",
            " 7   Valor         9520 non-null   object\n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 595.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filtrando datos por numero de indice, recordando que el indice empieza en cero.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "2i385oGsuzYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.iloc[300]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdFKIqRDu3kk",
        "outputId": "8a7f2f15-1c14-4215-b746-7f8652045e3a"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.iloc[300:305]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GRd_Oy_Hv2Jl",
        "outputId": "d5652ce7-58b9-40ed-f7d8-2632259205f2"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
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              "      <th>301</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Norte</td>\n",
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              "      <th>302</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Kennedy</td>\n",
              "      <td>3</td>\n",
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              "      <td>Kennedy</td>\n",
              "      <td>Kennedy Central</td>\n",
              "      <td>$ 237.000.000</td>\n",
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              "      <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",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles['Valor'][300:305]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PogUj8QKwCRl",
        "outputId": "a78914e0-1aab-420c-846f-6e5ab4c8c6e7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "300    $ 116.100.000\n",
              "301    $ 980.000.000\n",
              "302    $ 237.000.000\n",
              "303    $ 320.000.000\n",
              "304    $ 530.000.000\n",
              "Name: Valor, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculando el promedio de área"
      ],
      "metadata": {
        "id": "a12TEOz3xOWe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.Area.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqzWIzlkw9tn",
        "outputId": "7607ab11-af67-433d-fd33-fa6a81408054"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "146.66470588235293"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculamos la cantidad de propiedades en barrio \"Chico Reservado\"\n",
        "\n",
        "```\n",
        "(inmuebles.Barrio == \"Chico Reservado\")\n",
        "```\n",
        "Nos devuelve un falso o verdadero de cada uno de los registros\n"
      ],
      "metadata": {
        "id": "zNtbao_OyOWC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sum((inmuebles.Barrio == \"Chico Reservado\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1M1RSt4xrSq",
        "outputId": "40296381-caaf-4cc5-bf49-160ad2556d01"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "225"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles_Chico_Reservado = (inmuebles.Barrio == \"Chico Reservado\")\n",
        "type(inmuebles_Chico_Reservado)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMPjTICSzTIi",
        "outputId": "95c50205-adc4-462d-a624-9cba336c9815"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chico_Reservado = inmuebles[inmuebles_Chico_Reservado]\n",
        "chico_Reservado"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "igfaTcepzmlp",
        "outputId": "49442391-57d9-4328-e9eb-bc6f5cae8aae"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Tipo                              Descripcion  Habitaciones  \\\n",
              "2     Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "39    Apartamento  Apartamento en venta en Chico Reservado             1   \n",
              "72    Apartamento  Apartamento en venta en Chico Reservado             1   \n",
              "126   Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "143   Apartamento  Apartamento en venta en Chico Reservado             2   \n",
              "...           ...                                      ...           ...   \n",
              "9299  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "9335  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "9341  Apartamento  Apartamento en venta en Chico Reservado             2   \n",
              "9472  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "9506  Apartamento  Apartamento en venta en Chico Reservado             3   \n",
              "\n",
              "      Banos  Area           Barrio                                 UPZ  \\\n",
              "2         3   144  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "39        1    64  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "72        1    64  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "126       4   150  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "143       4   104  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "...     ...   ...              ...                                 ...   \n",
              "9299      3   148  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9335      3   176  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9341      3   118  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9472      1    49  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "9506      4   390  Chico Reservado  CHAPINERO: Chicó Lago + El Refugio   \n",
              "\n",
              "                Valor  \n",
              "2     $ 1.120.000.000  \n",
              "39      $ 495.000.000  \n",
              "72      $ 495.000.000  \n",
              "126   $ 1.300.000.000  \n",
              "143   $ 1.100.000.000  \n",
              "...               ...  \n",
              "9299    $ 780.000.000  \n",
              "9335    $ 850.000.000  \n",
              "9341    $ 700.000.000  \n",
              "9472    $ 200.000.000  \n",
              "9506  $ 3.200.000.000  \n",
              "\n",
              "[225 rows x 8 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
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              "      <td>Chico Reservado</td>\n",
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              "      <td>$ 1.120.000.000</td>\n",
              "    </tr>\n",
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              "      <th>39</th>\n",
              "      <td>Apartamento</td>\n",
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              "      <td>Chico Reservado</td>\n",
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              "      <td>$ 495.000.000</td>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>9299</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>148</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 780.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9335</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>176</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 850.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9341</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>118</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 700.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9472</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 200.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9506</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Apartamento en venta en Chico Reservado</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>390</td>\n",
              "      <td>Chico Reservado</td>\n",
              "      <td>CHAPINERO: Chicó Lago + El Refugio</td>\n",
              "      <td>$ 3.200.000.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>225 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80d7ba0d-72a3-4ce1-9dcb-6ae4cfd76918')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-80d7ba0d-72a3-4ce1-9dcb-6ae4cfd76918 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-80d7ba0d-72a3-4ce1-9dcb-6ae4cfd76918');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3a9b438f-bf5a-447f-84c9-38a6123153b1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a9b438f-bf5a-447f-84c9-38a6123153b1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3a9b438f-bf5a-447f-84c9-38a6123153b1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chico_Reservado.Area.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sCT71_j0R_0",
        "outputId": "11e2a586-a04c-4055-bfa7-9f80134e27a1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "142.78666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cantidad de barrios"
      ],
      "metadata": {
        "id": "uRXrKY2B0leV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(inmuebles.Barrio.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBFdDn580oiD",
        "outputId": "b92d8204-7fe7-4626-dd70-d6bf3cf1af3e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cantidad de inmuebles por barrio"
      ],
      "metadata": {
        "id": "HWR4cFO41DSE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.Barrio.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56Xme3se1GnE",
        "outputId": "bd0092c8-6c50-47a9-f79c-e3e84b88c94e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Usaquén               1105\n",
              "Zona Noroccidental     877\n",
              "Bosa                   589\n",
              "Kennedy                589\n",
              "Cedritos               554\n",
              "                      ... \n",
              "Colinas De Suba          1\n",
              "La Estanzuela            1\n",
              "Roma                     1\n",
              "Alameda                  1\n",
              "Prado Veraniego          1\n",
              "Name: Barrio, Length: 149, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cantidad de UPZ"
      ],
      "metadata": {
        "id": "sN26NYP-1Mbx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(inmuebles.UPZ.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dR6TpvEu1Owa",
        "outputId": "598d8547-793f-4a49-c1ff-5a2da5721b85"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grafico de inmuebles por barrio"
      ],
      "metadata": {
        "id": "UOrHhdt21kOy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles_barrio = inmuebles.Barrio.value_counts()\n",
        "inmuebles_barrio.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "LyX1J6gZ1ov7",
        "outputId": "7c7d3c06-6419-4bf2-915e-9f51ca444d3b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grafico de los 10 barrios con más inmuebles"
      ],
      "metadata": {
        "id": "u5zxtrQq2KFT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles_barrio.head(10).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "b7Q6cRDv1-JL",
        "outputId": "841f64e0-bd41-40f6-8531-690b42bbba1f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Desafíos de esta aula**\n",
        "\n",
        "1. Promedio del área de todos los inmuebles en los barrios en el dataset. El top 10."
      ],
      "metadata": {
        "id": "zTRBx1yZ2miK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculamos el promedio de las areas por barrio"
      ],
      "metadata": {
        "id": "DfVQfBil7ily"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "promedio_barrio = inmuebles[[\"Barrio\", \"Area\"]].groupby(\"Barrio\").mean()\n",
        "promedio_barrio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "hl8mnb0P2rnB",
        "outputId": "2e1dfefa-d398-40ad-b114-c7c8f2067581"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Area\n",
              "Barrio                        \n",
              "Alameda              97.000000\n",
              "Alhambra            190.411765\n",
              "Andes Norte         124.857143\n",
              "Antonio Nariño      163.062500\n",
              "Barrancas            70.200000\n",
              "...                        ...\n",
              "Villa Maria         207.153846\n",
              "Villas De Granada    86.590909\n",
              "Villemar             76.888889\n",
              "Zona Franca          54.000000\n",
              "Zona Noroccidental  135.785633\n",
              "\n",
              "[149 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7971a202-09b9-42d9-98be-6ca87f295185\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Barrio</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alameda</th>\n",
              "      <td>97.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alhambra</th>\n",
              "      <td>190.411765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andes Norte</th>\n",
              "      <td>124.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antonio Nariño</th>\n",
              "      <td>163.062500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Barrancas</th>\n",
              "      <td>70.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villa Maria</th>\n",
              "      <td>207.153846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villas De Granada</th>\n",
              "      <td>86.590909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villemar</th>\n",
              "      <td>76.888889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zona Franca</th>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zona Noroccidental</th>\n",
              "      <td>135.785633</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7971a202-09b9-42d9-98be-6ca87f295185')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7971a202-09b9-42d9-98be-6ca87f295185 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7971a202-09b9-42d9-98be-6ca87f295185');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8b86c54f-1a11-474a-a296-a094a8c01047\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b86c54f-1a11-474a-a296-a094a8c01047')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8b86c54f-1a11-474a-a296-a094a8c01047 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ordenamos de promedio mayor al menor y mostramos el top 10"
      ],
      "metadata": {
        "id": "KepsJkqz79lg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_barrios_area = promedio_barrio.sort_values([\"Area\"], ascending=False).head(10)\n",
        "top_barrios_area"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "BJJeFJGb8cUo",
        "outputId": "b2c35a44-c521-48d7-da18-2bee2e8a2d36"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Area\n",
              "Barrio                            \n",
              "Modelia                4316.750000\n",
              "Libertador              642.000000\n",
              "Bosa Piamonte           400.000000\n",
              "La Soledad              378.625000\n",
              "Restrepo                336.500000\n",
              "Santa Librada           328.000000\n",
              "Las Ferias              320.000000\n",
              "Santa Ana               309.375000\n",
              "Veraguas                302.666667\n",
              "Bellavista Occidental   286.200000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8db79348-73cf-4936-959e-62697994f6c8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\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>Area</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Barrio</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Modelia</th>\n",
              "      <td>4316.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Libertador</th>\n",
              "      <td>642.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bosa Piamonte</th>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>La Soledad</th>\n",
              "      <td>378.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Restrepo</th>\n",
              "      <td>336.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Santa Librada</th>\n",
              "      <td>328.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Las Ferias</th>\n",
              "      <td>320.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Santa Ana</th>\n",
              "      <td>309.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Veraguas</th>\n",
              "      <td>302.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bellavista Occidental</th>\n",
              "      <td>286.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8db79348-73cf-4936-959e-62697994f6c8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8db79348-73cf-4936-959e-62697994f6c8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-806f0ab5-33ff-4c69-b0cf-06ea2d4f718e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-806f0ab5-33ff-4c69-b0cf-06ea2d4f718e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-806f0ab5-33ff-4c69-b0cf-06ea2d4f718e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graficamos"
      ],
      "metadata": {
        "id": "N8w6mXg_8iin"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_barrios_area.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "i2qPU_jn8Kno",
        "outputId": "e4211895-458d-45c9-d967-753076be3527"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Barrio'>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos ver los inmuebles con mayor área de Modelia para confirmar la diferencia"
      ],
      "metadata": {
        "id": "2iFmgf5V-w4X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles_Modelia = (inmuebles.Barrio == \"Modelia\")\n",
        "Modelia = inmuebles[inmuebles_Modelia]\n",
        "Modelia.sort_values([\"Area\"], ascending=False).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "RqJW6CvR9HRH",
        "outputId": "a0efba49-e65a-4e7a-ea8c-49f736d31db6"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Tipo               Descripcion  Habitaciones  Banos    Area   Barrio  \\\n",
              "7073  Casa  Casa en venta en Modelia             4      3  166243  Modelia   \n",
              "8562  Casa  Casa en venta en Modelia             5      4     400  Modelia   \n",
              "4913  Casa  Casa en venta en Modelia             5      4     320  Modelia   \n",
              "2147  Casa  Casa en venta en Modelia             5      4     320  Modelia   \n",
              "4914  Casa  Casa en venta en Modelia             5      4     320  Modelia   \n",
              "5059  Casa  Casa en venta en Modelia             7      6     287  Modelia   \n",
              "7908  Casa  Casa en venta en Modelia             3      3     275  Modelia   \n",
              "9326  Casa  Casa en venta en Modelia             5      3     270  Modelia   \n",
              "2257  Casa  Casa en venta en Modelia             5      4     244  Modelia   \n",
              "1283  Casa  Casa en venta en Modelia             8      4     236  Modelia   \n",
              "\n",
              "           UPZ            Valor  \n",
              "7073  Fontibón    $ 850.000.000  \n",
              "8562  Fontibón    $ 950.000.000  \n",
              "4913  Fontibón    $ 750.000.000  \n",
              "2147  Fontibón    $ 750.000.000  \n",
              "4914  Fontibón    $ 750.000.000  \n",
              "5059  Fontibón    $ 750.000.000  \n",
              "7908  Fontibón    $ 850.000.000  \n",
              "9326  Fontibón  $ 1.100.000.000  \n",
              "2257  Fontibón    $ 950.000.000  \n",
              "1283  Fontibón  $ 1.120.000.000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-844d282c-28b4-4158-8a84-4d842888d97c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Banos</th>\n",
              "      <th>Area</th>\n",
              "      <th>Barrio</th>\n",
              "      <th>UPZ</th>\n",
              "      <th>Valor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7073</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>166243</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 850.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8562</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>400</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 950.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4913</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>320</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 750.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2147</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>320</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 750.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4914</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>320</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 750.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5059</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>287</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 750.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7908</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>275</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 850.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9326</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>270</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 1.100.000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2257</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Casa en venta en Modelia</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>244</td>\n",
              "      <td>Modelia</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>$ 950.000.000</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-844d282c-28b4-4158-8a84-4d842888d97c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-844d282c-28b4-4158-8a84-4d842888d97c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-844d282c-28b4-4158-8a84-4d842888d97c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-de0df089-edf5-443b-b3f5-29a876b1a567\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de0df089-edf5-443b-b3f5-29a876b1a567')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-de0df089-edf5-443b-b3f5-29a876b1a567 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Consultar otros datos estadísticos, conteo, mediana, valores mínimo y máximo."
      ],
      "metadata": {
        "id": "tZtueZo3_JRW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Promedio"
      ],
      "metadata": {
        "id": "7lDEt5Pz_Ukm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles[\"Habitaciones\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1s218Gd_LS-",
        "outputId": "5a2d9e49-d8ba-4b24-b797-19eeba9546b5"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.072478991596639"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mediana"
      ],
      "metadata": {
        "id": "CiCN8ps-_jpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles[\"Habitaciones\"].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iw9gSRwq_k3X",
        "outputId": "f58296d8-1766-4b3d-cc8c-8e3d572a6e39"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Describir todos los datos estadisticos"
      ],
      "metadata": {
        "id": "d2jEKeBb_9oU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles[[\"Habitaciones\",\"Area\"]].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "byyQRLyEAC4W",
        "outputId": "34b462b3-09a6-4487-c0ce-905f2f36f853"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Habitaciones           Area\n",
              "count   9520.000000    9520.000000\n",
              "mean       3.072479     146.664706\n",
              "std        2.049856    1731.379622\n",
              "min        1.000000       2.000000\n",
              "25%        2.000000      57.000000\n",
              "50%        3.000000      80.000000\n",
              "75%        3.000000     135.000000\n",
              "max      110.000000  166243.000000"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\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>146.664706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.049856</td>\n",
              "      <td>1731.379622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>57.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>135.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>110.000000</td>\n",
              "      <td>166243.000000</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1bb0f7fa-cf99-4441-8946-5eef7f0b51d6');\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-efcf0660-22a2-4f91-8bb3-5053c02c6c38 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Describir solo los datos que necesitemos"
      ],
      "metadata": {
        "id": "G8YzRqo9AjnP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inmuebles.agg(\n",
        "    {\n",
        "        \"Habitaciones\":[\"min\", \"max\", \"mean\", \"median\", \"count\"],\n",
        "        \"Area\":[\"min\", \"max\", \"mean\", \"median\", \"count\"]\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vPCS0sT-Anwd",
        "outputId": "1a12d87c-e52e-4e01-b31a-46245e04acc9"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Habitaciones           Area\n",
              "min         1.000000       2.000000\n",
              "max       110.000000  166243.000000\n",
              "mean        3.072479     146.664706\n",
              "median      3.000000      80.000000\n",
              "count    9520.000000    9520.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44222d23-efaf-4c0f-94a4-cfee92afed10\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Habitaciones</th>\n",
              "      <th>Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>110.000000</td>\n",
              "      <td>166243.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.072479</td>\n",
              "      <td>146.664706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9520.000000</td>\n",
              "      <td>9520.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44222d23-efaf-4c0f-94a4-cfee92afed10')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-44222d23-efaf-4c0f-94a4-cfee92afed10 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-44222d23-efaf-4c0f-94a4-cfee92afed10');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4b5bb245-2e8d-4dea-8f53-e006f9a2f14c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4b5bb245-2e8d-4dea-8f53-e006f9a2f14c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4b5bb245-2e8d-4dea-8f53-e006f9a2f14c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    }
  ]
}