Modulo_pandas.ipynb

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  "metadata": {
    "colab": {
      "name": "Modulo_pandas.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOtPy5em5MMeGBf+qTTp5Op",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CdeCMx-org/curso_python/blob/main/Modulo_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ux-81uD08hzT"
      },
      "source": [
        "En este módulo vamos a aprender sobre el paquete llamada **Pandas**. Este paquete es muy útil para hacer análisis de datos. Pandas nos permite leer con datos estructurados de forma fácil, rápida e intuitiva. \n",
        "\n",
        "La principal estructura de datos que utiliza se llama **DataFrame**,  este es muy similar a la de una hoja de cálculo como las de Excel. Se caracteriza por:\n",
        "\n",
        "1.   Almacenar datos en celdas\n",
        "2.   Tiene columnnas con etiquetas (nombres) y filas numeradas.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DYkN5VnS8WnS"
      },
      "source": [
        "## Vamos a importar los paquetes que necesitamos para los ejercicios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMK-53N2zvdk"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8yDrIxDowZAN"
      },
      "source": [
        "# Ejemplo 1\n",
        "## Ingresamos datos manualmente\n",
        "En el siguiente ejemplo vamos a crear un DataFrame con los datos de un equipo para el proyecto final de clubes de ciencia. La información que deberá tener es la siguiente:\n",
        "\n",
        "\n",
        "*   4 filas. Un fila por miembro del equipo.\n",
        "*   3 columnas. Una llamada `nombre`, `edad` y `ciudad`.\n",
        "\n",
        "El siguiente código crea un DataFrame con dicha información. Para ellos se requiere:\n",
        "\n",
        "\n",
        "1.   Proporcionar los datos para completar las 12 celdas. La celda de código llama a np.array para generar la matriz 4x3 NumPy.\n",
        "2.   Identificar los nombres de las dos columnas.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSw59MLiwYpD",
        "outputId": "b1dc66a7-b267-43a8-e32e-db12e233b8d5"
      },
      "source": [
        "# crear una matriz con Numpy de dimensiones 4x3.\n",
        "mi_equipo= np.array([[\"Ana Pau\",\"15\",\"Oaxaca\"],[\"Regina\", \"17\", \"Monterrey\"],[\"Ana Sofi\", \"19\", \"Xalapa\"],[\"Renata\",\"21\",\"Chihuahua\"]])\n",
        "print(mi_equipo)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['Ana Pau' '15' 'Oaxaca']\n",
            " ['Regina' '17' 'Monterrey']\n",
            " ['Ana Sofi' '19' 'Xalapa']\n",
            " ['Renata' '21' 'Chihuahua']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEX-VHQYzXt7",
        "outputId": "f6e2983e-96ca-4ab6-ac28-06b219b9402a"
      },
      "source": [
        "# crear una lista de Python con los nombres de las 3 columnas.\n",
        "nombres_columnas=[\"Nombre\",\"Edad\",\"Ciudad\"]\n",
        "\n",
        "# crear el DataFrame\n",
        "mi_dataframe=pd.DataFrame(data=mi_equipo,columns=nombres_columnas)\n",
        "\n",
        "# Veamos el DataFrame que creamos\n",
        "print(mi_dataframe)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Nombre Edad     Ciudad\n",
            "0   Ana Pau   15     Oaxaca\n",
            "1    Regina   17  Monterrey\n",
            "2  Ana Sofi   19     Xalapa\n",
            "3    Renata   21  Chihuahua\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O76sEMRr5hRp"
      },
      "source": [
        "Podemos extraer los valores de nuestro DataFrame de múltiples formas. Por ejemplo, obtener solo la columna de edades, o solo los datos de uno de los miembros del equipo, o solo los datos que cumplan con alguna condición. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNVNNdKk5gVL",
        "outputId": "a9fb037b-647f-47ae-e2c9-2f71c3774ba8"
      },
      "source": [
        "# Extraer las primeras tres filas de mi DataFrame\n",
        "print(mi_dataframe.head(3))\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Nombre Edad     Ciudad\n",
            "0   Ana Pau   15     Oaxaca\n",
            "1    Regina   17  Monterrey\n",
            "2  Ana Sofi   19     Xalapa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPjNZi546Hvs",
        "outputId": "e81d8702-56f3-4ad7-94c1-e87e89aaac1f"
      },
      "source": [
        "# Extraer solo la fila 2 (nota que Python empieza a contar desde zero por lo que restamos 1 al número de fila que queremos).\n",
        "print(mi_dataframe.iloc[[1]])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Nombre Edad     Ciudad\n",
            "1  Regina   17  Monterrey\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTbmR1lx6p9x",
        "outputId": "7d173915-6478-42d9-ed82-011853d0022d"
      },
      "source": [
        "# Extraer las filas 2 a 4\n",
        "print(mi_dataframe[1:4])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Nombre Edad     Ciudad\n",
            "1    Regina   17  Monterrey\n",
            "2  Ana Sofi   19     Xalapa\n",
            "3    Renata   21  Chihuahua\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JijSzngy6434",
        "outputId": "1f1f46ba-ea95-4512-947b-5fb5135b3d09"
      },
      "source": [
        "# Extraer la columna de edades\n",
        "print(mi_dataframe[\"Edad\"])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    15\n",
            "1    17\n",
            "2    19\n",
            "3    21\n",
            "Name: Edad, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfjml_HU7DEV"
      },
      "source": [
        "\n",
        "**Ejercicio:** Repite el ejercicio anterior pero creando tu propio set de datos. Aprovecha para conocer a tus compañeros, escoge a 3 estudiantes de clubes y preguntales su nombre, edad, ciudad, y comida favorita. Crea un DataFrame con 3 filas y 4 columnas (una fila por estudiante).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJTl5HMN7eOI"
      },
      "source": [
        "## ingresa tu código aquí"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qpOG3mV-RXi"
      },
      "source": [
        "# Ejemplo 2\n",
        "## Extraemos datos de un fuente externa\n",
        "\n",
        "Como ejemplo utilizaremos los datos ubicados en el siguiente repositorio de GitHub: https://github.com/CdeCMx-org/curso_python/tree/main/student\n",
        "\n",
        "**Información del conjunto de datos:** Estos datos se acercan al rendimiento de los estudiantes en la educación secundaria de dos escuelas portuguesas. Los atributos de los datos incluyen calificaciones de los estudiantes, características demográficas, sociales y relacionadas con la escuela) y se recopilaron mediante el uso de informes y cuestionarios escolares. Se proporcionan dos conjuntos de datos sobre el desempeño en dos materias distintas: Matemáticas (mat) y lengua portuguesa (por). Nota importante: el atributo objetivo G3 tiene una fuerte correlación con los atributos G2 y G1. Esto ocurre porque G3 es la calificación final del año (emitida en el tercer período), mientras que G1 y G2 corresponden a las calificaciones del primer y segundo período.\n",
        "\n",
        "La fuente original de los datos se encuentra en: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.\n",
        "[Web link](http://www3.dsi.uminho.pt/pcortez/student.pdf)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VkLs00TiaK4k"
      },
      "source": [
        "\n",
        "\n",
        "Puedes consultar la [descripción de datos aquí](https://github.com/CdeCMx-org/datasets-ejemplos/blob/main/student/student.txt). Algunos campos importantes para este ejercicio son los siguientes:\n",
        "\n",
        "*   Columna 1:  school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)\n",
        "*   Columna 2: - sex - student's sex (binary: 'F' - female or 'M' - male)\n",
        "*  Columna 3: age - student's age (numeric: from 15 to 22)\n",
        "*  Columna 14: studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)\n",
        "*  Columna 30: absences  - number of school absences (numeric: from 0 to 93)\n",
        "*  Columna 32: G3 - final grade (numeric: from 0 to 20, output target)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMBXRZ0D-zjX"
      },
      "source": [
        "Para leer los datos que contienen las calificaciones del curso de Matematicas, leeremos ingresando directamente el URL dentro de el repositorio de GitHub. \n",
        "\n",
        "Da click en el [link](https://raw.githubusercontent.com/CdeCMx-org/datasets-ejemplos/main/student/student-mat.csv) y ve la estructura de los datos. ¿Cómo están separados los campos? Nota que como están separados con \";\" vamos a tener que indicarlo al momento de leer los datos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juFnpyoE22VP"
      },
      "source": [
        "url = \"https://raw.githubusercontent.com/CdeCMx-org/curso_python/main/student/student-mat.csv\"\n",
        "df1 = pd.read_csv(url,sep=\";\")\n",
        "# Dataset is now stored in a Pandas Dataframe"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4nsqxCE8AmD"
      },
      "source": [
        "Vamos a llamar a los datos que acabamos de leer para ver la estructura de los datos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "oEqU0vSB3ShX",
        "outputId": "edbba904-b446-4201-c429-2e56c5cc840c"
      },
      "source": [
        "df1"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>1</td>\n",
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              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>R</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>R</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>at_home</td>\n",
              "      <td>course</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    school sex  age address famsize Pstatus  ...  Walc  health absences  G1  G2  G3\n",
              "0       GP   F   18       U     GT3       A  ...     1       3        6   5   6   6\n",
              "1       GP   F   17       U     GT3       T  ...     1       3        4   5   5   6\n",
              "2       GP   F   15       U     LE3       T  ...     3       3       10   7   8  10\n",
              "3       GP   F   15       U     GT3       T  ...     1       5        2  15  14  15\n",
              "4       GP   F   16       U     GT3       T  ...     2       5        4   6  10  10\n",
              "..     ...  ..  ...     ...     ...     ...  ...   ...     ...      ...  ..  ..  ..\n",
              "390     MS   M   20       U     LE3       A  ...     5       4       11   9   9   9\n",
              "391     MS   M   17       U     LE3       T  ...     4       2        3  14  16  16\n",
              "392     MS   M   21       R     GT3       T  ...     3       3        3  10   8   7\n",
              "393     MS   M   18       R     LE3       T  ...     4       5        0  11  12  10\n",
              "394     MS   M   19       U     LE3       T  ...     3       5        5   8   9   9\n",
              "\n",
              "[395 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-w-WPNqBOTk"
      },
      "source": [
        "Podemos extraer los valores de una columna de dos formas. Ya sea como si fuera un diccionario o un atributo. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6Okp8Wt7-xc",
        "outputId": "09d242af-c30f-40f9-8c59-095b501cd3bd"
      },
      "source": [
        "df1['age']"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      18\n",
              "1      17\n",
              "2      15\n",
              "3      15\n",
              "4      16\n",
              "       ..\n",
              "390    20\n",
              "391    17\n",
              "392    21\n",
              "393    18\n",
              "394    19\n",
              "Name: age, Length: 395, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2dnnDPjBKUy",
        "outputId": "5c37fead-896f-4bc4-9df6-e1624d2dde48"
      },
      "source": [
        "df1.age"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      18\n",
              "1      17\n",
              "2      15\n",
              "3      15\n",
              "4      16\n",
              "       ..\n",
              "390    20\n",
              "391    17\n",
              "392    21\n",
              "393    18\n",
              "394    19\n",
              "Name: age, Length: 395, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wty6WwCtBlSK"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "**Ejercicio:** imprime los valores de la columna 'absences'\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQTwV995BkZo"
      },
      "source": [
        "### Ingresa tu código aquí\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4ZEb_GvDcDq"
      },
      "source": [
        "\n",
        "## Ordenar datos\n",
        "\n",
        "Podemos tambien ordernar datos en base a una columna\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCNFFdqsGr2I"
      },
      "source": [
        "Ejemplo donde ordenamos los datos en base a la edad\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "LPxQ9RLVCPUF",
        "outputId": "908201b8-11f9-448c-ea01-80b0507c0402"
      },
      "source": [
        "df1.sort_values(by=\"age\")\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>school</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>address</th>\n",
              "      <th>famsize</th>\n",
              "      <th>Pstatus</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>Mjob</th>\n",
              "      <th>Fjob</th>\n",
              "      <th>reason</th>\n",
              "      <th>guardian</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>schoolsup</th>\n",
              "      <th>famsup</th>\n",
              "      <th>paid</th>\n",
              "      <th>activities</th>\n",
              "      <th>nursery</th>\n",
              "      <th>higher</th>\n",
              "      <th>internet</th>\n",
              "      <th>romantic</th>\n",
              "      <th>famrel</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>Dalc</th>\n",
              "      <th>Walc</th>\n",
              "      <th>health</th>\n",
              "      <th>absences</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>R</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>services</td>\n",
              "      <td>teacher</td>\n",
              "      <td>course</td>\n",
              "      <td>father</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>home</td>\n",
              "      <td>mother</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>health</td>\n",
              "      <td>reputation</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>376</th>\n",
              "      <td>MS</td>\n",
              "      <td>F</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>R</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>22</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    school sex  age address famsize Pstatus  ...  Walc  health absences  G1  G2  G3\n",
              "130     GP   F   15       R     GT3       T  ...     2       5        0  12   0   0\n",
              "102     GP   M   15       U     GT3       T  ...     1       5        4  10  13  14\n",
              "103     GP   F   15       U     GT3       T  ...     1       2       26   7   6   6\n",
              "104     GP   M   15       U     GT3       A  ...     1       1        0  16  18  18\n",
              "105     GP   F   15       U     GT3       A  ...     1       4       10  10  11  11\n",
              "..     ...  ..  ...     ...     ...     ...  ...   ...     ...      ...  ..  ..  ..\n",
              "376     MS   F   20       U     GT3       T  ...     1       3        4  15  14  15\n",
              "306     GP   M   20       U     GT3       A  ...     1       5        0  17  18  18\n",
              "390     MS   M   20       U     LE3       A  ...     5       4       11   9   9   9\n",
              "392     MS   M   21       R     GT3       T  ...     3       3        3  10   8   7\n",
              "247     GP   M   22       U     GT3       T  ...     5       1       16   6   8   8\n",
              "\n",
              "[395 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OWuNyheGz-8"
      },
      "source": [
        "También podemos ordenar en base a dos columnas, en este caso *edad* y *sexo*. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "dk6tqkuNG-a4",
        "outputId": "b13352a7-9c9c-4e1d-e3f9-bb3513996013"
      },
      "source": [
        "df1.sort_values(by=[\"age\",\"sex\"])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>school</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>address</th>\n",
              "      <th>famsize</th>\n",
              "      <th>Pstatus</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>Mjob</th>\n",
              "      <th>Fjob</th>\n",
              "      <th>reason</th>\n",
              "      <th>guardian</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>schoolsup</th>\n",
              "      <th>famsup</th>\n",
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              "      <th>activities</th>\n",
              "      <th>nursery</th>\n",
              "      <th>higher</th>\n",
              "      <th>internet</th>\n",
              "      <th>romantic</th>\n",
              "      <th>famrel</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>Dalc</th>\n",
              "      <th>Walc</th>\n",
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              "      <th>absences</th>\n",
              "      <th>G1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>GP</td>\n",
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              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>no</td>\n",
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              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>7</td>\n",
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              "      <td>10</td>\n",
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              "      <td>services</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>teacher</td>\n",
              "      <td>health</td>\n",
              "      <td>reputation</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>reputation</td>\n",
              "      <td>father</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>yes</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>R</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>services</td>\n",
              "      <td>health</td>\n",
              "      <td>course</td>\n",
              "      <td>mother</td>\n",
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              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>376</th>\n",
              "      <td>MS</td>\n",
              "      <td>F</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>R</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>GP</td>\n",
              "      <td>M</td>\n",
              "      <td>22</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    school sex  age address famsize Pstatus  ...  Walc  health absences  G1  G2  G3\n",
              "2       GP   F   15       U     LE3       T  ...     3       3       10   7   8  10\n",
              "3       GP   F   15       U     GT3       T  ...     1       5        2  15  14  15\n",
              "10      GP   F   15       U     GT3       T  ...     2       2        0  10   8   9\n",
              "11      GP   F   15       U     GT3       T  ...     1       4        4  10  12  12\n",
              "24      GP   F   15       R     GT3       T  ...     1       5        2  10   9   8\n",
              "..     ...  ..  ...     ...     ...     ...  ...   ...     ...      ...  ..  ..  ..\n",
              "376     MS   F   20       U     GT3       T  ...     1       3        4  15  14  15\n",
              "306     GP   M   20       U     GT3       A  ...     1       5        0  17  18  18\n",
              "390     MS   M   20       U     LE3       A  ...     5       4       11   9   9   9\n",
              "392     MS   M   21       R     GT3       T  ...     3       3        3  10   8   7\n",
              "247     GP   M   22       U     GT3       T  ...     5       1       16   6   8   8\n",
              "\n",
              "[395 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MQRxcebdCuk"
      },
      "source": [
        "**Ejercicio:** Ordena los datos en base a la calificación final (columna \"G3\"). \n",
        "¿En que orden se encuentran, es decir, sale primero las calificaciones altas o bajas?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7Tmgnc5ddt-"
      },
      "source": [
        "## Ingresa tu código aquí\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWpCCMv8dhIs"
      },
      "source": [
        "\n",
        "**Ejercicio:** Ahora ordena la calificación final en el orden contrario. \n",
        "Hint: agrega la especificación `ascending=TRUE` cuando llames a la funcion.  \n",
        "Por ejemplo, si ordenamos al edad de menor a mayor sería:\n",
        "`df1.sort_values(by=\"age\",ascending=TRUE)`\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFfxGf-Uef0V"
      },
      "source": [
        "## Ingresa tu código aquí\n",
        "\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXQ6YFZHXrdw"
      },
      "source": [
        "## Extraer un subconjunto de datos. \n",
        "Muchas veces nos interesa restringir nuestros análisis a solo una subpoblación. Para ellos podemos crear un nuevo set de datos que solo contenga los datos que nos interesan. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9h12xreZhUu"
      },
      "source": [
        "En el ejemplo que estamos utilizando tenemos datos sobre los alumnos de dos escuelas secundarias en Portugal. Supongamos que nos interesa analizar los datos de solo una escuela, la columna \"school\" indica a que escuela pertenece cada alumno, y nos interesa la escuela *Gabriel Pereira* (variable=GP)\n",
        "\n",
        "Pasos:\n",
        "\n",
        "\n",
        "1.   Definimos una nueva variable `df1_GP` que solo contiene los datos de la escuela que nos interesa\n",
        "2.   Nos fijamos en los valores de la columna \"school\" que son iguales \"GP\". Eso lo hacemos llamando a la columna y utilizando el signo `==`. Lo que nos da: `df1[\"school\"]==\"GP\"`. Si imprimimos esta línea solo, lo que nos da es un vector booleano, que indica que filas tienen valores iguales a GP y cuáles no. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9Qrtg_dhzas",
        "outputId": "ee8c8713-4de0-4b18-8928-027b1ab83ee0"
      },
      "source": [
        "df1[\"school\"]==\"GP\""
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       True\n",
              "1       True\n",
              "2       True\n",
              "3       True\n",
              "4       True\n",
              "       ...  \n",
              "390    False\n",
              "391    False\n",
              "392    False\n",
              "393    False\n",
              "394    False\n",
              "Name: school, Length: 395, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9k66qmLh3Y-"
      },
      "source": [
        "3. Como nos interes obtener todo DataFrame es decir todas las columnas del set de datos original, restringido solo a la escuela Gabriel Pereira, entonces escribmimos `df1[\"school\"]==\"GP\"`, dentro de df1. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWvULPuqfMfK"
      },
      "source": [
        "## Definimos una nueva variable df1_GP que solo contiene los datos de la escuela que nos interesa\n",
        "df1_GP=df1[df1[\"school\"]==\"GP\"]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmcnAFW6h8vc"
      },
      "source": [
        "Podemos comprobar revisando las dimensiones del DataFrame, esto se hace utilizando la función `shape`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLbzwC1PiIxJ",
        "outputId": "5e46da90-133c-49bb-e016-2c3a6f4124fb"
      },
      "source": [
        "## Dimensiones del los datos originales \n",
        "print(df1.shape)\n",
        "## Dimensiones del los datos restringidos a la escuela Gabriel Pereira \n",
        "print(df1_GP.shape)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(395, 33)\n",
            "(349, 33)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XpaLumjRSfeh"
      },
      "source": [
        "# Análisis de datos. Estadísitica descriptiva\n",
        "\n",
        "El paquete **pandas** te permite extraer información útil de forma simple. A continuación verás algunas funciones útiles que te permitiran realizar análisis de datos de forma sencilla. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yj-bujxqVYeI"
      },
      "source": [
        "## Extraer el promedio, mínimo y máximo de una columna\n",
        "La columna \"G3\" tiene los datos de la calificación final en la materia de matemáticas de las dos escuelas. Nos gustaría saber el promedio total que obtuvieron los alumnos en Matemáticas. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjsJyQb6UZF6",
        "outputId": "762ce1e7-8ba7-47da-9e19-a4f15651cbec"
      },
      "source": [
        "df1[\"G3\"].mean()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.415189873417722"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_ZhfHIJiwMf",
        "outputId": "37373e15-9dfd-4c78-b938-3ef795b1ab0a"
      },
      "source": [
        "df1[\"G3\"].max()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYK5BqVYi0p8",
        "outputId": "5a8e21c6-90b3-4308-8787-f900a5819c5e"
      },
      "source": [
        "df1[\"G3\"].min()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1TzY0Ebi4da"
      },
      "source": [
        "## Utiliza la función `describe` para calcular múltiples estadísticos al mismo tiempo y aplicarlo a todos los datos. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "welIYqFUTLHq",
        "outputId": "da29628b-f809-4a9b-97ba-69a6d1595a16"
      },
      "source": [
        "df1.describe()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>famrel</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>Dalc</th>\n",
              "      <th>Walc</th>\n",
              "      <th>health</th>\n",
              "      <th>absences</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16.696203</td>\n",
              "      <td>2.749367</td>\n",
              "      <td>2.521519</td>\n",
              "      <td>1.448101</td>\n",
              "      <td>2.035443</td>\n",
              "      <td>0.334177</td>\n",
              "      <td>3.944304</td>\n",
              "      <td>3.235443</td>\n",
              "      <td>3.108861</td>\n",
              "      <td>1.481013</td>\n",
              "      <td>2.291139</td>\n",
              "      <td>3.554430</td>\n",
              "      <td>5.708861</td>\n",
              "      <td>10.908861</td>\n",
              "      <td>10.713924</td>\n",
              "      <td>10.415190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.276043</td>\n",
              "      <td>1.094735</td>\n",
              "      <td>1.088201</td>\n",
              "      <td>0.697505</td>\n",
              "      <td>0.839240</td>\n",
              "      <td>0.743651</td>\n",
              "      <td>0.896659</td>\n",
              "      <td>0.998862</td>\n",
              "      <td>1.113278</td>\n",
              "      <td>0.890741</td>\n",
              "      <td>1.287897</td>\n",
              "      <td>1.390303</td>\n",
              "      <td>8.003096</td>\n",
              "      <td>3.319195</td>\n",
              "      <td>3.761505</td>\n",
              "      <td>4.581443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age        Medu        Fedu  ...          G1          G2          G3\n",
              "count  395.000000  395.000000  395.000000  ...  395.000000  395.000000  395.000000\n",
              "mean    16.696203    2.749367    2.521519  ...   10.908861   10.713924   10.415190\n",
              "std      1.276043    1.094735    1.088201  ...    3.319195    3.761505    4.581443\n",
              "min     15.000000    0.000000    0.000000  ...    3.000000    0.000000    0.000000\n",
              "25%     16.000000    2.000000    2.000000  ...    8.000000    9.000000    8.000000\n",
              "50%     17.000000    3.000000    2.000000  ...   11.000000   11.000000   11.000000\n",
              "75%     18.000000    4.000000    3.000000  ...   13.000000   13.000000   14.000000\n",
              "max     22.000000    4.000000    4.000000  ...   19.000000   19.000000   20.000000\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCt8JY_c8Ckp"
      },
      "source": [
        "**Ejercicio:** \n",
        "Repite el ejemplo anterior pero en lugar de utilizar todo el conjunto de datos, restringe los análisis a los alumnos de la escuela Gabriel Pereira. Si te gustan los retos, repite el análisis solo con las mujeres de la escuela Gabriel Pereira. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLEB8zMW8oSt"
      },
      "source": [
        "# Escribe tu código aquí\n",
        "\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HHAUInHrAhY"
      },
      "source": [
        "# Gráficas con Pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgFcq5H6v6jU"
      },
      "source": [
        "## Histogramas\n",
        "Podemos observar la distribución de una o múltiples variables. \n",
        "En el siguiente ejemplo:\n",
        "\n",
        "\n",
        "*   La primera parte `df1[[\"age\",\"studytime\",\"absences\",\"G3\"]]`extrae un subconjunto de variables del DataFrame original. \n",
        "\n",
        "*   La función `hist` llama para realizar un histograma\n",
        "*  Podemos agregar especificaciones adicionales por ejemplos `bins=50`significa que cuando las variables son continuas dividirá los datos en 50 grupos iguales. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "iwkhs4BprFCL",
        "outputId": "629234e0-42a4-46e6-843b-87c578f2faa5"
      },
      "source": [
        "df1[[\"age\",\"studytime\",\"absences\",\"G3\"]].hist(bins=50)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa9087c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa90302d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8fe0750>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8f97cd0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1iI9jdFw3C2"
      },
      "source": [
        "Si queremos tener una visión general de todos la distribución de todas las variables podemos llamar a todo el dataframe. Este tipo de análisis se conoce como *análisis exploratorio*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "oTPI_EeDw2E0",
        "outputId": "656775ef-0b1f-4fd7-878a-9833ca344d5b"
      },
      "source": [
        "df1.hist()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa9289190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8cd5bd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8c768d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8c29e50>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8bec410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8ba1990>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8b56f90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8b15510>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8b15550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8acbbd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa8ac3610>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaaa74a950>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaaa40c410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa99bc8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa93d7d90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa93c1210>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VhQHg_R3c0x"
      },
      "source": [
        "Podemos gráficar los datos dividiendo por una categoría. \n",
        "En el siguiente ejemplo, graficamos la distribución de las calificaciones dividiendo por escuela. \n",
        "\n",
        "\n",
        "\n",
        "*   La primera parte `df1[\"G3\"]` indica la variable cuya distribución queremos graficar.\n",
        "*   La function `hist` describe que queremos graficar un histograma. \n",
        "* La especificación `by=df1[\"school\"]` define que queremos dividir los datos en base al tipo de escuela.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "6zjq5CSs3M_l",
        "outputId": "75576101-89a3-42b0-ea16-0024fe4e76c9"
      },
      "source": [
        "df1[\"G3\"].hist(by=df1[\"school\"])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2251090>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa210f310>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bawASjkBxenX"
      },
      "source": [
        "## Box plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDVsp5tPyTXM"
      },
      "source": [
        "Podemos utilizar BoxPlots para visualizar la distribución de las variables una a lado de otra. \n",
        "\n",
        "En el siguiente ejemplo puedes ver la distribución de las 3 calificaciones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "M7LaVQauxhGd",
        "outputId": "bdef3e2e-7b67-4dd5-93ea-876168d107ef"
      },
      "source": [
        "df1[[\"G1\",\"G2\",\"G3\"]].plot.box()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffaa866b450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXnUMEjM0c1g"
      },
      "source": [
        "## Scatter matrix plot\n",
        "Podemos hacer matrices con \"scatter plots\", donde podemos ver la correlación entre múltiples variables. \n",
        "\n",
        "En el siguiente ejemplo podemos ver la relación que tienen las múltiples calificaciones. En la descripción de los datos se menciona que las calificaciones G1 y G2 son calificaciones de parciales y la calificación G3 está relacionada con las primeras dos. Podemos ver esa relación en la siguiente gráfica.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "2cwzvc7fzYx1",
        "outputId": "e4804894-5112-4b2e-e162-befd5b8b5c65"
      },
      "source": [
        "from pandas.plotting import scatter_matrix\n",
        "scatter_matrix(df1[[\"G1\",\"G2\",\"G3\"]])"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2fadcd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2ca2e50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2c4e350>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2c7d8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2c33e50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2bf4410>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2ba8a10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2b5fed0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffaa2b5ff10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}