Fundamentos_Estadística.ipynb

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      "name": "Fundamentos Estadística.ipynb",
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      "name": "python3",
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      "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/LinaTobon/Intro_Estadistica/blob/main/Fundamentos_Estad%C3%ADstica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HY8fYG-Psm4r"
      },
      "source": [
        "# **Fundamentos de estadística**\n",
        "En este taller vamos a explicar los siguientes conceptos básicos de estadística, para que puedas interpretar mejor tus datos.\n",
        "\n",
        "*   Tipos de datos\n",
        "*   Medidas de tendencia central\n",
        "*   Medidas de variabilidad\n",
        "\n",
        "Vamos a usar el famoso dataset del [titanic](https://www.kaggle.com/c/titanic/data) para poner en práctica nuestros conocimientos\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eE-QUSzJts-4"
      },
      "source": [
        "Los campos o columnas que contiene el dataset son las siguientes:\n",
        "- PassengerId -- un id numérico asignado a cada pasajero.\n",
        "- Survived -- si el pasajero sobrevivió (1), si no sobrevivió (0).\n",
        "- Pclass -- La clase en la que estaba el pasajero.\n",
        "- Name -- el nombre del pasajero.\n",
        "- Sex -- género del pasajero.\n",
        "- Age -- edad del pasajero.\n",
        "- SibSp -- número de hermanos y conyugues que el pasajero tenía a bordo.\n",
        "- Parch -- número de padres e hijos que el pasajero tenía a bordo.\n",
        "- Ticket -- número del tiquete del pasajero.\n",
        "- Fare -- precio del tiquete.\n",
        "- Cabin -- en qué cabina estaba ubicado el pasajero.\n",
        "- Embarked -- lugar de embarque del pasajero."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzFXQXtuv6U_",
        "outputId": "71a09c7c-4f18-4018-b8f2-4c2347a20a3a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLuzsFYyvha_"
      },
      "source": [
        "# Importando librerias\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "from scipy import stats\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vs7F2lo-_H1m"
      },
      "source": [
        "# Cargamos el archivo\n",
        "titanic = pd.read_csv(\"train.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P62N8Ore8ix1"
      },
      "source": [
        "# Exploremos nuestro dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "K8I83gcHziQQ",
        "outputId": "a5519eff-bc0d-47f9-ba5a-578b39672fa0"
      },
      "source": [
        "# Visualizamos 5 filas del titanic\n",
        "titanic.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248706</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>123</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mr. Nicholas</td>\n",
              "      <td>male</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>727</th>\n",
              "      <td>728</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Mannion, Miss. Margareth</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36866</td>\n",
              "      <td>7.7375</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>305</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 2466</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>499</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...      Fare    Cabin  Embarked\n",
              "15            16         1       2  ...   16.0000      NaN         S\n",
              "122          123         0       2  ...   30.0708      NaN         C\n",
              "727          728         1       3  ...    7.7375      NaN         Q\n",
              "304          305         0       3  ...    8.0500      NaN         S\n",
              "498          499         0       1  ...  151.5500  C22 C26         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32hmNKMuDmMv"
      },
      "source": [
        "Conclusiones\n",
        "* Tiquete tiene una mezcla de datos numéricos y  alfanumericos.\n",
        "* Cabina es alfanumerico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0qW25ByZaLl"
      },
      "source": [
        "# Vamos a renombrar las columnas, para traducirlas a español\n",
        "esp_colum ={'PassengerId':'IdPasajero','Survived':'Sobrevivio','Pclass':'Clase',\\\n",
        "           'Name':'Nombre','Sex':'Genero','Age':'Edad','SibSp':'HermEsp','Parch':'PadresHijos',\\\n",
        "           'Ticket':'Tiquete','Fare':'Precio','Cabin':'Cabina','Embarked':'Embarque'}\n",
        "titanic.rename(columns=esp_colum,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClsZ0f0n_cmS"
      },
      "source": [
        "### Conozcamos los campos del dataset\n",
        "\n",
        "*   ¿Cuántos registros tiene el dataset?\n",
        "    *El dataset tiene 890 entradas o registros*\n",
        "*   ¿Qué columnas tienen registros vacíos?\n",
        "    *Edad,Cabina y Embarque tienen registros vacíos*\n",
        "*   ¿Qué tipos de datos que tiene nuestro dataset?\n",
        "    *Cinco campos son enteros, 2 tienen decimales (float). Cinco campos son strings (object)*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgKPw8Cu1aRw",
        "outputId": "94d48082-d624-479d-a4da-355bd8403979"
      },
      "source": [
        "# Tipos de datos que tiene nuestro dataset y que cantidad de nulos tiene (non-null -->no nulo)\n",
        "titanic.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   IdPasajero   891 non-null    int64  \n",
            " 1   Sobrevivio   891 non-null    int64  \n",
            " 2   Clase        891 non-null    int64  \n",
            " 3   Nombre       891 non-null    object \n",
            " 4   Genero       891 non-null    object \n",
            " 5   Edad         714 non-null    float64\n",
            " 6   HermEsp      891 non-null    int64  \n",
            " 7   PadresHijos  891 non-null    int64  \n",
            " 8   Tiquete      891 non-null    object \n",
            " 9   Precio       891 non-null    float64\n",
            " 10  Cabina       204 non-null    object \n",
            " 11  Embarque     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sk0svzyt12AS"
      },
      "source": [
        "## **¿Qué tipos de datos son nuestras columnas?**\n",
        "\n",
        "### **Categóricos**\n",
        "\n",
        "**Nominales:**\n",
        "* IdPasajero\n",
        "* Sobrevivio\n",
        "* Nombre\n",
        "* Genero\n",
        "* Tiquete\n",
        "* Cabina\n",
        "* Embarque\n",
        "\n",
        "**Ordinales**\n",
        "* Clase\n",
        "\n",
        "### **Numéricos**\n",
        "\n",
        "**Continuos:**\n",
        "  * Edad**\n",
        "  * Precio\n",
        "**Discretos:**\n",
        "  * HermEsp\n",
        "  * PadresHijos\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klrWPl31FHqV"
      },
      "source": [
        "### Conozcamos los niveles de las variables categóricas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JFn5TC_3KT7",
        "outputId": "4d7877a4-b7d2-4088-9dfa-64ac62d5a236"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Sobrevivio' sin nulos\n",
        "titanic['Sobrevivio'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Sobrevivio, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxj1NYgGc04f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "824f8572-43d0-409c-fbcc-b53b1a813073"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Clase' sin nulos\n",
        "titanic['Clase'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: Clase, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FBqUYUP3jn0",
        "outputId": "1acdd456-732e-4f63-f2fc-d4a6bf20bb13"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Genero' sin nulos\n",
        "titanic['Genero'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Genero, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6eGGEc7Y5JqF",
        "outputId": "301f2044-7987-41fe-a1bf-9340d383f6ff"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Cabina' sin nulos\n",
        "titanic['Cabina'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
              "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
              "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
              "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
              "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
              "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
              "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
              "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
              "       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
              "       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
              "       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
              "       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
              "       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
              "       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
              "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
              "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
              "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
              "       'C148'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hoyznPP26S_",
        "outputId": "57acbeea-1f10-4233-a6f7-aef0facee27c"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Embarque' sin nulos\n",
        "titanic['Embarque'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarque, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "n42B2qDSIT2V",
        "outputId": "a4878b30-d859-4e07-d570-75a7f38b350f"
      },
      "source": [
        "titanic.describe(include=['O'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Dowdell, Miss. Elizabeth</td>\n",
              "      <td>male</td>\n",
              "      <td>1601</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
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              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
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              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "                            Name   Sex Ticket        Cabin Embarked\n",
              "count                        891   891    891          204      889\n",
              "unique                       891     2    681          147        3\n",
              "top     Dowdell, Miss. Elizabeth  male   1601  C23 C25 C27        S\n",
              "freq                           1   577      7            4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAnIao1kGJeS"
      },
      "source": [
        "### Conozcamos los posibles valores de las variables numéricos, para identificar si hay valores extraños"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sI6w-lvcqNGu",
        "outputId": "e5ca6f31-2215-43dc-f5e7-33975906f37e"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Edad' sin nulos\n",
        "titanic['Edad'].value_counts().sort_values().to_dict()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0.42: 1,\n",
              " 0.67: 1,\n",
              " 0.75: 2,\n",
              " 0.83: 2,\n",
              " 0.92: 1,\n",
              " 1.0: 7,\n",
              " 2.0: 10,\n",
              " 3.0: 6,\n",
              " 4.0: 10,\n",
              " 5.0: 4,\n",
              " 6.0: 3,\n",
              " 7.0: 3,\n",
              " 8.0: 4,\n",
              " 9.0: 8,\n",
              " 10.0: 2,\n",
              " 11.0: 4,\n",
              " 12.0: 1,\n",
              " 13.0: 2,\n",
              " 14.0: 6,\n",
              " 14.5: 1,\n",
              " 15.0: 5,\n",
              " 16.0: 17,\n",
              " 17.0: 13,\n",
              " 18.0: 26,\n",
              " 19.0: 25,\n",
              " 20.0: 15,\n",
              " 20.5: 1,\n",
              " 21.0: 24,\n",
              " 22.0: 27,\n",
              " 23.0: 15,\n",
              " 23.5: 1,\n",
              " 24.0: 30,\n",
              " 24.5: 1,\n",
              " 25.0: 23,\n",
              " 26.0: 18,\n",
              " 27.0: 18,\n",
              " 28.0: 25,\n",
              " 28.5: 2,\n",
              " 29.0: 20,\n",
              " 30.0: 25,\n",
              " 30.5: 2,\n",
              " 31.0: 17,\n",
              " 32.0: 18,\n",
              " 32.5: 2,\n",
              " 33.0: 15,\n",
              " 34.0: 15,\n",
              " 34.5: 1,\n",
              " 35.0: 18,\n",
              " 36.0: 22,\n",
              " 36.5: 1,\n",
              " 37.0: 6,\n",
              " 38.0: 11,\n",
              " 39.0: 14,\n",
              " 40.0: 13,\n",
              " 40.5: 2,\n",
              " 41.0: 6,\n",
              " 42.0: 13,\n",
              " 43.0: 5,\n",
              " 44.0: 9,\n",
              " 45.0: 12,\n",
              " 45.5: 2,\n",
              " 46.0: 3,\n",
              " 47.0: 9,\n",
              " 48.0: 9,\n",
              " 49.0: 6,\n",
              " 50.0: 10,\n",
              " 51.0: 7,\n",
              " 52.0: 6,\n",
              " 53.0: 1,\n",
              " 54.0: 8,\n",
              " 55.0: 2,\n",
              " 55.5: 1,\n",
              " 56.0: 4,\n",
              " 57.0: 2,\n",
              " 58.0: 5,\n",
              " 59.0: 2,\n",
              " 60.0: 4,\n",
              " 61.0: 3,\n",
              " 62.0: 4,\n",
              " 63.0: 2,\n",
              " 64.0: 2,\n",
              " 65.0: 3,\n",
              " 66.0: 1,\n",
              " 70.0: 2,\n",
              " 70.5: 1,\n",
              " 71.0: 2,\n",
              " 74.0: 1,\n",
              " 80.0: 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIJjl9Fo4RJV",
        "outputId": "c80fba8f-f36a-4e42-d062-b4241611398b"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'HermEsp' sin nulos\n",
        "titanic['HermEsp'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    608\n",
              "1    209\n",
              "2     28\n",
              "4     18\n",
              "3     16\n",
              "8      7\n",
              "5      5\n",
              "Name: HermEsp, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qcyJi1d4lUe",
        "outputId": "8b98890d-bd57-48cf-9d7e-1702ac57083c"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'PadresHijos' sin nulos\n",
        "titanic['PadresHijos'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    678\n",
              "1    118\n",
              "2     80\n",
              "5      5\n",
              "3      5\n",
              "4      4\n",
              "6      1\n",
              "Name: PadresHijos, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsa5fUPIsHV4",
        "outputId": "87a6c885-6d0f-48c1-d10d-0139adf63a8b"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Precio' sin nulos\n",
        "titanic['Precio'].value_counts().sort_values().to_dict()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0.0: 15,\n",
              " 4.0125: 1,\n",
              " 5.0: 1,\n",
              " 6.2375: 1,\n",
              " 6.4375: 1,\n",
              " 6.45: 1,\n",
              " 6.4958: 2,\n",
              " 6.75: 2,\n",
              " 6.8583: 1,\n",
              " 6.95: 1,\n",
              " 6.975: 2,\n",
              " 7.0458: 1,\n",
              " 7.05: 7,\n",
              " 7.0542: 2,\n",
              " 7.125: 4,\n",
              " 7.1417: 1,\n",
              " 7.225: 12,\n",
              " 7.2292: 15,\n",
              " 7.25: 13,\n",
              " 7.3125: 1,\n",
              " 7.4958: 3,\n",
              " 7.5208: 1,\n",
              " 7.55: 4,\n",
              " 7.6292: 1,\n",
              " 7.65: 4,\n",
              " 7.725: 1,\n",
              " 7.7292: 1,\n",
              " 7.7333: 4,\n",
              " 7.7375: 2,\n",
              " 7.7417: 1,\n",
              " 7.75: 34,\n",
              " 7.775: 16,\n",
              " 7.7875: 1,\n",
              " 7.7958: 6,\n",
              " 7.8: 1,\n",
              " 7.8292: 2,\n",
              " 7.8542: 13,\n",
              " 7.875: 1,\n",
              " 7.8792: 4,\n",
              " 7.8875: 1,\n",
              " 7.8958: 38,\n",
              " 7.925: 18,\n",
              " 8.0292: 1,\n",
              " 8.05: 43,\n",
              " 8.1125: 1,\n",
              " 8.1375: 1,\n",
              " 8.1583: 1,\n",
              " 8.3: 1,\n",
              " 8.3625: 1,\n",
              " 8.4042: 1,\n",
              " 8.4333: 1,\n",
              " 8.4583: 1,\n",
              " 8.5167: 1,\n",
              " 8.6542: 1,\n",
              " 8.6625: 13,\n",
              " 8.6833: 1,\n",
              " 8.7125: 1,\n",
              " 8.85: 1,\n",
              " 9.0: 2,\n",
              " 9.2167: 1,\n",
              " 9.225: 2,\n",
              " 9.35: 2,\n",
              " 9.475: 1,\n",
              " 9.4833: 1,\n",
              " 9.5: 9,\n",
              " 9.5875: 2,\n",
              " 9.825: 2,\n",
              " 9.8375: 1,\n",
              " 9.8417: 1,\n",
              " 9.8458: 1,\n",
              " 10.1708: 1,\n",
              " 10.4625: 2,\n",
              " 10.5: 24,\n",
              " 10.5167: 1,\n",
              " 11.1333: 3,\n",
              " 11.2417: 2,\n",
              " 11.5: 4,\n",
              " 12.0: 1,\n",
              " 12.275: 1,\n",
              " 12.2875: 1,\n",
              " 12.35: 3,\n",
              " 12.475: 4,\n",
              " 12.525: 1,\n",
              " 12.65: 1,\n",
              " 12.875: 1,\n",
              " 13.0: 42,\n",
              " 13.4167: 1,\n",
              " 13.5: 4,\n",
              " 13.7917: 1,\n",
              " 13.8583: 1,\n",
              " 13.8625: 1,\n",
              " 14.0: 1,\n",
              " 14.1083: 1,\n",
              " 14.4: 2,\n",
              " 14.4542: 7,\n",
              " 14.4583: 3,\n",
              " 14.5: 7,\n",
              " 15.0: 1,\n",
              " 15.0458: 1,\n",
              " 15.05: 1,\n",
              " 15.1: 1,\n",
              " 15.2458: 5,\n",
              " 15.5: 8,\n",
              " 15.55: 1,\n",
              " 15.7417: 2,\n",
              " 15.75: 1,\n",
              " 15.85: 4,\n",
              " 15.9: 2,\n",
              " 16.0: 1,\n",
              " 16.1: 9,\n",
              " 16.7: 2,\n",
              " 17.4: 1,\n",
              " 17.8: 2,\n",
              " 18.0: 3,\n",
              " 18.75: 3,\n",
              " 18.7875: 2,\n",
              " 19.2583: 4,\n",
              " 19.5: 2,\n",
              " 19.9667: 2,\n",
              " 20.2125: 2,\n",
              " 20.25: 2,\n",
              " 20.525: 3,\n",
              " 20.575: 2,\n",
              " 21.0: 6,\n",
              " 21.075: 4,\n",
              " 21.6792: 1,\n",
              " 22.025: 1,\n",
              " 22.3583: 2,\n",
              " 22.525: 1,\n",
              " 23.0: 4,\n",
              " 23.25: 2,\n",
              " 23.45: 2,\n",
              " 24.0: 2,\n",
              " 24.15: 8,\n",
              " 25.4667: 4,\n",
              " 25.5875: 1,\n",
              " 25.925: 1,\n",
              " 25.9292: 2,\n",
              " 26.0: 31,\n",
              " 26.25: 6,\n",
              " 26.2833: 1,\n",
              " 26.2875: 3,\n",
              " 26.3875: 1,\n",
              " 26.55: 15,\n",
              " 27.0: 2,\n",
              " 27.7208: 5,\n",
              " 27.75: 4,\n",
              " 27.9: 6,\n",
              " 28.5: 1,\n",
              " 28.7125: 1,\n",
              " 29.0: 2,\n",
              " 29.125: 5,\n",
              " 29.7: 3,\n",
              " 30.0: 6,\n",
              " 30.0708: 2,\n",
              " 30.5: 5,\n",
              " 30.6958: 2,\n",
              " 31.0: 3,\n",
              " 31.275: 7,\n",
              " 31.3875: 4,\n",
              " 32.3208: 1,\n",
              " 32.5: 1,\n",
              " 33.0: 3,\n",
              " 33.5: 1,\n",
              " 34.0208: 1,\n",
              " 34.375: 4,\n",
              " 34.6542: 1,\n",
              " 35.0: 1,\n",
              " 35.5: 4,\n",
              " 36.75: 2,\n",
              " 37.0042: 2,\n",
              " 38.5: 1,\n",
              " 39.0: 4,\n",
              " 39.4: 1,\n",
              " 39.6: 2,\n",
              " 39.6875: 6,\n",
              " 40.125: 1,\n",
              " 41.5792: 3,\n",
              " 42.4: 1,\n",
              " 46.9: 6,\n",
              " 47.1: 1,\n",
              " 49.5: 1,\n",
              " 49.5042: 2,\n",
              " 50.0: 1,\n",
              " 50.4958: 1,\n",
              " 51.4792: 1,\n",
              " 51.8625: 2,\n",
              " 52.0: 7,\n",
              " 52.5542: 3,\n",
              " 53.1: 5,\n",
              " 55.0: 2,\n",
              " 55.4417: 1,\n",
              " 55.9: 2,\n",
              " 56.4958: 7,\n",
              " 56.9292: 2,\n",
              " 57.0: 2,\n",
              " 57.9792: 2,\n",
              " 59.4: 1,\n",
              " 61.175: 1,\n",
              " 61.3792: 1,\n",
              " 61.9792: 1,\n",
              " 63.3583: 1,\n",
              " 65.0: 2,\n",
              " 66.6: 2,\n",
              " 69.3: 2,\n",
              " 69.55: 7,\n",
              " 71.0: 2,\n",
              " 71.2833: 1,\n",
              " 73.5: 5,\n",
              " 75.25: 1,\n",
              " 76.2917: 1,\n",
              " 76.7292: 3,\n",
              " 77.2875: 2,\n",
              " 77.9583: 3,\n",
              " 78.2667: 2,\n",
              " 78.85: 2,\n",
              " 79.2: 4,\n",
              " 79.65: 3,\n",
              " 80.0: 2,\n",
              " 81.8583: 1,\n",
              " 82.1708: 2,\n",
              " 83.1583: 3,\n",
              " 83.475: 2,\n",
              " 86.5: 3,\n",
              " 89.1042: 2,\n",
              " 90.0: 4,\n",
              " 91.0792: 2,\n",
              " 93.5: 2,\n",
              " 106.425: 2,\n",
              " 108.9: 2,\n",
              " 110.8833: 4,\n",
              " 113.275: 3,\n",
              " 120.0: 4,\n",
              " 133.65: 2,\n",
              " 134.5: 2,\n",
              " 135.6333: 3,\n",
              " 146.5208: 2,\n",
              " 151.55: 4,\n",
              " 153.4625: 3,\n",
              " 164.8667: 2,\n",
              " 211.3375: 3,\n",
              " 211.5: 1,\n",
              " 221.7792: 1,\n",
              " 227.525: 4,\n",
              " 247.5208: 2,\n",
              " 262.375: 2,\n",
              " 263.0: 4,\n",
              " 512.3292: 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9cozDzg4zXB"
      },
      "source": [
        "# Medidas de tendencia central\n",
        "\n",
        "\n",
        "*   Moda\n",
        "*   Media\n",
        "*   Mediana\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V06Fi8Pu5Z9z"
      },
      "source": [
        "# Medidas de dispercion\n",
        "\n",
        "\n",
        "*   Desviación estándar\n",
        "*   Varianza\n",
        "*   Rango intercuantil\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "n7tIpvSv0IbQ",
        "outputId": "fd5d1f45-f122-4802-c485-66056cc2790f"
      },
      "source": [
        "# Resumen de las columnas numericas\n",
        "titanic.describe()"
      ],
      "execution_count": null,
      "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>IdPasajero</th>\n",
              "      <th>Sobrevivio</th>\n",
              "      <th>Clase</th>\n",
              "      <th>Edad</th>\n",
              "      <th>HermEsp</th>\n",
              "      <th>PadresHijos</th>\n",
              "      <th>Precio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       IdPasajero  Sobrevivio       Clase  ...     HermEsp  PadresHijos      Precio\n",
              "count  891.000000  891.000000  891.000000  ...  891.000000   891.000000  891.000000\n",
              "mean   446.000000    0.383838    2.308642  ...    0.523008     0.381594   32.204208\n",
              "std    257.353842    0.486592    0.836071  ...    1.102743     0.806057   49.693429\n",
              "min      1.000000    0.000000    1.000000  ...    0.000000     0.000000    0.000000\n",
              "25%    223.500000    0.000000    2.000000  ...    0.000000     0.000000    7.910400\n",
              "50%    446.000000    0.000000    3.000000  ...    0.000000     0.000000   14.454200\n",
              "75%    668.500000    1.000000    3.000000  ...    1.000000     0.000000   31.000000\n",
              "max    891.000000    1.000000    3.000000  ...    8.000000     6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1ie0hpCWw5W",
        "outputId": "feeee17f-68ec-42fc-cbdd-95626e09879e"
      },
      "source": [
        "print('media de edad'+' ' +str(np.mean(titanic['Edad'])))\n",
        "print('mediana de edad'+' ' +str(np.median(titanic['Edad'].dropna().tolist())))\n",
        "print('moda de edad'+' ' +str(stats.mode(titanic['Edad'])[0]))\n",
        "\n",
        "print('media de precio'+' ' +str(np.mean(titanic['Precio'])))\n",
        "print('mediana de precio'+' ' +str(np.median(titanic['Precio'].dropna().tolist())))\n",
        "print('moda de precio'+' ' +str(stats.mode(titanic['Precio'])[0]))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "media de edad 29.69911764705882\n",
            "mediana de edad 28.0\n",
            "moda de edad [24.]\n",
            "media de precio 32.2042079685746\n",
            "mediana de precio 14.4542\n",
            "moda de precio [8.05]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24B1_V9zQVi-",
        "outputId": "201667a5-dda3-4f96-f5e8-b7183620af4a"
      },
      "source": [
        "# Rango Intercuartil con stats\n",
        "IQR_stats = stats.iqr(titanic['Edad'].value_counts().index.to_list(), interpolation = 'midpoint')\n",
        "\n",
        "\n",
        "# First quartile (Q1)\n",
        "Q1 = np.percentile(titanic['Edad'].value_counts().index.to_list(), 25, interpolation = 'midpoint')\n",
        "  \n",
        "# Third quartile (Q3)\n",
        "Q3 = np.percentile(titanic['Edad'].value_counts().index.to_list(), 75, interpolation = 'midpoint')\n",
        "  \n",
        "# Interquaritle range (IQR)\n",
        "IQR_np = Q3 - Q1\n",
        "print(\"IQR Edad con stats\"+\" \"+ str(IQR_stats))\n",
        "print(\"IQR Edad con numpy\"+\" \"+ str(IQR_np))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IQR Edad con stats 34.0\n",
            "IQR Edad con numpy 34.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhyqzupWazSK"
      },
      "source": [
        "# ¿Qué pasa si agregamos otro registro?\n",
        "\n",
        "\n",
        "nueva_fila ={'IdPasajero':[892],'Sobrevivio':[1],'Clase':[1],'Nombre':['Wadr.MissAnne'],\\\n",
        "             'Genero':['female'],'Edad':[35],'HermEsp':[0],'PadresHijos':[0],'Tiquete':['PC17755'],\\\n",
        "             'Precio':[5123292000],'Cabina':['A20'],'Embarque':['C']}\n",
        "df2 = pd.DataFrame.from_dict(nueva_fila)\n",
        "new_df = titanic.append(df2,ignore_index=True)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4_EYdqri9Ff",
        "outputId": "d5c3c069-b223-4635-cd08-c849c31cf723"
      },
      "source": [
        "\n",
        "print('nueva media de Precio'+' ' +str(round(np.mean(new_df['Precio']),4)))\n",
        "print('nueva mediana de Precio'+' ' +str(round(np.median(new_df['Precio']),4)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nueva media de Precio 5743633.065\n",
            "nueva mediana de Precio 14.4542\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9seAiDd68NTQ"
      },
      "source": [
        "# Gráficos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdvH2yE_lab4"
      },
      "source": [
        " **Variables Categóricas** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "TRyLZWZKO4Va",
        "outputId": "a9d35ea2-c416-49e6-a8a8-4a5e48b37c29"
      },
      "source": [
        "figure,axis = plt.subplots(1,2,figsize=(15,5))\n",
        "titanic.Genero.value_counts().plot(kind = 'pie', autopct='%1.1f%%',shadow=False, \\\n",
        "                                ax=axis[0], legend = False, fontsize=11, \\\n",
        "                                colors = ['c', 'g'],explode=[0.1,0])\n",
        "axis[0].set_title('Distribución de pasajeros por Genero')\n",
        "sns.countplot('Clase',data=titanic,ax=axis[1])\n",
        "axis[1].set_title('Pasajeros por Clase')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "y_hCUAJ9mTWN",
        "outputId": "5eebaa89-2cab-4ab7-adcb-0c97ae7ec91c"
      },
      "source": [
        "figure,axis = plt.subplots(1,2,figsize=(15,5))\n",
        "titanic.Clase.value_counts().plot.pie(autopct='%1.1f%%',shadow=False, \\\n",
        "                                ax=axis[0], legend = False, fontsize=11, \\\n",
        "                                colors = ['c', 'g','y'])\n",
        "axis[0].set_title('Distribución de pasajeros por Clase')\n",
        "titanic.Sobrevivio.value_counts().plot.pie(autopct='%1.1f%%',shadow=False, \\\n",
        "                                ax=axis[1], legend = False, fontsize=11)\n",
        "axis[1].set_title('Porcentaje de personas fallecidas y sobrevivientes')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qp9T0reIqtS-"
      },
      "source": [
        " **Variables Numéricas Continuas** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "FcjkiShH-Cwo",
        "outputId": "3a95d43b-5586-4419-e003-af35ff4c9e4b"
      },
      "source": [
        "ax = sns.boxplot(x=\"Edad\",data=titanic, palette=\"Set3\").set_title('Diagrama de caja variable Edad')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "07wd3azwDiUi",
        "outputId": "c9e0f2f2-00a3-4efa-e29c-887ed3e9dabc"
      },
      "source": [
        "sns.distplot(titanic['Edad']).set_title('Distribución variable Edad')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribución variable Edad')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cLJJzCmkAxJF",
        "outputId": "af57c09a-e5da-45d0-ba9a-675020f57015"
      },
      "source": [
        "ax = sns.boxplot(x=\"Precio\",data=titanic, palette=\"Set3\").set_title('Diagrama de caja variable Precio')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "X5S_DkhV7L0Z",
        "outputId": "ceff6c3e-a4c6-4fec-e295-a0dd5cebef81"
      },
      "source": [
        "sns.distplot(titanic['Precio']).set_title('Distribución variable Precio')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribución variable Precio')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yk6khrOOsq06"
      },
      "source": [
        "**¿Cómo cambian las gráficas con el nuevo registro que agregamos?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "o7PEUqE1tW_A",
        "outputId": "a5caa6e4-2ab5-4be8-adac-7f13ac4b9269"
      },
      "source": [
        "ax = sns.boxplot(x=\"Precio\",data=new_df,palette=\"Set3\").set_title('nuevo diagrama de caja variable precio')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "JLCEtE6vvwGL",
        "outputId": "74898779-2d73-44a4-c546-c39480bcc898"
      },
      "source": [
        "sns.distplot(new_df['Precio']).set_title('Nueva Distribución variable Precio')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Nueva Distribución variable Precio')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VL0NKJ6PrAXM"
      },
      "source": [
        " **Variables Numericas discretas** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "juoca2l2_6ke",
        "outputId": "56dec936-2535-48a6-d983-83aedbc17ba2"
      },
      "source": [
        "(figure, axis) = plt.subplots(nrows=1 , ncols=2, figsize=(15,7))\n",
        "ax = axis.flat\n",
        "discretas = ['HermEsp','PadresHijos']\n",
        "\n",
        "sns.set_style(\"darkgrid\")\n",
        "for i in range(len(discretas)):\n",
        "    sns.countplot(x = discretas[i],data=titanic,ax= ax[i],palette=\"Set2\")\n",
        "    ax[i].set_title('Frecuencia variable '+ discretas[i], fontsize = 16)\n",
        "    ax[i].set_ylabel('')\n",
        "    ax[i].set_xlabel('');\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}