From 6f6d7351ff0f17c43ebdcf12165af968002923cf Mon Sep 17 00:00:00 2001
From: JessicaFerreira <jessicapaula04@gmail.com>
Date: Wed, 25 Oct 2023 20:33:56 -0300
Subject: [PATCH]  Atividade sem12- Jessica Ferreira.

---
 .../para-casa/exerciciocs_s12_21102023.ipynb  | 898 ++++++++++++++++++
 .../para-casa}/titanic.csv                    |   0
 2 files changed, 898 insertions(+)
 create mode 100644 exercicios/para-casa/exerciciocs_s12_21102023.ipynb
 rename {material => exercicios/para-casa}/titanic.csv (100%)

diff --git a/exercicios/para-casa/exerciciocs_s12_21102023.ipynb b/exercicios/para-casa/exerciciocs_s12_21102023.ipynb
new file mode 100644
index 0000000..09a7758
--- /dev/null
+++ b/exercicios/para-casa/exerciciocs_s12_21102023.ipynb
@@ -0,0 +1,898 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercício sem12.\n",
+    "## Introdução-\n",
+    " \n",
+    "- Nesta base de dados temos informações sobre o Titanic e foram retiradas da rede Kaggle.\n",
+    "Após pequenas análises podemos perceber alguns elementos para justificar ou começarmos a pensar no por que de uma algumas pessoas de uma classe ou idade tenha sobrevivido ao acidente.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Processamento-\n",
+    "### -Funções de processamentos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_exc = pd.read_csv(\"titanic.csv\")\n",
+    "df_exc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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",
+    "novas_colunas = []\n",
+    "\n",
+    "for chave, valor in traducoes.items():\n",
+    "    novas_colunas.append(valor)\n",
+    "\n",
+    "novas_colunas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_exc.columns = novas_colunas\n",
+    "df_exc[:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   IdPassageiro     891 non-null    int64  \n",
+      " 1   Sobreviveu       891 non-null    int64  \n",
+      " 2   Classe           891 non-null    int64  \n",
+      " 3   Nome             891 non-null    object \n",
+      " 4   Sexo             891 non-null    object \n",
+      " 5   Idade            714 non-null    float64\n",
+      " 6   NumeroIrmaos     891 non-null    int64  \n",
+      " 7   NumeroPais       891 non-null    int64  \n",
+      " 8   NumeroTicket     891 non-null    object \n",
+      " 9   PrecoTicket      891 non-null    float64\n",
+      " 10  NumeroCabine     204 non-null    object \n",
+      " 11  PortoEmbarcacao  889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_exc.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>Idade</th>\n",
+       "      <th>NumeroIrmaos</th>\n",
+       "      <th>NumeroPais</th>\n",
+       "      <th>PrecoTicket</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       IdPassageiro  Sobreviveu      Classe       Idade  NumeroIrmaos  \\\n",
+       "count    891.000000  891.000000  891.000000  714.000000    891.000000   \n",
+       "mean     446.000000    0.383838    2.308642   29.699118      0.523008   \n",
+       "std      257.353842    0.486592    0.836071   14.526497      1.102743   \n",
+       "min        1.000000    0.000000    1.000000    0.420000      0.000000   \n",
+       "25%      223.500000    0.000000    2.000000   20.125000      0.000000   \n",
+       "50%      446.000000    0.000000    3.000000   28.000000      0.000000   \n",
+       "75%      668.500000    1.000000    3.000000   38.000000      1.000000   \n",
+       "max      891.000000    1.000000    3.000000   80.000000      8.000000   \n",
+       "\n",
+       "       NumeroPais  PrecoTicket  \n",
+       "count  891.000000   891.000000  \n",
+       "mean     0.381594    32.204208  \n",
+       "std      0.806057    49.693429  \n",
+       "min      0.000000     0.000000  \n",
+       "25%      0.000000     7.910400  \n",
+       "50%      0.000000    14.454200  \n",
+       "75%      0.000000    31.000000  \n",
+       "max      6.000000   512.329200  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_exc.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_exc.hist(figsize=(12,8), edgecolor='black');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"5\" halign=\"left\">Sobreviveu</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sexo</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
+       "      <th>1</th>\n",
+       "      <td>94</td>\n",
+       "      <td>0.968085</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>76</td>\n",
+       "      <td>0.921053</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>144</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
+       "      <th>1</th>\n",
+       "      <td>122</td>\n",
+       "      <td>0.368852</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>108</td>\n",
+       "      <td>0.157407</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>347</td>\n",
+       "      <td>0.135447</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sobreviveu                         \n",
+       "                   count      mean median min max\n",
+       "Sexo   Classe                                    \n",
+       "female 1              94  0.968085    1.0   0   1\n",
+       "       2              76  0.921053    1.0   0   1\n",
+       "       3             144  0.500000    0.5   0   1\n",
+       "male   1             122  0.368852    0.0   0   1\n",
+       "       2             108  0.157407    0.0   0   1\n",
+       "       3             347  0.135447    0.0   0   1"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sexo_classe = df_exc.groupby([\"Sexo\", \"Classe\"]).agg({\"Sobreviveu\": ['count', 'mean', 'median', 'min', 'max']})\n",
+    "sexo_classe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Demonstrativo de sexo por classe')"
+      ]
+     },
+     "execution_count": 18,
+     "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": [
+    "sexo_classe1 = sexo_classe\n",
+    "sexo_classe1.plot.bar(edgecolor=\"black\");\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Sexo\")\n",
+    "plt.title(\"Demonstrativo de sexo por classe\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "taxa_sob_classe = df_exc.groupby('Classe')[\"PrecoTicket\"].mean()\n",
+    "taxa_sob_classe.plot.bar(edgecolor=\"black\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Distribuição de Idade')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3wjk7ANxG9+7dHR6ZAQA3gnN2AJR6H3/8sbKzs/XZZ58pJCTE1eUAcDMc2QFQ6u3du1fDhw/XL7/8wgmxAIqMc3YAAIClcWQHAABYGmEHAABYGico69fnvxw7dky+vr5Ov50+AAAoGcYYnTt3TuHh4de9MShhR9KxY8cUERHh6jIAAEAxHD16VNWqVbtmP2FHkq+vr6Rff1iVKlVycTUAAOBGZGZmKiIiwv7v+LUQdiT7V1eVKlUi7AAA4GZ+7xQUTlAGAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACW5uHKnW/evFmvvfaadu7cqdTUVH355Zfq2bOnvf9aj2yfPn26XnzxRUlS9erVdeTIEYf+qVOnauzYsSVWN0qflJQUpaenu7qMIgkKClJkZKSrywAAy3Np2MnOzlaTJk00cOBAPfjggwX6U1NTHda//vprDRo0SL169XJonzx5soYMGWJf9/X1LZmCUSqlpKSobr36unjhvKtLKRJvn/JK2pdI4AGAEubSsBMbG6vY2Nhr9oeGhjqsL1u2THfffbdq1Kjh0O7r61tgLP440tPTdfHCeQXe97zKBUa4upwbknvqqE6tmKH09HTCDgCUMJeGnaI4fvy4Vq5cqQ8//LBA37Rp0/Tqq68qMjJSjz76qEaNGiUPD7eZGpykXGCEvEJruboMAEAp4zaJ4MMPP5Svr2+Br7tGjBihZs2aKSAgQN9++63i4uKUmpqqmTNnXnNbOTk5ysnJsa9nZmaWWN0AAMC13CbsfPDBB+rbt6+8vb0d2kePHm1/3bhxY3l6eurJJ5/U1KlT5eXlVei2pk6dqkmTJpVovQAAoHRwi0vP//vf/yopKUmDBw/+3bExMTG6fPmyDh8+fM0xcXFxOnv2rH05evSoE6sFAACliVsc2Xn//ffVvHlzNWnS5HfHJiQkqEyZMgoJCbnmGC8vr2se9QEAANbi0rCTlZWlgwcP2teTk5OVkJCggIAA+xUqmZmZ+uyzzzRjxowC74+Pj9e2bdt09913y9fXV/Hx8Ro1apT69eunypUr37J5AACA0sulYWfHjh26++677etXzr/p37+/5s+fL0lavHixjDHq06dPgfd7eXlp8eLFmjhxonJychQdHa1Ro0Y5nMcDAAD+2Fwadjp06CBjzHXHDB06VEOHDi20r1mzZtq6dWtJlAYAACzCLU5QBgAAKC7CDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDS3uKkgYFWJiYmuLqHIgoKCeFI7ALdC2AFcIC/rtGSzqV+/fq4upci8fcoraV8igQeA2yDsAC6Qn5MlGaPA+55XucAIV5dzw3JPHdWpFTOUnp5O2AHgNgg7gAuVC4yQV2gtV5cBAJbGCcoAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSXBp2Nm/erO7duys8PFw2m01Lly516B8wYIBsNpvD0qVLF4cxGRkZ6tu3rypVqiR/f38NGjRIWVlZt3AWAACgNHNp2MnOzlaTJk301ltvXXNMly5dlJqaal8+/vhjh/6+fftq7969WrNmjVasWKHNmzdr6NChJV06AABwEx6u3HlsbKxiY2OvO8bLy0uhoaGF9iUmJmrVqlXavn27WrRoIUmaM2eOunbtqn/84x8KDw93es0AAMC9lPpzdjZu3KiQkBDVrVtXTz/9tE6dOmXvi4+Pl7+/vz3oSFKnTp1UpkwZbdu27ZrbzMnJUWZmpsMCAACsqVSHnS5duuijjz7SunXr9Pe//12bNm1SbGys8vLyJElpaWkKCQlxeI+Hh4cCAgKUlpZ2ze1OnTpVfn5+9iUiIqJE5wEAAFzHpV9j/Z7evXvbX992221q3LixatasqY0bN6pjx47F3m5cXJxGjx5tX8/MzCTwAABgUaX6yM7VatSooaCgIB08eFCSFBoaqhMnTjiMuXz5sjIyMq55no/063lAlSpVclgAAIA1uVXY+fnnn3Xq1CmFhYVJklq1aqUzZ85o586d9jHr169Xfn6+YmJiXFUmAAAoRVz6NVZWVpb9KI0kJScnKyEhQQEBAQoICNCkSZPUq1cvhYaG6tChQ/rLX/6iWrVqqXPnzpKk+vXrq0uXLhoyZIjmzp2r3NxcDR8+XL179+ZKLAAAIMnFR3Z27Nihpk2bqmnTppKk0aNHq2nTpho/frzKli2r3bt3q0ePHqpTp44GDRqk5s2b67///a+8vLzs21i4cKHq1aunjh07qmvXrmrTpo3effddV00JAACUMi49stOhQwcZY67Z/8033/zuNgICArRo0SJnlgUAACzErc7ZAQAAKCrCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDSXhp3Nmzere/fuCg8Pl81m09KlS+19ubm5GjNmjG677TZVqFBB4eHhevzxx3Xs2DGHbVSvXl02m81hmTZt2i2eCQAAKK1cGnays7PVpEkTvfXWWwX6zp8/r127dmncuHHatWuXlixZoqSkJPXo0aPA2MmTJys1NdW+PPvss7eifAAA4AY8XLnz2NhYxcbGFtrn5+enNWvWOLS9+eabatmypVJSUhQZGWlv9/X1VWhoaInWCgAA3JNbnbNz9uxZ2Ww2+fv7O7RPmzZNgYGBatq0qV577TVdvnz5utvJyclRZmamwwIAAKzJpUd2iuLixYsaM2aM+vTpo0qVKtnbR4wYoWbNmikgIEDffvut4uLilJqaqpkzZ15zW1OnTtWkSZNuRdkAAMDF3CLs5Obm6uGHH5YxRu+8845D3+jRo+2vGzduLE9PTz355JOaOnWqvLy8Ct1eXFycw/syMzMVERFRMsUDAACXKvVh50rQOXLkiNavX+9wVKcwMTExunz5sg4fPqy6desWOsbLy+uaQQgAAFhLqQ47V4LOgQMHtGHDBgUGBv7uexISElSmTBmFhITcggoBAEBp59Kwk5WVpYMHD9rXk5OTlZCQoICAAIWFhenPf/6zdu3apRUrVigvL09paWmSpICAAHl6eio+Pl7btm3T3XffLV9fX8XHx2vUqFHq16+fKleu7KppAQCAUsSlYWfHjh26++677etXzqPp37+/Jk6cqOXLl0uSbr/9dof3bdiwQR06dJCXl5cWL16siRMnKicnR9HR0Ro1apTD+TgAAOCPzaVhp0OHDjLGXLP/en2S1KxZM23dutXZZQEAAAtxq/vsAAAAFBVhBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWFqpvoMygNIpMTHR1SUUSVBQkCIjI11dBgAXIewAuGF5Waclm039+vVzdSlF4u1TXkn7Egk8wB8UYQfADcvPyZKMUeB9z6tcYISry7khuaeO6tSKGUpPTyfsAH9QhB0ARVYuMEJeobVcXQYA3BBOUAYAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZW7LBz5swZvffee4qLi1NGRoYkadeuXfrll1+cVhwAAMDNKtbjInbv3q1OnTrJz89Phw8f1pAhQxQQEKAlS5YoJSVFH330kbPrBAAAKJZiHdkZPXq0BgwYoAMHDsjb29ve3rVrV23evNlpxQEAANysYoWd7du368knnyzQXrVqVaWlpd10UQAAAM5SrLDj5eWlzMzMAu379+9XcHDwTRcFAADgLMUKOz169NDkyZOVm5srSbLZbEpJSdGYMWPUq1cvpxYIAABwM4oVdmbMmKGsrCyFhITowoULat++vWrVqiVfX19NmTLF2TUCAAAUW7GuxvLz89OaNWu0ZcsW7d69W1lZWWrWrJk6derk7PoAAABuSrHCzhVt2rRRmzZtnFULAACA091w2Jk9e/YNb3TEiBHFKgYAAMDZbjjsvP766w7rJ0+e1Pnz5+Xv7y/p1zsqly9fXiEhIYQdAABQatzwCcrJycn2ZcqUKbr99tuVmJiojIwMZWRkKDExUc2aNdOrr75akvUCAAAUSbGuxho3bpzmzJmjunXr2tvq1q2r119/Xa+88orTigMAALhZxQo7qampunz5coH2vLw8HT9+/KaLAgAAcJZihZ2OHTvqySef1K5du+xtO3fu1NNPP83l5wAAoFQpVtj54IMPFBoaqhYtWsjLy0teXl5q2bKlqlSpovfee8/ZNQIAABRbse6zExwcrK+++kr79+/Xvn37JEn16tVTnTp1nFocAADAzbqpmwrWqVOHgAMAAEq1Yoedn3/+WcuXL1dKSoouXbrk0Ddz5sybLgwAAMAZihV21q1bpx49eqhGjRrat2+fGjVqpMOHD8sYo2bNmjm7RgAAgGIr1gnKcXFxeuGFF/TDDz/I29tbX3zxhY4ePar27dvroYceuuHtbN68Wd27d1d4eLhsNpuWLl3q0G+M0fjx4xUWFiYfHx916tRJBw4ccBiTkZGhvn37qlKlSvL399egQYOUlZVVnGkBAAALKlbYSUxM1OOPPy5J8vDw0IULF1SxYkVNnjxZf//73294O9nZ2WrSpIneeuutQvunT5+u2bNna+7cudq2bZsqVKigzp076+LFi/Yxffv21d69e7VmzRqtWLFCmzdv1tChQ4szLQAAYEHF+hqrQoUK9vN0wsLCdOjQITVs2FCSlJ6efsPbiY2NVWxsbKF9xhjNmjVLr7zyiu6//35J0kcffaQqVapo6dKl6t27txITE7Vq1Spt375dLVq0kCTNmTNHXbt21T/+8Q+Fh4cXZ3oAAMBCinVk584779SWLVskSV27dtXzzz+vKVOmaODAgbrzzjudUlhycrLS0tIcblLo5+enmJgYxcfHS5Li4+Pl7+9vDzqS1KlTJ5UpU0bbtm275rZzcnKUmZnpsAAAAGsq1pGdmTNn2s+LmTRpkrKysvTJJ5+odu3aTrsSKy0tTZJUpUoVh/YqVarY+9LS0hQSEuLQ7+HhoYCAAPuYwkydOlWTJk1ySp0AAKB0K1bYqVGjhv11hQoVNHfuXKcVdCvExcVp9OjR9vXMzExFRES4sCIAAFBSivU11q0QGhoqSQUeLHr8+HF7X2hoqE6cOOHQf/nyZWVkZNjHFMbLy0uVKlVyWAAAgDXd8JGdypUry2az3dDYjIyMYhd0RXR0tEJDQ7Vu3Trdfvvtkn49ArNt2zY9/fTTkqRWrVrpzJkz2rlzp5o3by5JWr9+vfLz8xUTE3PTNQAAAPd3w2Fn1qxZ9tenTp3SX//6V3Xu3FmtWrWS9OvJwt98843GjRt3wzvPysrSwYMH7evJyclKSEhQQECAIiMjNXLkSP31r39V7dq1FR0drXHjxik8PFw9e/aUJNWvX19dunTRkCFDNHfuXOXm5mr48OHq3bs3V2IBAABJRQg7/fv3t7/u1auXJk+erOHDh9vbRowYoTfffFNr167VqFGjbmibO3bs0N13321fv3IeTf/+/TV//nz95S9/UXZ2toYOHaozZ86oTZs2WrVqlby9ve3vWbhwoYYPH66OHTuqTJky6tWrl2bPnn2j0wIAABZXrBOUv/nmm0JvHtilSxeNHTv2hrfToUMHGWOu2W+z2TR58mRNnjz5mmMCAgK0aNGiG94nAAD4YynWCcqBgYFatmxZgfZly5YpMDDwposCAABwlmId2Zk0aZIGDx6sjRs32k8E3rZtm1atWqV//etfTi0QAADgZhQr7AwYMED169fX7NmztWTJEkm/niy8ZcsWroICAAClSrHCjiTFxMRo4cKFzqwFAADA6W447GRmZtpvvvd7z5LiJn0AAKC0KNJNBVNTUxUSEiJ/f/9CbzBojJHNZlNeXp5TiwQAACiuGw4769evV0BAgCRpw4YNJVYQAACAM91w2Gnfvr39dXR0tCIiIgoc3THG6OjRo86rDgAA4CYV6z470dHROnnyZIH2jIwMRUdH33RRAAAAzlKssHPl3JyrZWVlOTzKAQAAwNWKdOn5lWdX2Ww2jRs3TuXLl7f35eXladu2bfYnlAMAAJQGRQo73333naRfj+z88MMP8vT0tPd5enqqSZMmeuGFF5xbIQAAwE0oUti5chXWE088oTfeeIP76QAAgFKvWHdQnjdvnrPrAAAAKBHFCjvZ2dmaNm2a1q1bpxMnTig/P9+h/6effnJKcQAAADerWGFn8ODB2rRpkx577DGFhYUVemUWAABAaVCssPP1119r5cqVat26tbPrAQAAcKpi3WencuXK9kdHAAAAlGbFCjuvvvqqxo8fr/Pnzzu7HgAAAKcq1tdYM2bM0KFDh1SlShVVr15d5cqVc+jftWuXU4oDAAC4WcUKOz179nRyGQAAACWjWGFnwoQJzq4DAACgRBTrnB0AAAB3UawjO3l5eXr99df16aefKiUlRZcuXXLoz8jIcEpxAAAAN6tYR3YmTZqkmTNn6pFHHtHZs2c1evRoPfjggypTpowmTpzo5BIBAACKr1hhZ+HChfrXv/6l559/Xh4eHurTp4/ee+89jR8/Xlu3bnV2jQAAAMVWrLCTlpam2267TZJUsWJFnT17VpJ03333aeXKlc6rDgAA4CYVK+xUq1ZNqampkqSaNWtq9erVkqTt27fLy8vLedUBAADcpGKFnQceeEDr1q2TJD377LMaN26cateurccff1wDBw50aoEAAAA3o1hXY02bNs3++pFHHlFkZKTi4+NVu3Ztde/e3WnFAQAA3KxihZ2rtWrVSq1atXLGpgAAAJyqWGHno48+um7/448/XqxiAAAAnK1YYee5555zWM/NzdX58+fl6emp8uXLE3YAAECpUawTlE+fPu2wZGVlKSkpSW3atNHHH3/s7BoBAACKzWnPxqpdu7amTZtW4KgPAACAKzn1QaAeHh46duyYMzcJAABwU4p1zs7y5csd1o0xSk1N1ZtvvqnWrVs7pTAAAABnKFbY6dmzp8O6zWZTcHCw7rnnHs2YMcMZdQEAADhFsb7Gys/PV35+vo4fP67Tp08rLy9PaWlpWrRokcLCwpxaYPXq1WWz2Qosw4YNkyR16NChQN9TTz3l1BoAAID7KnLYOXPmjIYNG6agoCCFhoYqICBAoaGhiouL0/nz551e4Pbt25Wammpf1qxZI0l66KGH7GOGDBniMGb69OlOrwMAALinIn2NlZGRoVatWumXX35R3759Vb9+fUnSjz/+qDlz5mjNmjXasmWLdu/era1bt2rEiBE3XWBwcLDD+rRp01SzZk21b9/e3la+fHmFhobe9L4AAID1FCnsTJ48WZ6enjp06JCqVKlSoO/ee+/VY489ptWrV2v27NlOLVSSLl26pAULFmj06NGy2Wz29oULF2rBggUKDQ1V9+7dNW7cOJUvX97p+wcAAO6nSGFn6dKl+uc//1kg6EhSaGiopk+frq5du2rChAnq37+/04r87f7PnDmjAQMG2NseffRRRUVFKTw8XLt379aYMWOUlJSkJUuWXHM7OTk5ysnJsa9nZmY6vVYAAFA6FCnspKamqmHDhtfsb9SokcqUKaMJEybcdGGFef/99xUbG6vw8HB729ChQ+2vb7vtNoWFhaljx446dOiQatasWeh2pk6dqkmTJpVIjQAAoHQp0gnKQUFBOnz48DX7k5OTFRIScrM1FerIkSNau3atBg8efN1xMTExkqSDBw9ec0xcXJzOnj1rX44ePerUWgEAQOlRpLDTuXNnvfzyy7p06VKBvpycHI0bN05dunRxWnG/NW/ePIWEhKhbt27XHZeQkCBJ170E3svLS5UqVXJYAACANRX5BOUWLVqodu3aGjZsmOrVqydjjBITE/X2228rJydHH330kdOLzM/P17x589S/f395ePz/kg8dOqRFixapa9euCgwM1O7duzVq1Ci1a9dOjRs3dnodAADA/RQp7FSrVk3x8fF65plnFBcXJ2OMpF/voPynP/1Jb775piIjI51e5Nq1a5WSkqKBAwc6tHt6emrt2rWaNWuWsrOzFRERoV69eumVV15xeg0AAMA9FflxEdHR0fr66691+vRpHThwQJJUq1YtBQQEOL24K+699157sPqtiIgIbdq0qcT2CwAA3F+xno0lSZUrV1bLli2dWQsAAIDTFevZWAAAAO6CsAMAACyt2F9jAYA7SUxMdHUJRRIUFFQiF3wAf0SEHQCWlpd1WrLZ1K9fP1eXUiTePuWVtC+RwAM4AWEHgKXl52RJxijwvudVLjDC1eXckNxTR3VqxQylp6cTdgAnIOwA+EMoFxghr9Bari4DgAtwgjIAALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0D1cXYHUpKSlKT093dRlFEhQUpMjISFeXAQCAUxB2SlBKSorq1quvixfOu7qUIvH2Ka+kfYkEHgCAJRB2SlB6erouXjivwPueV7nACFeXc0NyTx3VqRUzlJ6eTtgBAFgCYecWKBcYIa/QWq4uAwCAPyROUAYAAJZWqsPOxIkTZbPZHJZ69erZ+y9evKhhw4YpMDBQFStWVK9evXT8+HEXVgwAAEqbUh12JKlhw4ZKTU21L1u2bLH3jRo1Sv/5z3/02WefadOmTTp27JgefPBBF1YLAABKm1J/zo6Hh4dCQ0MLtJ89e1bvv/++Fi1apHvuuUeSNG/ePNWvX19bt27VnXfeeatLBQAApVCpP7Jz4MABhYeHq0aNGurbt69SUlIkSTt37lRubq46depkH1uvXj1FRkYqPj7+utvMyclRZmamwwIAAKypVIedmJgYzZ8/X6tWrdI777yj5ORktW3bVufOnVNaWpo8PT3l7+/v8J4qVaooLS3tutudOnWq/Pz87EtEhHtcFg4AAIquVH+NFRsba3/duHFjxcTEKCoqSp9++ql8fHyKvd24uDiNHj3avp6ZmUngAQDAokr1kZ2r+fv7q06dOjp48KBCQ0N16dIlnTlzxmHM8ePHCz3H57e8vLxUqVIlhwUAAFiTW4WdrKwsHTp0SGFhYWrevLnKlSundevW2fuTkpKUkpKiVq1aubBKAABQmpTqr7FeeOEFde/eXVFRUTp27JgmTJigsmXLqk+fPvLz89OgQYM0evRoBQQEqFKlSnr22WfVqlUrrsQCAAB2pTrs/Pzzz+rTp49OnTql4OBgtWnTRlu3blVwcLAk6fXXX1eZMmXUq1cv5eTkqHPnznr77bddXDUAAChNSnXYWbx48XX7vb299dZbb+mtt966RRUBAAB3U6rDDlwnMTHR1SXcMHeqFQBw6xF24CAv67Rks6lfv36uLgUAAKcg7MBBfk6WZIwC73te5QLd495DF37aobP/XeDqMgCnc7ejlkFBQYqMjHR1GUABhB0UqlxghLxCa7m6jBuSe+qoq0sAnMpdj7B6+5RX0r5EAg9KHcIOAJQy7niENffUUZ1aMUPp6emEHZQ6hB0AKKXc6QgrUJq51R2UAQAAioqwAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALK1Uh52pU6fqjjvukK+vr0JCQtSzZ08lJSU5jOnQoYNsNpvD8tRTT7moYgAAUNqU6rCzadMmDRs2TFu3btWaNWuUm5ure++9V9nZ2Q7jhgwZotTUVPsyffp0F1UMAABKGw9XF3A9q1atclifP3++QkJCtHPnTrVr187eXr58eYWGht7q8gAAgBso1Ud2rnb27FlJUkBAgEP7woULFRQUpEaNGikuLk7nz5+/7nZycnKUmZnpsAAAAGsq1Ud2fis/P18jR45U69at1ahRI3v7o48+qqioKIWHh2v37t0aM2aMkpKStGTJkmtua+rUqZo0adKtKBsAALiY24SdYcOGac+ePdqyZYtD+9ChQ+2vb7vtNoWFhaljx446dOiQatasWei24uLiNHr0aPt6ZmamIiIiSqZwAPgDSUxMdHUJRRIUFKTIyEhXl4ES5hZhZ/jw4VqxYoU2b96satWqXXdsTEyMJOngwYPXDDteXl7y8vJyep0A8EeVl3VastnUr18/V5dSJN4+5ZW0L5HAY3GlOuwYY/Tss8/qyy+/1MaNGxUdHf2770lISJAkhYWFlXB1AIAr8nOyJGMUeN/zKhfoHkfKc08d1akVM5Senk7YsbhSHXaGDRumRYsWadmyZfL19VVaWpokyc/PTz4+Pjp06JAWLVqkrl27KjAwULt379aoUaPUrl07NW7c2MXVA8AfT7nACHmF1nJ1GYCDUh123nnnHUm/3jjwt+bNm6cBAwbI09NTa9eu1axZs5Sdna2IiAj16tVLr7zyiguqBQAApVGpDjvGmOv2R0REaNOmTbeoGgAA4I7c6j47AAAARUXYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlubh6gIAAHClxMREV5dQJEFBQYqMjHR1GW6FsAMA+EPKyzot2Wzq16+fq0spEm+f8kral0jgKQLCDgDgDyk/J0syRoH3Pa9ygRGuLueG5J46qlMrZig9PZ2wUwSEHQDAH1q5wAh5hdZydRkoQZygDAAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALM0yj4t466239NprryktLU1NmjTRnDlz1LJlS1eXBQCA0/Gk9qKxRNj55JNPNHr0aM2dO1cxMTGaNWuWOnfurKSkJIWEhLi6PAAAnIIntRePJcLOzJkzNWTIED3xxBOSpLlz52rlypX64IMPNHbsWBdXBwCAc/Ck9uJx+7Bz6dIl7dy5U3Fxcfa2MmXKqFOnToqPj3dhZQAAlAye1F40bh920tPTlZeXpypVqji0V6lSRfv27Sv0PTk5OcrJybGvnz17VpKUmZnp1NqysrJ+3V/aQeVfuujUbZeU3FNHJVFzSXPHmiX3rJuabw1qvjXcsuaMnyX9+m+is/+dvbI9Y8z1Bxo398svvxhJ5ttvv3Vof/HFF03Lli0Lfc+ECROMJBYWFhYWFhYLLEePHr1uVnD7IztBQUEqW7asjh8/7tB+/PhxhYaGFvqeuLg4jR492r6en5+vjIwMBQYGymaz3XRNmZmZioiI0NGjR1WpUqWb3l5pY/X5SczRCqw+P4k5WoHV5yeV7ByNMTp37pzCw8OvO87tw46np6eaN2+udevWqWfPnpJ+DS/r1q3T8OHDC32Pl5eXvLy8HNr8/f2dXlulSpUs+4dXsv78JOZoBVafn8QcrcDq85NKbo5+fn6/O8btw44kjR49Wv3791eLFi3UsmVLzZo1S9nZ2farswAAwB+XJcLOI488opMnT2r8+PFKS0vT7bffrlWrVhU4aRkAAPzxWCLsSNLw4cOv+bXVrebl5aUJEyYU+KrMKqw+P4k5WoHV5ycxRyuw+vyk0jFHmzG/d70WAACA++JBoAAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIO0721ltvqXr16vL29lZMTIz+97//ubqkYtu8ebO6d++u8PBw2Ww2LV261KHfGKPx48crLCxMPj4+6tSpkw4cOOCaYoth6tSpuuOOO+Tr66uQkBD17NlTSUlJDmMuXryoYcOGKTAwUBUrVlSvXr0K3K27NHvnnXfUuHFj+828WrVqpa+//tre7+7zu9q0adNks9k0cuRIe5u7z3HixImy2WwOS7169ez97j6/K3755Rf169dPgYGB8vHx0W233aYdO3bY+93990316tULfI42m03Dhg2T5P6fY15ensaNG6fo6Gj5+PioZs2aevXVVx2eWeXSz/Dmn06FKxYvXmw8PT3NBx98YPbu3WuGDBli/P39zfHjx11dWrF89dVX5uWXXzZLliwxksyXX37p0D9t2jTj5+dnli5dar7//nvTo0cPEx0dbS5cuOCagouoc+fOZt68eWbPnj0mISHBdO3a1URGRpqsrCz7mKeeespERESYdevWmR07dpg777zT3HXXXS6sumiWL19uVq5cafbv32+SkpLMSy+9ZMqVK2f27NljjHH/+f3W//73P1O9enXTuHFj89xzz9nb3X2OEyZMMA0bNjSpqan25eTJk/Z+d5+fMcZkZGSYqKgoM2DAALNt2zbz008/mW+++cYcPHjQPsbdf9+cOHHC4TNcs2aNkWQ2bNhgjHH/z3HKlCkmMDDQrFixwiQnJ5vPPvvMVKxY0bzxxhv2Ma78DAk7TtSyZUszbNgw+3peXp4JDw83U6dOdWFVznF12MnPzzehoaHmtddes7edOXPGeHl5mY8//tgFFd68EydOGElm06ZNxphf51OuXDnz2Wef2cckJiYaSSY+Pt5VZd60ypUrm/fee89S8zt37pypXbu2WbNmjWnfvr097FhhjhMmTDBNmjQptM8K8zPGmDFjxpg2bdpcs9+Kv2+ee+45U7NmTZOfn2+Jz7Fbt25m4MCBDm0PPvig6du3rzHG9Z8hX2M5yaVLl7Rz50516tTJ3lamTBl16tRJ8fHxLqysZCQnJystLc1hvn5+foqJiXHb+Z49e1aSFBAQIEnauXOncnNzHeZYr149RUZGuuUc8/LytHjxYmVnZ6tVq1aWmt+wYcPUrVs3h7lI1vkMDxw4oPDwcNWoUUN9+/ZVSkqKJOvMb/ny5WrRooUeeughhYSEqGnTpvrXv/5l77fa75tLly5pwYIFGjhwoGw2myU+x7vuukvr1q3T/v37JUnff/+9tmzZotjYWEmu/wwtcwdlV0tPT1deXl6BR1RUqVJF+/btc1FVJSctLU2SCp3vlT53kp+fr5EjR6p169Zq1KiRpF/n6OnpWeAhse42xx9++EGtWrXSxYsXVbFiRX355Zdq0KCBEhISLDG/xYsXa9euXdq+fXuBPit8hjExMZo/f77q1q2r1NRUTZo0SW3bttWePXssMT9J+umnn/TOO+9o9OjReumll7R9+3aNGDFCnp6e6t+/v+V+3yxdulRnzpzRgAEDJFnjz+nYsWOVmZmpevXqqWzZssrLy9OUKVPUt29fSa7/N4OwA+jXIwN79uzRli1bXF2K09WtW1cJCQk6e/asPv/8c/Xv31+bNm1ydVlOcfToUT333HNas2aNvL29XV1OibjyP2NJaty4sWJiYhQVFaVPP/1UPj4+LqzMefLz89WiRQv97W9/kyQ1bdpUe/bs0dy5c9W/f38XV+d877//vmJjYxUeHu7qUpzm008/1cKFC7Vo0SI1bNhQCQkJGjlypMLDw0vFZ8jXWE4SFBSksmXLFjh7/vjx4woNDXVRVSXnypysMN/hw4drxYoV2rBhg6pVq2ZvDw0N1aVLl3TmzBmH8e42R09PT9WqVUvNmzfX1KlT1aRJE73xxhuWmN/OnTt14sQJNWvWTB4eHvLw8NCmTZs0e/ZseXh4qEqVKm4/x6v5+/urTp06OnjwoCU+Q0kKCwtTgwYNHNrq169v/7rOSr9vjhw5orVr12rw4MH2Nit8ji+++KLGjh2r3r1767bbbtNjjz2mUaNGaerUqZJc/xkSdpzE09NTzZs317p16+xt+fn5WrdunVq1auXCykpGdHS0QkNDHeabmZmpbdu2uc18jTEaPny4vvzyS61fv17R0dEO/c2bN1e5cuUc5piUlKSUlBS3mWNh8vPzlZOTY4n5dezYUT/88IMSEhLsS4sWLdS3b1/7a3ef49WysrJ06NAhhYWFWeIzlKTWrVsXuO3D/v37FRUVJckav2+umDdvnkJCQtStWzd7mxU+x/Pnz6tMGcdIUbZsWeXn50sqBZ9hiZ8C/QeyePFi4+XlZebPn29+/PFHM3ToUOPv72/S0tJcXVqxnDt3znz33Xfmu+++M5LMzJkzzXfffWeOHDlijPn1MkJ/f3+zbNkys3v3bnP//fe71aWgTz/9tPHz8zMbN250uCT0/Pnz9jFPPfWUiYyMNOvXrzc7duwwrVq1Mq1atXJh1UUzduxYs2nTJpOcnGx2795txo4da2w2m1m9erUxxv3nV5jfXo1ljPvP8fnnnzcbN240ycnJ5v/+7/9Mp06dTFBQkDlx4oQxxv3nZ8yvtw3w8PAwU6ZMMQcOHDALFy405cuXNwsWLLCPcfffN8b8eoVuZGSkGTNmTIE+d/8c+/fvb6pWrWq/9HzJkiUmKCjI/OUvf7GPceVnSNhxsjlz5pjIyEjj6elpWrZsabZu3erqkoptw4YNRlKBpX///saYXy8lHDdunKlSpYrx8vIyHTt2NElJSa4tuggKm5skM2/ePPuYCxcumGeeecZUrlzZlC9f3jzwwAMmNTXVdUUX0cCBA01UVJTx9PQ0wcHBpmPHjvagY4z7z68wV4cdd5/jI488YsLCwoynp6epWrWqeeSRRxzuP+Pu87viP//5j2nUqJHx8vIy9erVM++++65Dv7v/vjHGmG+++cZIKrRud/8cMzMzzXPPPWciIyONt7e3qVGjhnn55ZdNTk6OfYwrP0ObMb+5vSEAAIDFcM4OAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOALfVoUMHjRw58qa2sXHjRtlstgLPJQJgHYQdAKXWgAED1LNnT1eXAcDNEXYAAIClEXYAuIXs7Gw9/vjjqlixosLCwjRjxowCY/7973+rRYsW8vX1VWhoqB599FGdOHHCYcxXX32lOnXqyMfHR3fffbcOHz5cYDtbtmxR27Zt5ePjo4iICI0YMULZ2dklNTUAJYywA8AtvPjii9q0aZOWLVum1atXa+PGjdq1a5fDmNzcXL366qv6/vvvtXTpUh0+fFgDBgyw9x89elQPPvigunfvroSEBA0ePFhjx4512MahQ4fUpUsX9erVS7t379Ynn3yiLVu2aPjw4bdimgBKAA8CBVBqDRgwQGfOnNGCBQsUGBioBQsW6KGHHpIkZWRkqFq1aho6dKhmzZpV6Pt37NihO+64Q+fOnVPFihX10ksvadmyZdq7d699zNixY/X3v/9dp0+flr+/vwYPHqyyZcvqn//8p33Mli1b1L59e2VnZ8vb27tE5wzA+TiyA6DUO3TokC5duqSYmBh7W0BAgOrWreswbufOnerevbsiIyPl6+ur9u3bS5JSUlIkSYmJiQ7bkKRWrVo5rH///feaP3++KlasaF86d+6s/Px8JScnl8T0AJQwD1cXAADOkJ2drc6dO6tz585auHChgoODlZKSos6dO+vSpUs3vJ2srCw9+eSTGjFiRIG+yMhIZ5YM4BYh7AAo9WrWrKly5cpp27Zt9sBx+vRp7d+/3370Zt++fTp16pSmTZumiIgISb9+jfVb9evX1/Llyx3atm7d6rDerFkz/fjjj6pVq1ZJTQfALcbXWABKvYoVK2rQoEF68cUXtX79eu3Zs0cDBgxQmTL//1dYZGSkPD09NWfOHP30009avny5Xn31VYftPPXUUzpw4IBefPFFJSUladGiRZo/f77DmDFjxujbb7/V8OHDlZCQoAMHDmjZsmWcoAy4McIOALfw2muvqW3bturevbs6deqkNm3aqHnz5vb+4OBgzZ8/X5999pkaNGigadOm6R//+IfDNiIjI/XFF19o6dKlatKkiebOnau//e1vDmMaN26sTZs2af/+/Wrbtq2aNm2q8ePHKzw8/JbME4DzcTUWAACwNI7sAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS/t/9wgDgj8/B8kAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_exc['Idade'].plot.hist(bins=10, edgecolor='black');\n",
+    "\n",
+    "plt.xlabel('Idade')\n",
+    "plt.ylabel('Quantidade')\n",
+    "plt.title('Distribuição de Idade')"
+   ]
+  }
+ ],
+ "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": "3.10.2"
+  }
+ },
+ "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