09. Practice - Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "702082cc",
   "metadata": {},
   "source": [
    "## <font color=green> Praticando: Analisando o Dataset do Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0560f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35d5e420",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "449aecf2",
   "metadata": {},
   "source": [
    "### Carregando Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9df8de49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic = pd.read_csv('datasets/titanic.csv')\n",
    "df_titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7df1b73b",
   "metadata": {},
   "outputs": [
    {
     "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>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>7925.0000</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  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket       Fare Cabin Embarked  \n",
       "0      0         A/5 21171     7.2500   NaN        S  \n",
       "1      0          PC 17599    71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282  7925.0000   NaN        S  \n",
       "3      0            113803    53.1000  C123        S  \n",
       "4      0            373450     8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2bf46d4",
   "metadata": {},
   "source": [
    "- **Passengerid**: ID do passageiro do navio (código primário).\n",
    "- **Survived**: Se sobreviveu ao naufrágio estará como 1 e caso esteja com 0 (zero) não sobreviveu.\n",
    "- **Pclass**: Tipo de classe de passagem (Do 1 ao 3), sendo 1 a melhor classe e 3 a pior classe.\n",
    "- **Name**: Nome do passageiro\n",
    "- **Sex**: Gênero do passageiro, sendo masculino e feminino.\n",
    "- **Age**: Idade do passageiro na data da ocorrência do naufrágio.\n",
    "- **SibSp**: Número de irmãos / cônjuges a bordo.\n",
    "- **Parch**: Número de pais / filhos a bordo.\n",
    "- **Ticket**: Código do ticket.\n",
    "- **Fare**: Valor da passagem.\n",
    "- **Cabin**: Código de identificação da Cabine.\n",
    "- **Embarked**: Local onde o passageiro embarcou no navio."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837cb876",
   "metadata": {},
   "source": [
    "### Verificando os tipos de dados contidos no Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b17f1ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de90aff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>1.00</td>\n",
       "      <td>328.00</td>\n",
       "      <td>655.0000</td>\n",
       "      <td>982.0000</td>\n",
       "      <td>1309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>0.377387</td>\n",
       "      <td>0.484918</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1046.0</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>14.413493</td>\n",
       "      <td>0.17</td>\n",
       "      <td>21.00</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>4425.079523</td>\n",
       "      <td>25407.652350</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.05</td>\n",
       "      <td>20.2125</td>\n",
       "      <td>57.9792</td>\n",
       "      <td>262375.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean           std   min     25%       50%  \\\n",
       "PassengerId  1309.0   655.000000    378.020061  1.00  328.00  655.0000   \n",
       "Survived     1309.0     0.377387      0.484918  0.00    0.00    0.0000   \n",
       "Pclass       1309.0     2.294882      0.837836  1.00    2.00    3.0000   \n",
       "Age          1046.0    29.881138     14.413493  0.17   21.00   28.0000   \n",
       "SibSp        1309.0     0.498854      1.041658  0.00    0.00    0.0000   \n",
       "Parch        1309.0     0.385027      0.865560  0.00    0.00    0.0000   \n",
       "Fare         1309.0  4425.079523  25407.652350  0.00    8.05   20.2125   \n",
       "\n",
       "                  75%       max  \n",
       "PassengerId  982.0000    1309.0  \n",
       "Survived       1.0000       1.0  \n",
       "Pclass         3.0000       3.0  \n",
       "Age           39.0000      80.0  \n",
       "SibSp          1.0000       8.0  \n",
       "Parch          0.0000       9.0  \n",
       "Fare          57.9792  262375.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bcdb0a",
   "metadata": {},
   "source": [
    "- A variável idade tem como valor máximo 80. Contudo o 3º quartil mostra a idade de 39 anos, o que indica que há outliers\n",
    "- Quanto a coluna Fare (valor da passagem), temos outliers distorcendo a distribuição dos valores desta variável, puxando a média para 4428.39 sendo que a mediana está em 20.08"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9759faf",
   "metadata": {},
   "source": [
    "### Explorando os dados categóricos e numéricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15ccabee",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>113803</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>373450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "                                                Name     Sex  \\\n",
       "0                            Braund, Mr. Owen Harris    male   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "2                             Heikkinen, Miss. Laina  female   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "4                           Allen, Mr. William Henry    male   \n",
       "\n",
       "             Ticket Cabin Embarked  \n",
       "0         A/5 21171   NaN        S  \n",
       "1          PC 17599   C85        C  \n",
       "2  STON/O2. 3101282   NaN        S  \n",
       "3            113803  C123        S  \n",
       "4            373450   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col = [var for var in df_titanic.columns if df_titanic[var].dtype == 'O']\n",
    "df_titanic.loc[:,cat_col].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f945b5ea",
   "metadata": {},
   "outputs": [
    {
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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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7925.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch       Fare\n",
       "0            1         0       3  22.0      1      0     7.2500\n",
       "1            2         1       1  38.0      1      0    71.2833\n",
       "2            3         1       3  26.0      0      0  7925.0000\n",
       "3            4         1       1  35.0      1      0    53.1000\n",
       "4            5         0       3  35.0      0      0     8.0500"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_col = [var for var in df_titanic.columns if df_titanic[var].dtype != 'O']\n",
    "df_titanic.loc[:,num_col].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998b3463",
   "metadata": {},
   "source": [
    "- 3 variáveis discretas: Pclass, SibSp e Parch\n",
    "- 2 variáveis contínuas: Fare e Age\n",
    "- 1 variável Id: PassengerId (chave primária).\n",
    "- 1 binário: Survived (booleano)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1689a89a",
   "metadata": {},
   "source": [
    "- Percentual de dados faltantes na base de treino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2dbe1b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.00\n",
       "Survived        0.00\n",
       "Pclass          0.00\n",
       "Name            0.00\n",
       "Sex            10.92\n",
       "Age            20.09\n",
       "SibSp           0.00\n",
       "Parch           0.00\n",
       "Ticket          0.00\n",
       "Fare            0.00\n",
       "Cabin          77.46\n",
       "Embarked        0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df_titanic.isnull().mean()*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16fdc69f",
   "metadata": {},
   "source": [
    "- Podemos observar que há 3 colunas com dados faltantes: Sex, Age e Cabin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f034d967",
   "metadata": {},
   "source": [
    "## Vamos explorar o dataset para analisar o que é possível fazer para preencher os dados faltantes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae628688",
   "metadata": {},
   "source": [
    "- Total de Passageiros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e38e51df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1309"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15dfce94",
   "metadata": {},
   "source": [
    "- Total de Passageiros com todos os registros preenchidos (linhas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b146247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.dropna().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3162a8f3",
   "metadata": {},
   "source": [
    "- Percentual de dados com 100% do preenchimento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ceb41129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.72"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df_titanic.dropna().shape[0] / len(df_titanic)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612294a0",
   "metadata": {},
   "source": [
    "- Selecionando os registros com dados ausentes para a coluna Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe2d09b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic['Pclass'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac77348",
   "metadata": {},
   "source": [
    "- Não temos nenhum registro faltante para a coluna Pclass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5970ca4e",
   "metadata": {},
   "source": [
    "- É interessante conseguir extrair os títulos dos nomes para verificar se temos realmente correlação entre as variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "338ff0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extrai_titulo(df):\n",
    "    df['Título'] = df['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip().lower())\n",
    "\n",
    "extrai_titulo(df_titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc31264",
   "metadata": {},
   "source": [
    "- Checando se a coluna Título foi criada corretamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d7fab67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Título</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",
       "      <td>mr</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",
       "      <td>mrs</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>7925.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</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",
       "      <td>mrs</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",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket       Fare Cabin Embarked Título  \n",
       "0      0         A/5 21171     7.2500   NaN        S     mr  \n",
       "1      0          PC 17599    71.2833   C85        C    mrs  \n",
       "2      0  STON/O2. 3101282  7925.0000   NaN        S   miss  \n",
       "3      0            113803    53.1000  C123        S    mrs  \n",
       "4      0            373450     8.0500   NaN        S     mr  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5712cbee",
   "metadata": {},
   "source": [
    "- Checando se há algum registro sem título"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26c1d25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic['Título'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7c97e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mr              757\n",
       "miss            260\n",
       "mrs             197\n",
       "master           61\n",
       "rev               8\n",
       "dr                8\n",
       "col               4\n",
       "mlle              2\n",
       "major             2\n",
       "ms                2\n",
       "the countess      1\n",
       "sir               1\n",
       "dona              1\n",
       "capt              1\n",
       "don               1\n",
       "jonkheer          1\n",
       "mme               1\n",
       "lady              1\n",
       "Name: Título, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = df_titanic['Título'].value_counts().plot.barh(figsize=(11,6))\n",
    "fig.set_title('Abreviações dos Títulos dos Passageiros')\n",
    "fig.set_ylabel('Nome dos Títulos')\n",
    "fig.set_xlabel('Quantidade por Título')\n",
    "\n",
    "df_titanic['Título'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62368eb3",
   "metadata": {},
   "source": [
    "- Conforme o gráfico acima, temos vários títulos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1465ad",
   "metadata": {},
   "source": [
    "- Vamos investigar os outliers como já identificamos acima nas variáveis Age e Fare. Para isso, vamos utilizar o boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e177c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Age'].plot.box(vert=False, figsize=(15,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e8b1fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Fare'].plot.box(vert=False, figsize=(15,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a609422",
   "metadata": {},
   "source": [
    "- Como já esperado, diversos Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999a1f05",
   "metadata": {},
   "source": [
    "- Para a coluna Fare, a quantidade de outiliers é tão grande que não conseguimos visualizar o box. Portanto, vamos filtrar por Fare menor que 500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0b7edd53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[df_titanic['Fare'] < 500].plot.box(vert=False, figsize=(15,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b48f47",
   "metadata": {},
   "source": [
    "- Analisando a distribuição das variáveis para descobrir se elas são gaussianas (normais) ou distorcidas (assimétricas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dce96bd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Age'].hist(bins = 25, figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "14dafccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[df_titanic['Fare'] < 500].hist(bins = 25, figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12b6a39",
   "metadata": {},
   "source": [
    "- Vamos ver a distribuição de pessoas por tipo de classe de passagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fa3dc50e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoEAAAFlCAYAAACUbjKxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAN7klEQVR4nO3dX4il9X3H8c+3O2sSo12TaousklEIQohFZZEWSygmtOqWpJcGUnKR4k0uIi2UDYFC7ra9CL0rSJI20DShTdI2KIVKmxACrXbWaNSsNtpsiMZkm4asWiFp7a8X80imMnHmjM8zu+v39YLDPOc5Z3/+/LJ/3vucc3ZqjBEAAHr5ubO9AQAA9p8IBABoSAQCADQkAgEAGhKBAAANiUAAgIbWllj00ksvHevr60ssDQDALp04ceIHY4zLtntskQhcX1/PxsbGEksDALBLVfXtn/WYl4MBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABpaW2LRh58+k/Vj9yyxNLCiU8ePnu0tAHAOciUQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZ2jMCqurKqvlRVJ6vq0ar60H5sDACA5azt4jn/k+T3xxgPVNXFSU5U1b1jjG8svDcAABay45XAMcYzY4wHpuPnkpxMcnjpjQEAsJyV3hNYVetJrk9y3zaP3VFVG1W18eILZ2baHgAAS9h1BFbVRUk+n+TOMcazL398jHHXGOPIGOPIgQsPzblHAABmtqsIrKqD2QzAT48xvrDslgAAWNpuPh1cST6R5OQY42PLbwkAgKXt5krgTUl+J8nNVfXgdLtt4X0BALCgHf+JmDHGV5PUPuwFAIB94juGAAA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQENrSyx67eFD2Th+dImlAQCYgSuBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoaG2JRR9++kzWj92zxNLAa8Sp40fP9hYAWnMlEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADe0YgVX1yao6XVWP7MeGAABY3m6uBP55klsW3gcAAPtoxwgcY3wlyQ/3YS8AAOwT7wkEAGhotgisqjuqaqOqNl584cxcywIAsIDZInCMcdcY48gY48iBCw/NtSwAAAvwcjAAQEO7+SdiPpPkn5NcU1VPVdUHlt8WAABLWtvpCWOM9+7HRgAA2D9eDgYAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgIREIANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQENrSyx67eFD2Th+dImlAQCYgSuBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0NDaEos+/PSZrB+7Z4mlAQDOS6eOHz3bW/h/XAkEAGhIBAIANCQCAQAaEoEAAA2JQACAhkQgAEBDIhAAoCERCADQkAgEAGhIBAIANCQCAQAaEoEAAA2JQACAhkQgAEBDIhAAoCERCADQkAgEAGhIBAIANCQCAQAaEoEAAA2JQACAhkQgAEBDIhAAoKEdI7CqXl9V91fVQ1X1aFV9dD82BgDActZ28ZwfJ7l5jPF8VR1M8tWq+vsxxr8svDcAABayYwSOMUaS56e7B6fbWHJTAAAsa1fvCayqA1X1YJLTSe4dY9y3zXPuqKqNqtp48YUzM28TAIA57SoCxxgvjjGuS3JFkhur6u3bPOeuMcaRMcaRAxcemnmbAADMaaVPB48xfpTky0luWWIzAADsj918OviyqrpkOn5DkncleWzhfQEAsKDdfDr48iSfqqoD2YzGvxpj3L3stgAAWNJuPh389STX78NeAADYJ75jCABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGhKBAAANiUAAgIZEIABAQyIQAKAhEQgA0JAIBABoSAQCADQkAgEAGlpbYtFrDx/KxvGjSywNAMAMXAkEAGhIBAIANCQCAQAaEoEAAA2JQACAhkQgAEBDIhAAoCERCADQkAgEAGhIBAIANCQCAQAaEoEAAA2JQACAhkQgAEBDIhAAoCERCADQkAgEAGhIBAIANCQCAQAaEoEAAA2JQACAhkQgAEBDIhAAoCERCADQkAgEAGhIBAIANFRjjPkXrXouyeOzL9zPpUl+cLY38RpgjvMxy3mY43zMch7mOJ9zbZZvGWNctt0Dawv9Bx8fYxxZaO02qmrDHF89c5yPWc7DHOdjlvMwx/mcT7P0cjAAQEMiEACgoaUi8K6F1u3GHOdhjvMxy3mY43zMch7mOJ/zZpaLfDAEAIBzm5eDAQAamjUCq+qWqnq8qp6oqmNzrv1aVFWfrKrTVfXIlnNvrqp7q+qb09c3bXnsw9NsH6+q3zw7uz73VNWVVfWlqjpZVY9W1Yem82a5gqp6fVXdX1UPTXP86HTeHPegqg5U1deq6u7pvjnuQVWdqqqHq+rBqtqYzpnliqrqkqr6XFU9Nv1e+avmuLqqumb6ufjS7dmquvO8neUYY5ZbkgNJnkxydZILkjyU5G1zrf9avCV5R5Ibkjyy5dwfJzk2HR9L8kfT8dummb4uyVXTrA+c7f+Hc+GW5PIkN0zHFyf5t2leZrnaHCvJRdPxwST3JfkVc9zzPH8vyV8muXu6b457m+OpJJe+7JxZrj7HTyX53en4giSXmOOrnumBJN9L8pbzdZZzXgm8MckTY4x/H2P8JMlnk7xnxvVfc8YYX0nyw5edfk82f7Fm+vrbW85/dozx4zHGt5I8kc2ZtzfGeGaM8cB0/FySk0kOxyxXMjY9P909ON1GzHFlVXVFkqNJPr7ltDnOxyxXUFU/n82LDp9IkjHGT8YYP4o5vlrvTPLkGOPbOU9nOWcEHk7ynS33n5rOsZpfGmM8k2zGTZJfnM6b7y5U1XqS67N5FcssVzS9hPlgktNJ7h1jmOPe/EmSP0jyv1vOmePejCT/UFUnquqO6ZxZrubqJP+R5M+mtyh8vKreGHN8tW5P8pnp+Lyc5ZwRWNuc89Hj+ZjvDqrqoiSfT3LnGOPZV3rqNufMMskY48UxxnVJrkhyY1W9/RWebo7bqKrfSnJ6jHFitz9km3Pt57jFTWOMG5LcmuSDVfWOV3iuWW5vLZtvPfrTMcb1Sf4rmy9Z/izmuIOquiDJu5P89U5P3ebcOTPLOSPwqSRXbrl/RZLvzrh+F9+vqsuTZPp6ejpvvq+gqg5mMwA/Pcb4wnTaLPdoeqnoy0luiTmu6qYk766qU9l8W8zNVfUXMcc9GWN8d/p6OsnfZPOlNLNczVNJnpqu7CfJ57IZhea4d7cmeWCM8f3p/nk5yzkj8F+TvLWqrpoK+fYkX5xx/S6+mOT90/H7k/zdlvO3V9XrquqqJG9Ncv9Z2N85p6oqm+91OTnG+NiWh8xyBVV1WVVdMh2/Icm7kjwWc1zJGOPDY4wrxhjr2fx98J/GGO+LOa6sqt5YVRe/dJzkN5I8ErNcyRjje0m+U1XXTKfemeQbMcdX47356UvByfk6y5k/KXNbNj+Z+WSSj5ztT72c67ds/gR6Jsl/Z/NvCx9I8gtJ/jHJN6evb97y/I9Ms308ya1ne//nyi3Jr2Xz8vrXkzw43W4zy5Xn+MtJvjbN8ZEkfzidN8e9z/TX89NPB5vj6vO7OpufrHwoyaMv/blilnua5XVJNqZf33+b5E3muOdZXpjkP5Mc2nLuvJyl7xgCANCQ7xgCANCQCAQAaEgEAgA0JAIBABoSgQAADYlAAICGRCAAQEMiEACgof8D87K6o4DMZI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Pclass'].value_counts().plot.barh(figsize=(11,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a47ae4",
   "metadata": {},
   "source": [
    "- Vamos identificar qual o nosso *threshold* de valor da passagem pago apenas pelos passageiros da Primeira Classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8f70ad9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1</th>\n",
       "      <th>25925.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40125.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61175.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83475.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106425.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113275.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227525.0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262375.0</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>12275.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12525.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12875.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">3</th>\n",
       "      <th>12475.0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13775.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20525.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20575.0</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21075.0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22025.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22525.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29125.0</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31275.0</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34375.0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 PassengerId\n",
       "Pclass Fare                 \n",
       "1      25925.0             1\n",
       "       40125.0             1\n",
       "       61175.0             2\n",
       "       83475.0             2\n",
       "       106425.0            3\n",
       "       113275.0            3\n",
       "       227525.0            5\n",
       "       262375.0            7\n",
       "2      12275.0             1\n",
       "       12525.0             1\n",
       "       12875.0             2\n",
       "3      12475.0             4\n",
       "       13775.0             3\n",
       "       20525.0             3\n",
       "       20575.0             4\n",
       "       21075.0             5\n",
       "       22025.0             3\n",
       "       22525.0             3\n",
       "       29125.0             6\n",
       "       31275.0             7\n",
       "       34375.0             5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.loc[df_titanic['Fare'] > 10000].groupby(['Pclass','Fare'])[['PassengerId']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b24edc7",
   "metadata": {},
   "source": [
    "- Podemos perceber que o valor mais alto pago na terceira classe foi de 34375. Ou seja, todo mundo que pagou um valor acima desse está na primeira classe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ba3599",
   "metadata": {},
   "source": [
    "- Vamos identificar agora a distribuição das classes das pessoas pelos seus títulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b057b984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</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",
       "    <tr>\n",
       "      <th>Título</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>capt</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>col</th>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>don</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dona</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dr</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jonkheer</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lady</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major</th>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">master</th>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">miss</th>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>135</td>\n",
       "      <td>103</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>4</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlle</th>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mme</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">mr</th>\n",
       "      <th>1</th>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>139</td>\n",
       "      <td>132</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>120</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>137</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>448</td>\n",
       "      <td>448</td>\n",
       "      <td>448</td>\n",
       "      <td>411</td>\n",
       "      <td>312</td>\n",
       "      <td>448</td>\n",
       "      <td>448</td>\n",
       "      <td>448</td>\n",
       "      <td>448</td>\n",
       "      <td>8</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">mrs</th>\n",
       "      <th>1</th>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>66</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>48</td>\n",
       "      <td>54</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>57</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ms</th>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev</th>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sir</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the countess</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PassengerId  Survived  Name  Sex  Age  SibSp  Parch  \\\n",
       "Título       Pclass                                                        \n",
       "capt         1                 1         1     1    1    1      1      1   \n",
       "col          1                 4         4     4    4    4      4      4   \n",
       "don          1                 1         1     1    0    1      1      1   \n",
       "dona         1                 1         1     1    1    1      1      1   \n",
       "dr           1                 6         6     6    6    5      6      6   \n",
       "             2                 2         2     2    1    2      2      2   \n",
       "jonkheer     1                 1         1     1    1    1      1      1   \n",
       "lady         1                 1         1     1    1    1      1      1   \n",
       "major        1                 2         2     2    2    2      2      2   \n",
       "master       1                 5         5     5    5    5      5      5   \n",
       "             2                11        11    11   10   11     11     11   \n",
       "             3                45        45    45   39   37     45     45   \n",
       "miss         1                60        60    60   49   59     60     60   \n",
       "             2                50        50    50   47   48     50     50   \n",
       "             3               150       150   150  135  103    150    150   \n",
       "mlle         1                 2         2     2    2    2      2      2   \n",
       "mme          1                 1         1     1    1    1      1      1   \n",
       "mr           1               159       159   159  139  132    159    159   \n",
       "             2               150       150   150  130  137    150    150   \n",
       "             3               448       448   448  411  312    448    448   \n",
       "mrs          1                77        77    77   65   67     77     77   \n",
       "             2                55        55    55   48   54     55     55   \n",
       "             3                65        65    65   57   49     65     65   \n",
       "ms           2                 1         1     1    1    1      1      1   \n",
       "             3                 1         1     1    1    0      1      1   \n",
       "rev          2                 8         8     8    7    8      8      8   \n",
       "sir          1                 1         1     1    1    1      1      1   \n",
       "the countess 1                 1         1     1    1    1      1      1   \n",
       "\n",
       "                     Ticket  Fare  Cabin  Embarked  \n",
       "Título       Pclass                                 \n",
       "capt         1            1     1      1         1  \n",
       "col          1            4     4      3         4  \n",
       "don          1            1     1      0         1  \n",
       "dona         1            1     1      1         1  \n",
       "dr           1            6     6      4         6  \n",
       "             2            2     2      0         2  \n",
       "jonkheer     1            1     1      0         1  \n",
       "lady         1            1     1      1         1  \n",
       "major        1            2     2      2         2  \n",
       "master       1            5     5      5         5  \n",
       "             2           11    11      3        11  \n",
       "             3           45    45      1        45  \n",
       "miss         1           60    60     48        60  \n",
       "             2           50    50      6        50  \n",
       "             3          150   150      4       150  \n",
       "mlle         1            2     2      2         2  \n",
       "mme          1            1     1      1         1  \n",
       "mr           1          159   159    120       159  \n",
       "             2          150   150      7       150  \n",
       "             3          448   448      8       448  \n",
       "mrs          1           77    77     66        77  \n",
       "             2           55    55      7        55  \n",
       "             3           65    65      3        65  \n",
       "ms           2            1     1      0         1  \n",
       "             3            1     1      0         1  \n",
       "rev          2            8     8      0         8  \n",
       "sir          1            1     1      1         1  \n",
       "the countess 1            1     1      1         1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.groupby(['Título','Pclass']).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549a3a10",
   "metadata": {},
   "source": [
    "- Todos que estão na terceira classe possuem o título de ms, mrs, mr e master. Todos os outros estão na segunda ou primeira classe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "68769440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    22\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.loc[df_titanic['Fare'] > 50000]['Pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05db7b16",
   "metadata": {},
   "source": [
    "- Vamos encontrar a média de preço para os passageiros da primeira, segunda e terceira classe (exlcuindo os Outliers para não subir a média)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd38ac8",
   "metadata": {},
   "source": [
    "- 1ª classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d6fc8139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[(df_titanic['Fare'] < 500) & (df_titanic['Pclass'] == 1)].plot.box(vert=False, figsize=(15,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9b80abeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.69911465201462"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[(df_titanic['Fare'] < 200) & (df_titanic['Pclass'] == 1)].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e4f6e1",
   "metadata": {},
   "source": [
    "- 2ª classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "368e71a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[(df_titanic['Fare'] < 500) & (df_titanic['Pclass'] == 2)].plot.box(vert=False, figsize=(15,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca94404b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.0673846743295"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[(df_titanic['Fare'] < 60) & (df_titanic['Pclass'] == 2)].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a655466",
   "metadata": {},
   "source": [
    "- 3ª classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ac1dcf2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[(df_titanic['Fare'] < 500) & (df_titanic['Pclass'] == 3)].plot.box(vert=False, figsize=(15,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b698ca4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.45326592178772"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic['Fare'].loc[(df_titanic['Fare'] < 30) & (df_titanic['Pclass'] == 3)].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c65c8c4",
   "metadata": {},
   "source": [
    "### Tratando dados ausentes da coluna Sex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d88ffe",
   "metadata": {},
   "source": [
    "- Como poderíamos fazer isso?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454d4585",
   "metadata": {},
   "source": [
    "- Uma alternativa é identicar seu gênero de acordo com o título do passageiro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "306f3c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mr', 'mrs', 'miss', 'master', 'don', 'rev', 'dr', 'mme', 'ms',\n",
       "       'major', 'lady', 'sir', 'mlle', 'col', 'capt', 'the countess',\n",
       "       'jonkheer', 'dona'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic['Título'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de76f5c",
   "metadata": {},
   "source": [
    "- Verificando se há algum passageiro que não possui título"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5ecc097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic['Título'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a5575a9",
   "metadata": {},
   "source": [
    "- Listando títulos masculinos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9a4e3de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_male = ['mr', 'master', 'don', 'rev', 'dr', 'major', 'sir', 'col', 'capt', 'jonkheer']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28596565",
   "metadata": {},
   "source": [
    "- Filtrando resultado que não possuem definição na coluna Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d8b03702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Título</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31275.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>334915</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1301</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "      <td>13775.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "      <td>mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7775.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "11             12         1       1   \n",
       "12             13         0       3   \n",
       "13             14         0       3   \n",
       "14             15         0       3   \n",
       "15             16         1       2   \n",
       "...           ...       ...     ...   \n",
       "1299         1300         1       3   \n",
       "1300         1301         1       3   \n",
       "1301         1302         1       3   \n",
       "1302         1303         1       1   \n",
       "1303         1304         1       3   \n",
       "\n",
       "                                                 Name  Sex   Age  SibSp  \\\n",
       "11                           Bonnell, Miss. Elizabeth  NaN  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry  NaN  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan  NaN  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  NaN  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   NaN  55.0      0   \n",
       "...                                               ...  ...   ...    ...   \n",
       "1299                  Riordan, Miss. Johanna Hannah\"\"  NaN   NaN      0   \n",
       "1300                        Peacock, Miss. Treasteall  NaN   3.0      1   \n",
       "1301                           Naughton, Miss. Hannah  NaN   NaN      0   \n",
       "1302  Minahan, Mrs. William Edward (Lillian E Thorpe)  NaN  37.0      1   \n",
       "1303                   Henriksson, Miss. Jenny Lovisa  NaN  28.0      0   \n",
       "\n",
       "      Parch              Ticket        Fare Cabin Embarked Título  \n",
       "11        0              113783     26.5500  C103        S   miss  \n",
       "12        0           A/5. 2151      8.0500   NaN        S     mr  \n",
       "13        5              347082  31275.0000   NaN        S     mr  \n",
       "14        0              350406      7.8542   NaN        S   miss  \n",
       "15        0              248706     16.0000   NaN        S    mrs  \n",
       "...     ...                 ...         ...   ...      ...    ...  \n",
       "1299      0              334915      7.7208   NaN        Q   miss  \n",
       "1300      1  SOTON/O.Q. 3101315  13775.0000   NaN        S   miss  \n",
       "1301      0              365237      7.7500   NaN        Q   miss  \n",
       "1302      0               19928     90.0000   C78        Q    mrs  \n",
       "1303      0              347086   7775.0000   NaN        S   miss  \n",
       "\n",
       "[143 rows x 13 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux = df_titanic[df_titanic['Sex'].isnull()]\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1b3c59",
   "metadata": {},
   "source": [
    "- Atribuindo o gênero do passageiro de acordo com o seu título"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e0e36b5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-60d78d20a5df>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_aux['Sex'] = df_aux['Título'].apply(lambda x: 'male' if x in list_male else 'female')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Título</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31275.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>334915</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1301</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "      <td>13775.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "      <td>mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7775.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "11             12         1       1   \n",
       "12             13         0       3   \n",
       "13             14         0       3   \n",
       "14             15         0       3   \n",
       "15             16         1       2   \n",
       "...           ...       ...     ...   \n",
       "1299         1300         1       3   \n",
       "1300         1301         1       3   \n",
       "1301         1302         1       3   \n",
       "1302         1303         1       1   \n",
       "1303         1304         1       3   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "...                                               ...     ...   ...    ...   \n",
       "1299                  Riordan, Miss. Johanna Hannah\"\"  female   NaN      0   \n",
       "1300                        Peacock, Miss. Treasteall  female   3.0      1   \n",
       "1301                           Naughton, Miss. Hannah  female   NaN      0   \n",
       "1302  Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0      1   \n",
       "1303                   Henriksson, Miss. Jenny Lovisa  female  28.0      0   \n",
       "\n",
       "      Parch              Ticket        Fare Cabin Embarked Título  \n",
       "11        0              113783     26.5500  C103        S   miss  \n",
       "12        0           A/5. 2151      8.0500   NaN        S     mr  \n",
       "13        5              347082  31275.0000   NaN        S     mr  \n",
       "14        0              350406      7.8542   NaN        S   miss  \n",
       "15        0              248706     16.0000   NaN        S    mrs  \n",
       "...     ...                 ...         ...   ...      ...    ...  \n",
       "1299      0              334915      7.7208   NaN        Q   miss  \n",
       "1300      1  SOTON/O.Q. 3101315  13775.0000   NaN        S   miss  \n",
       "1301      0              365237      7.7500   NaN        Q   miss  \n",
       "1302      0               19928     90.0000   C78        Q    mrs  \n",
       "1303      0              347086   7775.0000   NaN        S   miss  \n",
       "\n",
       "[143 rows x 13 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux['Sex'] = df_aux['Título'].apply(lambda x: 'male' if x in list_male else 'female')\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac70c61",
   "metadata": {},
   "source": [
    "- Combinando os dois dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5dc762cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_titanic = df_titanic.combine_first(df_aux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "930e75bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic['Sex'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f92b502",
   "metadata": {},
   "source": [
    "- Podemos observar que todos os passageiros possui o registro de gênero"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a73107",
   "metadata": {},
   "source": [
    "### Tratando a coluna Age (Idade) para valores faltantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cdda0017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic[\"Age\"].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb1126d",
   "metadata": {},
   "source": [
    "- Temos 263 passageiros sem idade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec686cb",
   "metadata": {},
   "source": [
    "- Podemos inserir as faixas de idades conforme classe e gênero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "163660d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
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       "      <th>male</th>\n",
       "      <td>41.029272</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>female</th>\n",
       "      <td>27.499223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>30.815380</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"2\" valign=\"top\">3.0</th>\n",
       "      <th>female</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Age\n",
       "Pclass Sex              \n",
       "1.0    female  37.037594\n",
       "       male    41.029272\n",
       "2.0    female  27.499223\n",
       "       male    30.815380\n",
       "3.0    female  22.185329\n",
       "       male    25.962264"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.groupby(['Pclass','Sex'])[['Age']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70267ce3",
   "metadata": {},
   "source": [
    "- Ou podemos tratar a idade por classe e título. Irá dar mais trabalho, mas ficará mais próximo da realidade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f37b8256",
   "metadata": {},
   "outputs": [
    {
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       "      <td>40.000000</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>dona</td>\n",
       "      <td>39.000000</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>dr</td>\n",
       "      <td>45.600000</td>\n",
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       "      <th>5</th>\n",
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       "      <td>38.000000</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>lady</td>\n",
       "      <td>48.000000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>major</td>\n",
       "      <td>48.500000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>6.984000</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>30.338983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>mr</td>\n",
       "      <td>41.450758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>43.208955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>sir</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>the countess</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>dr</td>\n",
       "      <td>38.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0</td>\n",
       "      <td>master</td>\n",
       "      <td>2.757273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>20.717083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>mr</td>\n",
       "      <td>32.346715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>mrs</td>\n",
       "      <td>33.518519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>ms</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>rev</td>\n",
       "      <td>41.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.0</td>\n",
       "      <td>master</td>\n",
       "      <td>6.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>17.360874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.0</td>\n",
       "      <td>mr</td>\n",
       "      <td>28.318910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.0</td>\n",
       "      <td>mrs</td>\n",
       "      <td>32.326531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>ms</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass        Título        Age\n",
       "0      1.0          capt  70.000000\n",
       "1      1.0           col  54.000000\n",
       "2      1.0           don  40.000000\n",
       "3      1.0          dona  39.000000\n",
       "4      1.0            dr  45.600000\n",
       "5      1.0      jonkheer  38.000000\n",
       "6      1.0          lady  48.000000\n",
       "7      1.0         major  48.500000\n",
       "8      1.0        master   6.984000\n",
       "9      1.0          miss  30.338983\n",
       "10     1.0          mlle  24.000000\n",
       "11     1.0           mme  24.000000\n",
       "12     1.0            mr  41.450758\n",
       "13     1.0           mrs  43.208955\n",
       "14     1.0           sir  49.000000\n",
       "15     1.0  the countess  33.000000\n",
       "16     2.0            dr  38.500000\n",
       "17     2.0        master   2.757273\n",
       "18     2.0          miss  20.717083\n",
       "19     2.0            mr  32.346715\n",
       "20     2.0           mrs  33.518519\n",
       "21     2.0            ms  28.000000\n",
       "22     2.0           rev  41.250000\n",
       "23     3.0        master   6.090000\n",
       "24     3.0          miss  17.360874\n",
       "25     3.0            mr  28.318910\n",
       "26     3.0           mrs  32.326531\n",
       "27     3.0            ms        NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux = df_titanic.groupby(['Pclass','Título'], as_index=False)[['Age']].mean()\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4b86f1",
   "metadata": {},
   "source": [
    "- Como pode ser visto, apena ms da terceira classe não possui idade. Vamos assumir então para essa situação o valor de ms da segunda classe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "55d38823",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andre/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n"
     ]
    },
    {
     "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>Título</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>capt</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>col</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>don</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>dona</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>dr</td>\n",
       "      <td>45.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>jonkheer</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>lady</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>major</td>\n",
       "      <td>48.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>master</td>\n",
       "      <td>6.984000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>30.338983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>mlle</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>mme</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>mr</td>\n",
       "      <td>41.450758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>mrs</td>\n",
       "      <td>43.208955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>sir</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>the countess</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>dr</td>\n",
       "      <td>38.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0</td>\n",
       "      <td>master</td>\n",
       "      <td>2.757273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>20.717083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>mr</td>\n",
       "      <td>32.346715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>mrs</td>\n",
       "      <td>33.518519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>ms</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>rev</td>\n",
       "      <td>41.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.0</td>\n",
       "      <td>master</td>\n",
       "      <td>6.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>17.360874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.0</td>\n",
       "      <td>mr</td>\n",
       "      <td>28.318910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.0</td>\n",
       "      <td>mrs</td>\n",
       "      <td>32.326531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>ms</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass        Título        Age\n",
       "0      1.0          capt  70.000000\n",
       "1      1.0           col  54.000000\n",
       "2      1.0           don  40.000000\n",
       "3      1.0          dona  39.000000\n",
       "4      1.0            dr  45.600000\n",
       "5      1.0      jonkheer  38.000000\n",
       "6      1.0          lady  48.000000\n",
       "7      1.0         major  48.500000\n",
       "8      1.0        master   6.984000\n",
       "9      1.0          miss  30.338983\n",
       "10     1.0          mlle  24.000000\n",
       "11     1.0           mme  24.000000\n",
       "12     1.0            mr  41.450758\n",
       "13     1.0           mrs  43.208955\n",
       "14     1.0           sir  49.000000\n",
       "15     1.0  the countess  33.000000\n",
       "16     2.0            dr  38.500000\n",
       "17     2.0        master   2.757273\n",
       "18     2.0          miss  20.717083\n",
       "19     2.0            mr  32.346715\n",
       "20     2.0           mrs  33.518519\n",
       "21     2.0            ms  28.000000\n",
       "22     2.0           rev  41.250000\n",
       "23     3.0        master   6.090000\n",
       "24     3.0          miss  17.360874\n",
       "25     3.0            mr  28.318910\n",
       "26     3.0           mrs  32.326531\n",
       "27     3.0            ms  28.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux['Age'].loc[27] = df_aux['Age'].loc[21]\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "603fd943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic['Age'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966d3cd3",
   "metadata": {},
   "source": [
    "- Podemos ver que há 263 registros sem Age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3648c0ba",
   "metadata": {},
   "source": [
    "- Vamos utilizar uma função que retorna a média de idade baseada na média de idade de classe e título."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "de6d8903",
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(age_pclass_titulo):\n",
    "    age = age_pclass_titulo[0]\n",
    "    pclass = age_pclass_titulo[1]\n",
    "    titulo = age_pclass_titulo[2]\n",
    "    if pd.isnull(age):\n",
    "        return df_aux['Age'].loc[(df_aux['Pclass']==pclass) & (df_aux['Título']==titulo)].values[0]\n",
    "    else:\n",
    "        return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cb4de415",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_titanic['Age'] = df_titanic[['Age','Pclass','Título']].apply(impute_age, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8354a7d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic['Age'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2c1c37",
   "metadata": {},
   "source": [
    "### Tratando a coluna Cabin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072d0f51",
   "metadata": {},
   "source": [
    "- Agora só temos a coluna Cabin com dados faltantes que possui ~33% dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d1fa8a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.00\n",
       "Survived        0.00\n",
       "Pclass          0.00\n",
       "Name            0.00\n",
       "Sex             0.00\n",
       "Age             0.00\n",
       "SibSp           0.00\n",
       "Parch           0.00\n",
       "Ticket          0.00\n",
       "Fare            0.00\n",
       "Cabin          77.46\n",
       "Embarked        0.00\n",
       "Título          0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df_titanic.isnull().mean()*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c5f62851",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>mrs</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7925.0000</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
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       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>35.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.31891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>349909</td>\n",
       "      <td>21075.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>master</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>mrs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          1.0       0.0     3.0   \n",
       "1          2.0       1.0     1.0   \n",
       "2          3.0       1.0     3.0   \n",
       "3          4.0       1.0     1.0   \n",
       "4          5.0       0.0     3.0   \n",
       "5          6.0       0.0     3.0   \n",
       "6          7.0       0.0     1.0   \n",
       "7          8.0       0.0     3.0   \n",
       "8          9.0       1.0     3.0   \n",
       "9         10.0       1.0     2.0   \n",
       "\n",
       "                                                Name     Sex       Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.00000    1.0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.00000    1.0   \n",
       "2                             Heikkinen, Miss. Laina  female  26.00000    0.0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.00000    1.0   \n",
       "4                           Allen, Mr. William Henry    male  35.00000    0.0   \n",
       "5                                   Moran, Mr. James    male  28.31891    0.0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.00000    0.0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.00000    3.0   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.00000    0.0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.00000    1.0   \n",
       "\n",
       "   Parch            Ticket        Fare Cabin Embarked  Título  \n",
       "0    0.0         A/5 21171      7.2500   NaN        S      mr  \n",
       "1    0.0          PC 17599     71.2833   C85        C     mrs  \n",
       "2    0.0  STON/O2. 3101282   7925.0000   NaN        S    miss  \n",
       "3    0.0            113803     53.1000  C123        S     mrs  \n",
       "4    0.0            373450      8.0500   NaN        S      mr  \n",
       "5    0.0            330877      8.4583   NaN        Q      mr  \n",
       "6    0.0             17463     51.8625   E46        S      mr  \n",
       "7    1.0            349909  21075.0000   NaN        S  master  \n",
       "8    2.0            347742     11.1333   NaN        S     mrs  \n",
       "9    0.0            237736     30.0708   NaN        C     mrs  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e7f163",
   "metadata": {},
   "source": [
    "- Vamos verificar como estão distribuídas as cabines por classe, selecionando apenas a letra inicial de cada observação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "20e67407",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_titanic['Ale_cabin'] = df_titanic['Cabin'].str[0].str.lower().str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "44016d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Ale_cabin\n",
       "1.0     c            94\n",
       "        b            65\n",
       "        d            40\n",
       "        e            34\n",
       "        a            22\n",
       "        t             1\n",
       "2.0     f            13\n",
       "        d             6\n",
       "        e             4\n",
       "3.0     f             8\n",
       "        g             5\n",
       "        e             3\n",
       "Name: Ale_cabin, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.groupby('Pclass')['Ale_cabin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e059256f",
   "metadata": {},
   "source": [
    "- Temos a seguinte distribuição das cabines por classe:\n",
    "    - Classe 1 = a, b, c, d, e, t\n",
    "    - Classe 2 = d, e, f\n",
    "    - Classe 3 = e, g, f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122d224d",
   "metadata": {},
   "source": [
    "- Podemos remover a coluna Ale_cabin pois ja extraimos o que precisamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "310777d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_titanic['Ale_cabin']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecf3219",
   "metadata": {},
   "source": [
    "- Vamos criar um dicionário onde a chave é a classe e os valores são as possíveis letras das cabines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "78e0a322",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_classe = {1:['a', 'b', 'c', 'd', 'e', 't'],\n",
    "               2:['d', 'e', 'f'],\n",
    "               3:['e', 'g', 'h']}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50d379d",
   "metadata": {},
   "source": [
    "- Filtrando registros de Cabin nula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4eb16fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Título</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7925.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.31891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>349909</td>\n",
       "      <td>21075.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>master</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1304.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7775.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>28.31891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>28.31891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>6.09000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>master</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass                            Name     Sex  \\\n",
       "0             1.0       0.0     3.0         Braund, Mr. Owen Harris    male   \n",
       "2             3.0       1.0     3.0          Heikkinen, Miss. Laina  female   \n",
       "4             5.0       0.0     3.0        Allen, Mr. William Henry    male   \n",
       "5             6.0       0.0     3.0                Moran, Mr. James    male   \n",
       "7             8.0       0.0     3.0  Palsson, Master. Gosta Leonard    male   \n",
       "...           ...       ...     ...                             ...     ...   \n",
       "1303       1304.0       1.0     3.0  Henriksson, Miss. Jenny Lovisa  female   \n",
       "1304       1305.0       0.0     3.0              Spector, Mr. Woolf    male   \n",
       "1306       1307.0       0.0     3.0    Saether, Mr. Simon Sivertsen    male   \n",
       "1307       1308.0       0.0     3.0             Ware, Mr. Frederick    male   \n",
       "1308       1309.0       0.0     3.0        Peter, Master. Michael J    male   \n",
       "\n",
       "           Age  SibSp  Parch              Ticket        Fare Cabin Embarked  \\\n",
       "0     22.00000    1.0    0.0           A/5 21171      7.2500   NaN        S   \n",
       "2     26.00000    0.0    0.0    STON/O2. 3101282   7925.0000   NaN        S   \n",
       "4     35.00000    0.0    0.0              373450      8.0500   NaN        S   \n",
       "5     28.31891    0.0    0.0              330877      8.4583   NaN        Q   \n",
       "7      2.00000    3.0    1.0              349909  21075.0000   NaN        S   \n",
       "...        ...    ...    ...                 ...         ...   ...      ...   \n",
       "1303  28.00000    0.0    0.0              347086   7775.0000   NaN        S   \n",
       "1304  28.31891    0.0    0.0           A.5. 3236      8.0500   NaN        S   \n",
       "1306  38.50000    0.0    0.0  SOTON/O.Q. 3101262      7.2500   NaN        S   \n",
       "1307  28.31891    0.0    0.0              359309      8.0500   NaN        S   \n",
       "1308   6.09000    1.0    1.0                2668     22.3583   NaN        C   \n",
       "\n",
       "      Título  \n",
       "0         mr  \n",
       "2       miss  \n",
       "4         mr  \n",
       "5         mr  \n",
       "7     master  \n",
       "...      ...  \n",
       "1303    miss  \n",
       "1304      mr  \n",
       "1306      mr  \n",
       "1307      mr  \n",
       "1308  master  \n",
       "\n",
       "[1014 rows x 13 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux = df_titanic[df_titanic['Cabin'].isnull()]\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088cab4d",
   "metadata": {},
   "source": [
    "- Vamos imputar os dados com a função choice do numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2ff68458",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-55-d900d826106b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_aux['Cabin'] = df_titanic['Pclass'].apply(lambda x: np.random.choice(dict_classe[x], size=1)[0])\n"
     ]
    }
   ],
   "source": [
    "df_aux['Cabin'] = df_titanic['Pclass'].apply(lambda x: np.random.choice(dict_classe[x], size=1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d3b07c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Título</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>e</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7925.0000</td>\n",
       "      <td>g</td>\n",
       "      <td>S</td>\n",
       "      <td>miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>h</td>\n",
       "      <td>S</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.31891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>e</td>\n",
       "      <td>Q</td>\n",
       "      <td>mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>349909</td>\n",
       "      <td>21075.0000</td>\n",
       "      <td>g</td>\n",
       "      <td>S</td>\n",
       "      <td>master</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass                            Name     Sex  \\\n",
       "0          1.0       0.0     3.0         Braund, Mr. Owen Harris    male   \n",
       "2          3.0       1.0     3.0          Heikkinen, Miss. Laina  female   \n",
       "4          5.0       0.0     3.0        Allen, Mr. William Henry    male   \n",
       "5          6.0       0.0     3.0                Moran, Mr. James    male   \n",
       "7          8.0       0.0     3.0  Palsson, Master. Gosta Leonard    male   \n",
       "\n",
       "        Age  SibSp  Parch            Ticket        Fare Cabin Embarked  Título  \n",
       "0  22.00000    1.0    0.0         A/5 21171      7.2500     e        S      mr  \n",
       "2  26.00000    0.0    0.0  STON/O2. 3101282   7925.0000     g        S    miss  \n",
       "4  35.00000    0.0    0.0            373450      8.0500     h        S      mr  \n",
       "5  28.31891    0.0    0.0            330877      8.4583     e        Q      mr  \n",
       "7   2.00000    3.0    1.0            349909  21075.0000     g        S  master  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e386a27f",
   "metadata": {},
   "source": [
    "- Combinando os dois dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d1944474",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_titanic = df_titanic.combine_first(df_aux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9b78886e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[df_titanic['Cabin'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f7808d",
   "metadata": {},
   "source": [
    "- Podemos observar que todos os registros de Cabin possuem pelo menos um letra associada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "733b848a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0.0\n",
       "Survived       0.0\n",
       "Pclass         0.0\n",
       "Name           0.0\n",
       "Sex            0.0\n",
       "Age            0.0\n",
       "SibSp          0.0\n",
       "Parch          0.0\n",
       "Ticket         0.0\n",
       "Fare           0.0\n",
       "Cabin          0.0\n",
       "Embarked       0.0\n",
       "Título         0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df_titanic.isnull().mean()*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5a37d6",
   "metadata": {},
   "source": [
    "- E com isso, finalizamos nossa atividade de limpeza dos dados. Todas as colunas possuem dados."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851ce9e8",
   "metadata": {},
   "source": [
    "- Vamos explorar visualmente nossos dados. Vamos visualizar as pessoas que sobreviveram baseado na classe e gênero"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ab169b",
   "metadata": {},
   "source": [
    "- Pessoas que sobreviveram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "798025d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived = df_titanic.loc[df_titanic['Survived'] == 1]\n",
    "percentual = pd.crosstab(survived.Sex,\n",
    "                         survived.Pclass,\n",
    "                         aggfunc = 'count',\n",
    "                         values = survived.Survived)\n",
    "\n",
    "percentual.T.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f1cae4",
   "metadata": {},
   "source": [
    "- Pessoas que não sobreviveram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7944e8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "not_survived = df_titanic.loc[df_titanic['Survived'] == 0]\n",
    "percentual = pd.crosstab(not_survived.Sex,\n",
    "                         not_survived.Pclass,\n",
    "                         aggfunc = 'count',\n",
    "                         values = not_survived.Survived)\n",
    "\n",
    "percentual.T.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022bd2d1",
   "metadata": {},
   "source": [
    "- Conforme nosso gráfico acima, em todas as classes o percentual de mulheres sobrevivente é superior aos homens e fica mais expressivo quando olhamos para a primeria e segunda classe.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3505c3b",
   "metadata": {},
   "source": [
    "- Média de idade dos que sobreviveram e dos que morreram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b99b6490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>30.103622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>28.552944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age\n",
       "Survived           \n",
       "0.0       30.103622\n",
       "1.0       28.552944"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.groupby('Survived')[['Age']].mean()"
   ]
  }
 ],
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  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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