From 1ebf58d06ff7d4fc12784cfff0471fe2c0c1622b Mon Sep 17 00:00:00 2001
From: Taianne <tairocha06@gmail.com>
Date: Wed, 25 Oct 2023 20:09:50 -0300
Subject: [PATCH 1/2] exercicio para casa

---
 exercicios/para-casa/paracasa.ipynb           |  809 ++++
 .../para-casa}/titanic.csv                    |    0
 material/aula_s12.ipynb                       | 3385 ++++++++++++++++-
 3 files changed, 4134 insertions(+), 60 deletions(-)
 create mode 100644 exercicios/para-casa/paracasa.ipynb
 rename {material => exercicios/para-casa}/titanic.csv (100%)

diff --git a/exercicios/para-casa/paracasa.ipynb b/exercicios/para-casa/paracasa.ipynb
new file mode 100644
index 0000000..45156cf
--- /dev/null
+++ b/exercicios/para-casa/paracasa.ipynb
@@ -0,0 +1,809 @@
+{
+ "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 homens"
+   ]
+  }
+ ],
+ "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 690a7b6..43633f9 100644
--- a/material/aula_s12.ipynb
+++ b/material/aula_s12.ipynb
@@ -114,16 +114,270 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "id": "_eorJ3GNS_mm",
     "outputId": "14f2b233-cc14-4f8a-a041-98b4cb90d2cc"
    },
-   "outputs": [],
+   "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": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "df = pd.read_csv(\"data/titanic.csv\")\n",
+    "df = pd.read_csv(\"titanic.csv\")\n",
     "df"
    ]
   },
@@ -138,12 +392,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "id": "ZuehjTU7S_mt",
     "outputId": "6ce66e1f-01d4-4889-f97f-50d5edae71e6"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket', 'NumeroCabine', 'PortoEmbarcacao']\n"
+     ]
+    }
+   ],
    "source": [
     "# Resolução\n",
     "\n",
@@ -173,12 +435,279 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "id": "h6tvl5HPS_mw",
     "outputId": "4639f186-8484-4780-f4dc-0a29f3cc759e"
    },
-   "outputs": [],
+   "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>...</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": [
+       "     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",
+       "..            ...         ...     ...   \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",
+       "                                                  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",
+       "..                                                 ...     ...    ...   \n",
+       "886                              Montvila, Rev. Juozas    male   27.0   \n",
+       "887                       Graham, Miss. Margaret Edith  female   19.0   \n",
+       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female    NaN   \n",
+       "889                              Behr, Mr. Karl Howell    male   26.0   \n",
+       "890                                Dooley, Mr. Patrick    male   32.0   \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",
+       "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",
+       "..            ...         ...               ...          ...          ...   \n",
+       "886             0           0            211536      13.0000          NaN   \n",
+       "887             0           0            112053      30.0000          B42   \n",
+       "888             1           2        W./C. 6607      23.4500          NaN   \n",
+       "889             0           0            111369      30.0000         C148   \n",
+       "890             0           0            370376       7.7500          NaN   \n",
+       "\n",
+       "    PortoEmbarcacao  \n",
+       "0                 S  \n",
+       "1                 C  \n",
+       "2                 S  \n",
+       "3                 S  \n",
+       "4                 S  \n",
+       "..              ...  \n",
+       "886               S  \n",
+       "887               S  \n",
+       "888               S  \n",
+       "889               C  \n",
+       "890               Q  \n",
+       "\n",
+       "[891 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.columns = novas_colunas\n",
     "df"
@@ -203,12 +732,279 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "id": "gZKfzWYAS_mx",
     "outputId": "cdc7a73b-0f3b-42ce-cdf2-f8322b15e686"
    },
-   "outputs": [],
+   "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>...</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": [
+       "     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",
+       "..            ...         ...     ...   \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",
+       "                                                  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",
+       "..                                                 ...     ...    ...   \n",
+       "886                              Montvila, Rev. Juozas    male   27.0   \n",
+       "887                       Graham, Miss. Margaret Edith  female   19.0   \n",
+       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female    NaN   \n",
+       "889                              Behr, Mr. Karl Howell    male   26.0   \n",
+       "890                                Dooley, Mr. Patrick    male   32.0   \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",
+       "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",
+       "..            ...         ...               ...          ...          ...   \n",
+       "886             0           0            211536      13.0000          NaN   \n",
+       "887             0           0            112053      30.0000          B42   \n",
+       "888             1           2        W./C. 6607      23.4500          NaN   \n",
+       "889             0           0            111369      30.0000         C148   \n",
+       "890             0           0            370376       7.7500          NaN   \n",
+       "\n",
+       "    PortoEmbarcacao  \n",
+       "0                 S  \n",
+       "1                 C  \n",
+       "2                 S  \n",
+       "3                 S  \n",
+       "4                 S  \n",
+       "..              ...  \n",
+       "886               S  \n",
+       "887               S  \n",
+       "888               S  \n",
+       "889               C  \n",
+       "890               Q  \n",
+       "\n",
+       "[891 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# dataframe originial\n",
     "df"
@@ -216,12 +1012,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "id": "xzHvkQRrS_mx",
     "outputId": "ca4d371d-386e-433d-e49b-6e5a6749f767"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(305, 12)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Vamos começar com um filtro baseado em uma única condição: idade > 30\n",
     "df_filtrado = df[df['Idade'] > 30]\n",
@@ -230,12 +1037,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "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,
+   "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>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,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Nome'].str.contains('Rose')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {
     "id": "ZFZ7LOT6S_my",
     "outputId": "e41628cd-501b-4009-d643-c91f7fe9d7e4"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 12)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Agora um filtro com múltiplas condições\n",
     "df_filtrado = df[(df['Idade'] > 30) & (df['Sexo'] == 'F')]\n",
@@ -244,28 +1202,182 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "id": "BpueQ0G0S_my",
     "outputId": "ba8ca09f-6788-4332-9a11-786aa02700e4"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 12)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Selecionando entradas que possuem uma determinada 'string'\n",
     "# Vamos procurar por nomes que possuam 'Good'\n",
     "\n",
-    "df_filtrado = df[df['Nome'].str.contains('Good')]\n",
+    "df_filtrado = df[df['Nome'].str.contains('Ros')]\n",
     "df_filtrado.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {
     "id": "ODR1BQRyS_my",
     "outputId": "a2289b09-9eb0-460f-da3b-265bfaf400fb"
    },
-   "outputs": [],
+   "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>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,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Por último, utilizando a função isin()\n",
     "df_filtrado = df[df['PortoEmbarcacao'].isin(['Q'])]\n",
@@ -288,30 +1400,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {
     "id": "W-Ktv45VS_mz",
     "outputId": "00f9464e-df1b-4cd2-c0b3-30eac23016f1"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(491, 12)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Para ilustrar essa função, vamos procurar por pessoas que estavam na classe 3:\n",
     "df_consulta = df.query(\"Classe == 3\")\n",
-    "df_consulta.head()\n"
+    "df_consulta.shape\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {
     "id": "uVSmQ1zeS_mz",
     "outputId": "555cf64e-c5a6-4b3d-d69a-89fb77dd1012"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(707, 12)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# A função query também pode ser utilizada para filtrar baseada em uma lista de valores\n",
     "df_consulta = df.query(\"Classe in (1,3)\")\n",
-    "df_consulta.head()"
+    "df_consulta.shape"
    ]
   },
   {
@@ -355,12 +1489,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {
     "id": "cr5Lt68uS_m4",
     "outputId": "8786bf85-9a78-4ff2-a2e5-12a514640843"
    },
-   "outputs": [],
+   "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>Sobreviveu</th>\n",
+       "      <th>Idade</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>1</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>0</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Sobreviveu  Idade\n",
+       "0             0   22.0\n",
+       "1             1   38.0\n",
+       "2             1   26.0\n",
+       "3             1   35.0\n",
+       "4             0   35.0\n",
+       "..          ...    ...\n",
+       "886           0   27.0\n",
+       "887           1   19.0\n",
+       "888           0    NaN\n",
+       "889           1   26.0\n",
+       "890           0   32.0\n",
+       "\n",
+       "[891 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.loc[:, [\"Sobreviveu\", \"Idade\"]]"
    ]
@@ -381,24 +1623,225 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "id": "NzQE8tnOS_m4",
     "outputId": "7e798f09-5b8e-4561-d1ab-bc2b460fd619"
    },
-   "outputs": [],
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>887</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>888</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>889</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>890</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>891</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     IdPassageiro  Sobreviveu\n",
+       "0               1           0\n",
+       "1               2           1\n",
+       "2               3           1\n",
+       "3               4           1\n",
+       "4               5           0\n",
+       "..            ...         ...\n",
+       "886           887           0\n",
+       "887           888           1\n",
+       "888           889           0\n",
+       "889           890           1\n",
+       "890           891           0\n",
+       "\n",
+       "[891 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.iloc[:, [0,1]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "id": "PSjsxwSuS_m4",
     "outputId": "cb5f5ab9-92b9-4de7-f718-90515d59ab14"
    },
-   "outputs": [],
+   "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": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.iloc[[0,1],:]"
    ]
@@ -417,6 +1860,1083 @@
     "- Utilizar a função loc para obter a idade e sobrevivência do passageiro;\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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": {},
@@ -426,7 +2946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,16 +2975,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "id": "noSCxvLQS_m6",
     "outputId": "fd434c19-19d3-4395-a595-83703e140d1b"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000024016F53DD0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Agrupar passageiros por classe\n",
     "dado_agrupado = df.groupby('Classe')\n",
-    "\n",
+    "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",
@@ -474,12 +3027,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {
     "id": "UxbPOZJfS_m7",
     "outputId": "f2031638-fbad-45b1-94a6-1415795d6800"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
    "source": [
     "# Média de Idade de passageiros em cada classe\n",
     "media_idade = dado_agrupado['Idade'].mean()\n",
@@ -490,12 +3056,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {
     "id": "jxk2rE1hS_m7",
     "outputId": "55e2c10a-49b5-4a8a-f979-162b4bc89e1b"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valor máximo do preço da passagem por classe\n",
+      "Classe\n",
+      "1    512.3292\n",
+      "2     73.5000\n",
+      "3     69.5500\n",
+      "Name: PrecoTicket, dtype: float64\n",
+      "Valor mínimo do preço da passagem por classe\n",
+      "Classe\n",
+      "1    0.0\n",
+      "2    0.0\n",
+      "3    0.0\n",
+      "Name: PrecoTicket, dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
     "# Obter o valor máximo e mínimo pago por passageiros em cada classe:\n",
     "valor_max = dado_agrupado['PrecoTicket'].max()\n",
@@ -514,9 +3099,92 @@
    "source": [
     "### Exercício 04 parte 02\n",
     "\n",
-    "- Agrupar passageiros por classe\n",
-    "- Obter a média de idade de passageiros por classe\n",
-    "- Obter o valor máximo e mínimo pago por passageiros em cada classe"
+    "- Agrupar passageiros por genero\n",
+    "- Obter a média de idade de passageiros por genero\n",
+    "- 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)"
    ]
   },
   {
@@ -563,7 +3231,459 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "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": [
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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",
+      "  Downloading pyparsing-3.1.1-py3-none-any.whl (103 kB)\n",
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+      "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",
+      "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",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\taian\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib) (23.2)\n",
+      "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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+      "     ---------------------------------------- 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,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -572,7 +3692,35 @@
     "id": "KS8389rdXMEu",
     "outputId": "386b4ea1-0ef0-4e2a-891a-e1ea0dee0ed5"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "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"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#exemplo mais simples de gráfico:\n",
     "\n",
@@ -596,12 +3744,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "id": "bmZg5LjvS_nJ",
     "outputId": "f3874a08-9ec1-4235-8db1-2250deeb54ea"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<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"
+    }
+   ],
    "source": [
     "# Criação de um gráfico de barras:  matplotlib + Pandas\n",
     "# plot() + bar\n",
@@ -614,12 +3783,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {
     "id": "zpZmI2e9S_nJ",
     "outputId": "3f16a4f4-3039-4426-ac46-d0f8178d87a6"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Número de Passageiros por Classe')"
+      ]
+     },
+     "execution_count": 29,
+     "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"
+    }
+   ],
    "source": [
     "contagem_passageiros = df['Classe'].value_counts()\n",
     "contagem_passageiros.plot(kind='bar')\n",
@@ -631,12 +3821,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "id": "j7lUqI3BS_nK",
     "outputId": "758058f2-185d-412d-835e-38d8ca773f3e"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Criando um gráfico de barras para calcular a taxa de sobrevivência por sexo\n",
     "# plot.bar()\n",
@@ -654,12 +3855,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {
     "id": "RzV9cvoOS_nK",
     "outputId": "eea06c73-5805-427d-f2b1-b82d3481b8a8"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Distribuição de Idade')"
+      ]
+     },
+     "execution_count": 31,
+     "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"
+    }
+   ],
    "source": [
     "# Criando um histograma com a distribuição de idade em passageiros do Titanic\n",
     "\n",
@@ -707,7 +3929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "id": "6qrRb5EIS_nL"
    },
@@ -736,12 +3958,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "id": "suI74yqZS_nL",
     "outputId": "eb87a166-4d92-4068-e5ad-e7ce283ed244"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n",
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
    "source": [
     "# Conversão de lista (estrutura de dados Python) para um array unidimensional com  uma linha e quatro colunas:\n",
     "lista_1 = [1, 2, 3, 4, 5]\n",
@@ -755,12 +3986,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "id": "DxCmoOkhS_nM",
     "outputId": "2c7699a1-519f-4b81-d4e1-836895767375"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1, 2, 3], [4, 5, 6]]\n",
+      "[[1 2 3]\n",
+      " [4 5 6]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Array bidimensional de uma lista Python (uma lista contendo listas)\n",
     "\n",
@@ -775,12 +4016,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "id": "KZ-JzM6US_nM",
     "outputId": "9c7580bf-627b-4926-ed86-8441bb436861"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10 20 30 40 50]\n"
+     ]
+    }
+   ],
    "source": [
     "# operações matemáticas podem ser realizadas em todos os valores de um array Numpy de uma vez (impossível em listas python sem loops)\n",
     "\n",
@@ -870,12 +4119,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "id": "LHfl6fALS_nN",
     "outputId": "5805c47a-4db3-4a7f-8790-dad45dc7ff76"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     1\n",
+       "1     4\n",
+       "2     9\n",
+       "3    16\n",
+       "4    25\n",
+       "dtype: int32"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Operações matemáticas podem ser realizadas em séries e Dataframes Pandas usando funções do numpy\n",
     "\n",

From be11cce6ee12c0077504bdd80bae82fb7f4f1402 Mon Sep 17 00:00:00 2001
From: Taianne <tairocha06@gmail.com>
Date: Wed, 25 Oct 2023 20:22:05 -0300
Subject: [PATCH 2/2] exercicio para casa

---
 exercicios/para-casa/paracasa.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/exercicios/para-casa/paracasa.ipynb b/exercicios/para-casa/paracasa.ipynb
index 45156cf..8d2866e 100644
--- a/exercicios/para-casa/paracasa.ipynb
+++ b/exercicios/para-casa/paracasa.ipynb
@@ -781,7 +781,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "De acordo com a analise em tela, estima-se que o a maioria dos passageiros no navio titanic eram homens"
+    "De acordo com a analise em tela, estima-se que o a maioria dos passageiros no navio titanic eram do gênero masculino."
    ]
   }
  ],