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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Analise de Dados do Titanic*\n",
+    "\n",
+    " Temos um conjunto de de dados, com informações detalhadas sobre passageiros que estavam a bordo do navio Titanic.\n",
+    " Dados que contem informações sobre nome,idade,genero, classe da cabine e sobreviventes.\n",
+    " O próposito dessa analise é obter insights sobre os passageiros do navio o que nos permite ter informações relevantes, detalhadas e visualvelmente mais intuitivas.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>887</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>888</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>889</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.4500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>890</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>891</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 12 columns</p>\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",
+       "886          887         0       2   \n",
+       "887          888         1       1   \n",
+       "888          889         0       3   \n",
+       "889          890         1       1   \n",
+       "890          891         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",
+       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
+       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
+       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
+       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
+       "890                                Dooley, Mr. Patrick    male  32.0      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   7.9250   NaN        S  \n",
+       "3        0            113803  53.1000  C123        S  \n",
+       "4        0            373450   8.0500   NaN        S  \n",
+       "..     ...               ...      ...   ...      ...  \n",
+       "886      0            211536  13.0000   NaN        S  \n",
+       "887      0            112053  30.0000   B42        S  \n",
+       "888      2        W./C. 6607  23.4500   NaN        S  \n",
+       "889      0            111369  30.0000  C148        C  \n",
+       "890      0            370376   7.7500   NaN        Q  \n",
+       "\n",
+       "[891 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"titanic.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\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   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['IdPassageiro',\n",
+       " 'Sobreviveu',\n",
+       " 'Classe',\n",
+       " 'Nome',\n",
+       " 'Sexo',\n",
+       " 'Idade',\n",
+       " 'NumeroIrmaos',\n",
+       " 'NumeroPais',\n",
+       " 'NumeroTicket',\n",
+       " 'PrecoTicket',\n",
+       " 'NumeroCabine',\n",
+       " 'PortoEmbarcacao']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Traduzir as colunas, para que melhore o entedimento e a criação de novas definições\n",
+    "\n",
+    "# Resolução\n",
+    "\n",
+    "traducoes = {\n",
+    "    'PassengerId': 'IdPassageiro',\n",
+    "    'Survived': 'Sobreviveu', # 0 = Não, 1 = Sim\n",
+    "    'Pclass': 'Classe', # 1, 2, 3\n",
+    "    'Name': 'Nome',\n",
+    "    'Sex': 'Sexo',\n",
+    "    'Age': 'Idade',\n",
+    "    'SibSp': 'NumeroIrmaos',\n",
+    "    'Parch':  'NumeroPais',\n",
+    "    'Ticket': 'NumeroTicket',\n",
+    "    'Fare': 'PrecoTicket',\n",
+    "    'Cabin' : 'NumeroCabine',\n",
+    "    'Embarked': 'PortoEmbarcacao' # C = Cherbourg, Q = Queenstown, S = Southampton\n",
+    "}\n",
+    "\n",
+    "# result = [(d['color'], d['value']) for d in dictionarylist]\n",
+    "\n",
+    "novas_colunas = []\n",
+    "for chave, valor in traducoes.items():\n",
+    "    novas_colunas.append(valor)\n",
+    "\n",
+    "novas_colunas "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Sexo</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>NumeroIrmaos</th>\n",
+       "      <th>NumeroPais</th>\n",
+       "      <th>NumeroTicket</th>\n",
+       "      <th>PrecoTicket</th>\n",
+       "      <th>NumeroCabine</th>\n",
+       "      <th>PortoEmbarcacao</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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   IdPassageiro  Sobreviveu  Classe  \\\n",
+       "0             1           0       3   \n",
+       "1             2           1       1   \n",
+       "\n",
+       "                                                Nome    Sexo  Idade  \\\n",
+       "0                            Braund, Mr. Owen Harris    male   22.0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
+       "\n",
+       "   NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "0             1           0    A/5 21171       7.2500          NaN   \n",
+       "1             1           0     PC 17599      71.2833          C85   \n",
+       "\n",
+       "  PortoEmbarcacao  \n",
+       "0               S  \n",
+       "1               C  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns = novas_colunas\n",
+    "df[:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Função de processamento dos dados* - Essa função é importante para que seja feito um tratamento inicial em um DataFrame do Pandas com base no conjunto de dadosa ser tratado, no caso em tela iremos ultilizar o conjunto de dados do Navio Titanic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Criando a função de processamento dos dados\n",
+    "def processamento_df(df):\n",
+    "    \n",
+    "    # 3 primeiras linhas do df\n",
+    "    df_3_linhas = df.head(3)\n",
+    "    \n",
+    "    # selecionando aleatoriamente 100 linhas do df\n",
+    "    df_menor = df.sample(100)\n",
+    "    \n",
+    "    # removendo colunas com muitos dados nulos\n",
+    "    df = df.drop(columns='Cabin')\n",
+    "    \n",
+    "    # ordenando por idade menor para o maior\n",
+    "    df = df.sort_values(\"Age\")\n",
+    "      \n",
+    "    # removendo dados duplicados\n",
+    "    df = df.drop_duplicates()\n",
+    "    \n",
+    "    # reset index\n",
+    "    df = df.reset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Visualização e Insights atraves de gráficos* - Usaremos a biblioteca MATPLOPIT \n",
+    "\n",
+    "\n",
+    "*MATPLOPIT* - É uma biblioteca ultilizada em python muito usada na analise de dados, para criação de graficos e visualização de dados. Tornando-se mais visual e intuitivo.\n",
+    "- Para a analise em tela, ultilazamos a integração do *matplopit* com o *Jupyter Notebook*\n",
+    "- Como tambem a integração com o *Pandas*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Grafico 1 - Distribuição de idade dos passageiros \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Quantidade de Passageiros')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(5,8))\n",
+    "plt.hist(df['Idade'].dropna(), bins=10, color='red', edgecolor= 'yellow')\n",
+    "plt.title('Distribuição de Idade por Passageiros')\n",
+    "plt.xlabel('Idade')\n",
+    "plt.ylabel('Quantidade de Passageiros')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- De acordo com analise apresentada pelo o grafico. Conclui-se que a idade da maioria dos passageiros era entre 20 e 30 anos "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Grafico 2- Taxa de Sobrevivencia por Classe na Cabine \n",
+    "- De inicio é necessario agrupar os dados por classe de cada cabine\n",
+    "- Calcular a taxa de sobrivência"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Agrupar os dados por classe de cabine e calcular a taxa de sobrevivência\n",
+    "Classe_sobrevivente = df.groupby('Classe')['Sobreviveu'].mean()\n",
+    "\n",
+    "# Código do gráfico \n",
+    "plt.figure(figsize=(8, 6))\n",
+    "Classe_sobrevivente.plot(kind='bar', color='green')\n",
+    "plt.title('Taxa de Sobrevivência por Classe de Cabine')\n",
+    "plt.xlabel('Classe de Cabine')\n",
+    "plt.ylabel('Taxa de Sobrevivência')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "De acordo com a analise em tela, estima-se que a classe 2,3 são as classes com menos sobrevivetes. E a classe 1 com mais sobrevivente."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Grafico 3 - Numero de passageiros por gênero\n",
+    "- Para essa analise foi apresentado o grafico em pizza\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "contagem_por_genero = df['Sexo'].value_counts()\n",
+    "\n",
+    "# Código do Gráfico\n",
+    "plt.figure(figsize=(3, 6))\n",
+    "plt.pie(contagem_por_genero, labels=contagem_por_genero.index, autopct='%1.1f%%', colors=['blue', 'red'])\n",
+    "plt.title('Contagem Por Gênero')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "De acordo com a analise em tela, estima-se que o a maioria dos passageiros no navio titanic eram do gênero masculino."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/material/titanic.csv b/exercicios/para-casa/titanic.csv
similarity index 100%
rename from material/titanic.csv
rename to exercicios/para-casa/titanic.csv
diff --git a/material/aula_s12.ipynb b/material/aula_s12.ipynb
index 6a3a013..ec40aab 100644
--- a/material/aula_s12.ipynb
+++ b/material/aula_s12.ipynb
@@ -115,6 +115,8 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+<<<<<<< HEAD
+=======
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,6 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "_eorJ3GNS_mm",
     "outputId": "14f2b233-cc14-4f8a-a041-98b4cb90d2cc"
@@ -379,13 +382,22 @@
        "[891 rows x 12 columns]"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 1,
+=======
      "execution_count": 2,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+<<<<<<< HEAD
+    "import pandas as pd\n",
+    "\n",
+=======
     "# carregando o dataset \n",
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
     "df = pd.read_csv(\"titanic.csv\")\n",
     "df"
    ]
@@ -401,13 +413,24 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 2,
+=======
    "execution_count": 3,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "ZuehjTU7S_mt",
     "outputId": "6ce66e1f-01d4-4889-f97f-50d5edae71e6"
    },
    "outputs": [
     {
+<<<<<<< HEAD
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket', 'NumeroCabine', 'PortoEmbarcacao']\n"
+     ]
+=======
      "data": {
       "text/plain": [
        "['IdPassageiro',\n",
@@ -427,6 +450,7 @@
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
     }
    ],
    "source": [
@@ -456,7 +480,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 3,
+=======
    "execution_count": 4,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "h6tvl5HPS_mw",
     "outputId": "4639f186-8484-4780-f4dc-0a29f3cc759e"
@@ -724,7 +752,11 @@
        "[891 rows x 12 columns]"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 3,
+=======
      "execution_count": 4,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,7 +785,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 4,
+=======
    "execution_count": 5,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "gZKfzWYAS_mx",
     "outputId": "cdc7a73b-0f3b-42ce-cdf2-f8322b15e686"
@@ -1021,7 +1057,11 @@
        "[891 rows x 12 columns]"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 4,
+=======
      "execution_count": 5,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1033,6 +1073,9 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 5,
+=======
    "execution_count": 6,
    "metadata": {},
    "outputs": [
@@ -1310,6 +1353,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "xzHvkQRrS_mx",
     "outputId": "ca4d371d-386e-433d-e49b-6e5a6749f767"
@@ -1321,7 +1365,11 @@
        "(305, 12)"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 5,
+=======
      "execution_count": 7,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1334,7 +1382,71 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 19,
+   "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>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Sexo</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>NumeroIrmaos</th>\n",
+       "      <th>NumeroPais</th>\n",
+       "      <th>NumeroTicket</th>\n",
+       "      <th>PrecoTicket</th>\n",
+       "      <th>NumeroCabine</th>\n",
+       "      <th>PortoEmbarcacao</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [IdPassageiro, Sobreviveu, Classe, Nome, Sexo, Idade, NumeroIrmaos, NumeroPais, NumeroTicket, PrecoTicket, NumeroCabine, PortoEmbarcacao]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df['Idade'] > 30) & (df['Sexo'] == 'F')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+=======
    "execution_count": 8,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {},
    "outputs": [
     {
@@ -1374,6 +1486,38 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+<<<<<<< HEAD
+       "      <th>855</th>\n",
+       "      <td>856</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Aks, Mrs. Sam (Leah Rosen)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>392091</td>\n",
+       "      <td>9.35</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     IdPassageiro  Sobreviveu  Classe                        Nome    Sexo  \\\n",
+       "855           856           1       3  Aks, Mrs. Sam (Leah Rosen)  female   \n",
+       "\n",
+       "     Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "855   18.0             0           1       392091         9.35          NaN   \n",
+       "\n",
+       "    PortoEmbarcacao  \n",
+       "855               S  "
+      ]
+     },
+     "execution_count": 20,
+=======
        "      <th>1</th>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
@@ -1600,17 +1744,26 @@
       ]
      },
      "execution_count": 8,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+<<<<<<< HEAD
+    "df[df['Nome'].str.contains('Rose')]"
+=======
     "df[(df['Idade'] > 30) & (df['Sexo'] == 'female')]"
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 21,
+=======
    "execution_count": 9,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "ZFZ7LOT6S_my",
     "outputId": "e41628cd-501b-4009-d643-c91f7fe9d7e4"
@@ -1619,10 +1772,17 @@
     {
      "data": {
       "text/plain": [
+<<<<<<< HEAD
+       "(0, 12)"
+      ]
+     },
+     "execution_count": 21,
+=======
        "(103, 12)"
       ]
      },
      "execution_count": 9,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1635,6 +1795,9 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 22,
+=======
    "execution_count": 10,
    "metadata": {},
    "outputs": [
@@ -1716,6 +1879,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "BpueQ0G0S_my",
     "outputId": "ba8ca09f-6788-4332-9a11-786aa02700e4"
@@ -1724,10 +1888,17 @@
     {
      "data": {
       "text/plain": [
+<<<<<<< HEAD
+       "(10, 12)"
+      ]
+     },
+     "execution_count": 22,
+=======
        "(1, 12)"
       ]
      },
      "execution_count": 11,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1736,12 +1907,19 @@
     "# Selecionando entradas que possuem uma determinada 'string'\n",
     "# Vamos procurar por nomes que possuam 'Good'\n",
     "\n",
+<<<<<<< HEAD
+    "df_filtrado = df[df['Nome'].str.contains('Ros')]\n",
+=======
     "df_filtrado = df[df['Nome'].str.contains('Jack')]\n",
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
     "df_filtrado.shape"
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 23,
+=======
    "execution_count": 12,
    "metadata": {},
    "outputs": [
@@ -1823,6 +2001,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "ODR1BQRyS_my",
     "outputId": "a2289b09-9eb0-460f-da3b-265bfaf400fb"
@@ -1830,11 +2009,151 @@
    "outputs": [
     {
      "data": {
+<<<<<<< HEAD
+      "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>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Sexo</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>NumeroIrmaos</th>\n",
+       "      <th>NumeroPais</th>\n",
+       "      <th>NumeroTicket</th>\n",
+       "      <th>PrecoTicket</th>\n",
+       "      <th>NumeroCabine</th>\n",
+       "      <th>PortoEmbarcacao</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Moran, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Rice, Master. Eugene</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>382652</td>\n",
+       "      <td>29.1250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330923</td>\n",
+       "      <td>8.0292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330959</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Glynn, Miss. Mary Agatha</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>335677</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    IdPassageiro  Sobreviveu  Classe                           Nome    Sexo  \\\n",
+       "5              6           0       3               Moran, Mr. James    male   \n",
+       "16            17           0       3           Rice, Master. Eugene    male   \n",
+       "22            23           1       3    McGowan, Miss. Anna \"Annie\"  female   \n",
+       "28            29           1       3  O'Dwyer, Miss. Ellen \"Nellie\"  female   \n",
+       "32            33           1       3       Glynn, Miss. Mary Agatha  female   \n",
+       "\n",
+       "    Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "5     NaN             0           0       330877       8.4583          NaN   \n",
+       "16    2.0             4           1       382652      29.1250          NaN   \n",
+       "22   15.0             0           0       330923       8.0292          NaN   \n",
+       "28    NaN             0           0       330959       7.8792          NaN   \n",
+       "32    NaN             0           0       335677       7.7500          NaN   \n",
+       "\n",
+       "   PortoEmbarcacao  \n",
+       "5                Q  \n",
+       "16               Q  \n",
+       "22               Q  \n",
+       "28               Q  \n",
+       "32               Q  "
+      ]
+     },
+     "execution_count": 23,
+=======
       "text/plain": [
        "(77, 12)"
       ]
      },
      "execution_count": 13,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1861,7 +2180,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 24,
+=======
    "execution_count": 14,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "W-Ktv45VS_mz",
     "outputId": "00f9464e-df1b-4cd2-c0b3-30eac23016f1"
@@ -1873,7 +2196,11 @@
        "(491, 12)"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 24,
+=======
      "execution_count": 14,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1886,7 +2213,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 25,
+=======
    "execution_count": 15,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "uVSmQ1zeS_mz",
     "outputId": "555cf64e-c5a6-4b3d-d69a-89fb77dd1012"
@@ -1898,14 +2229,22 @@
        "(707, 12)"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 25,
+=======
      "execution_count": 15,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# A função query também pode ser utilizada para filtrar baseada em uma lista de valores\n",
+<<<<<<< HEAD
+    "df_consulta = df.query(\"Classe in (1,3)\")\n",
+=======
     "df_consulta = df.query(\"Classe in (1, 3)\")\n",
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
     "df_consulta.shape"
    ]
   },
@@ -2100,7 +2439,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 26,
+=======
    "execution_count": 17,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "cr5Lt68uS_m4",
     "outputId": "8786bf85-9a78-4ff2-a2e5-12a514640843"
@@ -2209,7 +2552,11 @@
        "[891 rows x 2 columns]"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 26,
+=======
      "execution_count": 17,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2235,7 +2582,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 27,
+=======
    "execution_count": 18,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "NzQE8tnOS_m4",
     "outputId": "7e798f09-5b8e-4561-d1ab-bc2b460fd619"
@@ -2344,7 +2695,11 @@
        "[891 rows x 2 columns]"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 27,
+=======
      "execution_count": 18,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2355,7 +2710,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 28,
+=======
    "execution_count": 19,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "PSjsxwSuS_m4",
     "outputId": "cb5f5ab9-92b9-4de7-f718-90515d59ab14"
@@ -2398,6 +2757,19 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+<<<<<<< HEAD
+       "      <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",
+=======
        "      <th>10</th>\n",
        "      <td>11</td>\n",
        "      <td>1</td>\n",
@@ -2439,10 +2811,26 @@
        "      <td>0</td>\n",
        "      <td>A/5. 2151</td>\n",
        "      <td>8.0500</td>\n",
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+<<<<<<< HEAD
+       "      <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",
+=======
        "      <th>13</th>\n",
        "      <td>14</td>\n",
        "      <td>0</td>\n",
@@ -2471,12 +2859,33 @@
        "      <td>7.8542</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
+<<<<<<< HEAD
+       "   IdPassageiro  Sobreviveu  Classe  \\\n",
+       "0             1           0       3   \n",
+       "1             2           1       1   \n",
+       "\n",
+       "                                                Nome    Sexo  Idade  \\\n",
+       "0                            Braund, Mr. Owen Harris    male   22.0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
+       "\n",
+       "   NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "0             1           0    A/5 21171       7.2500          NaN   \n",
+       "1             1           0     PC 17599      71.2833          C85   \n",
+       "\n",
+       "  PortoEmbarcacao  \n",
+       "0               S  \n",
+       "1               C  "
+      ]
+     },
+     "execution_count": 28,
+=======
        "    IdPassageiro  Sobreviveu  Classe                                  Nome  \\\n",
        "10            11           1       3       Sandstrom, Miss. Marguerite Rut   \n",
        "11            12           1       1              Bonnell, Miss. Elizabeth   \n",
@@ -2500,6 +2909,7 @@
       ]
      },
      "execution_count": 19,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2523,25 +2933,1106 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# Alomoço"
+    "import pandas as pd"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"300\"\n",
-       "            height=\"250\"\n",
-       "            src=\"https://giphy.com/embed/7AKbdZiyTx98fPHG0Z\"\n",
+      "text/plain": [
+       "(0, 12)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#filtro condição unica\n",
+    "\n",
+    "df_filtro1 = df[df['Sobreviveu']> 3]\n",
+    "df_filtro1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 12)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#filtro com mais de uma condição\n",
+    "df_filtro = df[(df['Sobreviveu']>2) & (df['Sexo']=='famale')]\n",
+    "df_filtro.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Sexo</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>NumeroIrmaos</th>\n",
+       "      <th>NumeroPais</th>\n",
+       "      <th>NumeroTicket</th>\n",
+       "      <th>PrecoTicket</th>\n",
+       "      <th>NumeroCabine</th>\n",
+       "      <th>PortoEmbarcacao</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Moran, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>McCarthy, Mr. Timothy J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17463</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>E46</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Palsson, Master. Gosta Leonard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>347742</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>237736</td>\n",
+       "      <td>30.0708</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
+       "      <td>female</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>PP 9549</td>\n",
+       "      <td>16.7000</td>\n",
+       "      <td>G6</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\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",
+       "    </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>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",
+       "    </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>31.2750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</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>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",
+       "    </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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Rice, Master. Eugene</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>382652</td>\n",
+       "      <td>29.1250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Williams, Mr. Charles Eugene</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>244373</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>345763</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Masselmani, Mrs. Fatima</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2649</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Fynney, Mr. Joseph J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>239865</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Beesley, Mr. Lawrence</td>\n",
+       "      <td>male</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>248698</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>D56</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330923</td>\n",
+       "      <td>8.0292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sloper, Mr. William Thompson</td>\n",
+       "      <td>male</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113788</td>\n",
+       "      <td>35.5000</td>\n",
+       "      <td>A6</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Palsson, Miss. Torborg Danira</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>347077</td>\n",
+       "      <td>31.3875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Emir, Mr. Farred Chehab</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2631</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fortune, Mr. Charles Alexander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>19950</td>\n",
+       "      <td>263.0000</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330959</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Todoroff, Mr. Lalio</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>349216</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    IdPassageiro  Sobreviveu  Classe  \\\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",
+       "5              6           0       3   \n",
+       "6              7           0       1   \n",
+       "7              8           0       3   \n",
+       "8              9           1       3   \n",
+       "9             10           1       2   \n",
+       "10            11           1       3   \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",
+       "16            17           0       3   \n",
+       "17            18           1       2   \n",
+       "18            19           0       3   \n",
+       "19            20           1       3   \n",
+       "20            21           0       2   \n",
+       "21            22           1       2   \n",
+       "22            23           1       3   \n",
+       "23            24           1       1   \n",
+       "24            25           0       3   \n",
+       "25            26           1       3   \n",
+       "26            27           0       3   \n",
+       "27            28           0       1   \n",
+       "28            29           1       3   \n",
+       "29            30           0       3   \n",
+       "\n",
+       "                                                 Nome    Sexo  Idade  \\\n",
+       "0                             Braund, Mr. Owen Harris    male   22.0   \n",
+       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
+       "2                              Heikkinen, Miss. Laina  female   26.0   \n",
+       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35.0   \n",
+       "4                            Allen, Mr. William Henry    male   35.0   \n",
+       "5                                    Moran, Mr. James    male    NaN   \n",
+       "6                             McCarthy, Mr. Timothy J    male   54.0   \n",
+       "7                      Palsson, Master. Gosta Leonard    male    2.0   \n",
+       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27.0   \n",
+       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female   14.0   \n",
+       "10                    Sandstrom, Miss. Marguerite Rut  female    4.0   \n",
+       "11                           Bonnell, Miss. Elizabeth  female   58.0   \n",
+       "12                     Saundercock, Mr. William Henry    male   20.0   \n",
+       "13                        Andersson, Mr. Anders Johan    male   39.0   \n",
+       "14               Vestrom, Miss. Hulda Amanda Adolfina  female   14.0   \n",
+       "15                   Hewlett, Mrs. (Mary D Kingcome)   female   55.0   \n",
+       "16                               Rice, Master. Eugene    male    2.0   \n",
+       "17                       Williams, Mr. Charles Eugene    male    NaN   \n",
+       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female   31.0   \n",
+       "19                            Masselmani, Mrs. Fatima  female    NaN   \n",
+       "20                               Fynney, Mr. Joseph J    male   35.0   \n",
+       "21                              Beesley, Mr. Lawrence    male   34.0   \n",
+       "22                        McGowan, Miss. Anna \"Annie\"  female   15.0   \n",
+       "23                       Sloper, Mr. William Thompson    male   28.0   \n",
+       "24                      Palsson, Miss. Torborg Danira  female    8.0   \n",
+       "25  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female   38.0   \n",
+       "26                            Emir, Mr. Farred Chehab    male    NaN   \n",
+       "27                     Fortune, Mr. Charles Alexander    male   19.0   \n",
+       "28                      O'Dwyer, Miss. Ellen \"Nellie\"  female    NaN   \n",
+       "29                                Todoroff, Mr. Lalio    male    NaN   \n",
+       "\n",
+       "    NumeroIrmaos  NumeroPais      NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "0              1           0         A/5 21171       7.2500          NaN   \n",
+       "1              1           0          PC 17599      71.2833          C85   \n",
+       "2              0           0  STON/O2. 3101282       7.9250          NaN   \n",
+       "3              1           0            113803      53.1000         C123   \n",
+       "4              0           0            373450       8.0500          NaN   \n",
+       "5              0           0            330877       8.4583          NaN   \n",
+       "6              0           0             17463      51.8625          E46   \n",
+       "7              3           1            349909      21.0750          NaN   \n",
+       "8              0           2            347742      11.1333          NaN   \n",
+       "9              1           0            237736      30.0708          NaN   \n",
+       "10             1           1           PP 9549      16.7000           G6   \n",
+       "11             0           0            113783      26.5500         C103   \n",
+       "12             0           0         A/5. 2151       8.0500          NaN   \n",
+       "13             1           5            347082      31.2750          NaN   \n",
+       "14             0           0            350406       7.8542          NaN   \n",
+       "15             0           0            248706      16.0000          NaN   \n",
+       "16             4           1            382652      29.1250          NaN   \n",
+       "17             0           0            244373      13.0000          NaN   \n",
+       "18             1           0            345763      18.0000          NaN   \n",
+       "19             0           0              2649       7.2250          NaN   \n",
+       "20             0           0            239865      26.0000          NaN   \n",
+       "21             0           0            248698      13.0000          D56   \n",
+       "22             0           0            330923       8.0292          NaN   \n",
+       "23             0           0            113788      35.5000           A6   \n",
+       "24             3           1            349909      21.0750          NaN   \n",
+       "25             1           5            347077      31.3875          NaN   \n",
+       "26             0           0              2631       7.2250          NaN   \n",
+       "27             3           2             19950     263.0000  C23 C25 C27   \n",
+       "28             0           0            330959       7.8792          NaN   \n",
+       "29             0           0            349216       7.8958          NaN   \n",
+       "\n",
+       "   PortoEmbarcacao  \n",
+       "0                S  \n",
+       "1                C  \n",
+       "2                S  \n",
+       "3                S  \n",
+       "4                S  \n",
+       "5                Q  \n",
+       "6                S  \n",
+       "7                S  \n",
+       "8                S  \n",
+       "9                C  \n",
+       "10               S  \n",
+       "11               S  \n",
+       "12               S  \n",
+       "13               S  \n",
+       "14               S  \n",
+       "15               S  \n",
+       "16               Q  \n",
+       "17               S  \n",
+       "18               S  \n",
+       "19               C  \n",
+       "20               S  \n",
+       "21               S  \n",
+       "22               Q  \n",
+       "23               S  \n",
+       "24               S  \n",
+       "25               S  \n",
+       "26               C  \n",
+       "27               S  \n",
+       "28               Q  \n",
+       "29               S  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_amostra = df[:30]\n",
+    "df_amostra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Sexo</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>NumeroIrmaos</th>\n",
+       "      <th>NumeroPais</th>\n",
+       "      <th>NumeroTicket</th>\n",
+       "      <th>PrecoTicket</th>\n",
+       "      <th>NumeroCabine</th>\n",
+       "      <th>PortoEmbarcacao</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>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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>133</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Robins, Mrs. Alexander A (Grace Charity Laury)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5. 3337</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>154</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
+       "      <td>male</td>\n",
+       "      <td>40.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>A/5. 851</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>205</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Cohen, Mr. Gurshon \"Gus\"</td>\n",
+       "      <td>male</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 3540</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>213</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Perkin, Mr. John Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21174</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>228</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lovell, Mr. John Hall (\"Henry\")</td>\n",
+       "      <td>male</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21173</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>254</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lobb, Mr. William Arthur</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5. 3336</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>284</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dorking, Mr. Edward Arthur</td>\n",
+       "      <td>male</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5. 10482</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>305</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 2466</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>321</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dennis, Mr. Samuel</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21172</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>421</th>\n",
+       "      <td>422</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Charters, Mr. David</td>\n",
+       "      <td>male</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5. 13032</td>\n",
+       "      <td>7.7333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>455</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Peduzzi, Mr. Joseph</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 2817</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>483</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Rouse, Mr. Richard Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 3594</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Elsbury, Mr. William James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 3902</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>617</th>\n",
+       "      <td>618</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lobb, Mrs. William Arthur (Cordelia K Stanlick)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5. 3336</td>\n",
+       "      <td>16.1000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>669</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Cook, Mr. Jacob</td>\n",
+       "      <td>male</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 3536</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     IdPassageiro  Sobreviveu  Classe  \\\n",
+       "0               1           0       3   \n",
+       "12             13           0       3   \n",
+       "132           133           0       3   \n",
+       "153           154           0       3   \n",
+       "204           205           1       3   \n",
+       "212           213           0       3   \n",
+       "227           228           0       3   \n",
+       "253           254           0       3   \n",
+       "283           284           1       3   \n",
+       "304           305           0       3   \n",
+       "320           321           0       3   \n",
+       "421           422           0       3   \n",
+       "454           455           0       3   \n",
+       "482           483           0       3   \n",
+       "592           593           0       3   \n",
+       "617           618           0       3   \n",
+       "668           669           0       3   \n",
+       "\n",
+       "                                                Nome    Sexo  Idade  \\\n",
+       "0                            Braund, Mr. Owen Harris    male   22.0   \n",
+       "12                    Saundercock, Mr. William Henry    male   20.0   \n",
+       "132   Robins, Mrs. Alexander A (Grace Charity Laury)  female   47.0   \n",
+       "153                  van Billiard, Mr. Austin Blyler    male   40.5   \n",
+       "204                         Cohen, Mr. Gurshon \"Gus\"    male   18.0   \n",
+       "212                           Perkin, Mr. John Henry    male   22.0   \n",
+       "227                  Lovell, Mr. John Hall (\"Henry\")    male   20.5   \n",
+       "253                         Lobb, Mr. William Arthur    male   30.0   \n",
+       "283                       Dorking, Mr. Edward Arthur    male   19.0   \n",
+       "304                Williams, Mr. Howard Hugh \"Harry\"    male    NaN   \n",
+       "320                               Dennis, Mr. Samuel    male   22.0   \n",
+       "421                              Charters, Mr. David    male   21.0   \n",
+       "454                              Peduzzi, Mr. Joseph    male    NaN   \n",
+       "482                         Rouse, Mr. Richard Henry    male   50.0   \n",
+       "592                       Elsbury, Mr. William James    male   47.0   \n",
+       "617  Lobb, Mrs. William Arthur (Cordelia K Stanlick)  female   26.0   \n",
+       "668                                  Cook, Mr. Jacob    male   43.0   \n",
+       "\n",
+       "     NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "0               1           0    A/5 21171       7.2500          NaN   \n",
+       "12              0           0    A/5. 2151       8.0500          NaN   \n",
+       "132             1           0    A/5. 3337      14.5000          NaN   \n",
+       "153             0           2     A/5. 851      14.5000          NaN   \n",
+       "204             0           0     A/5 3540       8.0500          NaN   \n",
+       "212             0           0    A/5 21174       7.2500          NaN   \n",
+       "227             0           0    A/5 21173       7.2500          NaN   \n",
+       "253             1           0    A/5. 3336      16.1000          NaN   \n",
+       "283             0           0   A/5. 10482       8.0500          NaN   \n",
+       "304             0           0     A/5 2466       8.0500          NaN   \n",
+       "320             0           0    A/5 21172       7.2500          NaN   \n",
+       "421             0           0   A/5. 13032       7.7333          NaN   \n",
+       "454             0           0     A/5 2817       8.0500          NaN   \n",
+       "482             0           0     A/5 3594       8.0500          NaN   \n",
+       "592             0           0     A/5 3902       7.2500          NaN   \n",
+       "617             1           0    A/5. 3336      16.1000          NaN   \n",
+       "668             0           0     A/5 3536       8.0500          NaN   \n",
+       "\n",
+       "    PortoEmbarcacao  \n",
+       "0                 S  \n",
+       "12                S  \n",
+       "132               S  \n",
+       "153               S  \n",
+       "204               S  \n",
+       "212               S  \n",
+       "227               S  \n",
+       "253               S  \n",
+       "283               S  \n",
+       "304               S  \n",
+       "320               S  \n",
+       "421               Q  \n",
+       "454               S  \n",
+       "482               S  \n",
+       "592               S  \n",
+       "617               S  \n",
+       "668               S  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_string = df[df['NumeroTicket'].str.contains('A/5')]\n",
+    "df_string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Alomoço"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 3,
+=======
+   "execution_count": 20,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"300\"\n",
+       "            height=\"250\"\n",
+       "            src=\"https://giphy.com/embed/7AKbdZiyTx98fPHG0Z\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "            \n",
@@ -2583,7 +4074,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 10,
+=======
    "execution_count": 21,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "noSCxvLQS_m6",
     "outputId": "fd434c19-19d3-4395-a595-83703e140d1b"
@@ -2592,10 +4087,17 @@
     {
      "data": {
       "text/plain": [
+<<<<<<< HEAD
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000024016F53DD0>"
+      ]
+     },
+     "execution_count": 10,
+=======
        "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002BED4E60F50>"
       ]
      },
      "execution_count": 21,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2603,11 +4105,45 @@
    "source": [
     "# Agrupar passageiros por classe\n",
     "dado_agrupado = df.groupby('Classe')\n",
+<<<<<<< HEAD
+    "dado_agrupado\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Contagem de passageiros\n",
+      "Classe\n",
+      "1    216\n",
+      "2    184\n",
+      "3    491\n",
+      "Name: IdPassageiro, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Contagem de passageiros em cada classe, usando os dados agrupados no passo anterior\n",
+    "contagem_passageiros = dado_agrupado['IdPassageiro'].count()\n",
+    "\n",
+    "print(\"Contagem de passageiros\")\n",
+    "print(contagem_passageiros)"
+=======
     "dado_agrupado"
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 34,
+=======
    "execution_count": 30,
    "metadata": {},
    "outputs": [
@@ -2660,12 +4196,25 @@
   {
    "cell_type": "code",
    "execution_count": 24,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "UxbPOZJfS_m7",
     "outputId": "f2031638-fbad-45b1-94a6-1415795d6800"
    },
    "outputs": [
     {
+<<<<<<< HEAD
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Média de Idade de passageiros por classe\n",
+      "Classe\n",
+      "1    38.233441\n",
+      "2    29.877630\n",
+      "3    25.140620\n",
+      "Name: Idade, dtype: float64\n"
+     ]
+=======
      "data": {
       "text/plain": [
        "Classe\n",
@@ -2678,6 +4227,7 @@
      "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
     }
    ],
    "source": [
@@ -2688,6 +4238,9 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 35,
+=======
    "execution_count": 41,
    "metadata": {},
    "outputs": [
@@ -2907,6 +4460,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "jxk2rE1hS_m7",
     "outputId": "55e2c10a-49b5-4a8a-f979-162b4bc89e1b"
@@ -3194,7 +4748,94 @@
     "\n",
     "- Agrupar passageiros por genero\n",
     "- Obter a média de idade de passageiros por genero\n",
+<<<<<<< HEAD
+    "- Obter o valor máximo e mínimo pago por passageiros em cada genero\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Contagem de passageiros\n",
+      "Sexo\n",
+      "female    314\n",
+      "male      577\n",
+      "Name: IdPassageiro, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "dado_agrupado = df.groupby('Sexo')\n",
+    "dado_agrupado\n",
+    "contagem_passageiros = dado_agrupado['IdPassageiro'].count()\n",
+    "\n",
+    "print(\"Contagem de passageiros\")\n",
+    "print(contagem_passageiros)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Média de Idade de passageiros por classe\n",
+      "Sexo\n",
+      "female    27.915709\n",
+      "male      30.726645\n",
+      "Name: Idade, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "media_idade = dado_agrupado['Idade'].mean()\n",
+    "\n",
+    "print(\"Média de Idade de passageiros por classe\")\n",
+    "print(media_idade)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valor máximo do preço da passagem por classe\n",
+      "Sexo\n",
+      "female    512.3292\n",
+      "male      512.3292\n",
+      "Name: PrecoTicket, dtype: float64\n",
+      "valor minimo do preco da passagem por classe\n",
+      "Sexo\n",
+      "female    6.75\n",
+      "male      0.00\n",
+      "Name: PrecoTicket, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#obter o valor maximo e minimo pago por passageiros em cada classe\n",
+    "valor_max = dado_agrupado['PrecoTicket'].max()\n",
+    "valor_min = dado_agrupado['PrecoTicket'].min()\n",
+    "\n",
+    "print(\"Valor máximo do preço da passagem por classe\")\n",
+    "print(valor_max)\n",
+    "print(\"valor minimo do preco da passagem por classe\")\n",
+    "print(valor_min)"
+=======
     "- Obter o valor máximo e mínimo pago por passageiros em cada genero"
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    ]
   },
   {
@@ -3241,7 +4882,463 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.1.2 -> 23.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting matplotlib\n",
+      "  Downloading matplotlib-3.8.0-cp311-cp311-win_amd64.whl (7.6 MB)\n",
+      "                                              0.0/7.6 MB ? eta -:--:--\n",
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+      "Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
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+      "Collecting pillow>=6.2.0 (from matplotlib)\n",
+      "  Downloading Pillow-10.1.0-cp311-cp311-win_amd64.whl (2.6 MB)\n",
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+      "Collecting pyparsing>=2.3.1 (from matplotlib)\n",
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+      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
+      "Successfully installed contourpy-1.1.1 cycler-0.12.1 fonttools-4.43.1 kiwisolver-1.4.5 matplotlib-3.8.0 pillow-10.1.0 pyparsing-3.1.1\n",
+      "Collecting matplotlib\n",
+      "  Using cached matplotlib-3.8.0-cp311-cp311-win_amd64.whl (7.6 MB)\n",
+      "Collecting contourpy>=1.0.1 (from matplotlib)\n",
+      "  Using cached contourpy-1.1.1-cp311-cp311-win_amd64.whl (480 kB)\n",
+      "Collecting cycler>=0.10 (from matplotlib)\n",
+      "  Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
+      "  Using cached fonttools-4.43.1-cp311-cp311-win_amd64.whl (2.1 MB)\n",
+      "Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
+      "  Using cached kiwisolver-1.4.5-cp311-cp311-win_amd64.whl (56 kB)\n",
+      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\taian\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib) (1.26.0)\n",
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+      "Collecting pillow>=6.2.0 (from matplotlib)\n",
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+      "     ---------------------------------------  2.6/2.6 MB 594.2 kB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.6/2.6 MB 594.2 kB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.6/2.6 MB 594.2 kB/s eta 0:00:01\n",
+      "     ---------------------------------------  2.6/2.6 MB 594.2 kB/s eta 0:00:01\n",
+      "     ---------------------------------------- 2.6/2.6 MB 547.1 kB/s eta 0:00:00\n",
+      "Collecting pyparsing>=2.3.1 (from matplotlib)\n",
+      "  Using cached pyparsing-3.1.1-py3-none-any.whl (103 kB)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\taian\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\taian\\appdata\\roaming\\python\\python311\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
+      "Successfully installed contourpy-1.1.1 cycler-0.12.1 fonttools-4.43.1 kiwisolver-1.4.5 matplotlib-3.8.0 pillow-10.1.0 pyparsing-3.1.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.1.2 -> 23.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+=======
    "execution_count": 58,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3252,6 +5349,26 @@
    },
    "outputs": [
     {
+<<<<<<< HEAD
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Matplotlib is building the font cache; this may take a moment.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x240246b2710>]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+=======
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "data": {
       "image/png": 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",
       "text/plain": [
@@ -3283,7 +5400,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 28,
+=======
    "execution_count": 59,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "bmZg5LjvS_nJ",
     "outputId": "f3874a08-9ec1-4235-8db1-2250deeb54ea"
@@ -3292,6 +5413,24 @@
     {
      "data": {
       "text/plain": [
+<<<<<<< HEAD
+       "<Axes: xlabel='Classe'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+=======
        "Classe\n",
        "3    491\n",
        "1    216\n",
@@ -3302,6 +5441,7 @@
      "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
     }
    ],
    "source": [
@@ -3320,6 +5460,9 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 29,
+=======
    "execution_count": 62,
    "metadata": {},
    "outputs": [
@@ -3392,6 +5535,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "zpZmI2e9S_nJ",
     "outputId": "3f16a4f4-3039-4426-ac46-d0f8178d87a6"
@@ -3403,7 +5547,11 @@
        "Text(0.5, 1.0, 'Número de Passageiros por Classe')"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 29,
+=======
      "execution_count": 68,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3430,7 +5578,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 30,
+=======
    "execution_count": 69,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "j7lUqI3BS_nK",
     "outputId": "758058f2-185d-412d-835e-38d8ca773f3e"
@@ -3461,7 +5613,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 31,
+=======
    "execution_count": 74,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "RzV9cvoOS_nK",
     "outputId": "eea06c73-5805-427d-f2b1-b82d3481b8a8"
@@ -3473,13 +5629,21 @@
        "Text(0.5, 1.0, 'Distribuição de Idade')"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 31,
+=======
      "execution_count": 74,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
+<<<<<<< HEAD
+      "image/png": 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",
+=======
       "image/png": 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",
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3655,7 +5819,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 2,
+=======
    "execution_count": 92,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "6qrRb5EIS_nL"
    },
@@ -3684,7 +5852,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 3,
+=======
    "execution_count": 93,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "suI74yqZS_nL",
     "outputId": "eb87a166-4d92-4068-e5ad-e7ce283ed244"
@@ -3710,7 +5882,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 4,
+=======
    "execution_count": 98,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "DxCmoOkhS_nM",
     "outputId": "2c7699a1-519f-4b81-d4e1-836895767375"
@@ -3720,9 +5896,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+<<<<<<< HEAD
+      "[[1, 2, 3], [4, 5, 6]]\n",
+=======
       "Uma lista de listas\n",
       "[[1, 2, 3], [4, 5, 6]]\n",
       "Array bidimensional\n",
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
       "[[1 2 3]\n",
       " [4 5 6]]\n"
      ]
@@ -3741,6 +5921,20 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 5,
+   "metadata": {
+    "id": "KZ-JzM6US_nM",
+    "outputId": "9c7580bf-627b-4926-ed86-8441bb436861"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10 20 30 40 50]\n"
+     ]
+=======
    "execution_count": 121,
    "metadata": {},
    "outputs": [
@@ -3753,6 +5947,7 @@
      "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
     }
    ],
    "source": [
@@ -3993,6 +6188,9 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 6,
+=======
    "execution_count": 106,
    "metadata": {},
    "outputs": [
@@ -4061,6 +6259,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    "metadata": {
     "id": "LHfl6fALS_nN",
     "outputId": "5805c47a-4db3-4a7f-8790-dad45dc7ff76"
@@ -4077,7 +6276,11 @@
        "dtype: int32"
       ]
      },
+<<<<<<< HEAD
+     "execution_count": 6,
+=======
      "execution_count": 107,
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
      "metadata": {},
      "output_type": "execute_result"
     }