diff --git a/exercicios/para-casa/TheresNothingIFear.ipynb b/exercicios/para-casa/TheresNothingIFear.ipynb
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+++ b/exercicios/para-casa/TheresNothingIFear.ipynb
@@ -0,0 +1,514 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TITANIC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"300\"\n",
+       "            height=\"250\"\n",
+       "            src=\"https://i.gifer.com/42eU.gif\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x215e221c710>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import IFrame\n",
+    "iframe_url = \"https://i.gifer.com/42eU.gif\"\n",
+    "IFrame(src=iframe_url, width=300, height=250)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "𝑩𝒂𝒔𝒆 𝒅𝒆 𝒅𝒂𝒅𝒐𝒔 𝒐𝒓𝒊𝒈𝒊𝒏𝒂𝒍 𝒅𝒊𝒗𝒖𝒍𝒈𝒂𝒅𝒂 𝒒𝒖𝒆 𝒄𝒐𝒏𝒕𝒆𝒎 𝒂𝒍𝒈𝒖𝒎𝒂𝒔 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒄̧𝒐̃𝒆𝒔 𝒔𝒐𝒃𝒓𝒆 𝒐𝒔 𝒑𝒂𝒔𝒔𝒂𝒈𝒆𝒊𝒓𝒐𝒔 𝒒𝒖𝒆 𝒆𝒎𝒃𝒂𝒓𝒄𝒂𝒓𝒂𝒎 𝒏𝒐 𝒏𝒂𝒗𝒊𝒐 𝑹𝑴𝑺 𝑻𝒊𝒕𝒂𝒏𝒊𝒄."
+   ]
+  },
+  {
+   "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 = pd.read_csv(\"titanic.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WGDhwoBESEmL4+fkZ3bp1Mw4ePFji1nPDMIysrCwjOTnZqFWrluHp6WlEREQYnTp1Mt55550ybevChQvGe++9Z9x2221GYGCg4enpaURFRRl9+vSxuy29tFvPV69ebbRu3dqoUqWKERkZaTz//PPGwoULDUnG8uXLDcMwjF9//dV47LHHjLp16xo+Pj5GcHCwcfvttxtLliyxraesx1NBQYHx73//27jhhhsMb29vo1q1akbLli2N0aNHG7m5uZe1LsBVLIbxl/8lAAAXiYuL0/PPP6/OnTu7uhQAJsI1OwAqjG7dutl9ZQYAOAPX7ABwuc8//1ynT5/WrFmzFBYW5upyAJgMZ3YAuNz27duVkpKiw4cP69lnn3V1OQBMhmt2AACAqXFmBwAAmBphBwAAmBoXKOuPp5YeOXJEVatWdfrj9gEAQPkwDEMnT55UZGTkJR8cStiRdOTIEdsX2gEAgGvLwYMHVbNmzYv2E3YkVa1aVdIfOysgIMDF1QAAgLLIy8tTrVq1bH/HL4awI9k+ugoICCDsAABwjfm7S1C4QBkAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJiaS8POqFGjZLFY7F6NGjWy9Z87d07JyckKCQmRv7+/EhMTlZWVZbeOjIwMJSQkyNfXV2FhYXruued04cKFqz0VAABQQbn81vMbbrhBS5Yssb338Ph/JQ0ePFjz5s3TrFmzFBgYqJSUFPXo0UOrV6+WJBUWFiohIUERERH66aefdPToUT3yyCPy9PTUq6++etXnAgAAKh6Xhx0PDw9FRESUaM/NzdX777+vzz77TB07dpQkTZ8+XY0bN9aaNWvUunVrLVq0SDt27NCSJUsUHh6u5s2b6+WXX9aQIUM0atQoeXl5Xe3pAACACsbl1+zs2bNHkZGRuv7669WrVy9lZGRIkjZu3Kjz588rLi7ONrZRo0aqXbu20tLSJElpaWlq2rSpwsPDbWPi4+OVl5en7du3X92JAACACsmlZ3ZiY2M1Y8YMNWzYUEePHtXo0aN12223adu2bcrMzJSXl5eCgoLslgkPD1dmZqYkKTMz0y7oFPcX911Mfn6+8vPzbe/z8vKcNCMAAFDRuDTsdOnSxfZzs2bNFBsbq6ioKM2cOVNVqlQpt+2OHTtWo0ePLrf1AwCAisPlH2P9WVBQkBo0aKC9e/cqIiJCBQUFysnJsRuTlZVlu8YnIiKixN1Zxe9Luw6o2NChQ5Wbm2t7HTx40LkTAQAAFUaFCjunTp3Svn37VKNGDbVs2VKenp5aunSprX/37t3KyMiQ1WqVJFmtVm3dulXZ2dm2MYsXL1ZAQIBiYmIuuh1vb2/bl37y5Z8AAJibSz/GevbZZ9WtWzdFRUXpyJEjGjlypNzd3fXQQw8pMDBQjz/+uFJTUxUcHKyAgAANGDBAVqtVrVu3liR17txZMTEx6t27t8aPH6/MzEwNGzZMycnJ8vb2duXUAABABeHSsHPo0CE99NBD+u233xQaGqq2bdtqzZo1Cg0NlSRNmjRJbm5uSkxMVH5+vuLj4zV16lTb8u7u7po7d6769+8vq9UqPz8/JSUlacyYMa6aEgAAqGAshmEYri7C1fLy8hQYGKjc3Fznf6RVVCi5uTt3nQAuD/8OAVMq699vlz9U0PTc3KUPe0mZO11dCVA5RTSWkj51dRUAXIiwczVk7pQObXZ1FQAAVEoV6m4sAAAAZyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU6swYWfcuHGyWCwaNGiQre3cuXNKTk5WSEiI/P39lZiYqKysLLvlMjIylJCQIF9fX4WFhem5557ThQsXrnL1AACgoqoQYWf9+vV6++231axZM7v2wYMH6/vvv9esWbO0cuVKHTlyRD169LD1FxYWKiEhQQUFBfrpp5/04YcfasaMGRoxYsTVngIAAKigXB52Tp06pV69eundd99VtWrVbO25ubl6//33NXHiRHXs2FEtW7bU9OnT9dNPP2nNmjWSpEWLFmnHjh365JNP1Lx5c3Xp0kUvv/yypkyZooKCAldNCQAAVCAuDzvJyclKSEhQXFycXfvGjRt1/vx5u/ZGjRqpdu3aSktLkySlpaWpadOmCg8Pt42Jj49XXl6etm/fftFt5ufnKy8vz+4FAADMycOVG//iiy+0adMmrV+/vkRfZmamvLy8FBQUZNceHh6uzMxM25g/B53i/uK+ixk7dqxGjx59hdUDAIBrgcvO7Bw8eFBPP/20Pv30U/n4+FzVbQ8dOlS5ubm218GDB6/q9gEAwNXjsrCzceNGZWdn66abbpKHh4c8PDy0cuVKTZ48WR4eHgoPD1dBQYFycnLslsvKylJERIQkKSIiosTdWcXvi8eUxtvbWwEBAXYvAABgTi4LO506ddLWrVu1ZcsW26tVq1bq1auX7WdPT08tXbrUtszu3buVkZEhq9UqSbJardq6dauys7NtYxYvXqyAgADFxMRc9TkBAICKx2XX7FStWlVNmjSxa/Pz81NISIit/fHHH1dqaqqCg4MVEBCgAQMGyGq1qnXr1pKkzp07KyYmRr1799b48eOVmZmpYcOGKTk5Wd7e3ld9TgAAoOJx6QXKf2fSpElyc3NTYmKi8vPzFR8fr6lTp9r63d3dNXfuXPXv319Wq1V+fn5KSkrSmDFjXFg1AACoSCyGYRiuLsLV8vLyFBgYqNzc3PK5fuffN0mHNjt/vQD+Xs0W0pBNrq4CQDko699vlz9nBwAAoDwRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKk5HHZyzuXovU3vaeiSoTpx9oQkadPRTTqcd9hpxQEAAFwpD0cW+jnrZ8V9FKdAn0Dtz9mvJ1o+oeAqwfp659fKyM3QR/d+5Ow6AQAAHOLQmZ3Uhal6tPmj2jNgj3w8fGztd9W/S6sOrHJacQAAAFfKobCz/sh6/bPlP0u0X1f1OmWeyrziogAAAJzFobDj7e6tvPy8Eu2//PaLQv1Cy7yet956S82aNVNAQIACAgJktVo1f/58W/+5c+eUnJyskJAQ+fv7KzExUVlZWXbryMjIUEJCgnx9fRUWFqbnnntOFy5ccGRaAADAhBwKO90bdteYVWN0vvC8JMkiizJyMzRkyRAlNk4s83pq1qypcePGaePGjdqwYYM6duyou+++W9u3b5ckDR48WN9//71mzZqllStX6siRI+rRo4dt+cLCQiUkJKigoEA//fSTPvzwQ82YMUMjRoxwZFoAAMCELIZhGJe7UO65XN036z5tOLJBJ/NPKrJqpDJPZcpay6of/vGD/Lz8HC4oODhYr732mu677z6Fhobqs88+03333SdJ2rVrlxo3bqy0tDS1bt1a8+fPV9euXXXkyBGFh4dLkqZNm6YhQ4bo2LFj8vLyKtM28/LyFBgYqNzcXAUEBDhc+0X9+ybp0GbnrxfA36vZQhqyydVVACgHZf377dDdWIE+gVrce7F+zPhRP2f9rFMFp3RTjZsUd32cwwUXFhZq1qxZOn36tKxWqzZu3Kjz588rLu7/rbNRo0aqXbu2LeykpaWpadOmtqAjSfHx8erfv7+2b9+uFi1aOFwPAAAwB4fCTrG2tduqbe22V1TA1q1bZbVade7cOfn7+2vOnDmKiYnRli1b5OXlpaCgILvx4eHhysz84yLozMxMu6BT3F/cdzH5+fnKz8+3vc/LK3n9EQAAMIcyh53JayeXeaUDYweWeWzDhg21ZcsW5ebmavbs2UpKStLKlSvLvLwjxo4dq9GjR5frNgAAQMVQ5rAzac0ku/fHTh/TmfNnFOQTJOmPJyr7evoqzC/sssKOl5eX6tWrJ0lq2bKl1q9frzfeeEMPPvigCgoKlJOTY3d2JysrSxEREZKkiIgIrVu3zm59xXdrFY8pzdChQ5Wammp7n5eXp1q1apW5ZgAAcO0oc9hJfzrd9vNnWz/T1PVT9X7399WwekNJ0u7ju/XE90+U+vydy1FUVKT8/Hy1bNlSnp6eWrp0qRIT/7jDa/fu3crIyJDVapUkWa1WvfLKK8rOzlZYWJgkafHixQoICFBMTMxFt+Ht7S1vb+8rqhMAAFwbHLpmZ/jy4Zp9/2xb0JGkhtUbalL8JN036z71atarTOsZOnSounTpotq1a+vkyZP67LPPtGLFCi1cuFCBgYF6/PHHlZqaquDgYAUEBGjAgAGyWq1q3bq1JKlz586KiYlR7969NX78eGVmZmrYsGFKTk4mzAAAAEkOhp2jJ4/qQlHJB/cVGoXKOpVVyhKly87O1iOPPKKjR48qMDBQzZo108KFC3XHHXdIkiZNmiQ3NzclJiYqPz9f8fHxmjp1qm15d3d3zZ07V/3795fVapWfn5+SkpI0ZswYR6YFAABMyKHn7HT7vJsO5x3We93f0001bpIkbTyyUf3m9tN1Va/Tdw995/RCyxPP2QFMjOfsAKZVrs/Z+aD7B0r6Jkmt3mklT3dPSdKFoguKrxuv97q/51jFAAAA5cChsBPqF6ofev2gX377RbuO75IkNareSA1CGji1OAAAgCt1RQ8VbBDSgIADAAAqNIfDzqG8Q/pu93fKyM1QQWGBXd/E+IlXXBgAAIAzOBR2lv66VN2/6K7rq12vXcd3qUlYE+3P2S/DMGwXLAMAAFQEbo4sNHTpUD1rfVZb+2+Vj4ePvnrgKx0cfFDt67TX/TH3O7tGAAAAhzkUdnYe36lHbnxEkuTh5qGz58/K38tfYzqM0b9X/9upBQIAAFwJh8KOn6ef7TqdGv41tO/3fba+42eOO6cyAAAAJ3Domp3WNVvrx4wf1Ti0se6qf5eeWfSMtmZt1de7vlbrmq2dXSMAAIDDHAo7E+Mn6lTBKUnS6A6jdarglL7c/qXqh9TXxM7ciQUAACoOh8LO9dWut/3s5+WnaV2nOa0gAAAAZ3Lomh0AAIBrRZnP7FT7dzVZZCnT2BNDTjhcEAAAgDOVOey8Hv+67effzv6mf636l+Lrxcta0ypJSjuUpoV7F2p4u+FOLxIAAMBRZQ47Sc2TbD8nzkzUmNvHKOWWFFvbwNiB+s+6/2jJr0s02DrYuVUCAAA4yKFrdhbuXag7691Zov3Oendqya9LrrgoAAAAZ3Eo7IT4hujbXd+WaP9217cK8Q254qIAAACcxaFbz0d3GK2+3/XVigMrFHtdrCRp7eG1WrB3gd7t9q5TCwQAALgSDoWdR5s/qsbVG2vyusn6eufXkqTGoY31Y58fFVsz1qkFAgAAXAmHwo4kxdaM1ac1P3VmLQAAAE5X5rCTl5+nAO8A28+XUjwOAADA1S7roYJHnzmqML8wBY0LksVS8gGDhmHIYrGocEShU4sEAABwVJnDzrJHlim4SrAkaXnS8nIrCAAAwJnKHHba12lv+zm6WrRqBdQqcXbHMAwdzDvovOoAAACukEPP2Yl+I1rHzhwr0X7i7AlFvxF9xUUBAAA4i0NhxzCMUr8U9FTBKfl4+FxxUQAAAM5yWbeepy5MlSRZLBYNXz5cvp6+tr7CokKtPbxWzSOaO7VAAACAK3FZYWdz5mZJf5zZ2Zq9VV7uXrY+L3cv3Rh+o5699VnnVggAAHAFLivsFN+F1efbPnrjzjd4ng4AAKjwHHqC8vS7pzu7DgAAgHLhUNg5XXBa434cp6XpS5V9OltFRpFd/69P/+qU4gAAAK6UQ2Gn7/d9tXL/SvVu1ls1qtYo9c4sAACAisChsDN/z3zN+8c8tandxtn1AAAAOJVDz9mpVqWa7asjAAAAKjKHws7Lt7+sEStG6Mz5M86uBwAAwKkc+hhrQtoE7TuxT+H/F646QXXk6eZp17/pn5ucUhwAAMCVcijs3NPwHieXAQAAUD4cCjsjO4x0dh0AAADlwqFrdgAAAK4VDp3ZKSwq1KQ1kzRz+0xl5GaooLDArv/EkBNOKQ4AAOBKOXRmZ/TK0ZqYNlEP3vCgcvNzlWpNVY/GPeRmcdOoDqOcXCIAAIDjHAo7n279VO92e1fP3PqMPNw89FCTh/Re9/c0ov0IrTm0xtk1AgAAOMyhsJN5KlNNw5tKkvy9/JWbnytJ6tqgq+btmee86gAAAK6QQ2GnZkBNHT15VJJUt1pdLdq3SJK0/vB6ebt7O686AACAK+TQBcr3NrpXS9OXKrZmrAbcMkAPz3lY729+Xxm5GRrcerCzawQAAHCYQ2FnXNw4288PNnlQtQNrK+1QmuoH11e3ht2cVhwAAMCVcijs/JW1llXWWlZnrAoAAMCpHAo7H/3vo0v2P3LjIw4VAwAA4GwOhZ2nFzxt9/584XmdOX9GXu5e8vX0JewAAIAKw6Gw8/uQ30u07fltj/rP66/nbn3uiosCAABwFqd9N1b9kPoaFzeuxFkfAAAAV3LqF4F6uHnoyMkjzlwlAADAFXHoY6zvdn9n994wDB09dVT/WfcftandximFAQAAOINDYeeeL+6xe2+xWBTqG6qO0R01ofMEZ9QFAADgFA6FnaKRRZKkY6ePycvdS4E+gU4tCgAAwFkuO+zknMvRS0tf0pfbv9Tv5/64KyvUN1R9mvfR8PbD5evp6/QiAQAAHHVZYefE2ROyvm/V4bzD6tW0lxqHNpYk7Ti2Q2+ue1OLf12sHx/7UT9n/aw1h9ZoYOzAcikaAMqsaoRUVCi5ubu6EqDycvG/wcsKO2NWjpGXu5f2DdyncP9w+77bx6jzx53Ve05vLdq3SJPvnOzUQgHAIb5Bf/yS/bCXlLnT1dUAlU9EYynpU5eWcFlh55td3+jtrm+XCDqSFOEfofF3jNddn96lke1HKql5ktOKBIArlrlTOrTZ1VUAcIHLes7O0VNHdUPYDRftbxLWRG4WN43sMPKKCwMAAHCGywo71X2ra3/O/ov2p/+erjC/sCutCQAAwGkuK+zE143XS8teUkFhQYm+/Av5Gr58uO6sd2eZ1zd27FjdfPPNqlq1qsLCwnTPPfdo9+7ddmPOnTun5ORkhYSEyN/fX4mJicrKyrIbk5GRoYSEBPn6+iosLEzPPfecLly4cDlTAwAAJnV5FyjfPkat3mml+m/WV/LNyWpUvZEMw9DO4zs1df1U5Rfm66N7Pyrz+lauXKnk5GTdfPPNunDhgl588UV17txZO3bskJ+fnyRp8ODBmjdvnmbNmqXAwEClpKSoR48eWr16tSSpsLBQCQkJioiI0E8//aSjR4/qkUcekaenp1599dXLmR4AADAhi2EYxuUskP57up764Skt2rdIxYtaLBbdcf0d+s9d/1G94HoOF3Ps2DGFhYVp5cqVateunXJzcxUaGqrPPvtM9913nyRp165daty4sdLS0tS6dWvNnz9fXbt21ZEjRxQe/seF09OmTdOQIUN07NgxeXl5/e128/LyFBgYqNzcXAUEBDhc/0X9+yYujARcpeVD0qOf8e8QcJWaLaQhm8pl1WX9+33ZDxWMrhat+b3m6/ezv2vPiT2SpHrB9RRcJdjxav9/ubm5kqTg4D/WtXHjRp0/f15xcXG2MY0aNVLt2rVtYSctLU1Nmza1BR1Jio+PV//+/bV9+3a1aNGixHby8/OVn59ve5+Xl3fFtQMAgIrJoa+LkKRqVarplutucVohRUVFGjRokNq0aaMmTZpIkjIzM+Xl5aWgoCC7seHh4crMzLSN+XPQKe4v7ivN2LFjNXr0aKfVDgAAKq7LukC5PCUnJ2vbtm364osvyn1bQ4cOVW5uru118ODBct8mAABwDYfP7DhTSkqK5s6dq1WrVqlmzZq29oiICBUUFCgnJ8fu7E5WVpYiIiJsY9atW2e3vuK7tYrH/JW3t7e8vb2dPAsAAFARufTMjmEYSklJ0Zw5c7Rs2TJFR0fb9bds2VKenp5aunSprW337t3KyMiQ1WqVJFmtVm3dulXZ2dm2MYsXL1ZAQIBiYmKuzkQAAECF5dIzO8nJyfrss8/07bffqmrVqrZrbAIDA1WlShUFBgbq8ccfV2pqqoKDgxUQEKABAwbIarWqdevWkqTOnTsrJiZGvXv31vjx45WZmalhw4YpOTmZszcAAMC1Yeett96SJHXo0MGuffr06Xr00UclSZMmTZKbm5sSExOVn5+v+Ph4TZ061TbW3d1dc+fOVf/+/WW1WuXn56ekpCSNGTPmak0DAABUYC4NO2V5xI+Pj4+mTJmiKVOmXHRMVFSUfvjhB2eWBgAATKLC3I0FAABQHgg7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1FwadlatWqVu3bopMjJSFotF33zzjV2/YRgaMWKEatSooSpVqiguLk579uyxG3PixAn16tVLAQEBCgoK0uOPP65Tp05dxVkAAICKzKVh5/Tp07rxxhs1ZcqUUvvHjx+vyZMna9q0aVq7dq38/PwUHx+vc+fO2cb06tVL27dv1+LFizV37lytWrVK/fr1u1pTAAAAFZyHKzfepUsXdenSpdQ+wzD0+uuva9iwYbr77rslSR999JHCw8P1zTffqGfPntq5c6cWLFig9evXq1WrVpKkN998U3fddZf+7//+T5GRkVdtLgAAoGKqsNfspKenKzMzU3Fxcba2wMBAxcbGKi0tTZKUlpamoKAgW9CRpLi4OLm5uWnt2rUXXXd+fr7y8vLsXgAAwJwqbNjJzMyUJIWHh9u1h4eH2/oyMzMVFhZm1+/h4aHg4GDbmNKMHTtWgYGBtletWrWcXD0AAKgoKmzYKU9Dhw5Vbm6u7XXw4EFXlwQAAMpJhQ07ERERkqSsrCy79qysLFtfRESEsrOz7fovXLigEydO2MaUxtvbWwEBAXYvAABgThU27ERHRysiIkJLly61teXl5Wnt2rWyWq2SJKvVqpycHG3cuNE2ZtmyZSoqKlJsbOxVrxkAAFQ8Lr0b69SpU9q7d6/tfXp6urZs2aLg4GDVrl1bgwYN0r/+9S/Vr19f0dHRGj58uCIjI3XPPfdIkho3bqw777xTTzzxhKZNm6bz588rJSVFPXv25E4sAAAgycVhZ8OGDbr99ttt71NTUyVJSUlJmjFjhp5//nmdPn1a/fr1U05Ojtq2basFCxbIx8fHtsynn36qlJQUderUSW5ubkpMTNTkyZOv+lwAAEDF5NKw06FDBxmGcdF+i8WiMWPGaMyYMRcdExwcrM8++6w8ygMAACZQYa/ZAQAAcAbCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXThJ0pU6aoTp068vHxUWxsrNatW+fqkgAAQAVgirDz5ZdfKjU1VSNHjtSmTZt04403Kj4+XtnZ2a4uDQAAuJgpws7EiRP1xBNPqE+fPoqJidG0adPk6+urDz74wNWlAQAAF7vmw05BQYE2btyouLg4W5ubm5vi4uKUlpbmwsoAAEBF4OHqAq7U8ePHVVhYqPDwcLv28PBw7dq1q9Rl8vPzlZ+fb3ufm5srScrLyyufIgPrSecKy2fdAC6tSqSUl8e/Q8BVAuv98W+wHBT/3TYM45Ljrvmw44ixY8dq9OjRJdpr1arlgmoAlK+fJU1wdRFAJfaz1H9WuW7h5MmTCgwMvGj/NR92qlevLnd3d2VlZdm1Z2VlKSIiotRlhg4dqtTUVNv7oqIinThxQiEhIbJYLA7VkZeXp1q1aungwYMKCAhwaB3Xusq+Dyr7/CX2gcQ+qOzzl9gH0tXbB4Zh6OTJk4qMjLzkuGs+7Hh5eally5ZaunSp7rnnHkl/hJelS5cqJSWl1GW8vb3l7e1t1xYUFOSUegICAirtwV2ssu+Dyj5/iX0gsQ8q+/wl9oF0dfbBpc7oFLvmw44kpaamKikpSa1atdItt9yi119/XadPn1afPn1cXRoAAHAxU4SdBx98UMeOHdOIESOUmZmp5s2ba8GCBSUuWgYAAJWPKcKOJKWkpFz0Y6urwdvbWyNHjizx8VhlUtn3QWWfv8Q+kNgHlX3+EvtAqnj7wGL83f1aAAAA17Br/qGCAAAAl0LYAQAApkbYAQAApkbYAQAApkbYcYIpU6aoTp068vHxUWxsrNatW+fqksrNqlWr1K1bN0VGRspiseibb76x6zcMQyNGjFCNGjVUpUoVxcXFac+ePa4ptpyMHTtWN998s6pWraqwsDDdc8892r17t92Yc+fOKTk5WSEhIfL391diYmKJp3xfq9566y01a9bM9rAwq9Wq+fPn2/rNPPeLGTdunCwWiwYNGmRrM/t+GDVqlCwWi92rUaNGtn6zz1+SDh8+rIcfflghISGqUqWKmjZtqg0bNtj6zf77sE6dOiWOAYvFouTkZEkV6xgg7FyhL7/8UqmpqRo5cqQ2bdqkG2+8UfHx8crOznZ1aeXi9OnTuvHGGzVlypRS+8ePH6/Jkydr2rRpWrt2rfz8/BQfH69z585d5UrLz8qVK5WcnKw1a9Zo8eLFOn/+vDp37qzTp0/bxgwePFjff/+9Zs2apZUrV+rIkSPq0aOHC6t2npo1a2rcuHHauHGjNmzYoI4dO+ruu+/W9u3bJZl77qVZv3693n77bTVr1syuvTLshxtuuEFHjx61vX788Udbn9nn//vvv6tNmzby9PTU/PnztWPHDk2YMEHVqlWzjTH778P169fb/fdfvHixJOn++++XVMGOAQNX5JZbbjGSk5Nt7wsLC43IyEhj7NixLqzq6pBkzJkzx/a+qKjIiIiIMF577TVbW05OjuHt7W18/vnnLqjw6sjOzjYkGStXrjQM4485e3p6GrNmzbKN2blzpyHJSEtLc1WZ5apatWrGe++9V+nmfvLkSaN+/frG4sWLjfbt2xtPP/20YRiV4xgYOXKkceONN5baVxnmP2TIEKNt27YX7a+Mvw+ffvppo27dukZRUVGFOwY4s3MFCgoKtHHjRsXFxdna3NzcFBcXp7S0NBdW5hrp6enKzMy02x+BgYGKjY019f7Izc2VJAUHB0uSNm7cqPPnz9vth0aNGql27dqm2w+FhYX64osvdPr0aVmt1ko1d0lKTk5WQkKC3XylynMM7NmzR5GRkbr++uvVq1cvZWRkSKoc8//uu+/UqlUr3X///QoLC1OLFi307rvv2vor2+/DgoICffLJJ3rsscdksVgq3DFA2LkCx48fV2FhYYmvpQgPD1dmZqaLqnKd4jlXpv1RVFSkQYMGqU2bNmrSpImkP/aDl5dXiS+XNdN+2Lp1q/z9/eXt7a0nn3xSc+bMUUxMTKWYe7EvvvhCmzZt0tixY0v0VYb9EBsbqxkzZmjBggV66623lJ6erttuu00nT56sFPP/9ddf9dZbb6l+/fpauHCh+vfvr4EDB+rDDz+UVPl+H37zzTfKycnRo48+Kqni/RswzddFAK6QnJysbdu22V2rUBk0bNhQW7ZsUW5urmbPnq2kpCStXLnS1WVdNQcPHtTTTz+txYsXy8fHx9XluESXLl1sPzdr1kyxsbGKiorSzJkzVaVKFRdWdnUUFRWpVatWevXVVyVJLVq00LZt2zRt2jQlJSW5uLqr7/3331eXLl0UGRnp6lJKxZmdK1C9enW5u7uXuLo8KytLERERLqrKdYrnXFn2R0pKiubOnavly5erZs2atvaIiAgVFBQoJyfHbryZ9oOXl5fq1aunli1bauzYsbrxxhv1xhtvVIq5S398TJOdna2bbrpJHh4e8vDw0MqVKzV58mR5eHgoPDy8UuyHPwsKClKDBg20d+/eSnEc1KhRQzExMXZtjRs3tn2UV5l+Hx44cEBLlixR3759bW0V7Rgg7FwBLy8vtWzZUkuXLrW1FRUVaenSpbJarS6szDWio6MVERFhtz/y8vK0du1aU+0PwzCUkpKiOXPmaNmyZYqOjrbrb9mypTw9Pe32w+7du5WRkWGq/fBnRUVFys/PrzRz79Spk7Zu3aotW7bYXq1atVKvXr1sP1eG/fBnp06d0r59+1SjRo1KcRy0adOmxCMnfvnlF0VFRUmqPL8PJWn69OkKCwtTQkKCra3CHQNX/ZJok/niiy8Mb29vY8aMGcaOHTuMfv36GUFBQUZmZqarSysXJ0+eNDZv3mxs3rzZkGRMnDjR2Lx5s3HgwAHDMAxj3LhxRlBQkPHtt98aP//8s3H33Xcb0dHRxtmzZ11cufP079/fCAwMNFasWGEcPXrU9jpz5oxtzJNPPmnUrl3bWLZsmbFhwwbDarUaVqvVhVU7zwsvvGCsXLnSSE9PN37++WfjhRdeMCwWi7Fo0SLDMMw990v5891YhmH+/fDMM88YK1asMNLT043Vq1cbcXFxRvXq1Y3s7GzDMMw//3Xr1hkeHh7GK6+8YuzZs8f49NNPDV9fX+OTTz6xjakMvw8LCwuN2rVrG0OGDCnRV5GOAcKOE7z55ptG7dq1DS8vL+OWW24x1qxZ4+qSys3y5csNSSVeSUlJhmH8cbvl8OHDjfDwcMPb29vo1KmTsXv3btcW7WSlzV+SMX36dNuYs2fPGk899ZRRrVo1w9fX17j33nuNo0ePuq5oJ3rssceMqKgow8vLywgNDTU6depkCzqGYe65X8pfw47Z98ODDz5o1KhRw/Dy8jKuu+4648EHHzT27t1r6zf7/A3DML7//nujSZMmhre3t9GoUSPjnXfeseuvDL8PFy5caEgqdV4V6RiwGIZhXP3zSQAAAFcH1+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAuOZYRlv0za5vXF0GgGsE33oOoMLJPJWpV1a9onl75unwycMK8wtT84jmGhQ7SJ2u7+Tq8gBcYwg7ACqU/Tn71eaDNgryCdJrd7ympuFNdb7wvBbuW6jkH5K1K2WXq0sEcI0h7ACoUJ6a95Qssmhd33Xy8/Kztd8QdoMea/FYqcsMWTxEc3bN0aG8Q4rwj1Cvpr00ov0Iebp7SpL+l/k/DVo4SBuObJBFFtUPqa+3u76tVpGtdCDngFLmp+jHjB9VUFigOkF19Nodr+mu+ndJkrZlb9Nzi5/Tfw/8V35efupct7MmxU9Sdd/q5b8zADgFYQdAhXHi7Akt2LtAr3R8xS7oFAvyCSp1uareVTXjnhmKrBqprVlb9cT3T6iqd1U93+Z5SVKvr3upRY0WeivhLblb3LUlc4s83f4IQsk/JKugsECrHl0lPy8/7Ti2Q/5e/pKknHM56vhhR/W9qa8mxU/S2fNnNWTJED0w6wEtS1pWPjsBgNMRdgBUGHtP7JUhQ42qN7qs5Ya1G2b7uU5QHT3727P6YtsXtrCTkZuh5259zrbe+iH1beMzcjOU2DhRTcObSpKur3a9re8/6/6jFjVa6NVOr9raPrj7A9WaVEu//PaLGoQ0uPxJArjqCDsAKgzDMBxa7sttX2ryusnad2KfThWc0oWiCwrwDrD1p1pT1ff7vvr4548Vd32c7o+5X3WD60qSBsYOVP95/bXo10WKi45TYkyimoU3kyT9L+t/Wp6+XP6v+pfY5r4T+wg7wDWCW88BVBj1Q+rLIot2HS/7RchpB9PU6+teuqveXZr7j7na/M/Neum2l1RQWGAbM6rDKG1/arsS6idoWfoyxUyN0ZydcyRJfW/qq18H/qrezXpra/ZWtXqnld5c+6Yk6VTBKXVr2E1bntxi99ozYI/aRbVz7uQBlBvCDoAKI7hKsOLrxWvK+ik6XXC6RH/OuZwSbT8d/ElRQVF6qd1LahXZSvVD6utA7oES4xqENNBg62At6r1IPRr30PQt0219tQJr6clWT+rrB7/WM9Zn9O6mdyVJN0XcpO3Z21UnqI7qBdeze5V2TRGAiomwA6BCmXLXFBUahbrlvVv01Y6vtOe3Pdp5bKcmr50s6/vWEuPrh9RXRm6Gvtj2hfad2KfJaydrzq45tv6z588q5YcUrdi/QgdyDmh1xmqtP7xejas3liQNWjBIC/cuVPrv6dp0dJOW71+uxqF/9CXfkqwTZ0/ooa8e0vrD67XvxD4t3LtQfb7to8KiwquzQwBcMa7ZAVChXF/tem3qt0mv/PcVPbPoGR09dVShvqFqGdlSbyW8VWJ894bdNbj1YKX8kKL8wnwl1E/Q8HbDNWrFKEmSu5u7fjv7mx6Z84iyTmepum919WjUQ6NvHy1JKiwqVPIPyTqUd0gB3gG6s96dmhQ/SZIUWTVSqx9brSFLhqjzJ52VfyFfUUFRurPunXKz8P+KwLXCYjh6RSAAAMA1gP81AQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApvb/AVB5EdLUSTi/AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Gráfico númerico (1 variável)\n",
+    "# filtro com condicional\n",
+    "filter_hist = (df['Age'] > 0.8) & (df['Age'] < 70.72)\n",
+    "\n",
+    "# gerando o histograma\n",
+    "df.loc[filter_hist, 'Age'].plot.hist(bins=2, edgecolor=\"White\", color = \"#ff6c00\")\n",
+    "\n",
+    "plt.xlabel(\"Classe\", color = \"Green\") \n",
+    "plt.ylabel(\"Quantidade\", color = \"Green\") \n",
+    "plt.title(\"Distribuição de Classes\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.loc[(df['Age'] == 80), :]\n",
+    "df.loc[filter_hist, 'Age'].plot.hist(bins=100, edgecolor=\"White\", color = \"#ff6c00\")\n",
+    "\n",
+    "plt.xlabel(\"Classe\", color = \"Green\") \n",
+    "plt.ylabel(\"Quantidade\", color = \"Green\") \n",
+    "plt.title(\"Distribuição de Classes\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filter_hist = (df['Age'] > 0.8) & (df['Age'] < 70.72)\n",
+    "fig = plt.figure(figsize=(10, 8))\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "plt.title('Ambiente de Plotagem 3D', fontsize=20)\n",
+    "plt.xlabel('Eixo X', fontsize=15)\n",
+    "plt.ylabel('Eixo Y', fontsize=15)\n",
+    "plt.show;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "# Gráficos de barras 3D\n",
+    "df.loc[(df['Age'] == 80), :]\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "num_barras = 15\n",
+    "x_pos = random.sample(range(20), num_barras)\n",
+    "y_pos = random.sample(range(20), num_barras)\n",
+    "z_pos = [0] * num_barras\n",
+    "x_size = np.ones(num_barras)\n",
+    "y_size = np.ones(num_barras)\n",
+    "z_size = random.sample(range(20), num_barras)\n",
+    "plt.plot(x_pos, y_pos, z_pos, x_size, y_size, z_size, color='#ff6c00')\n",
+    "plt.title('Gráfico de barras 3D', fontsize=18);\n",
+    "plt.xlabel('Eixo X', fontsize=15)\n",
+    "plt.ylabel('Eixo Y', fontsize=15)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"150\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://i.gifer.com/54Gq.gif\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x215e2401350>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import IFrame\n",
+    "iframe_url = \"https://i.gifer.com/54Gq.gif\"\n",
+    "IFrame(src=iframe_url, width=150, height=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "THE END ? !"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prof eu queria pedir minhas sinceras desculpas, eu sei que poderia ter ido além e ter me esforçado mais, mas, não consegui."
+   ]
+  }
+ ],
+ "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/exercicios/para-casa/titanic.csv b/exercicios/para-casa/titanic.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/exercicios/para-casa/titanic.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
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diff --git a/material/aula_revisao.ipynb b/material/aula_revisao.ipynb
index 916e3c7..6ff0ebe 100644
--- a/material/aula_revisao.ipynb
+++ b/material/aula_revisao.ipynb
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -1970,7 +1970,7 @@
        "99          100         0       2                                  Kantor, Mr. Sinai    male  34.00      1      0            244367   26.00          NaN        S"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/material/aula_s12.ipynb b/material/aula_s12.ipynb
index 6a3a013..7305d41 100644
--- a/material/aula_s12.ipynb
+++ b/material/aula_s12.ipynb
@@ -114,17 +114,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# carregando a biblioteca \n",
-    "import pandas as pd"
+    "Teste"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "id": "_eorJ3GNS_mm",
     "outputId": "14f2b233-cc14-4f8a-a041-98b4cb90d2cc"
@@ -379,13 +378,14 @@
        "[891 rows x 12 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# carregando o dataset \n",
+    "import pandas as pd\n",
+    "\n",
     "df = pd.read_csv(\"titanic.csv\")\n",
     "df"
    ]
@@ -401,36 +401,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "id": "ZuehjTU7S_mt",
     "outputId": "6ce66e1f-01d4-4889-f97f-50d5edae71e6"
    },
    "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": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket', 'NumeroCabine', 'PortoEmbarcacao']\n"
+     ]
     }
    ],
    "source": [
     "# Resolução\n",
+    "\n",
     "traducoes = {\n",
     "    'PassengerId': 'IdPassageiro',\n",
     "    'Survived': 'Sobreviveu', # 0 = Não, 1 = Sim\n",
@@ -446,17 +433,18 @@
     "    'Embarked': 'PortoEmbarcacao' # C = Cherbourg, Q = Queenstown, S = Southampton\n",
     "}\n",
     "\n",
-    "novas_colunas = []\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"
+    "print(novas_colunas)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "id": "h6tvl5HPS_mw",
     "outputId": "4639f186-8484-4780-f4dc-0a29f3cc759e"
@@ -724,7 +712,7 @@
        "[891 rows x 12 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "id": "gZKfzWYAS_mx",
     "outputId": "cdc7a73b-0f3b-42ce-cdf2-f8322b15e686"
@@ -1021,7 +1009,7 @@
        "[891 rows x 12 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1031,9 +1019,86 @@
     "df"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "xzHvkQRrS_mx",
+    "outputId": "ca4d371d-386e-433d-e49b-6e5a6749f767"
+   },
+   "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",
+    "df_filtrado.shape"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {
+    "id": "ZFZ7LOT6S_my",
+    "outputId": "e41628cd-501b-4009-d643-c91f7fe9d7e4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(103, 12)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Agora um filtro com múltiplas condições\n",
+    "df_filtrado = df[(df['Idade'] > 30) & (df['Sexo'] == 'female')]\n",
+    "df_filtrado.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "BpueQ0G0S_my",
+    "outputId": "ba8ca09f-6788-4332-9a11-786aa02700e4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 12)"
+      ]
+     },
+     "execution_count": 7,
+     "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.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1073,269 +1138,141 @@
        "  </thead>\n",
        "  <tbody>\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>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",
+       "      <th>59</th>\n",
+       "      <td>60</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>Goodwin, Master. William Frederick</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>11.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CA 2144</td>\n",
+       "      <td>46.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</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",
+       "      <th>71</th>\n",
+       "      <td>72</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>11</th>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Bonnell, Miss. Elizabeth</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Goodwin, Miss. Lillian Amy</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>16.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CA 2144</td>\n",
+       "      <td>46.9</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>873</th>\n",
-       "      <td>874</td>\n",
+       "      <th>386</th>\n",
+       "      <td>387</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
+       "      <td>Goodwin, Master. Sidney Leonard</td>\n",
        "      <td>male</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>345765</td>\n",
-       "      <td>9.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CA 2144</td>\n",
+       "      <td>46.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>879</th>\n",
-       "      <td>880</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11767</td>\n",
-       "      <td>83.1583</td>\n",
-       "      <td>C50</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>881</th>\n",
-       "      <td>882</td>\n",
+       "      <th>480</th>\n",
+       "      <td>481</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Markun, Mr. Johann</td>\n",
+       "      <td>Goodwin, Master. Harold Victor</td>\n",
        "      <td>male</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>349257</td>\n",
-       "      <td>7.8958</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CA 2144</td>\n",
+       "      <td>46.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>885</th>\n",
-       "      <td>886</td>\n",
+       "      <th>678</th>\n",
+       "      <td>679</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
+       "      <td>Goodwin, Mrs. Frederick (Augusta Tyler)</td>\n",
        "      <td>female</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>382652</td>\n",
-       "      <td>29.1250</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>CA 2144</td>\n",
+       "      <td>46.9</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Q</td>\n",
+       "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>890</th>\n",
-       "      <td>891</td>\n",
+       "      <th>683</th>\n",
+       "      <td>684</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>Goodwin, Mr. Charles Edward</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>14.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CA 2144</td>\n",
+       "      <td>46.9</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Q</td>\n",
+       "      <td>S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>305 rows × 12 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "     IdPassageiro  Sobreviveu  Classe  \\\n",
-       "1               2           1       1   \n",
-       "3               4           1       1   \n",
-       "4               5           0       3   \n",
-       "6               7           0       1   \n",
-       "11             12           1       1   \n",
-       "..            ...         ...     ...   \n",
-       "873           874           0       3   \n",
-       "879           880           1       1   \n",
-       "881           882           0       3   \n",
-       "885           886           0       3   \n",
-       "890           891           0       3   \n",
-       "\n",
-       "                                                  Nome    Sexo  Idade  \\\n",
-       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
-       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35.0   \n",
-       "4                             Allen, Mr. William Henry    male   35.0   \n",
-       "6                              McCarthy, Mr. Timothy J    male   54.0   \n",
-       "11                            Bonnell, Miss. Elizabeth  female   58.0   \n",
-       "..                                                 ...     ...    ...   \n",
-       "873                        Vander Cruyssen, Mr. Victor    male   47.0   \n",
-       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female   56.0   \n",
-       "881                                 Markun, Mr. Johann    male   33.0   \n",
-       "885               Rice, Mrs. William (Margaret Norton)  female   39.0   \n",
-       "890                                Dooley, Mr. Patrick    male   32.0   \n",
-       "\n",
-       "     NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
-       "1               1           0     PC 17599      71.2833          C85   \n",
-       "3               1           0       113803      53.1000         C123   \n",
-       "4               0           0       373450       8.0500          NaN   \n",
-       "6               0           0        17463      51.8625          E46   \n",
-       "11              0           0       113783      26.5500         C103   \n",
-       "..            ...         ...          ...          ...          ...   \n",
-       "873             0           0       345765       9.0000          NaN   \n",
-       "879             0           1        11767      83.1583          C50   \n",
-       "881             0           0       349257       7.8958          NaN   \n",
-       "885             0           5       382652      29.1250          NaN   \n",
-       "890             0           0       370376       7.7500          NaN   \n",
+       "59             60           0       3   \n",
+       "71             72           0       3   \n",
+       "386           387           0       3   \n",
+       "480           481           0       3   \n",
+       "678           679           0       3   \n",
+       "683           684           0       3   \n",
        "\n",
-       "    PortoEmbarcacao  \n",
-       "1                 C  \n",
-       "3                 S  \n",
-       "4                 S  \n",
-       "6                 S  \n",
-       "11                S  \n",
-       "..              ...  \n",
-       "873               S  \n",
-       "879               C  \n",
-       "881               S  \n",
-       "885               Q  \n",
-       "890               Q  \n",
+       "                                        Nome    Sexo  Idade  NumeroIrmaos  \\\n",
+       "59        Goodwin, Master. William Frederick    male   11.0             5   \n",
+       "71                Goodwin, Miss. Lillian Amy  female   16.0             5   \n",
+       "386          Goodwin, Master. Sidney Leonard    male    1.0             5   \n",
+       "480           Goodwin, Master. Harold Victor    male    9.0             5   \n",
+       "678  Goodwin, Mrs. Frederick (Augusta Tyler)  female   43.0             1   \n",
+       "683              Goodwin, Mr. Charles Edward    male   14.0             5   \n",
        "\n",
-       "[305 rows x 12 columns]"
+       "     NumeroPais NumeroTicket  PrecoTicket NumeroCabine PortoEmbarcacao  \n",
+       "59            2      CA 2144         46.9          NaN               S  \n",
+       "71            2      CA 2144         46.9          NaN               S  \n",
+       "386           2      CA 2144         46.9          NaN               S  \n",
+       "480           2      CA 2144         46.9          NaN               S  \n",
+       "678           6      CA 2144         46.9          NaN               S  \n",
+       "683           2      CA 2144         46.9          NaN               S  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[df['Idade'] > 30]"
+    "df_filtrado #Mostrando"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {
-    "id": "xzHvkQRrS_mx",
-    "outputId": "ca4d371d-386e-433d-e49b-6e5a6749f767"
+    "id": "ODR1BQRyS_my",
+    "outputId": "a2289b09-9eb0-460f-da3b-265bfaf400fb"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(305, 12)"
-      ]
-     },
-     "execution_count": 7,
-     "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",
-    "df_filtrado.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1374,269 +1311,147 @@
        "  </thead>\n",
        "  <tbody>\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>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>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",
+       "      <th>5</th>\n",
+       "      <td>6</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>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>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>248706</td>\n",
-       "      <td>16.0000</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>8.4583</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>19</td>\n",
+       "      <th>16</th>\n",
+       "      <td>17</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>Rice, Master. Eugene</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>345763</td>\n",
-       "      <td>18.0000</td>\n",
+       "      <td>382652</td>\n",
+       "      <td>29.1250</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",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>862</th>\n",
-       "      <td>863</td>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
        "      <td>female</td>\n",
-       "      <td>48.0</td>\n",
+       "      <td>15.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>17466</td>\n",
-       "      <td>25.9292</td>\n",
-       "      <td>D17</td>\n",
-       "      <td>S</td>\n",
+       "      <td>330923</td>\n",
+       "      <td>8.0292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>865</th>\n",
-       "      <td>866</td>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
+       "      <td>3</td>\n",
+       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
        "      <td>female</td>\n",
-       "      <td>42.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>236852</td>\n",
-       "      <td>13.0000</td>\n",
+       "      <td>330959</td>\n",
+       "      <td>7.8792</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>871</th>\n",
-       "      <td>872</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11751</td>\n",
-       "      <td>52.5542</td>\n",
-       "      <td>D35</td>\n",
-       "      <td>S</td>\n",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>879</th>\n",
-       "      <td>880</td>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11767</td>\n",
-       "      <td>83.1583</td>\n",
-       "      <td>C50</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>885</th>\n",
-       "      <td>886</td>\n",
-       "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
+       "      <td>Glynn, Miss. Mary Agatha</td>\n",
        "      <td>female</td>\n",
-       "      <td>39.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>382652</td>\n",
-       "      <td>29.1250</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",
-       "<p>103 rows × 12 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     IdPassageiro  Sobreviveu  Classe  \\\n",
-       "1               2           1       1   \n",
-       "3               4           1       1   \n",
-       "11             12           1       1   \n",
-       "15             16           1       2   \n",
-       "18             19           0       3   \n",
-       "..            ...         ...     ...   \n",
-       "862           863           1       1   \n",
-       "865           866           1       2   \n",
-       "871           872           1       1   \n",
-       "879           880           1       1   \n",
-       "885           886           0       3   \n",
-       "\n",
-       "                                                  Nome    Sexo  Idade  \\\n",
-       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
-       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35.0   \n",
-       "11                            Bonnell, Miss. Elizabeth  female   58.0   \n",
-       "15                    Hewlett, Mrs. (Mary D Kingcome)   female   55.0   \n",
-       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female   31.0   \n",
-       "..                                                 ...     ...    ...   \n",
-       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female   48.0   \n",
-       "865                           Bystrom, Mrs. (Karolina)  female   42.0   \n",
-       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female   47.0   \n",
-       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female   56.0   \n",
-       "885               Rice, Mrs. William (Margaret Norton)  female   39.0   \n",
+       "    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",
-       "     NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
-       "1               1           0     PC 17599      71.2833          C85   \n",
-       "3               1           0       113803      53.1000         C123   \n",
-       "11              0           0       113783      26.5500         C103   \n",
-       "15              0           0       248706      16.0000          NaN   \n",
-       "18              1           0       345763      18.0000          NaN   \n",
-       "..            ...         ...          ...          ...          ...   \n",
-       "862             0           0        17466      25.9292          D17   \n",
-       "865             0           0       236852      13.0000          NaN   \n",
-       "871             1           1        11751      52.5542          D35   \n",
-       "879             0           1        11767      83.1583          C50   \n",
-       "885             0           5       382652      29.1250          NaN   \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",
-       "1                 C  \n",
-       "3                 S  \n",
-       "11                S  \n",
-       "15                S  \n",
-       "18                S  \n",
-       "..              ...  \n",
-       "862               S  \n",
-       "865               S  \n",
-       "871               S  \n",
-       "879               C  \n",
-       "885               Q  \n",
-       "\n",
-       "[103 rows x 12 columns]"
+       "   PortoEmbarcacao  \n",
+       "5                Q  \n",
+       "16               Q  \n",
+       "22               Q  \n",
+       "28               Q  \n",
+       "32               Q  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[(df['Idade'] > 30) & (df['Sexo'] == 'female')]"
+    "# Por último, utilizando a função isin() = Dentre\n",
+    "df_filtrado = df[df['PortoEmbarcacao'].isin(['Q'])]\n",
+    "df_filtrado.head()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
+   "cell_type": "markdown",
    "metadata": {
-    "id": "ZFZ7LOT6S_my",
-    "outputId": "e41628cd-501b-4009-d643-c91f7fe9d7e4"
+    "id": "8gULcYPzS_mz"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(103, 12)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "# Agora um filtro com múltiplas condições\n",
-    "df_filtrado = df[(df['Idade'] > 30) & (df['Sexo'] == 'female')]\n",
-    "df_filtrado.shape"
+    "### Consultas e filtros\n",
+    "### Consultas\n",
+    "\n",
+    "Pandas também possui a função query(), que realiza consultas no DataFrame através de uma expressão booleana (T/F).\n",
+    "\n",
+    "**Docs**: [https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
+   "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "# Shape mostra a quantidade\n",
+    "# Head mostra impresso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "W-Ktv45VS_mz",
+    "outputId": "00f9464e-df1b-4cd2-c0b3-30eac23016f1"
+   },
    "outputs": [
     {
      "data": {
@@ -1675,17 +1490,77 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>855</th>\n",
-       "      <td>856</td>\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>2</th>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Aks, Mrs. Sam (Leah Rosen)</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
        "      <td>female</td>\n",
-       "      <td>18.0</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>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>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>392091</td>\n",
-       "      <td>9.35</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>21.0750</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
@@ -1694,37 +1569,26 @@
        "</div>"
       ],
       "text/plain": [
-       "     IdPassageiro  Sobreviveu  Classe                        Nome    Sexo  \\\n",
-       "855           856           1       3  Aks, Mrs. Sam (Leah Rosen)  female   \n",
+       "   IdPassageiro  Sobreviveu  Classe                            Nome    Sexo  \\\n",
+       "0             1           0       3         Braund, Mr. Owen Harris    male   \n",
+       "2             3           1       3          Heikkinen, Miss. Laina  female   \n",
+       "4             5           0       3        Allen, Mr. William Henry    male   \n",
+       "5             6           0       3                Moran, Mr. James    male   \n",
+       "7             8           0       3  Palsson, Master. Gosta Leonard    male   \n",
        "\n",
-       "     Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
-       "855   18.0             0           1       392091         9.35          NaN   \n",
+       "   Idade  NumeroIrmaos  NumeroPais      NumeroTicket  PrecoTicket  \\\n",
+       "0   22.0             1           0         A/5 21171       7.2500   \n",
+       "2   26.0             0           0  STON/O2. 3101282       7.9250   \n",
+       "4   35.0             0           0            373450       8.0500   \n",
+       "5    NaN             0           0            330877       8.4583   \n",
+       "7    2.0             3           1            349909      21.0750   \n",
        "\n",
-       "    PortoEmbarcacao  \n",
-       "855               S  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df['Nome'].str.contains('Rose')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "id": "BpueQ0G0S_my",
-    "outputId": "ba8ca09f-6788-4332-9a11-786aa02700e4"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 12)"
+       "  NumeroCabine PortoEmbarcacao  \n",
+       "0          NaN               S  \n",
+       "2          NaN               S  \n",
+       "4          NaN               S  \n",
+       "5          NaN               Q  \n",
+       "7          NaN               S  "
       ]
      },
      "execution_count": 11,
@@ -1733,17 +1597,18 @@
     }
    ],
    "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('Jack')]\n",
-    "df_filtrado.shape"
+    "# 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"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "id": "uVSmQ1zeS_mz",
+    "outputId": "555cf64e-c5a6-4b3d-d69a-89fb77dd1012"
+   },
    "outputs": [
     {
      "data": {
@@ -1782,33 +1647,112 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>766</th>\n",
-       "      <td>767</td>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>Brewe, Dr. Arthur Jackson</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>112379</td>\n",
-       "      <td>39.6</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
+       "      <td>S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     IdPassageiro  Sobreviveu  Classe                       Nome  Sexo  Idade  \\\n",
-       "766           767           0       1  Brewe, Dr. Arthur Jackson  male    NaN   \n",
+       "   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",
+       "                                                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",
-       "     NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
-       "766             0           0       112379         39.6          NaN   \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",
-       "    PortoEmbarcacao  \n",
-       "766               C  "
+       "  PortoEmbarcacao  \n",
+       "0               S  \n",
+       "1               C  \n",
+       "2               S  \n",
+       "3               S  \n",
+       "4               S  "
       ]
      },
      "execution_count": 12,
@@ -1816,97 +1760,10 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "df_filtrado"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "ODR1BQRyS_my",
-    "outputId": "a2289b09-9eb0-460f-da3b-265bfaf400fb"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(77, 12)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Por último, utilizando a função isin()\n",
-    "df_filtrado = df[df['PortoEmbarcacao'].isin(['Q'])]\n",
-    "df_filtrado.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "8gULcYPzS_mz"
-   },
-   "source": [
-    "### Consultas e filtros\n",
-    "### Consultas\n",
-    "\n",
-    "Pandas também possui a função query(), que realiza consultas no DataFrame através de uma expressão booleana (T/F).\n",
-    "\n",
-    "**Docs**: [https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "W-Ktv45VS_mz",
-    "outputId": "00f9464e-df1b-4cd2-c0b3-30eac23016f1"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(491, 12)"
-      ]
-     },
-     "execution_count": 14,
-     "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.shape\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "id": "uVSmQ1zeS_mz",
-    "outputId": "555cf64e-c5a6-4b3d-d69a-89fb77dd1012"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(707, 12)"
-      ]
-     },
-     "execution_count": 15,
-     "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.shape"
+    "df_consulta = df.query(\"Classe in (1,3)\")\n",
+    "df_consulta.head()"
    ]
   },
   {
@@ -1922,7 +1779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
     "id": "H3dlaegrS_m0",
     "outputId": "1c7ad974-196c-40db-8eb7-fa78f65ff480"
@@ -2073,7 +1930,7 @@
        "4               S  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2100,7 +1957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "id": "cr5Lt68uS_m4",
     "outputId": "8786bf85-9a78-4ff2-a2e5-12a514640843"
@@ -2209,14 +2066,13 @@
        "[891 rows x 2 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_loc = df.loc[:, [\"Sobreviveu\", \"Idade\"]]\n",
-    "df_loc"
+    "df.loc[:, [\"Sobreviveu\", \"Idade\"]]"
    ]
   },
   {
@@ -2235,7 +2091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {
     "id": "NzQE8tnOS_m4",
     "outputId": "7e798f09-5b8e-4561-d1ab-bc2b460fd619"
@@ -2344,7 +2200,7 @@
        "[891 rows x 2 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2355,7 +2211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {
     "id": "PSjsxwSuS_m4",
     "outputId": "cb5f5ab9-92b9-4de7-f718-90515d59ab14"
@@ -2398,114 +2254,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>11</td>\n",
+       "      <th>0</th>\n",
        "      <td>1</td>\n",
+       "      <td>0</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>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
        "      <td>1</td>\n",
-       "      <td>PP 9549</td>\n",
-       "      <td>16.7000</td>\n",
-       "      <td>G6</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>11</th>\n",
-       "      <td>12</td>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>Bonnell, Miss. Elizabeth</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td>female</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</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",
+       "      <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                                  Nome  \\\n",
-       "10            11           1       3       Sandstrom, Miss. Marguerite Rut   \n",
-       "11            12           1       1              Bonnell, Miss. Elizabeth   \n",
-       "12            13           0       3        Saundercock, Mr. William Henry   \n",
-       "13            14           0       3           Andersson, Mr. Anders Johan   \n",
-       "14            15           0       3  Vestrom, Miss. Hulda Amanda Adolfina   \n",
+       "   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",
-       "      Sexo  Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket  \\\n",
-       "10  female    4.0             1           1      PP 9549      16.7000   \n",
-       "11  female   58.0             0           0       113783      26.5500   \n",
-       "12    male   20.0             0           0    A/5. 2151       8.0500   \n",
-       "13    male   39.0             1           5       347082      31.2750   \n",
-       "14  female   14.0             0           0       350406       7.8542   \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",
-       "   NumeroCabine PortoEmbarcacao  \n",
-       "10           G6               S  \n",
-       "11         C103               S  \n",
-       "12          NaN               S  \n",
-       "13          NaN               S  \n",
-       "14          NaN               S  "
+       "  PortoEmbarcacao  \n",
+       "0               S  \n",
+       "1               C  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.iloc[10:15, :]"
+    "df.iloc[[0,1],:]"
    ]
   },
   {
@@ -2522,173 +2328,51 @@
     "- Utilizar a função loc para obter a idade e sobrevivência do passageiro;\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Alomoço"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"300\"\n",
-       "            height=\"250\"\n",
-       "            src=\"https://giphy.com/embed/7AKbdZiyTx98fPHG0Z\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x2becd951610>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import IFrame\n",
-    "\n",
-    "# GIF link used as IFrame\n",
-    "iframe_url = \"https://giphy.com/embed/7AKbdZiyTx98fPHG0Z\"\n",
-    "\n",
-    "# resized output IFrame\n",
-    "IFrame(src=iframe_url, width=300, height=250)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nES063ppS_m4"
-   },
-   "source": [
-    "### Agrupamento / Agregação\n",
-    "\n",
-    "Podemos criar um agrupamento de categorias e aplicar uma função às categorias. É um conceito simples, mas é uma técnica extremamente valiosa, amplamente utilizada na ciência de dados. Em projetos reais de ciência de dados, você lidará com grandes quantidades de dados e tentará várias vezes, portanto, para eficiência, usamos o conceito Groupby. O conceito de Groupby é realmente importante por causa de sua capacidade de resumir, agregar e agrupar dados com eficiência.\n",
-    "\n",
-    "\n",
-    "[Fonte](https://acervolima.com/pandas-groupby-resumindo-agregando-e-agrupando-dados-em-python/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "id": "noSCxvLQS_m6",
-    "outputId": "fd434c19-19d3-4395-a595-83703e140d1b"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002BED4E60F50>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Agrupar passageiros por classe\n",
-    "dado_agrupado = df.groupby('Classe')\n",
-    "dado_agrupado"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Classe\n",
-       "1    216\n",
-       "2    184\n",
-       "3    491\n",
-       "Name: IdPassageiro, dtype: int64"
+       "(342, 12)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Contagem de passageiros em cada classe, usando os dados agrupados no passo anterior\n",
-    "contagem_passageiros = dado_agrupado['IdPassageiro'].count()\n",
-    "contagem_passageiros"
+    "df_filtrado = df[df['Sobreviveu'] == 1]\n",
+    "df_filtrado.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Classe\n",
-       "3    491\n",
-       "1    216\n",
-       "2    184\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# função value counts\n",
-    "df['Classe'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "id": "UxbPOZJfS_m7",
-    "outputId": "f2031638-fbad-45b1-94a6-1415795d6800"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Classe\n",
-       "1    38.233441\n",
-       "2    29.877630\n",
-       "3    25.140620\n",
-       "Name: Idade, dtype: float64"
+       "(0, 12)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Média de Idade de passageiros em cada classe\n",
-    "media_idade = dado_agrupado['Idade'].mean()\n",
-    "media_idade"
+    "df_filtrado = df[(df['Idade'] > 50) & (df['Sexo'] == 'female') & (df['Nome'] == 'Nome')]\n",
+    "df_filtrado.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2712,49 +2396,129 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>99%</th>\n",
-       "      <th>99.5%</th>\n",
-       "      <th>max</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>Idade</th>\n",
-       "      <td>714.0</td>\n",
-       "      <td>29.699118</td>\n",
-       "      <td>14.526497</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>65.87</td>\n",
-       "      <td>70.7175</td>\n",
-       "      <td>80.0</td>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Arnold-Franchi, Mrs. Josef (Josefine Franchi)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>349237</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>100</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kantor, Mr. Sinai</td>\n",
+       "      <td>male</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>244367</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       count       mean        std   min   50%    99%    99.5%   max\n",
-       "Idade  714.0  29.699118  14.526497  0.42  28.0  65.87  70.7175  80.0"
+       "    IdPassageiro  Sobreviveu  Classe  \\\n",
+       "49            50           0       3   \n",
+       "99           100           0       2   \n",
+       "\n",
+       "                                             Nome    Sexo  Idade  \\\n",
+       "49  Arnold-Franchi, Mrs. Josef (Josefine Franchi)  female   18.0   \n",
+       "99                              Kantor, Mr. Sinai    male   34.0   \n",
+       "\n",
+       "    NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "49             1           0       349237         17.8          NaN   \n",
+       "99             1           0       244367         26.0          NaN   \n",
+       "\n",
+       "   PortoEmbarcacao  \n",
+       "49               S  \n",
+       "99               S  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_consulta = df.query(\"IdPassageiro in (50, 100)\")\n",
+    "df_consulta.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.head of      IdPassageiro  Sobreviveu  Classe                               Nome  \\\n",
+       "143           144           0       3                Burke, Mr. Jeremiah   \n",
+       "337           338           1       1    Burns, Miss. Elizabeth Margaret   \n",
+       "387           388           1       2                   Buss, Miss. Kate   \n",
+       "536           537           0       1  Butt, Major. Archibald Willingham   \n",
+       "666           667           0       2        Butler, Mr. Reginald Fenton   \n",
+       "846           847           0       3           Sage, Mr. Douglas Bullen   \n",
+       "\n",
+       "       Sexo  Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket  \\\n",
+       "143    male   19.0             0           0       365222         6.75   \n",
+       "337  female   41.0             0           0        16966       134.50   \n",
+       "387  female   36.0             0           0        27849        13.00   \n",
+       "536    male   45.0             0           0       113050        26.55   \n",
+       "666    male   25.0             0           0       234686        13.00   \n",
+       "846    male    NaN             8           2     CA. 2343        69.55   \n",
+       "\n",
+       "    NumeroCabine PortoEmbarcacao  \n",
+       "143          NaN               Q  \n",
+       "337          E40               C  \n",
+       "387          NaN               S  \n",
+       "536          B38               S  \n",
+       "666          NaN               S  \n",
+       "846          NaN               S  >"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[['Idade']].describe(percentiles=[0.99, 0.995]).T"
+    "df_filtrado = df[df['Nome'].str.contains('Bu')]\n",
+    "df_filtrado.head"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2794,77 +2558,2402 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>97</td>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>Goldschmidt, Mr. George B</td>\n",
-       "      <td>male</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>PC 17754</td>\n",
-       "      <td>34.6542</td>\n",
-       "      <td>A5</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>116</th>\n",
-       "      <td>117</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Connors, Mr. Patrick</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
        "      <td>male</td>\n",
-       "      <td>70.5</td>\n",
-       "      <td>0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>370369</td>\n",
-       "      <td>7.7500</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Q</td>\n",
+       "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>493</th>\n",
-       "      <td>494</td>\n",
-       "      <td>0</td>\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>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>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>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>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>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",
+       "    <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",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Wheadon, Mr. Edward H</td>\n",
+       "      <td>male</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C.A. 24579</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Mamee, Mr. Hanna</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2677</td>\n",
+       "      <td>7.2292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Cann, Mr. Ernest Charles</td>\n",
+       "      <td>male</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A./5. 2152</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Vander Planke, Miss. Augusta Maria</td>\n",
+       "      <td>female</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>345764</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Nicola-Yarred, Miss. Jamila</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2651</td>\n",
+       "      <td>11.2417</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Ahlin, Mrs. Johan (Johanna Persdotter Larsson)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7546</td>\n",
+       "      <td>9.4750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Turpin, Mrs. William John Robert (Dorothy Ann ...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11668</td>\n",
+       "      <td>21.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kraeff, Mr. Theodor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>349253</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
+       "      <td>female</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SC/Paris 2123</td>\n",
+       "      <td>41.5792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Devaney, Miss. Margaret Delia</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330958</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Rogers, Mr. William John</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>S.C./A.4. 23567</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lennon, Mr. Denis</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370371</td>\n",
+       "      <td>15.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>O'Driscoll, Miss. Bridget</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14311</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Samaan, Mr. Youssef</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2662</td>\n",
+       "      <td>21.6792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Arnold-Franchi, Mrs. Josef (Josefine Franchi)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>349237</td>\n",
+       "      <td>17.8000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Panula, Master. Juha Niilo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101295</td>\n",
+       "      <td>39.6875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>52</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Nosworthy, Mr. Richard Cater</td>\n",
+       "      <td>male</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/4. 39886</td>\n",
+       "      <td>7.8000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>54</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2926</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>57</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rugg, Miss. Emily</td>\n",
+       "      <td>female</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C.A. 31026</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>58</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Novel, Mr. Mansouer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2697</td>\n",
+       "      <td>7.2292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>59</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>West, Miss. Constance Mirium</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>C.A. 34651</td>\n",
+       "      <td>27.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Goodwin, Master. William Frederick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CA 2144</td>\n",
+       "      <td>46.9000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>61</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Sirayanian, Mr. Orsen</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2669</td>\n",
+       "      <td>7.2292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Skoog, Master. Harald</td>\n",
+       "      <td>male</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>347088</td>\n",
+       "      <td>27.9000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>66</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Moubarek, Master. Gerios</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2661</td>\n",
+       "      <td>15.2458</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    IdPassageiro  Sobreviveu  Classe  \\\n",
+       "0              1           0       3   \n",
+       "2              3           1       3   \n",
+       "4              5           0       3   \n",
+       "5              6           0       3   \n",
+       "7              8           0       3   \n",
+       "8              9           1       3   \n",
+       "9             10           1       2   \n",
+       "10            11           1       3   \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",
+       "24            25           0       3   \n",
+       "25            26           1       3   \n",
+       "26            27           0       3   \n",
+       "28            29           1       3   \n",
+       "29            30           0       3   \n",
+       "32            33           1       3   \n",
+       "33            34           0       2   \n",
+       "36            37           1       3   \n",
+       "37            38           0       3   \n",
+       "38            39           0       3   \n",
+       "39            40           1       3   \n",
+       "40            41           0       3   \n",
+       "41            42           0       2   \n",
+       "42            43           0       3   \n",
+       "43            44           1       2   \n",
+       "44            45           1       3   \n",
+       "45            46           0       3   \n",
+       "46            47           0       3   \n",
+       "47            48           1       3   \n",
+       "48            49           0       3   \n",
+       "49            50           0       3   \n",
+       "50            51           0       3   \n",
+       "51            52           0       3   \n",
+       "53            54           1       2   \n",
+       "56            57           1       2   \n",
+       "57            58           0       3   \n",
+       "58            59           1       2   \n",
+       "59            60           0       3   \n",
+       "60            61           0       3   \n",
+       "63            64           0       3   \n",
+       "65            66           1       3   \n",
+       "\n",
+       "                                                 Nome    Sexo  Idade  \\\n",
+       "0                             Braund, Mr. Owen Harris    male   22.0   \n",
+       "2                              Heikkinen, Miss. Laina  female   26.0   \n",
+       "4                            Allen, Mr. William Henry    male   35.0   \n",
+       "5                                    Moran, Mr. James    male    NaN   \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",
+       "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",
+       "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",
+       "28                      O'Dwyer, Miss. Ellen \"Nellie\"  female    NaN   \n",
+       "29                                Todoroff, Mr. Lalio    male    NaN   \n",
+       "32                           Glynn, Miss. Mary Agatha  female    NaN   \n",
+       "33                              Wheadon, Mr. Edward H    male   66.0   \n",
+       "36                                   Mamee, Mr. Hanna    male    NaN   \n",
+       "37                           Cann, Mr. Ernest Charles    male   21.0   \n",
+       "38                 Vander Planke, Miss. Augusta Maria  female   18.0   \n",
+       "39                        Nicola-Yarred, Miss. Jamila  female   14.0   \n",
+       "40     Ahlin, Mrs. Johan (Johanna Persdotter Larsson)  female   40.0   \n",
+       "41  Turpin, Mrs. William John Robert (Dorothy Ann ...  female   27.0   \n",
+       "42                                Kraeff, Mr. Theodor    male    NaN   \n",
+       "43           Laroche, Miss. Simonne Marie Anne Andree  female    3.0   \n",
+       "44                      Devaney, Miss. Margaret Delia  female   19.0   \n",
+       "45                           Rogers, Mr. William John    male    NaN   \n",
+       "46                                  Lennon, Mr. Denis    male    NaN   \n",
+       "47                          O'Driscoll, Miss. Bridget  female    NaN   \n",
+       "48                                Samaan, Mr. Youssef    male    NaN   \n",
+       "49      Arnold-Franchi, Mrs. Josef (Josefine Franchi)  female   18.0   \n",
+       "50                         Panula, Master. Juha Niilo    male    7.0   \n",
+       "51                       Nosworthy, Mr. Richard Cater    male   21.0   \n",
+       "53  Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...  female   29.0   \n",
+       "56                                  Rugg, Miss. Emily  female   21.0   \n",
+       "57                                Novel, Mr. Mansouer    male   28.5   \n",
+       "58                       West, Miss. Constance Mirium  female    5.0   \n",
+       "59                 Goodwin, Master. William Frederick    male   11.0   \n",
+       "60                              Sirayanian, Mr. Orsen    male   22.0   \n",
+       "63                              Skoog, Master. Harald    male    4.0   \n",
+       "65                           Moubarek, Master. Gerios    male    NaN   \n",
+       "\n",
+       "    NumeroIrmaos  NumeroPais      NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "0              1           0         A/5 21171       7.2500          NaN   \n",
+       "2              0           0  STON/O2. 3101282       7.9250          NaN   \n",
+       "4              0           0            373450       8.0500          NaN   \n",
+       "5              0           0            330877       8.4583          NaN   \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",
+       "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",
+       "24             3           1            349909      21.0750          NaN   \n",
+       "25             1           5            347077      31.3875          NaN   \n",
+       "26             0           0              2631       7.2250          NaN   \n",
+       "28             0           0            330959       7.8792          NaN   \n",
+       "29             0           0            349216       7.8958          NaN   \n",
+       "32             0           0            335677       7.7500          NaN   \n",
+       "33             0           0        C.A. 24579      10.5000          NaN   \n",
+       "36             0           0              2677       7.2292          NaN   \n",
+       "37             0           0        A./5. 2152       8.0500          NaN   \n",
+       "38             2           0            345764      18.0000          NaN   \n",
+       "39             1           0              2651      11.2417          NaN   \n",
+       "40             1           0              7546       9.4750          NaN   \n",
+       "41             1           0             11668      21.0000          NaN   \n",
+       "42             0           0            349253       7.8958          NaN   \n",
+       "43             1           2     SC/Paris 2123      41.5792          NaN   \n",
+       "44             0           0            330958       7.8792          NaN   \n",
+       "45             0           0   S.C./A.4. 23567       8.0500          NaN   \n",
+       "46             1           0            370371      15.5000          NaN   \n",
+       "47             0           0             14311       7.7500          NaN   \n",
+       "48             2           0              2662      21.6792          NaN   \n",
+       "49             1           0            349237      17.8000          NaN   \n",
+       "50             4           1           3101295      39.6875          NaN   \n",
+       "51             0           0        A/4. 39886       7.8000          NaN   \n",
+       "53             1           0              2926      26.0000          NaN   \n",
+       "56             0           0        C.A. 31026      10.5000          NaN   \n",
+       "57             0           0              2697       7.2292          NaN   \n",
+       "58             1           2        C.A. 34651      27.7500          NaN   \n",
+       "59             5           2           CA 2144      46.9000          NaN   \n",
+       "60             0           0              2669       7.2292          NaN   \n",
+       "63             3           2            347088      27.9000          NaN   \n",
+       "65             1           1              2661      15.2458          NaN   \n",
+       "\n",
+       "   PortoEmbarcacao  \n",
+       "0                S  \n",
+       "2                S  \n",
+       "4                S  \n",
+       "5                Q  \n",
+       "7                S  \n",
+       "8                S  \n",
+       "9                C  \n",
+       "10               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",
+       "24               S  \n",
+       "25               S  \n",
+       "26               C  \n",
+       "28               Q  \n",
+       "29               S  \n",
+       "32               Q  \n",
+       "33               S  \n",
+       "36               C  \n",
+       "37               S  \n",
+       "38               S  \n",
+       "39               C  \n",
+       "40               S  \n",
+       "41               S  \n",
+       "42               C  \n",
+       "43               C  \n",
+       "44               Q  \n",
+       "45               S  \n",
+       "46               Q  \n",
+       "47               Q  \n",
+       "48               C  \n",
+       "49               S  \n",
+       "50               S  \n",
+       "51               S  \n",
+       "53               S  \n",
+       "56               S  \n",
+       "57               C  \n",
+       "58               S  \n",
+       "59               S  \n",
+       "60               C  \n",
+       "63               S  \n",
+       "65               C  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_consulta = df.query(\"Classe in (2,3)\")\n",
+    "df_consulta.head(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "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": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filtrado = df[:30]\n",
+    "df_filtrado.head(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Uruchurtu, Don. Manuel E</td>\n",
+       "      <td>male</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17601</td>\n",
+       "      <td>27.7208</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17569</td>\n",
+       "      <td>146.5208</td>\n",
+       "      <td>B78</td>\n",
+       "      <td>C</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",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Wheadon, Mr. Edward H</td>\n",
+       "      <td>male</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C.A. 24579</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Meyer, Mr. Edgar Joseph</td>\n",
+       "      <td>male</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17604</td>\n",
+       "      <td>82.1708</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>36</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Holverson, Mr. Alexander Oskar</td>\n",
+       "      <td>male</td>\n",
+       "      <td>42.0</td>\n",
        "      <td>1</td>\n",
-       "      <td>Artagaveytia, Mr. Ramon</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113789</td>\n",
+       "      <td>52.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Mamee, Mr. Hanna</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2677</td>\n",
+       "      <td>7.2292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Cann, Mr. Ernest Charles</td>\n",
+       "      <td>male</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A./5. 2152</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Vander Planke, Miss. Augusta Maria</td>\n",
+       "      <td>female</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>345764</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Nicola-Yarred, Miss. Jamila</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2651</td>\n",
+       "      <td>11.2417</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Ahlin, Mrs. Johan (Johanna Persdotter Larsson)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7546</td>\n",
+       "      <td>9.4750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Turpin, Mrs. William John Robert (Dorothy Ann ...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11668</td>\n",
+       "      <td>21.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kraeff, Mr. Theodor</td>\n",
        "      <td>male</td>\n",
-       "      <td>71.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>PC 17609</td>\n",
-       "      <td>49.5042</td>\n",
+       "      <td>349253</td>\n",
+       "      <td>7.8958</td>\n",
        "      <td>NaN</td>\n",
        "      <td>C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>630</th>\n",
-       "      <td>631</td>\n",
+       "      <th>43</th>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
+       "      <td>female</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SC/Paris 2123</td>\n",
+       "      <td>41.5792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>45</td>\n",
        "      <td>1</td>\n",
-       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Devaney, Miss. Margaret Delia</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330958</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Rogers, Mr. William John</td>\n",
        "      <td>male</td>\n",
-       "      <td>80.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>27042</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>A23</td>\n",
+       "      <td>S.C./A.4. 23567</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>851</th>\n",
-       "      <td>852</td>\n",
+       "      <th>46</th>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lennon, Mr. Denis</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370371</td>\n",
+       "      <td>15.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>O'Driscoll, Miss. Bridget</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14311</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Svensson, Mr. Johan</td>\n",
+       "      <td>Samaan, Mr. Youssef</td>\n",
        "      <td>male</td>\n",
-       "      <td>74.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2662</td>\n",
+       "      <td>21.6792</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
        "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Arnold-Franchi, Mrs. Josef (Josefine Franchi)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>347060</td>\n",
-       "      <td>7.7750</td>\n",
+       "      <td>349237</td>\n",
+       "      <td>17.8000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
@@ -2873,194 +4962,551 @@
        "</div>"
       ],
       "text/plain": [
-       "     IdPassageiro  Sobreviveu  Classe                                  Nome  \\\n",
-       "96             97           0       1             Goldschmidt, Mr. George B   \n",
-       "116           117           0       3                  Connors, Mr. Patrick   \n",
-       "493           494           0       1               Artagaveytia, Mr. Ramon   \n",
-       "630           631           1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
-       "851           852           0       3                   Svensson, Mr. Johan   \n",
+       "    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",
+       "30            31           0       1   \n",
+       "31            32           1       1   \n",
+       "32            33           1       3   \n",
+       "33            34           0       2   \n",
+       "34            35           0       1   \n",
+       "35            36           0       1   \n",
+       "36            37           1       3   \n",
+       "37            38           0       3   \n",
+       "38            39           0       3   \n",
+       "39            40           1       3   \n",
+       "40            41           0       3   \n",
+       "41            42           0       2   \n",
+       "42            43           0       3   \n",
+       "43            44           1       2   \n",
+       "44            45           1       3   \n",
+       "45            46           0       3   \n",
+       "46            47           0       3   \n",
+       "47            48           1       3   \n",
+       "48            49           0       3   \n",
+       "49            50           0       3   \n",
        "\n",
-       "     Sexo  Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket  \\\n",
-       "96   male   71.0             0           0     PC 17754      34.6542   \n",
-       "116  male   70.5             0           0       370369       7.7500   \n",
-       "493  male   71.0             0           0     PC 17609      49.5042   \n",
-       "630  male   80.0             0           0        27042      30.0000   \n",
-       "851  male   74.0             0           0       347060       7.7750   \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",
+       "30                           Uruchurtu, Don. Manuel E    male   40.0   \n",
+       "31     Spencer, Mrs. William Augustus (Marie Eugenie)  female    NaN   \n",
+       "32                           Glynn, Miss. Mary Agatha  female    NaN   \n",
+       "33                              Wheadon, Mr. Edward H    male   66.0   \n",
+       "34                            Meyer, Mr. Edgar Joseph    male   28.0   \n",
+       "35                     Holverson, Mr. Alexander Oskar    male   42.0   \n",
+       "36                                   Mamee, Mr. Hanna    male    NaN   \n",
+       "37                           Cann, Mr. Ernest Charles    male   21.0   \n",
+       "38                 Vander Planke, Miss. Augusta Maria  female   18.0   \n",
+       "39                        Nicola-Yarred, Miss. Jamila  female   14.0   \n",
+       "40     Ahlin, Mrs. Johan (Johanna Persdotter Larsson)  female   40.0   \n",
+       "41  Turpin, Mrs. William John Robert (Dorothy Ann ...  female   27.0   \n",
+       "42                                Kraeff, Mr. Theodor    male    NaN   \n",
+       "43           Laroche, Miss. Simonne Marie Anne Andree  female    3.0   \n",
+       "44                      Devaney, Miss. Margaret Delia  female   19.0   \n",
+       "45                           Rogers, Mr. William John    male    NaN   \n",
+       "46                                  Lennon, Mr. Denis    male    NaN   \n",
+       "47                          O'Driscoll, Miss. Bridget  female    NaN   \n",
+       "48                                Samaan, Mr. Youssef    male    NaN   \n",
+       "49      Arnold-Franchi, Mrs. Josef (Josefine Franchi)  female   18.0   \n",
        "\n",
-       "    NumeroCabine PortoEmbarcacao  \n",
-       "96            A5               C  \n",
-       "116          NaN               Q  \n",
-       "493          NaN               C  \n",
-       "630          A23               S  \n",
-       "851          NaN               S  "
+       "    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",
+       "30             0           0          PC 17601      27.7208          NaN   \n",
+       "31             1           0          PC 17569     146.5208          B78   \n",
+       "32             0           0            335677       7.7500          NaN   \n",
+       "33             0           0        C.A. 24579      10.5000          NaN   \n",
+       "34             1           0          PC 17604      82.1708          NaN   \n",
+       "35             1           0            113789      52.0000          NaN   \n",
+       "36             0           0              2677       7.2292          NaN   \n",
+       "37             0           0        A./5. 2152       8.0500          NaN   \n",
+       "38             2           0            345764      18.0000          NaN   \n",
+       "39             1           0              2651      11.2417          NaN   \n",
+       "40             1           0              7546       9.4750          NaN   \n",
+       "41             1           0             11668      21.0000          NaN   \n",
+       "42             0           0            349253       7.8958          NaN   \n",
+       "43             1           2     SC/Paris 2123      41.5792          NaN   \n",
+       "44             0           0            330958       7.8792          NaN   \n",
+       "45             0           0   S.C./A.4. 23567       8.0500          NaN   \n",
+       "46             1           0            370371      15.5000          NaN   \n",
+       "47             0           0             14311       7.7500          NaN   \n",
+       "48             2           0              2662      21.6792          NaN   \n",
+       "49             1           0            349237      17.8000          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  \n",
+       "30               C  \n",
+       "31               C  \n",
+       "32               Q  \n",
+       "33               S  \n",
+       "34               C  \n",
+       "35               S  \n",
+       "36               C  \n",
+       "37               S  \n",
+       "38               S  \n",
+       "39               C  \n",
+       "40               S  \n",
+       "41               S  \n",
+       "42               C  \n",
+       "43               C  \n",
+       "44               Q  \n",
+       "45               S  \n",
+       "46               Q  \n",
+       "47               Q  \n",
+       "48               C  \n",
+       "49               S  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[:, [\"Sobreviveu\", \"Idade\"]]\n",
+    "df.head(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"300\"\n",
+       "            height=\"250\"\n",
+       "            src=\"https://i.gifer.com/XoRB.gif\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x1cc10e85c90>"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import IFrame\n",
+    "#Link do gif\n",
+    "iframe_url = \"https://i.gifer.com/XoRB.gif\"\n",
+    "#Resized output IFrame\n",
+    "IFrame(src=iframe_url, width=300, height=250)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Alomoço"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from IPython.display import IFrame\n",
+    "\n",
+    "# # GIF link used as IFrame\n",
+    "# iframe_url = \"https://giphy.com/embed/7AKbdZiyTx98fPHG0Z\"\n",
+    "\n",
+    "# # resized output IFrame\n",
+    "# IFrame(src=iframe_url, width=300, height=250)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nES063ppS_m4"
+   },
+   "source": [
+    "### Agrupamento / Agregação\n",
+    "\n",
+    "Podemos criar um agrupamento de categorias e aplicar uma função às categorias. É um conceito simples, mas é uma técnica extremamente valiosa, amplamente utilizada na ciência de dados. Em projetos reais de ciência de dados, você lidará com grandes quantidades de dados e tentará várias vezes, portanto, para eficiência, usamos o conceito Groupby. O conceito de Groupby é realmente importante por causa de sua capacidade de resumir, agregar e agrupar dados com eficiência.\n",
+    "\n",
+    "\n",
+    "[Fonte](https://acervolima.com/pandas-groupby-resumindo-agregando-e-agrupando-dados-em-python/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "id": "noSCxvLQS_m6",
+    "outputId": "fd434c19-19d3-4395-a595-83703e140d1b"
+   },
+   "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": [
+    "# Agrupar passageiros por classe\n",
+    "dado_agrupado = df.groupby('Classe')\n",
+    "\n",
+    "# Contagem de passageiros em cada classe, usando os dados agrupados no passo anterior\n",
+    "contagem_passageiros = dado_agrupado['IdPassageiro'].count()\n",
+    "\n",
+    "print(\"Contagem de passageiros\")\n",
+    "print(contagem_passageiros)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "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.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Classe\n",
+       "3    491\n",
+       "1    216\n",
+       "2    184\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[df['Idade'] > 70]"
+    "df[\"Classe\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "id": "UxbPOZJfS_m7",
+    "outputId": "f2031638-fbad-45b1-94a6-1415795d6800"
+   },
+   "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",
+    "\n",
+    "print(\"Média de Idade de passageiros por classe\")\n",
+    "print(media_idade)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "id": "jxk2rE1hS_m7",
+    "outputId": "55e2c10a-49b5-4a8a-f979-162b4bc89e1b"
+   },
+   "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",
+    "valor_min = dado_agrupado['PrecoTicket'].min()\n",
+    "\n",
+    "print(\"Valor máximo do preço da passagem por classe\")\n",
+    "print(valor_max)\n",
+    "\n",
+    "print(\"Valor mínimo do preço da passagem por classe\")\n",
+    "print(valor_min)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercício 04 parte 02\n",
+    "\n",
+    "- 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "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",
+    "# print(dado_agrupado)\n",
+    "df.groupby('Sexo')\n",
+    "contagem_passageiros = dado_agrupado['IdPassageiro'].count()\n",
+    "\n",
+    "print(\"Contagem de passageiros\")\n",
+    "print(contagem_passageiros)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "id": "jxk2rE1hS_m7",
-    "outputId": "55e2c10a-49b5-4a8a-f979-162b4bc89e1b"
-   },
+   "execution_count": 87,
+   "metadata": {},
    "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"
+      "Média de Idade de passageiros por sexo\n",
+      "Sexo\n",
+      "female    27.915709\n",
+      "male      30.726645\n",
+      "Name: Idade, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "# Obter o valor máximo e mínimo 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",
+    "media_idade = dado_agrupado['Idade'].mean()\n",
     "\n",
-    "print(\"Valor mínimo do preço da passagem por classe\")\n",
-    "print(valor_min)"
+    "print(\"Média de Idade de passageiros por sexo\")\n",
+    "print(media_idade)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 92,
    "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 colspan=\"5\" halign=\"left\">PrecoTicket</th>\n",
-       "      <th colspan=\"5\" halign=\"left\">Idade</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>median</th>\n",
-       "      <th>min</th>\n",
-       "      <th>max</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></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>female</th>\n",
-       "      <td>314</td>\n",
-       "      <td>44.479818</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>6.75</td>\n",
-       "      <td>512.3292</td>\n",
-       "      <td>261</td>\n",
-       "      <td>27.915709</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>63.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>male</th>\n",
-       "      <td>577</td>\n",
-       "      <td>25.523893</td>\n",
-       "      <td>10.5</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>512.3292</td>\n",
-       "      <td>453</td>\n",
-       "      <td>30.726645</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>80.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       PrecoTicket                                   Idade                    \\\n",
-       "             count       mean median   min       max count       mean median   \n",
-       "Sexo                                                                           \n",
-       "female         314  44.479818   23.0  6.75  512.3292   261  27.915709   27.0   \n",
-       "male           577  25.523893   10.5  0.00  512.3292   453  30.726645   29.0   \n",
-       "\n",
-       "                    \n",
-       "         min   max  \n",
-       "Sexo                \n",
-       "female  0.75  63.0  \n",
-       "male    0.42  80.0  "
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valor máximo do preço da passagem por sexoSexo\n",
+      "female    512.3292\n",
+      "male      512.3292\n",
+      "Name: PrecoTicket, dtype: float64\n",
+      "\n",
+      "Valor mínimo do preço da passagem por sexoSexo\n",
+      "female    6.75\n",
+      "male      0.00\n",
+      "Name: PrecoTicket, dtype: float64\n"
+     ]
     }
    ],
    "source": [
-    "df.groupby('Sexo').agg({'PrecoTicket': ['count', 'mean', 'median', 'min', 'max'], \n",
-    "                        'Idade': ['count', 'mean', 'median', 'min', 'max']})"
+    "valor_max = dado_agrupado['PrecoTicket'].max()\n",
+    "valor_min = dado_agrupado['PrecoTicket'].min()\n",
+    "\n",
+    "print(f\"Valor máximo do preço da passagem por sexo{valor_max}\")\n",
+    "print(f\"\\nValor mínimo do preço da passagem por sexo{valor_min}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -3102,7 +5548,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sexo</th>\n",
-       "      <th>PortoEmbarcacao</th>\n",
+       "      <th>Idade</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -3112,89 +5558,127 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
-       "      <th>C</th>\n",
-       "      <td>73</td>\n",
-       "      <td>75.169805</td>\n",
-       "      <td>56.92920</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>512.3292</td>\n",
+       "      <th rowspan=\"5\" valign=\"top\">female</th>\n",
+       "      <th>0.75</th>\n",
+       "      <td>2</td>\n",
+       "      <td>19.258300</td>\n",
+       "      <td>19.2583</td>\n",
+       "      <td>19.2583</td>\n",
+       "      <td>19.2583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Q</th>\n",
-       "      <td>36</td>\n",
-       "      <td>12.634958</td>\n",
-       "      <td>7.76875</td>\n",
-       "      <td>6.7500</td>\n",
-       "      <td>90.0000</td>\n",
+       "      <th>1.00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>13.437500</td>\n",
+       "      <td>13.4375</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>15.7417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>S</th>\n",
-       "      <td>203</td>\n",
-       "      <td>38.740929</td>\n",
-       "      <td>24.15000</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>263.0000</td>\n",
+       "      <th>2.00</th>\n",
+       "      <td>6</td>\n",
+       "      <td>43.245833</td>\n",
+       "      <td>26.9500</td>\n",
+       "      <td>10.4625</td>\n",
+       "      <td>151.5500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
-       "      <th>C</th>\n",
-       "      <td>95</td>\n",
-       "      <td>48.262109</td>\n",
-       "      <td>24.00000</td>\n",
-       "      <td>4.0125</td>\n",
-       "      <td>512.3292</td>\n",
+       "      <th>3.00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>31.327100</td>\n",
+       "      <td>31.3271</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>41.5792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Q</th>\n",
-       "      <td>41</td>\n",
-       "      <td>13.838922</td>\n",
-       "      <td>7.75000</td>\n",
-       "      <td>6.7500</td>\n",
-       "      <td>90.0000</td>\n",
+       "      <th>4.00</th>\n",
+       "      <td>5</td>\n",
+       "      <td>22.828340</td>\n",
+       "      <td>22.0250</td>\n",
+       "      <td>13.4167</td>\n",
+       "      <td>39.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>S</th>\n",
-       "      <td>441</td>\n",
-       "      <td>21.711996</td>\n",
-       "      <td>10.50000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>263.0000</td>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">male</th>\n",
+       "      <th>70.00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>40.750000</td>\n",
+       "      <td>40.7500</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>71.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70.50</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.750000</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>7.7500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71.00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>42.079200</td>\n",
+       "      <td>42.0792</td>\n",
+       "      <td>34.6542</td>\n",
+       "      <td>49.5042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74.00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.775000</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>7.7750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80.00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>30.0000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>145 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                       PrecoTicket                                       \n",
-       "                             count       mean    median     min       max\n",
-       "Sexo   PortoEmbarcacao                                                   \n",
-       "female C                        73  75.169805  56.92920  7.2250  512.3292\n",
-       "       Q                        36  12.634958   7.76875  6.7500   90.0000\n",
-       "       S                       203  38.740929  24.15000  7.2500  263.0000\n",
-       "male   C                        95  48.262109  24.00000  4.0125  512.3292\n",
-       "       Q                        41  13.838922   7.75000  6.7500   90.0000\n",
-       "       S                       441  21.711996  10.50000  0.0000  263.0000"
+       "             PrecoTicket                                       \n",
+       "                   count       mean   median      min       max\n",
+       "Sexo   Idade                                                   \n",
+       "female 0.75            2  19.258300  19.2583  19.2583   19.2583\n",
+       "       1.00            2  13.437500  13.4375  11.1333   15.7417\n",
+       "       2.00            6  43.245833  26.9500  10.4625  151.5500\n",
+       "       3.00            2  31.327100  31.3271  21.0750   41.5792\n",
+       "       4.00            5  22.828340  22.0250  13.4167   39.0000\n",
+       "...                  ...        ...      ...      ...       ...\n",
+       "male   70.00           2  40.750000  40.7500  10.5000   71.0000\n",
+       "       70.50           1   7.750000   7.7500   7.7500    7.7500\n",
+       "       71.00           2  42.079200  42.0792  34.6542   49.5042\n",
+       "       74.00           1   7.775000   7.7750   7.7750    7.7750\n",
+       "       80.00           1  30.000000  30.0000  30.0000   30.0000\n",
+       "\n",
+       "[145 rows x 5 columns]"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby(['Sexo', 'PortoEmbarcacao']).agg({'PrecoTicket': ['count', 'mean', 'median', 'min', 'max']})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercício 04 parte 02\n",
-    "\n",
-    "- 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"
+    "df.groupby([\"Sexo\", \"Idade\"]).agg({\"PrecoTicket\": [\"count\", \"mean\", \"median\", \"min\", \"max\"]})"
    ]
   },
   {
@@ -3241,7 +5725,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3263,9 +5756,11 @@
     }
    ],
    "source": [
-    "# exemplo mais simples de gráfico:\n",
+    "#exemplo mais simples de gráfico:\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot([1, 2, 3, 4], [2, 4, 6, 8]);"
    ]
@@ -3283,100 +5778,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "id": "bmZg5LjvS_nJ",
-    "outputId": "f3874a08-9ec1-4235-8db1-2250deeb54ea"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Classe\n",
-       "3    491\n",
-       "1    216\n",
-       "2    184\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Criação de um gráfico de barras:  matplotlib + Pandas\n",
-    "# plot() + bar\n",
-    "contagem_passageiros = df['Classe'].value_counts()\n",
-    "contagem_passageiros"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Doc:** https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# pandas: gráfico\n",
-    "contagem_passageiros.plot(kind='barh');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "contagem_passageiros.plot(kind='bar');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
+   "execution_count": 112,
+   "metadata": {
+    "id": "bmZg5LjvS_nJ",
+    "outputId": "f3874a08-9ec1-4235-8db1-2250deeb54ea"
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Axes: ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3386,12 +5796,18 @@
     }
    ],
    "source": [
-    "df['Idade'].plot(kind='hist')"
+    "# Criação de um gráfico de barras:  matplotlib + Pandas\n",
+    "# plot() + bar\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "contagem_passageiros = df['Classe'].value_counts()\n",
+    "contagem_passageiros.plot(kind='bar');\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 128,
    "metadata": {
     "id": "zpZmI2e9S_nJ",
     "outputId": "3f16a4f4-3039-4426-ac46-d0f8178d87a6"
@@ -3399,17 +5815,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Número de Passageiros por Classe')"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3419,18 +5825,17 @@
     }
    ],
    "source": [
-    "# título, legendas (Y e X)\n",
     "contagem_passageiros = df['Classe'].value_counts()\n",
-    "contagem_passageiros.plot(kind='bar');\n",
+    "contagem_passageiros.plot(kind='pie')\n",
     "\n",
     "plt.xlabel('Classe do Passageiro')\n",
     "plt.ylabel('Quantidade')\n",
-    "plt.title('Número de Passageiros por Classe')\n"
+    "plt.title('Número de Passageiros por Classe');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 129,
    "metadata": {
     "id": "j7lUqI3BS_nK",
     "outputId": "758058f2-185d-412d-835e-38d8ca773f3e"
@@ -3450,18 +5855,21 @@
    "source": [
     "# Criando um gráfico de barras para calcular a taxa de sobrevivência por sexo\n",
     "# plot.bar()\n",
+    "\n",
+    "\n",
     "taxa_sob_sexo = df.groupby('Sexo')['Sobreviveu'].mean()\n",
     "taxa_sob_sexo.plot.bar()\n",
     "\n",
     "plt.xlabel('Sexo')\n",
     "plt.ylabel('Taxa de Sobrevivência')\n",
     "plt.title('Taxa de Sobrevivência por Sexo')\n",
+    "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 133,
    "metadata": {
     "id": "RzV9cvoOS_nK",
     "outputId": "eea06c73-5805-427d-f2b1-b82d3481b8a8"
@@ -3473,13 +5881,13 @@
        "Text(0.5, 1.0, 'Distribuição de Idade')"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3491,7 +5899,7 @@
    "source": [
     "# Criando um histograma com a distribuição de idade em passageiros do Titanic\n",
     "\n",
-    "df['Idade'].plot.hist(bins=10, edgecolor='black')\n",
+    "df['Idade'].plot.hist(bins=10, edgecolor='orange')\n",
     "\n",
     "plt.xlabel('Idade')\n",
     "plt.ylabel('Quantidade')\n",
@@ -3509,122 +5917,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# exemplo dúvida: como pegar uma única classe (a importância do reset_index())\n",
-    "agg_classe = df.groupby(['Classe', 'Sexo']).agg({'Sobreviveu': ['count']})\n",
-    "agg_classe.reset_index(inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "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",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>Classe</th>\n",
-       "      <th>Sexo</th>\n",
-       "      <th>Sobreviveu</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>female</td>\n",
-       "      <td>94</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Classe    Sexo Sobreviveu\n",
-       "                      count\n",
-       "0      1  female         94"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agg_classe[(agg_classe[(    'Classe',      '')] == 1) & (agg_classe[(    'Sexo',      '')] == 'female')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "MultiIndex([(    'Classe',      ''),\n",
-       "            (      'Sexo',      ''),\n",
-       "            ('Sobreviveu', 'count')],\n",
-       "           )"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 88,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "agg_classe.columns"
+    "df[\"Idade\"].plot.hist(bins = 100, edgecolor = \"silver\")\n",
+    "plt.xlabel(\"Shows\")\n",
+    "plt.ylabel(\"Public\")\n",
+    "plt.title(\"Renaissance World Tour\"); #Alterei os nomes para saber como funciona"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "Index(['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade',\n",
-       "       'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket',\n",
-       "       'NumeroCabine', 'PortoEmbarcacao'],\n",
-       "      dtype='object')"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 91,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "df[df['PortoEmbarcacao']]"
+    "taxa_sob_sexo = df.groupby('Sexo')['Sobreviveu'].mean()\n",
+    "taxa_sob_sexo.plot.bar()\n",
+    "\n",
+    "plt.xlabel('Singers')\n",
+    "plt.ylabel('Of generation')\n",
+    "plt.title('Category Best of generation')\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -3655,7 +5993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 155,
    "metadata": {
     "id": "6qrRb5EIS_nL"
    },
@@ -3684,7 +6022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 156,
    "metadata": {
     "id": "suI74yqZS_nL",
     "outputId": "eb87a166-4d92-4068-e5ad-e7ce283ed244"
@@ -3702,15 +6040,17 @@
    "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",
+    "\n",
     "array_1 = np.array(lista_1)\n",
     "\n",
+    "\n",
     "print(lista_1)\n",
     "print(array_1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 157,
    "metadata": {
     "id": "DxCmoOkhS_nM",
     "outputId": "2c7699a1-519f-4b81-d4e1-836895767375"
@@ -3720,9 +6060,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Uma lista de listas\n",
       "[[1, 2, 3], [4, 5, 6]]\n",
-      "Array bidimensional\n",
       "[[1 2 3]\n",
       " [4 5 6]]\n"
      ]
@@ -3730,88 +6068,46 @@
    ],
    "source": [
     "# Array bidimensional de uma lista Python (uma lista contendo listas)\n",
+    "\n",
     "lista_2 = [[1,2,3],[4,5,6]]\n",
+    "\n",
     "array_2 = np.array(lista_2)\n",
     "\n",
-    "print('Uma lista de listas')\n",
+    "\n",
     "print(lista_2)\n",
-    "print('Array bidimensional')\n",
     "print(array_2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lista_2[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3])"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array_2[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 158,
    "metadata": {
     "id": "KZ-JzM6US_nM",
     "outputId": "9c7580bf-627b-4926-ed86-8441bb436861"
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([10, 20, 30, 40, 50])"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
+     "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",
     "# loja com preços\n",
-    "array_precos = np.array([12, 22, 32, 42, 52])\n",
+    "array_precos = np.array([12,22,32,42,52])\n",
     "\n",
     "# queremos entrar em liquidação diminuindo 2 reais no preço de cada item\n",
     "array_promocional = array_precos - 2\n",
-    "array_promocional\n"
+    "print(array_promocional)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 160,
    "metadata": {
     "id": "SWy82LKAS_nM",
     "outputId": "ee7e4c2e-5553-405f-cbdf-878534650298"
@@ -3824,7 +6120,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\Debora\\Documents\\GitHub\\on26-python-s12-pandas-numpy-II\\material\\aula_s12.ipynb Cell 70\u001b[0m line \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Debora/Documents/GitHub/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y106sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# se fosse uma lista Python, seria impossível pois retornaria um TypeError\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Debora/Documents/GitHub/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y106sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m lista_precos \u001b[39m=\u001b[39m [\u001b[39m12\u001b[39m,\u001b[39m22\u001b[39m,\u001b[39m32\u001b[39m,\u001b[39m42\u001b[39m,\u001b[39m52\u001b[39m]\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Debora/Documents/GitHub/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y106sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m lista_promocial \u001b[39m=\u001b[39m lista_precos \u001b[39m-\u001b[39;49m \u001b[39m2\u001b[39;49m\n",
+      "\u001b[1;32mc:\\Users\\Bodecam\\Estudos\\on26-python-s12-pandas-numpy-II\\material\\aula_s12.ipynb Cell 70\u001b[0m line \u001b[0;36m4\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Bodecam/Estudos/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y101sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# se fosse uma lista Python, seria impossível pois retornaria um TypeError\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Bodecam/Estudos/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y101sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m lista_precos \u001b[39m=\u001b[39m [\u001b[39m12\u001b[39m,\u001b[39m22\u001b[39m,\u001b[39m32\u001b[39m,\u001b[39m42\u001b[39m,\u001b[39m52\u001b[39m]\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Bodecam/Estudos/on26-python-s12-pandas-numpy-II/material/aula_s12.ipynb#Y101sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m lista_promocial \u001b[39m=\u001b[39m lista_precos \u001b[39m-\u001b[39;49m \u001b[39m2\u001b[39;49m\n",
       "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'list' and 'int'"
      ]
     }
@@ -3832,7 +6128,8 @@
    "source": [
     "# se fosse uma lista Python, seria impossível pois retornaria um TypeError\n",
     "lista_precos = [12,22,32,42,52]\n",
-    "lista_promocial = lista_precos - 2"
+    "\n",
+    "lista_promocial = lista_precos - 2\n"
    ]
   },
   {
@@ -3846,33 +6143,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 161,
    "metadata": {
     "id": "LljyqgRgS_nM",
     "outputId": "17c514e4-0fa0-4d36-c6f8-46293589ce4b"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([100, 200, 300])"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Criando Séries em Pandas usando um array numpy:\n",
-    "precos = np.array([100,200,300])\n",
-    "precos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3883,19 +6158,23 @@
        "dtype: int32"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Criando Séries em Pandas usando um array numpy:\n",
+    "\n",
+    "precos = np.array([100,200,300])\n",
     "serie_precos = pd.Series(precos)\n",
+    "\n",
     "serie_precos"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 162,
    "metadata": {
     "id": "4m4a1TDIS_nN",
     "outputId": "d60ac614-bd45-430f-d375-bafdf4252478"
@@ -3910,20 +6189,22 @@
        "dtype: int32"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# criando série e customizando índices da série\n",
-    "serie_precos_produtos = pd.Series(precos, index=['Bala', 'Chocolate', 'Bolo'])\n",
+    "\n",
+    "serie_precos_produtos = pd.Series(precos,index=['Bala', 'Chocolate', 'Bolo'])\n",
+    "\n",
     "serie_precos_produtos"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 163,
    "metadata": {
     "id": "ZxBABq3pS_nN",
     "outputId": "d11786b2-ac75-43da-9ce9-5e3c8d3869d4"
@@ -3978,89 +6259,25 @@
        "1  4  5  6"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Criando DataFrames Pandas com arrays Numpy:\n",
+    "\n",
     "lista_2 = [[1,2,3],[4,5,6]]\n",
     "array_2 = np.array(lista_2)\n",
+    "\n",
     "df = pd.DataFrame(array_2, columns=['A', 'B', 'C'])\n",
+    "\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
-   "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>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A  B  C  D\n",
-       "0  1  2  3  1\n",
-       "1  4  5  6  5"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['D'] = [1,5]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 164,
    "metadata": {
     "id": "LHfl6fALS_nN",
     "outputId": "5805c47a-4db3-4a7f-8790-dad45dc7ff76"
@@ -4077,7 +6294,7 @@
        "dtype: int32"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4085,15 +6302,13 @@
    "source": [
     "# Operações matemáticas podem ser realizadas em séries e Dataframes Pandas usando funções do numpy\n",
     "\n",
-    "# criando um array (numpay)\n",
     "lista_1 = [1, 2, 3, 4, 5]\n",
-    "array_1 = np.array(lista_1)\n",
     "\n",
-    "# criando uma series (pandas)\n",
+    "array_1 = np.array(lista_1)\n",
     "serie_precos = pd.Series(array_1)\n",
     "\n",
-    "# calculo matematico\n",
     "serie_precos_ao_quadrado = np.square(serie_precos)\n",
+    "\n",
     "serie_precos_ao_quadrado"
    ]
   },
@@ -4105,69 +6320,56 @@
     "\n",
     "- Gerar arrays de números aleatórios de com tamanho de 5, 50 e 500 elementos\n",
     "- Criar um DF Pandas a partir de um array numpy criado de uma lista Python\n",
-    "- Utilize métodos do Numpy e séries de Pandas para dobrar os números da lista"
+    "- Utilize métodos do Numpy e séries de Pandas para dobrar os números da lista a seguir"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 184,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([10, 23, 13, 32, 25, 11, 20, 49, 26,  6, 40, 14, 42, 17, 11,  6, 43,\n",
-       "       15, 19, 12, 29, 47,  5, 12, 42, 47, 40, 15,  7, 22,  8, 41, 35, 25,\n",
-       "       14,  5, 31, 13, 42, 49, 49, 47, 41, 37,  5, 22, 34, 21, 14, 10, 13,\n",
-       "       10, 37, 27, 24, 31, 34, 31, 23, 32, 23, 47, 22, 30, 14, 40, 31, 42,\n",
-       "       35, 21, 40, 42, 36, 37, 11,  8, 14, 46, 11, 28, 39, 35, 29,  5, 46,\n",
-       "       18, 24, 17, 29, 48, 35, 14, 38, 30, 11, 10, 47, 48, 38,  5, 37,  5,\n",
-       "       26, 10, 10, 29,  6, 49, 15, 48, 13, 30, 24, 19, 15, 40, 43,  5, 35,\n",
-       "       46, 45, 44, 28,  7, 13, 44,  5, 47, 14, 13, 17, 43, 33, 46, 32, 16,\n",
-       "       39, 46, 23, 38, 21, 46, 18, 31,  7, 43,  8, 31, 11, 18, 37, 40, 48,\n",
-       "       41, 13,  6, 33, 13, 11,  6, 15, 23, 30, 16, 31, 34, 16,  6, 14,  5,\n",
-       "       34, 27, 34, 45, 45,  6, 28, 15,  8, 27, 25, 22, 46, 32, 23, 49, 45,\n",
-       "       35, 30, 34, 45, 27, 27,  7, 16, 18,  7, 34, 47, 45, 30, 30, 46, 47,\n",
-       "       29, 29, 45, 16, 47, 22, 35,  9, 31, 49, 36,  9,  8, 21, 37, 47, 49,\n",
-       "       48, 38, 11, 11, 12, 11, 25, 31, 47, 13, 41, 33, 14,  6, 41, 24, 15,\n",
-       "       44, 33, 37, 28, 43, 33, 45,  9, 33, 29, 33, 40, 21, 33, 15, 47, 41,\n",
-       "       47, 22, 46, 27, 12, 10, 43, 40, 39, 19, 30, 17, 49, 44, 26,  8, 44,\n",
-       "       27, 42, 37, 48, 13, 12, 36, 15, 41,  6, 40, 19, 17, 14, 27, 39, 24,\n",
-       "       17, 29, 14, 24, 27, 21, 12, 44, 12, 39, 18,  7, 34, 20, 17, 19, 27,\n",
-       "       31, 19, 18, 13, 47, 38, 47, 14, 39, 26, 30,  6, 18, 37, 44, 42, 21,\n",
-       "       20,  6, 48,  6, 29,  7, 35, 47, 17, 22, 42, 32, 24, 48, 22, 28, 27,\n",
-       "       34, 21, 48,  7, 39, 26,  8, 43,  7, 19, 22, 49, 29,  6, 22, 30, 32,\n",
-       "       49, 35,  9, 40, 21, 18, 21, 33,  6, 14, 47,  6, 10, 46, 10, 24, 24,\n",
-       "       27, 18, 40, 34, 28, 45, 16, 38, 38, 45, 23, 37, 25, 46, 25, 37, 22,\n",
-       "       38, 30, 18, 44, 11, 39, 32, 26, 12,  7,  8,  7, 18, 23, 41, 37, 39,\n",
-       "       40, 45, 38, 43, 48, 19, 13, 25, 11, 37,  9, 11, 42, 18, 49, 24, 40,\n",
-       "       40, 17,  7, 31, 21, 37, 18, 15, 35, 49, 19, 41, 13, 42,  8, 30, 10,\n",
-       "       35, 12, 25, 40, 16, 42, 40, 42, 15, 25, 33, 30, 40, 30, 20, 22,  7,\n",
-       "       16,  8, 34,  8, 38,  8, 31,  9, 28, 23, 36,  8, 13, 38, 31, 29, 18,\n",
-       "       42, 45, 34, 41, 37, 28, 24, 36, 45, 49, 45, 23, 14, 25, 30, 27, 44,\n",
-       "       45, 19, 18, 24, 35, 34, 42])"
+       "array([49, 22, 34, 41, 29, 38, 47, 32, 46, 49, 29, 22, 25, 17, 22, 42, 37,\n",
+       "       19, 24, 34, 18, 48, 20, 19, 28, 38, 27, 19, 49,  5, 45, 49, 19, 35,\n",
+       "       16, 12, 38, 15, 38, 29, 35,  6, 13, 12, 17, 17, 35,  5, 47, 20, 22,\n",
+       "       25,  5, 11,  8, 34, 28, 44, 42, 23, 13, 43, 47, 45,  8,  5, 27, 25,\n",
+       "       19, 39, 42, 40, 49, 21, 28, 32, 37, 18, 13, 29, 36, 29,  9, 36, 11,\n",
+       "       10, 38,  8,  7, 21, 36, 26,  8, 27, 43,  5, 47, 40, 35, 12,  6, 29,\n",
+       "       34, 26, 45,  7, 11, 39, 33, 44, 49, 21,  5, 41, 34, 25, 29, 11,  7,\n",
+       "       36,  6, 41, 28, 14, 33, 33, 27, 27, 49, 25, 15,  5,  7, 29, 11, 49,\n",
+       "       29, 20,  7, 15, 31, 48, 15, 34, 49, 34, 31, 23, 31, 37, 42, 11, 36,\n",
+       "       46, 40, 18, 25, 32, 48, 11, 39, 46, 45, 12, 48,  5, 41, 21, 27, 39,\n",
+       "       15, 41, 24, 24, 14, 20, 35, 30, 22, 45, 42, 20, 25,  6, 27,  5, 43,\n",
+       "       20, 14, 17, 47, 33, 13, 25, 44, 20, 17, 14, 39, 33, 33, 28, 40,  7,\n",
+       "       34, 34, 34,  5, 31, 19, 26, 36, 33, 31, 23, 30, 46,  7, 30, 45, 46,\n",
+       "        5, 15,  9, 19, 46, 37, 39,  9, 34, 10, 45, 45, 20, 45, 16, 19, 14,\n",
+       "       18, 18, 14, 48, 39, 24, 18,  5, 36, 26, 47, 39, 27, 28, 23, 23, 19,\n",
+       "       34, 46, 27, 40, 10, 17, 12, 40, 16, 25, 18, 39, 43, 36, 45, 45, 20,\n",
+       "       42,  9, 15, 34, 38, 41,  8,  8,  9, 18, 33, 43, 26, 40, 39, 16, 17,\n",
+       "        9,  6, 14,  9, 10, 13,  5, 45, 20, 15, 31, 10, 48, 47, 45, 15, 18,\n",
+       "       14, 19, 34,  9, 10, 46, 29,  6, 41, 43, 41,  8, 42, 29, 24, 24, 48,\n",
+       "       10,  8, 12, 27, 29, 32, 31, 34, 13, 34, 15, 12, 11,  5, 37, 39, 14,\n",
+       "        7, 30, 34, 32, 31, 34, 14, 23,  7,  9, 22, 42, 42, 39, 40, 38, 20,\n",
+       "       44, 37, 42, 29, 26, 34, 10, 25, 11, 34, 29, 34,  9, 42, 32, 36, 35,\n",
+       "        6, 46, 11, 11, 35, 49,  9, 42, 42,  8,  8, 47, 33, 45, 33, 45, 29,\n",
+       "       37, 13, 42, 17, 23, 32, 14, 38, 44, 44, 36, 32, 15, 16, 44, 35, 16,\n",
+       "        5, 33, 21, 10, 11, 47, 20, 40, 45,  9, 30, 34, 29, 44, 21, 26, 28,\n",
+       "       40, 13, 34, 13, 49, 28, 24, 22, 36, 28, 41, 13, 39, 28, 12, 46, 12,\n",
+       "       20, 32, 29, 32, 37, 21, 28, 19, 14, 21,  8, 16, 11, 21, 10, 25, 14,\n",
+       "       12, 26, 47, 31, 17,  7, 18, 32, 37, 24,  7, 49,  6, 29, 16, 23, 41,\n",
+       "       36, 22, 30, 24, 45, 20, 44, 27, 10, 20,  5, 11, 13, 30, 28, 49, 40,\n",
+       "       34, 31, 30, 44, 44, 45, 31, 11,  5, 34, 13,  6, 17, 18, 46, 22, 36])"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.random.randint(5, 50, 500)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Links Bibliotecas:\n",
-    "\n",
-    "https://pandas.pydata.org/\n",
-    "\n",
-    "https://numpy.org/pt/\n",
-    "\n",
-    "https://matplotlib.org/"
+    "np.random.randint(5, 50, 510)\n"
    ]
   }
  ],