Tarea_Titanic.ipynb

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    "colab": {
      "name": "Tarea_Titanic.ipynb",
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    },
    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVLeSpz4Zf0Q"
      },
      "source": [
        "# data analysis and wrangling\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random as rnd\n",
        "\n",
        "# visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# machine learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-fZXypiZqa2"
      },
      "source": [
        "train_df = pd.read_csv('/content/train.csv')\n",
        "test_df = pd.read_csv('/content/test.csv')\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2DLJ7Wja8Q0",
        "outputId": "f5dacbfd-8dc8-4a3a-f487-69af4727cbd7"
      },
      "source": [
        "print(train_df.columns.values)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
            " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "d4cHdMtcbBcU",
        "outputId": "782b09b8-8d53-4ea2-def2-301524c14ded"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "7uCVJ7FUbKiu",
        "outputId": "6687b5ec-b238-4596-b47a-d05a5644f6c2"
      },
      "source": [
        "train_df.tail()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.00</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.00</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...   Fare Cabin  Embarked\n",
              "886          887         0       2  ...  13.00   NaN         S\n",
              "887          888         1       1  ...  30.00   B42         S\n",
              "888          889         0       3  ...  23.45   NaN         S\n",
              "889          890         1       1  ...  30.00  C148         C\n",
              "890          891         0       3  ...   7.75   NaN         Q\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgGqtvlCbsRr",
        "outputId": "f6ce038c-16d6-4034-85af-52b00d0fd605"
      },
      "source": [
        "train_df.info()\n",
        "print('_'*40)\n",
        "test_df.info()"
      ],
      "execution_count": 10,
      "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   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n",
            "________________________________________\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "CEiTFCOhb6H6",
        "outputId": "68a1139c-dcc7-430d-d720-57d50d585b11"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</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": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "5XgRHnGcc3En",
        "outputId": "2247c21b-9ff0-4166-b47d-dc1e1519e83f"
      },
      "source": [
        "train_df.describe(include=['O'])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "      <td>681</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Eklund, Mr. Hans Linus</td>\n",
              "      <td>male</td>\n",
              "      <td>1601</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          Name   Sex Ticket        Cabin Embarked\n",
              "count                      891   891    891          204      889\n",
              "unique                     891     2    681          147        3\n",
              "top     Eklund, Mr. Hans Linus  male   1601  C23 C25 C27        S\n",
              "freq                         1   577      7            4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "MZmIhinw9yJr",
        "outputId": "141ed730-01b8-4fb5-ef9d-fcdf8ed55325"
      },
      "source": [
        "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C</td>\n",
              "      <td>0.553571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Q</td>\n",
              "      <td>0.389610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>S</td>\n",
              "      <td>0.336957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Embarked  Survived\n",
              "0        C  0.553571\n",
              "1        Q  0.389610\n",
              "2        S  0.336957"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "0-9OAGVod5-X",
        "outputId": "b11b52bb-d77e-4657-bd6e-4cb0f195717e"
      },
      "source": [
        "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 42,
      "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",
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              "\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>Pclass</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.629630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.472826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.242363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Survived\n",
              "0       1  0.629630\n",
              "1       2  0.472826\n",
              "2       3  0.242363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "Czg7WKmE8ptl",
        "outputId": "00340590-7a31-4ec1-af2f-f8c99771972a"
      },
      "source": [
        "train_df.loc[train_df['Survived'] == 1,['Pclass', 'Age']].groupby(['Pclass'], as_index=False).median().sort_values(by='Pclass', ascending=True)\n"
      ],
      "execution_count": 39,
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass   Age\n",
              "0       1  35.0\n",
              "1       2  28.0\n",
              "2       3  22.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "q6OH2DDy9dzi",
        "outputId": "0eb7721a-76d6-4536-9fba-2a1b2f0e3898"
      },
      "source": [
        "train_df.loc[train_df['Survived'] == 0,['Pclass', 'Age']].groupby(['Pclass'], as_index=False).median().sort_values(by='Pclass', ascending=True)\n"
      ],
      "execution_count": 40,
      "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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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>45.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>30.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass    Age\n",
              "0       1  45.25\n",
              "1       2  30.50\n",
              "2       3  25.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "zh0daezXeraF",
        "outputId": "242f2e2e-985e-429a-db9a-f9c5ffa9240f"
      },
      "source": [
        "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 41,
      "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",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sex</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>female</td>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>male</td>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Sex  Survived\n",
              "0  female  0.742038\n",
              "1    male  0.188908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "0JUslMYb_ocJ",
        "outputId": "16c7b426-1986-424e-e85b-3900649fe49f"
      },
      "source": [
        "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 57,
      "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",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.535885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.464286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.345395</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SibSp  Survived\n",
              "1      1  0.535885\n",
              "2      2  0.464286\n",
              "0      0  0.345395\n",
              "3      3  0.250000\n",
              "4      4  0.166667\n",
              "5      5  0.000000\n",
              "6      8  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "pNySd5dn_sOZ",
        "outputId": "91749ab0-f90d-45e8-ae97-d5bafdb3adcd"
      },
      "source": [
        "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 58,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Parch</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.550847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.343658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Parch  Survived\n",
              "3      3  0.600000\n",
              "1      1  0.550847\n",
              "2      2  0.500000\n",
              "0      0  0.343658\n",
              "5      5  0.200000\n",
              "4      4  0.000000\n",
              "6      6  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "Ej07NuHb-av_",
        "outputId": "97ee8cf6-3d5b-4f84-ccaa-0f89d83bd05d"
      },
      "source": [
        "g = sns.FacetGrid(train_df, col='Survived')\n",
        "g.map(plt.hist, 'Age', bins=20)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f92c02ced10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "opKdRfQT-gW6",
        "outputId": "f37346a0-eb72-41f2-c204-ec45aedea1bf"
      },
      "source": [
        "g = sns.FacetGrid(train_df, col='Survived')\n",
        "g.map(plt.hist, 'Fare', bins=20)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f92c0a53b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trBNVEqNAPEW"
      },
      "source": [
        "Vamos a eliminar las variables que creemos que no influyen en el outcome, como lo son: Name, PassengerID, Cabin, Ticket."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "SKPy6e84AzXj",
        "outputId": "10ea717d-1323-4507-9512-b1346b3a315a"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 59,
      "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>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>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SvqCncXAN6V"
      },
      "source": [
        "train_df.drop([\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"], axis=1, inplace = True)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pLSZ310BF5_"
      },
      "source": [
        "test_df.drop([\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"], axis=1,inplace = True)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgp3DBkuCeuj",
        "outputId": "df654316-3067-4c00-8ec1-1b7bbe599619"
      },
      "source": [
        "train_df.info()\n",
        "print('_'*40)\n",
        "test_df.info()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Sex       891 non-null    object \n",
            " 3   Age       891 non-null    float64\n",
            " 4   SibSp     891 non-null    int64  \n",
            " 5   Parch     891 non-null    int64  \n",
            " 6   Fare      891 non-null    float64\n",
            " 7   Embarked  889 non-null    object \n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 55.8+ KB\n",
            "________________________________________\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Pclass    418 non-null    int64  \n",
            " 1   Sex       418 non-null    object \n",
            " 2   Age       418 non-null    float64\n",
            " 3   SibSp     418 non-null    int64  \n",
            " 4   Parch     418 non-null    int64  \n",
            " 5   Fare      417 non-null    float64\n",
            " 6   Embarked  418 non-null    object \n",
            "dtypes: float64(2), int64(3), object(2)\n",
            "memory usage: 23.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQieJeBKJF2i"
      },
      "source": [
        "Vamos a rellenar los faltantes de la edad en los dos datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-j-JopFJPBo"
      },
      "source": [
        "average_age_titanic   = train_df[\"Age\"].mean()\n",
        "std_age_titanic       = train_df[\"Age\"].std()\n"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tPinQjnMyn0"
      },
      "source": [
        "train_df[\"Age\"] = train_df[\"Age\"].fillna(np.random.randint(average_age_titanic - std_age_titanic, average_age_titanic + std_age_titanic))\n",
        "test_df[\"Age\"] = test_df[\"Age\"].fillna(np.random.randint(average_age_titanic - std_age_titanic, average_age_titanic + std_age_titanic))"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n19qbxdgNi-_",
        "outputId": "59f15317-869a-4a99-be7d-ec6f0b50d68f"
      },
      "source": [
        "train_df.info()\n",
        "print('_'*40)\n",
        "test_df.info()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Sex       891 non-null    object \n",
            " 3   Age       891 non-null    float64\n",
            " 4   SibSp     891 non-null    int64  \n",
            " 5   Parch     891 non-null    int64  \n",
            " 6   Fare      891 non-null    float64\n",
            " 7   Embarked  889 non-null    object \n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 55.8+ KB\n",
            "________________________________________\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Pclass    418 non-null    int64  \n",
            " 1   Sex       418 non-null    object \n",
            " 2   Age       418 non-null    float64\n",
            " 3   SibSp     418 non-null    int64  \n",
            " 4   Parch     418 non-null    int64  \n",
            " 5   Fare      417 non-null    float64\n",
            " 6   Embarked  418 non-null    object \n",
            "dtypes: float64(2), int64(3), object(2)\n",
            "memory usage: 23.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "vj5ZDkhwOlMI",
        "outputId": "5e74a827-cfac-4f44-ae22-673b8bc38260"
      },
      "source": [
        "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
        "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
      ],
      "execution_count": 82,
      "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>AgeBand</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(0.34, 16.336]</td>\n",
              "      <td>0.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>0.344168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>0.404255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(48.168, 64.084]</td>\n",
              "      <td>0.434783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(64.084, 80.0]</td>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AgeBand  Survived\n",
              "0    (0.34, 16.336]  0.550000\n",
              "1  (16.336, 32.252]  0.344168\n",
              "2  (32.252, 48.168]  0.404255\n",
              "3  (48.168, 64.084]  0.434783\n",
              "4    (64.084, 80.0]  0.090909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "uu9BOFouO0Zg",
        "outputId": "111c27bb-f0bc-4bc1-fd6d-2b9281065b78"
      },
      "source": [
        "\n",
        "train_df.head()"
      ],
      "execution_count": 83,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>AgeBand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex   Age  ...  Parch     Fare  Embarked           AgeBand\n",
              "0         0       3    male  22.0  ...      0   7.2500         S  (16.336, 32.252]\n",
              "1         1       1  female  38.0  ...      0  71.2833         C  (32.252, 48.168]\n",
              "2         1       3  female  26.0  ...      0   7.9250         S  (16.336, 32.252]\n",
              "3         1       1  female  35.0  ...      0  53.1000         S  (32.252, 48.168]\n",
              "4         0       3    male  35.0  ...      0   8.0500         S  (32.252, 48.168]\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHTBtuqPP69F",
        "outputId": "18d28532-025e-4b21-ab39-e70c4e8863e3"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count  Dtype   \n",
            "---  ------    --------------  -----   \n",
            " 0   Survived  891 non-null    int64   \n",
            " 1   Pclass    891 non-null    int64   \n",
            " 2   Sex       891 non-null    object  \n",
            " 3   Age       891 non-null    float64 \n",
            " 4   SibSp     891 non-null    int64   \n",
            " 5   Parch     891 non-null    int64   \n",
            " 6   Fare      891 non-null    float64 \n",
            " 7   Embarked  889 non-null    object  \n",
            " 8   AgeBand   891 non-null    category\n",
            "dtypes: category(1), float64(2), int64(4), object(2)\n",
            "memory usage: 56.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "d07dcZYTRu6U",
        "outputId": "26c51a6f-d695-431f-aad7-4e906b819d54"
      },
      "source": [
        "def RangoEdad(row):\n",
        "    if row['Age'] <= 16.336:\n",
        "        val = 'Niños'\n",
        "    elif row['Age'] <= 32.252:\n",
        "        val = 'Jovenes'\n",
        "    elif row['Age'] <= 48.168:\n",
        "        val = 'Adultos'\n",
        "    elif row['Age'] <= 64.084:\n",
        "        val = 'Viejos'\n",
        "    else:\n",
        "        val = 'Vejetes'\n",
        "    return val\n",
        "\n",
        "train_df['Edad_Nueva'] = train_df.apply(RangoEdad, axis=1)\n",
        "train_df.head()"
      ],
      "execution_count": 86,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>AgeBand</th>\n",
              "      <th>Edad_Nueva</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>Jovenes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>Adultos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>Jovenes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>Adultos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>Adultos</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex  ...  Embarked           AgeBand  Edad_Nueva\n",
              "0         0       3    male  ...         S  (16.336, 32.252]     Jovenes\n",
              "1         1       1  female  ...         C  (32.252, 48.168]     Adultos\n",
              "2         1       3  female  ...         S  (16.336, 32.252]     Jovenes\n",
              "3         1       1  female  ...         S  (32.252, 48.168]     Adultos\n",
              "4         0       3    male  ...         S  (32.252, 48.168]     Adultos\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "PHQCvgR1TX4x",
        "outputId": "53bba056-3008-46ba-f3cf-ece4f699908c"
      },
      "source": [
        "test_df['Edad_Nueva'] = test_df.apply(RangoEdad, axis=1)\n",
        "test_df.head()"
      ],
      "execution_count": 87,
      "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>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Edad_Nueva</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>Q</td>\n",
              "      <td>Adultos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>Adultos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>Q</td>\n",
              "      <td>Viejos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>S</td>\n",
              "      <td>Jovenes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</td>\n",
              "      <td>Jovenes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked Edad_Nueva\n",
              "0       3    male  34.5      0      0   7.8292        Q    Adultos\n",
              "1       3  female  47.0      1      0   7.0000        S    Adultos\n",
              "2       2    male  62.0      0      0   9.6875        Q     Viejos\n",
              "3       3    male  27.0      0      0   8.6625        S    Jovenes\n",
              "4       3  female  22.0      1      1  12.2875        S    Jovenes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6MC6v_tTgGr",
        "outputId": "16e1b65d-de32-4cf4-f951-b923ec13df40"
      },
      "source": [
        ""
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 10 columns):\n",
            " #   Column      Non-Null Count  Dtype   \n",
            "---  ------      --------------  -----   \n",
            " 0   Survived    891 non-null    int64   \n",
            " 1   Pclass      891 non-null    int64   \n",
            " 2   Sex         891 non-null    object  \n",
            " 3   Age         891 non-null    float64 \n",
            " 4   SibSp       891 non-null    int64   \n",
            " 5   Parch       891 non-null    int64   \n",
            " 6   Fare        891 non-null    float64 \n",
            " 7   Embarked    889 non-null    object  \n",
            " 8   AgeBand     891 non-null    category\n",
            " 9   Edad_Nueva  891 non-null    object  \n",
            "dtypes: category(1), float64(2), int64(4), object(3)\n",
            "memory usage: 63.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "528BYNDTTtQz",
        "outputId": "5896ee82-0afa-4cd7-f12f-501d80b892fd"
      },
      "source": [
        "train_df.drop([\"Age\", \"Ageband\"], axis=1,inplace = True)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-89-17d7f3f54b8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Age\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Ageband\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4172\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4173\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4174\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4175\u001b[0m         )\n\u001b[1;32m   4176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3887\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3888\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3889\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3921\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3922\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3923\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3924\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5285\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5286\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5288\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5289\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Ageband'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R6koY-ILexVO",
        "outputId": "cddbf537-141a-4526-88d4-ed9da54d4721"
      },
      "source": [
        "# Funcion para graficar distribucion\n",
        "def getBarplot(data, col, objetivo = 'Survived'):\n",
        "    \n",
        "    # Crear figura y establecer su tamaño\n",
        "    fig, ax = plt.subplots(figsize=(12,5))\n",
        "    \n",
        "    # Genera un grafico de barras contra variable objetivo\n",
        "    sns.barplot(x = objetivo, y = col, data = data, ax=ax)\n",
        "    \n",
        "    # Asigna titulo a cada grafico\n",
        "    plt.title(f\"Comparativo {col} vs {objetivo}\")\n",
        "    \n",
        "    # Muestra grafico\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "# Bucle para graficar la distribucion de todas las variables utilizando funcion getBoxplot\n",
        "for col in train_df.columns:\n",
        "    if col != \"Survived\":\n",
        "        getBarplot(train_df, col)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1ZJiWJEmSejJMS5IkST0ZpiVJkqSeDNOSJElST4ZpSZIkqSfDtCRJktSTYVqSJEnqyTAtSZIk9WSYliRJknoyTEuSJEk9GaYlSZKkngzTkiRJUk8jC9NJNkry70n+I8lVSd7R2ndO8rUky5Kck2SD1r5hm1/Wlu80qtokSZKkuTDKnun7gH2r6tnA7sALkuwNvAd4f1XtAtwBHNHWPwK4o7W/v60nSZIkTayRhenq3Ntm12+PAvYFzm/tpwEHtekD2zxt+X5JMqr6JEmSpEdqpGOmkyxIcjlwC3Ah8F3gzqpa2Va5EVjcphcDNwC05XcBW65in0cmWZpk6a233jrK8iVJkqQ1WrimhUk+RNebvEpV9ftr2r6qHgB2T7I58EngaX2KnLbPk4GTAZYsWbLa2iRJkqRRm6lneilwKbAR8BzgO+2xO7DBsC9SVXcCXwL2ATZPMhXitwOWt+nlwPYAbflmwO3DvoYkSZK0tq0xTFfVaVV1GvAs4PlV9aGq+hCwH12gXq0ki1qPNEkeA/wv4Gq6UP2SttrhwKfb9AVtnrb8oqqy51mSJEkTa43DPAY8Hngc8P02v0lrW5NtgNOSLKAL7edW1WeTfBM4O8lxwDeAU9r6pwAfS7Ksvc4hwx+GJEmStPYNG6bfDXwjyZeAAL8IHLumDarqCuBnV9F+DbDnKtp/DLx0yHokSZKksRsqTFfV3yf5R2Cv1vTmqloxurIkSZKkyTfT3TyeM63phva8bZJtq+qy0ZQlSZIkTb6Zeqbft4ZlUz/AIkmSJD0qrTFMV9UvJ1kP2KeqvrqWapIkSZLmhRl/AbGqHgQ+vBZqkSRJkuaVYX9O/ItJfjNJRlqNJEmSNI8MG6Z/GzgP+EmSu5Pck+TuEdYlSZIkTbxhb4236agLkSRJkuaboXqm0zksyZ+2+e2TPOyHVyRJkqRHk2GHeZwI7AO8vM3fC5wwkookSZKkeWLYnxPfq6qek+QbAFV1R5INRliXJEmSNPGG7Zm+P8kCuh9qIcki4MGRVSVJkiTNA8OG6Q8CnwSekOSdwFeAd42sKkmSJGkeGPZuHmckuRTYDwhwUFVdPdLKJEmSpAk3VJhOsgVwC3DWQNv6VXX/qAqTJEmSJt2wwzwuA24Fvg18p01fm+SyJHuMqjhJkiRpkg0bpi8EXlhVW1XVlsABwGeB36O7bZ4kSZL0qDNsmN67qj4/NVNVXwD2qapLgA1HUpkkSZI04Ya9z/RNSd4MnN3mXwbc3G6X5y3yJEmS9Kg0bM/0y4HtgE+1xw6tbQFw8GhKkyRJkibbsLfGuw14/WoWL5u7ciRJkqT5Y9hb4z0FeCOw0+A2VbXvaMqSJEmSJt+wY6bPAz4C/B3wwOjKkSRJkuaPYcP0yqo6aaSVSJIkSfPMsBcgfibJ7yXZJskWU4+RViZJkiRNuGF7pg9vz28aaCvgSXNbjiRJkjR/DHs3j51HXYgkSZI03ww1zCPJxknemuTkNr9rkheNtjRJkiRpsg07ZvrvgZ8AP9fmlwPHjaQiSZIkaZ4YNkw/uareC9wPUFU/BDKyqiRJkqR5YNgw/ZMkj6G76JAkTwbuG1lVkiRJ0jww7N083g78E7B9kjOA5wGvHlVRkiRJ0nww7N08LkxyGbA33fCOo6rqtpFWJkmSJE24Ye/m8Tzgx1X1D8DmwFuS7DjSyiRJkqQJN+yY6ZOAHyZ5NvAG4LvA6SOrSpIkSZoHhg3TK6uqgAOBE6rqBGDT0ZUlSZIkTb5hL0C8J8kfA4cBv5hkPWD90ZUlSZIkTb5he6ZfRncrvCOqagWwHfCXI6tKkiRJmgeG7pkGjq+qB5I8BXgacNboypIkSZIm37A9018GNkyyGPgC8Erg1FEVJUmSJM0Hw4bptJ8QfzFwYlW9FHjG6MqSJEmSJt/QYTrJPsArgH+Y5baSJEnSOmnYQHwU8MfAJ6vqqiRPAr40urIkSZKkyTfsz4l/mW7c9NT8NcDvj6ooSZIkaT4YKkwnWQQcDTwd2Giqvar2HVFdkiRJ0sQbdpjHGcB/ATsD7wCuBb6+pg2SbJ/kS0m+meSqJEe19i2SXJjkO+358a09ST6YZFmSK5I8p/dRSZIkSWvBsGF6y6o6Bbi/qv6lqn4LmKlXeiXwR1W1G7A38NokuwHHAF+sql2BL7Z5gAOAXdvjSOCk2R2KJEmStHYNG6bvb883Jfm1JD8LbLGmDarqpqq6rE3fA1wNLAYOBE5rq50GHNSmDwROr84lwOZJthn+UCRJkqS1a9hfQDwuyWbAHwEfAh4H/OGwL5JkJ+Bnga8BT6yqm9qiFcAT2/Ri4IaBzW5sbTchSZIkTaA1hukkGwG/A+xCF2xPqapfns0LJNkE+L/AH1TV3Un+Z1lVVZKa5f6OpBsGwg477DCbTSVJkqQ5NdMwj9OAJcCVdGOa3zebnSdZny5In1FVn2jNN08N32jPt7T25cD2A5tv19oeoqpOrqolVbVk0aJFsylHkiRJmlMzhendquqwqvob4CXALwy743Rd0KcAV1fVXw8sugA4vE0fDnx6oP1V7a4eewN3DQwHkSRJkibOTGOmpy48pKpWDg7RGMLzgFcCVya5vLW9BXg3cG6SI4DrgIPbss8BLwSWAT8EXjObF5MkSZLWtpnC9LOT3N2mAzymzYduyPPjVrdhVX2lrbcq+61i/QJeO3PJkiRJ0mRYY5iuqgVrqxBJkiRpvhn2PtOSJEmSpjFMS5IkST0ZpiVJkqSeDNOSJElST4ZpSZIkqSfDtCRJktSTYVqSJEnqyTAtSZIk9WSYliRJknoyTEuSJEk9GaYlSZKkngzTkiRJUk+GaUmSJKknw7QkSZLUk2FakiRJ6skwLUmSJPVkmJYkSZJ6MkxLkiRJPRmmJUmSpJ4M05IkSVJPhmlJkiSpJ8O0JEmS1JNhWpIkSerJMC1JkiT1ZJiWJEmSejJMS5IkST0ZpiVJkqSeDNOSJElST4ZpSZIkqSfDtCRJktSTYVqSJEnqyTAtSZIk9WSYliRJknoyTEuSJEk9GaYlSZKkngzTkiRJUk+GaUmSJKknw7QkSZLUk2FakiRJ6skwLUmSJPVkmJYkSZJ6MkxLkiRJPRmmJUmSpJ4M05IkSVJPhmlJkiSpp5GF6SQfTXJLkv8caNsiyYVJvtOeH9/ak+SDSZYluSLJc0ZVlyRJkjRXRtkzfSrwgmltxwBfrKpdgS+2eYADgF3b40jgpBHWJUmSJM2JkYXpqvoy8P1pzQcCp7Xp04CDBtpPr84lwOZJthlVbZIkSdJcWNtjpp9YVTe16RXAE9v0YuCGgfVubG2SJEnSxBrbBYhVVUDNdrskRyZZmmTprbfeOoLKJEmSpOGs7TB989TwjfZ8S2tfDmw/sN52re1hqurkqlpSVUsWLVo00mIlSZKkNVnbYfoC4PA2fTjw6YH2V7W7euwN3DUwHESSJEmaSAtHteMkZwHPB7ZKciPwduDdwLlJjgCuAw5uq38OeCGwDPgh8JpR1SVJkiTNlZGF6ao6dDWL9lvFugW8dlS1SJIkSaPgLyBKkiRJPRmmJUmSpJ4M05IkSVJPhmlJkiSpJ8O0JEmS1JNhWpIkSerJMC1JkiT1ZJiWJEmSejJMS5IkST0ZpiVJkqSeDNOSJElST4ZpSZIkqSfDtCRJktSTYVqSJEnqyTAtSZIk9WSYliRJknoyTEuSJEk9GaYlSZKkngzTkiRJUk+GaUmSJKknw7QkSZLUk2FakiRJ6skwLUmSJPVkmJYkSZJ6MkxLkiRJPRmmJUmSpJ4M05IkSVJPhmlJkiSpJ8O0JEmS1JNhWpIkSerJMC1JkiT1ZJiWJEmSejJMS5IkST0ZpiVJkqSeDNOSJElST4ZpSZIkqSfDtCRJktSTYVqSJEnqyTAtSZIk9WSYliRJknoyTEuSJEk9GaYlSZKkngzTkiRJUk+GaUmSJKknw7QkSZLUk2FakiRJ6mmiwnSSFyT5VpJlSY4Zdz2SJEnSmkxMmE6yADgBOADYDTg0yW7jrUqSJElavYkJ08CewLKquqaqfgKcDRw45pokSZKk1ZqkML0YuGFg/sbWJkmSJE2kheMuYLaSHAkc2WbvTfKtcdYjrcZWwG3jLkKTJX91+LhLkCad/3bq4d6ecVcwZcdVNU5SmF4ObD8wv11re4iqOhk4eW0VJfWRZGlVLRl3HZI0n/hvp+ajSRrm8XVg1yQ7J9kAOAS4YMw1SZIkSas1MT3TVbUyyeuAzwMLgI9W1VVjLkuSJElarYkJ0wBV9Tngc+OuQ5oDDkWSpNnz307NO6mqcdcgSZIkzUuTNGZakiRJmlcM09IcS/KCJN9KsizJMeOuR5ImXZKPJrklyX+OuxZptgzT0hxKsgA4ATgA2A04NMlu461KkibeqcALxl2E1IdhWppbewLLquqaqvoJcDZw4JhrkqSJVlVfBr4/7jqkPgzT0txaDNwwMH9ja5MkSesgw7QkSZLUk2FamlvLge0H5rdrbZIkaR1kmJbm1teBXZPsnGQD4BDggjHXJEmSRsQwLc2hqloJvA74PHA1cG5VXTXeqiRpsiU5C/g34KlJbkxyxLhrkoblLyBKkiRJPdkzLUmSJPVkmJYkSZJ6MkxLkiRJPRmmJUmSpJ4M05IkSVJPhmlJmnBJ/iTJVUmuSHJ5kr3mYJ+/nuSYOarv3rnYjyTNR94aT5ImWJJ9gL8Gnl9V9yXZCtigqr43xLYL273PR13jvVW1yahfR5ImkT3TkjTZtgFuq6r7AKrqtqr6XpJrW7AmyZIkF7fpY5N8LMlXgY8luSTJ06d2luTitv6rk3w4yWZJrkuyXlv+2CQ3JFk/yZOT/FOSS5P8a5KntXV2TvJvSa5MctxaPh+SNFEM05I02b4AbJ/k20lOTPJLQ2yzG7B/VR0KnAMcDJBkG2Cbqlo6tWJV3QVcDkzt90XA56vqfuBk4PVVtQfwRuDEts7xwElV9Uzgpkd8hJI0jxmmJWmCVdW9wB7AkcCtwDlJXj3DZhdU1Y/a9LnAS9r0wcD5q1j/HOBlbfqQ9hqbAD8HnJfkcuBv6HrJAZ4HnNWmPzarA5KkdczCcRcgSVqzqnoAuBi4OMmVwOHASn7aIbLRtE1+MLDt8iS3J3kWXWD+nVW8xAXAu5JsQRfcLwIeC9xZVbuvrqyehyNJ6xR7piVpgiV5apJdB5p2B64DrqULvgC/OcNuzgGOBjarqiumL2y931+nG77x2ap6oKruBv47yUtbHUny7LbJV+l6sAFeMfujkqR1h2FakibbJsBpSb6Z5Aq68dDHAu8Ajk+yFHhghn2cTxd+z13DOucAh7XnKa8AjkjyH8BVwIGt/Sjgta2XfPHsDkeS1i3eGk+SJEnqyZ5pSZIkqSfDtCRJktSTYVqSJEnqyTAtSZIk9WSYliRJknoyTEuSJEk9GaYlSZKkngzTkiRJUk//HyHBJbW2OV7CAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "Fxa6peufft8L",
        "outputId": "8e4bd583-eed9-4131-9c55-ef75cd3f465c"
      },
      "source": [
        "corr = train_df.corr()\n",
        "corr"
      ],
      "execution_count": 17,
      "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005007</td>\n",
              "      <td>-0.035144</td>\n",
              "      <td>0.036847</td>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.012658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>-0.005007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>-0.077221</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.257307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.035144</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.369226</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.549500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.036847</td>\n",
              "      <td>-0.077221</td>\n",
              "      <td>-0.369226</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.308247</td>\n",
              "      <td>-0.189119</td>\n",
              "      <td>0.096067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>-0.308247</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>0.159651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.189119</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.012658</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.096067</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             PassengerId  Survived    Pclass  ...     SibSp     Parch      Fare\n",
              "PassengerId     1.000000 -0.005007 -0.035144  ... -0.057527 -0.001652  0.012658\n",
              "Survived       -0.005007  1.000000 -0.338481  ... -0.035322  0.081629  0.257307\n",
              "Pclass         -0.035144 -0.338481  1.000000  ...  0.083081  0.018443 -0.549500\n",
              "Age             0.036847 -0.077221 -0.369226  ... -0.308247 -0.189119  0.096067\n",
              "SibSp          -0.057527 -0.035322  0.083081  ...  1.000000  0.414838  0.159651\n",
              "Parch          -0.001652  0.081629  0.018443  ...  0.414838  1.000000  0.216225\n",
              "Fare            0.012658  0.257307 -0.549500  ...  0.159651  0.216225  1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "JNjtjnLngE94",
        "outputId": "63a79aa9-b411-45af-da25-68d9177eed76"
      },
      "source": [
        "# Grafiquemos la matriz de correlacion para tener un \n",
        "fig, ax = plt.subplots(figsize=(15,8))\n",
        "\n",
        "# Preparacion de datos para mostrar triangulo inferior\n",
        "mask = np.zeros_like(corr, dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "# Crear heatmap con matriz de correlacion\n",
        "sns.heatmap(corr, vmin=-1, vmax=1, cmap=\"RdBu\", annot=True, linewidths=.5, fmt=\".2f\", mask=mask)\n",
        "\n",
        "# Asignar titulo al grafico\n",
        "ax.set_title(\"Mapa de calor: Matriz de correlacion\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}