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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Introdução\n",
+    "No presente trabalho vamos fazer um breve tratamento dos dados coletados do acidente com o navio Titanic ocorrido em meados do ano de 1912.\n",
+    "Para fazer os tratamentos dos dados, foram ultizadas as bibliotecas Numpy e Pandas e por meio da leitura dos dados diposbinilizados de um arquivo csv exibimos gráficos criados pela biblioteca Matplotlib visando melhor compreensão dos fatos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "df = pd.read_csv(\"titanic.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Gênero', 'Idade', 'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket', 'NumeroCabine', 'PortoEmbarcacao']\n"
+     ]
+    }
+   ],
+   "source": [
+    "traducoes = {\n",
+    "    'PassengerId': 'IdPassageiro',\n",
+    "    'Survived': 'Sobreviveu', # 0 = Não, 1 = Sim\n",
+    "    'Pclass': 'Classe', # 1, 2, 3\n",
+    "    'Name': 'Nome',\n",
+    "    'Sex': 'Gênero',\n",
+    "    'Age': 'Idade',\n",
+    "    'SibSp': 'NumeroIrmaos',\n",
+    "    'Parch':  'NumeroPais',\n",
+    "    'Ticket': 'NumeroTicket',\n",
+    "    'Fare': 'PrecoTicket',\n",
+    "    'Cabin' : 'NumeroCabine',\n",
+    "    'Embarked': 'PortoEmbarcacao' # C = Cherbourg, Q = Queenstown, S = Southampton\n",
+    "}\n",
+    "\n",
+    "# result = [(d['color'], d['value']) for d in dictionarylist]\n",
+    "\n",
+    "novas_colunas = []\n",
+    "for chave, valor in traducoes.items():\n",
+    "    novas_colunas.append(valor)\n",
+    "\n",
+    "print(novas_colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Gênero</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>NumeroIrmaos</th>\n",
+       "      <th>NumeroPais</th>\n",
+       "      <th>NumeroTicket</th>\n",
+       "      <th>PrecoTicket</th>\n",
+       "      <th>NumeroCabine</th>\n",
+       "      <th>PortoEmbarcacao</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>887</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>888</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>889</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.4500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>890</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>891</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     IdPassageiro  Sobreviveu  Classe  \\\n",
+       "0               1           0       3   \n",
+       "1               2           1       1   \n",
+       "2               3           1       3   \n",
+       "3               4           1       1   \n",
+       "4               5           0       3   \n",
+       "..            ...         ...     ...   \n",
+       "886           887           0       2   \n",
+       "887           888           1       1   \n",
+       "888           889           0       3   \n",
+       "889           890           1       1   \n",
+       "890           891           0       3   \n",
+       "\n",
+       "                                                  Nome  Gênero  Idade  \\\n",
+       "0                              Braund, Mr. Owen Harris    male   22.0   \n",
+       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
+       "2                               Heikkinen, Miss. Laina  female   26.0   \n",
+       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35.0   \n",
+       "4                             Allen, Mr. William Henry    male   35.0   \n",
+       "..                                                 ...     ...    ...   \n",
+       "886                              Montvila, Rev. Juozas    male   27.0   \n",
+       "887                       Graham, Miss. Margaret Edith  female   19.0   \n",
+       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female    NaN   \n",
+       "889                              Behr, Mr. Karl Howell    male   26.0   \n",
+       "890                                Dooley, Mr. Patrick    male   32.0   \n",
+       "\n",
+       "     NumeroIrmaos  NumeroPais      NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "0               1           0         A/5 21171       7.2500          NaN   \n",
+       "1               1           0          PC 17599      71.2833          C85   \n",
+       "2               0           0  STON/O2. 3101282       7.9250          NaN   \n",
+       "3               1           0            113803      53.1000         C123   \n",
+       "4               0           0            373450       8.0500          NaN   \n",
+       "..            ...         ...               ...          ...          ...   \n",
+       "886             0           0            211536      13.0000          NaN   \n",
+       "887             0           0            112053      30.0000          B42   \n",
+       "888             1           2        W./C. 6607      23.4500          NaN   \n",
+       "889             0           0            111369      30.0000         C148   \n",
+       "890             0           0            370376       7.7500          NaN   \n",
+       "\n",
+       "    PortoEmbarcacao  \n",
+       "0                 S  \n",
+       "1                 C  \n",
+       "2                 S  \n",
+       "3                 S  \n",
+       "4                 S  \n",
+       "..              ...  \n",
+       "886               S  \n",
+       "887               S  \n",
+       "888               S  \n",
+       "889               C  \n",
+       "890               Q  \n",
+       "\n",
+       "[891 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns = novas_colunas\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Contagem de passageiros\n",
+      "Gênero\n",
+      "female    314\n",
+      "male      577\n",
+      "Name: IdPassageiro, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#agrupando os dados\n",
+    "agrupando_genero = df.groupby('Gênero')\n",
+    "contagem_passageiros = agrupando_genero['IdPassageiro'].count()\n",
+    "\n",
+    "print(\"Contagem de passageiros\")\n",
+    "print(contagem_passageiros)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Média de Idade de passageiros por gênero\n",
+      "Gênero\n",
+      "female    27.915709\n",
+      "male      30.726645\n",
+      "Name: Idade, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#calculando média de idade por Genero\n",
+    "media_idade = agrupando_genero['Idade'].mean()\n",
+    "\n",
+    "print(\"Média de Idade de passageiros por gênero\")\n",
+    "print(media_idade)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valor mínimo do preço da passagem por gênero Gênero\n",
+      "female    6.75\n",
+      "male      0.00\n",
+      "Name: PrecoTicket, dtype: float64, Gênero\n",
+      "female    512.3292\n",
+      "male      512.3292\n",
+      "Name: PrecoTicket, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#calculando valor max e min pago por Genero\n",
+    "valor_max = agrupando_genero['PrecoTicket'].max()\n",
+    "valor_min = agrupando_genero['PrecoTicket'].min()\n",
+    "\n",
+    "\n",
+    "print(f'Valor mínimo do preço da passagem por gênero {valor_min}, {valor_max}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotando Gráficos\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Gênero'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "valor_max = df['Gênero'].value_counts()\n",
+    "contagem_passageiros.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Distribuição de Idades')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Idade'].plot.hist(bins=20, edgecolor='black')\n",
+    "\n",
+    "plt.xlabel('Idades')\n",
+    "plt.ylabel('Quantidade')\n",
+    "plt.title('Distribuição de Idades')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "taxa_sob_sexo = df.groupby('Gênero')['PrecoTicket'].mean()\n",
+    "taxa_sob_sexo.plot.bar()\n",
+    "\n",
+    "plt.xlabel('Gênero')\n",
+    "plt.ylabel('Média paga')\n",
+    "plt.title('Média paga por gênero')\n",
+    " \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "taxa_sob_classe = df.groupby('Classe')[\"PrecoTicket\"].mean()\n",
+    "taxa_sob_classe.plot.barh()\n",
+    "\n",
+    "plt.title = (\"Valor Médio pago por classe\")\n",
+    "plt.xlabel= (\"classe\")\n",
+    "plt.ylabel= (\"Arrecadação por cor\")\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insigths: No insigths pra essas semanas."
+   ]
+  }
+ ],
+ "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
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
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+693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
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+699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
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+717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
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+719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
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+727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
+728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
+729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
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+755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
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+759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
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+769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
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+772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
+773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
+774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
+775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
+776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
+777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
+778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
+779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
+780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
+781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
+782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
+783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
+784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
+785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
+786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
+787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
+788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
+789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
+790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
+791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
+792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
+793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
+794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
+795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
+796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
+797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
+798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
+799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
+800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
+801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
+802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
+803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
+804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
+805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
+806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
+807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
+808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
+809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
+810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
+811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
+812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
+813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
+814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
+815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
+816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
+817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
+818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
+819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
+820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
+821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
+822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
+823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
+824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
+825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
+826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
+827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
+828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
+829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
+830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
+831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
+832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
+833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
+834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
+835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
+836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
+837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
+838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
+839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
+840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
+841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
+842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
+843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
+844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
+845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
+846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
+847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
+848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
+849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
+850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
+851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
+852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
+853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
+854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
+855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
+856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
+857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
+858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
+862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
+863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
+865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
+866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
+867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
+869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
+870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
+871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
+873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
+874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
diff --git a/material/aula_s12.ipynb b/material/aula_s12.ipynb
index 6a3a013..36dff9f 100644
--- a/material/aula_s12.ipynb
+++ b/material/aula_s12.ipynb
@@ -114,17 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# carregando a biblioteca \n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "id": "_eorJ3GNS_mm",
     "outputId": "14f2b233-cc14-4f8a-a041-98b4cb90d2cc"
@@ -379,13 +369,14 @@
        "[891 rows x 12 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# carregando o dataset \n",
+    "import pandas as pd\n",
+    "\n",
     "df = pd.read_csv(\"titanic.csv\")\n",
     "df"
    ]
@@ -401,32 +392,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "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": [
@@ -456,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "id": "h6tvl5HPS_mw",
     "outputId": "4639f186-8484-4780-f4dc-0a29f3cc759e"
@@ -724,7 +701,7 @@
        "[891 rows x 12 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "id": "gZKfzWYAS_mx",
     "outputId": "cdc7a73b-0f3b-42ce-cdf2-f8322b15e686"
@@ -1021,7 +998,7 @@
        "[891 rows x 12 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1033,8 +1010,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "id": "xzHvkQRrS_mx",
+    "outputId": "ca4d371d-386e-433d-e49b-6e5a6749f767"
+   },
+   "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": {
+    "id": "ZFZ7LOT6S_my",
+    "outputId": "e41628cd-501b-4009-d643-c91f7fe9d7e4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 12)"
+      ]
+     },
+     "execution_count": 8,
+     "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": 9,
+   "metadata": {
+    "id": "BpueQ0G0S_my",
+    "outputId": "ba8ca09f-6788-4332-9a11-786aa02700e4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 12)"
+      ]
+     },
+     "execution_count": 9,
+     "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('Jack')]\n",
+    "df_filtrado.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "ODR1BQRyS_my",
+    "outputId": "a2289b09-9eb0-460f-da3b-265bfaf400fb"
+   },
    "outputs": [
     {
      "data": {
@@ -1073,269 +1130,139 @@
        "  </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>5</th>\n",
+       "      <td>6</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>Moran, Mr. James</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>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>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>...</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>330877</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>873</th>\n",
-       "      <td>874</td>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
+       "      <td>Rice, Master. Eugene</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>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>S</td>\n",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>879</th>\n",
-       "      <td>880</td>\n",
+       "      <th>22</th>\n",
+       "      <td>23</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",
-       "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Markun, Mr. Johann</td>\n",
-       "      <td>male</td>\n",
-       "      <td>33.0</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>349257</td>\n",
-       "      <td>7.8958</td>\n",
+       "      <td>330923</td>\n",
+       "      <td>8.0292</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>885</th>\n",
-       "      <td>886</td>\n",
-       "      <td>0</td>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
+       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</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>330959</td>\n",
+       "      <td>7.8792</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>890</th>\n",
-       "      <td>891</td>\n",
-       "      <td>0</td>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
        "      <td>3</td>\n",
-       "      <td>Dooley, Mr. Patrick</td>\n",
-       "      <td>male</td>\n",
-       "      <td>32.0</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>370376</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>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",
-       "\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",
-       "\n",
-       "[305 rows x 12 columns]"
+       "    IdPassageiro  Sobreviveu  Classe                           Nome    Sexo  \\\n",
+       "5              6           0       3               Moran, Mr. James    male   \n",
+       "16            17           0       3           Rice, Master. Eugene    male   \n",
+       "22            23           1       3    McGowan, Miss. Anna \"Annie\"  female   \n",
+       "28            29           1       3  O'Dwyer, Miss. Ellen \"Nellie\"  female   \n",
+       "32            33           1       3       Glynn, Miss. Mary Agatha  female   \n",
+       "\n",
+       "    Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "5     NaN             0           0       330877       8.4583          NaN   \n",
+       "16    2.0             4           1       382652      29.1250          NaN   \n",
+       "22   15.0             0           0       330923       8.0292          NaN   \n",
+       "28    NaN             0           0       330959       7.8792          NaN   \n",
+       "32    NaN             0           0       335677       7.7500          NaN   \n",
+       "\n",
+       "   PortoEmbarcacao  \n",
+       "5                Q  \n",
+       "16               Q  \n",
+       "22               Q  \n",
+       "28               Q  \n",
+       "32               Q  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[df['Idade'] > 30]"
+    "# Por último, utilizando a função isin()\n",
+    "df_filtrado = df[df['PortoEmbarcacao'].isin(['Q'])]\n",
+    "df_filtrado.shape"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
+   "cell_type": "markdown",
    "metadata": {
-    "id": "xzHvkQRrS_mx",
-    "outputId": "ca4d371d-386e-433d-e49b-6e5a6749f767"
+    "id": "8gULcYPzS_mz"
    },
-   "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"
+    "### 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": 8,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "id": "W-Ktv45VS_mz",
+    "outputId": "00f9464e-df1b-4cd2-c0b3-30eac23016f1"
+   },
    "outputs": [
     {
      "data": {
@@ -1374,269 +1301,125 @@
        "  </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",
+       "      <th>0</th>\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>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</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>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>16</td>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
        "      <td>female</td>\n",
-       "      <td>55.0</td>\n",
+       "      <td>26.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>248706</td>\n",
-       "      <td>16.0000</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>18</th>\n",
-       "      <td>19</td>\n",
+       "      <th>4</th>\n",
+       "      <td>5</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>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>345763</td>\n",
-       "      <td>18.0000</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>862</th>\n",
-       "      <td>863</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
-       "      <td>female</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
        "      <td>0</td>\n",
-       "      <td>17466</td>\n",
-       "      <td>25.9292</td>\n",
-       "      <td>D17</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>865</th>\n",
-       "      <td>866</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>42.0</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>236852</td>\n",
-       "      <td>13.0000</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>8.4583</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",
-       "    </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",
+       "      <td>Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>885</th>\n",
-       "      <td>886</td>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>Rice, Mrs. William (Margaret Norton)</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>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>Q</td>\n",
+       "      <td>S</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",
-       "\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",
-       "\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]"
+       "   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  \\\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",
+       "  NumeroCabine PortoEmbarcacao  \n",
+       "0          NaN               S  \n",
+       "2          NaN               S  \n",
+       "4          NaN               S  \n",
+       "5          NaN               Q  \n",
+       "7          NaN               S  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[(df['Idade'] > 30) & (df['Sexo'] == 'female')]"
+    "# 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": 9,
+   "execution_count": 12,
    "metadata": {
-    "id": "ZFZ7LOT6S_my",
-    "outputId": "e41628cd-501b-4009-d643-c91f7fe9d7e4"
+    "id": "uVSmQ1zeS_mz",
+    "outputId": "555cf64e-c5a6-4b3d-d69a-89fb77dd1012"
    },
-   "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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1675,17 +1458,77 @@
        "  </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>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>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>3</th>\n",
+       "      <td>4</td>\n",
        "      <td>1</td>\n",
-       "      <td>392091</td>\n",
-       "      <td>9.35</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",
@@ -1694,121 +1537,33 @@
        "</div>"
       ],
       "text/plain": [
-       "     IdPassageiro  Sobreviveu  Classe                        Nome    Sexo  \\\n",
-       "855           856           1       3  Aks, Mrs. Sam (Leah Rosen)  female   \n",
-       "\n",
-       "     Idade  NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
-       "855   18.0             0           1       392091         9.35          NaN   \n",
-       "\n",
-       "    PortoEmbarcacao  \n",
-       "855               S  "
-      ]
-     },
-     "execution_count": 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)"
-      ]
-     },
-     "execution_count": 11,
-     "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('Jack')]\n",
-    "df_filtrado.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "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",
+       "   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",
-       "    .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>766</th>\n",
-       "      <td>767</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Brewe, Dr. Arthur Jackson</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>112379</td>\n",
-       "      <td>39.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</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",
+       "                                                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,
@@ -1816,93 +1571,6 @@
      "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",
@@ -1922,7 +1590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
     "id": "H3dlaegrS_m0",
     "outputId": "1c7ad974-196c-40db-8eb7-fa78f65ff480"
@@ -2073,7 +1741,7 @@
        "4               S  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2100,7 +1768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "id": "cr5Lt68uS_m4",
     "outputId": "8786bf85-9a78-4ff2-a2e5-12a514640843"
@@ -2209,7 +1877,7 @@
        "[891 rows x 2 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2235,7 +1903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {
     "id": "NzQE8tnOS_m4",
     "outputId": "7e798f09-5b8e-4561-d1ab-bc2b460fd619"
@@ -2344,7 +2012,7 @@
        "[891 rows x 2 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2355,7 +2023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {
     "id": "PSjsxwSuS_m4",
     "outputId": "cb5f5ab9-92b9-4de7-f718-90515d59ab14"
@@ -2398,515 +2066,196 @@
        "  </thead>\n",
        "  <tbody>\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",
+       "      <th>0</th>\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>Braund, Mr. Owen Harris</td>\n",
        "      <td>male</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>A/5. 2151</td>\n",
-       "      <td>8.0500</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>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",
+       "      <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>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",
-       "  </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",
-       "\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",
-       "\n",
-       "   NumeroCabine PortoEmbarcacao  \n",
-       "10           G6               S  \n",
-       "11         C103               S  \n",
-       "12          NaN               S  \n",
-       "13          NaN               S  \n",
-       "14          NaN               S  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[10:15, :]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercício 04 parte 01\n",
-    "\n",
-    "- Realizar filtro com apenas uma única condição;\n",
-    "- Realizar filtro com múltiplas condições;\n",
-    "- Selecionar entradas com string específica;\n",
-    "- Realizar uma consulta passageiros da segunda e terceira classe;\n",
-    "- Obter subamostra do dataset com 30 amostras utilizando o método de slicing;\n",
-    "- 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,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Classe\n",
-       "1    216\n",
-       "2    184\n",
-       "3    491\n",
-       "Name: IdPassageiro, dtype: int64"
-      ]
-     },
-     "execution_count": 30,
-     "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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "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"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Média de Idade de passageiros em cada classe\n",
-    "media_idade = dado_agrupado['Idade'].mean()\n",
-    "media_idade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "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>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",
-       "    </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",
-       "    </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"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[['Idade']].describe(percentiles=[0.99, 0.995]).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "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>96</th>\n",
-       "      <td>97</td>\n",
-       "      <td>0</td>\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>male</td>\n",
-       "      <td>70.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>370369</td>\n",
-       "      <td>7.7500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Q</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>493</th>\n",
-       "      <td>494</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Artagaveytia, Mr. Ramon</td>\n",
-       "      <td>male</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>PC 17609</td>\n",
-       "      <td>49.5042</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>630</th>\n",
-       "      <td>631</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
-       "      <td>male</td>\n",
-       "      <td>80.0</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</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>851</th>\n",
-       "      <td>852</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Svensson, Mr. Johan</td>\n",
-       "      <td>male</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>347060</td>\n",
-       "      <td>7.7750</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",
-       "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",
+       "\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",
-       "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",
+       "   NumeroIrmaos  NumeroPais NumeroTicket  PrecoTicket NumeroCabine  \\\n",
+       "0             1           0    A/5 21171       7.2500          NaN   \n",
+       "1             1           0     PC 17599      71.2833          C85   \n",
+       "\n",
+       "  PortoEmbarcacao  \n",
+       "0               S  \n",
+       "1               C  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[10:15, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercício 04 parte 01\n",
+    "\n",
+    "- Realizar filtro com apenas uma única condição;\n",
+    "- Realizar filtro com múltiplas condições;\n",
+    "- Selecionar entradas com string específica;\n",
+    "- Realizar uma consulta passageiros da segunda e terceira classe;\n",
+    "- Obter subamostra do dataset com 30 amostras utilizando o método de slicing;\n",
+    "- Utilizar a função loc para obter a idade e sobrevivência do passageiro;\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Alomoço"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
        "\n",
-       "    NumeroCabine PortoEmbarcacao  \n",
-       "96            A5               C  \n",
-       "116          NaN               Q  \n",
-       "493          NaN               C  \n",
-       "630          A23               S  \n",
-       "851          NaN               S  "
+       "        <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": 42,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[df['Idade'] > 70]"
+    "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": 18,
+   "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",
+    "dado_agrupado"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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",
+    "media_idade"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 20,
    "metadata": {
     "id": "jxk2rE1hS_m7",
     "outputId": "55e2c10a-49b5-4a8a-f979-162b4bc89e1b"
@@ -3241,7 +2590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 21,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3263,7 +2612,8 @@
     }
    ],
    "source": [
-    "# exemplo mais simples de gráfico:\n",
+    "#exemplo mais simples de gráfico:\n",
+    "#!pip install matplotlib \n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "fig, ax = plt.subplots()\n",
@@ -3283,7 +2633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 22,
    "metadata": {
     "id": "bmZg5LjvS_nJ",
     "outputId": "f3874a08-9ec1-4235-8db1-2250deeb54ea"
@@ -3292,58 +2642,13 @@
     {
      "data": {
       "text/plain": [
-       "Classe\n",
-       "3    491\n",
-       "1    216\n",
-       "2    184\n",
-       "Name: count, dtype: int64"
+       "<Axes: xlabel='Classe'>"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 22,
      "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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",
@@ -3356,42 +2661,24 @@
     }
    ],
    "source": [
-    "contagem_passageiros.plot(kind='bar');"
+    "# 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"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 65,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "df['Idade'].plot(kind='hist')"
+    "**Doc:** https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 23,
    "metadata": {
     "id": "zpZmI2e9S_nJ",
     "outputId": "3f16a4f4-3039-4426-ac46-d0f8178d87a6"
@@ -3403,7 +2690,7 @@
        "Text(0.5, 1.0, 'Número de Passageiros por Classe')"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3430,7 +2717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 24,
    "metadata": {
     "id": "j7lUqI3BS_nK",
     "outputId": "758058f2-185d-412d-835e-38d8ca773f3e"
@@ -3461,7 +2748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 25,
    "metadata": {
     "id": "RzV9cvoOS_nK",
     "outputId": "eea06c73-5805-427d-f2b1-b82d3481b8a8"
@@ -3473,7 +2760,7 @@
        "Text(0.5, 1.0, 'Distribuição de Idade')"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3655,7 +2942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 26,
    "metadata": {
     "id": "6qrRb5EIS_nL"
    },
@@ -3684,7 +2971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 27,
    "metadata": {
     "id": "suI74yqZS_nL",
     "outputId": "eb87a166-4d92-4068-e5ad-e7ce283ed244"
@@ -3710,7 +2997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 28,
    "metadata": {
     "id": "DxCmoOkhS_nM",
     "outputId": "2c7699a1-519f-4b81-d4e1-836895767375"
@@ -3720,9 +3007,7 @@
      "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"
      ]
@@ -3741,77 +3026,27 @@
   },
   {
    "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": 29,
    "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",
-    "\n",
-    "# queremos entrar em liquidação diminuindo 2 reais no preço de cada item\n",
-    "array_promocional = array_precos - 2\n",
-    "array_promocional\n"
+    "lista_2[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 30,
    "metadata": {
     "id": "SWy82LKAS_nM",
     "outputId": "ee7e4c2e-5553-405f-cbdf-878534650298"
@@ -3824,7 +3059,7 @@
      "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\\Valcineia Soares\\estudos\\on26-python-s12-pandas-numpy-II\\material\\aula_s12.ipynb Cell 51\u001b[0m line \u001b[0;36m4\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Valcineia%20Soares/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/Valcineia%20Soares/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/Valcineia%20Soares/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'"
      ]
     }
diff --git "a/material/exerc\303\255ciossala.ipynb" "b/material/exerc\303\255ciossala.ipynb"
new file mode 100644
index 0000000..96de7bd
--- /dev/null
+++ "b/material/exerc\303\255ciossala.ipynb"
@@ -0,0 +1,451 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# def tratamento (df)\n",
+    "\n",
+    "df = pd.read_csv(\"titanic.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Gênero', 'Idade', 'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket', 'NumeroCabine', 'PortoEmbarcacao']\n"
+     ]
+    }
+   ],
+   "source": [
+    "traducoes = {\n",
+    "    'PassengerId': 'IdPassageiro',\n",
+    "    'Survived': 'Sobreviveu', # 0 = Não, 1 = Sim\n",
+    "    'Pclass': 'Classe', # 1, 2, 3\n",
+    "    'Name': 'Nome',\n",
+    "    'Sex': 'Gênero',\n",
+    "    'Age': 'Idade',\n",
+    "    'SibSp': 'NumeroIrmaos',\n",
+    "    'Parch':  'NumeroPais',\n",
+    "    'Ticket': 'NumeroTicket',\n",
+    "    'Fare': 'PrecoTicket',\n",
+    "    'Cabin' : 'NumeroCabine',\n",
+    "    'Embarked': 'PortoEmbarcacao' # C = Cherbourg, Q = Queenstown, S = Southampton\n",
+    "}\n",
+    "\n",
+    "# result = [(d['color'], d['value']) for d in dictionarylist]\n",
+    "\n",
+    "novas_colunas = []\n",
+    "for chave, valor in traducoes.items():\n",
+    "    novas_colunas.append(valor)\n",
+    "\n",
+    "print(novas_colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Gênero'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Valcineia Soares\\estudos\\on26-python-s12-pandas-numpy-II\\exercicios\\para-casa\\desafiosemana12.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Valcineia%20Soares/estudos/on26-python-s12-pandas-numpy-II/exercicios/para-casa/desafiosemana12.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m agrupando_genero \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39;49mgroupby(\u001b[39m'\u001b[39;49m\u001b[39mGênero\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Valcineia%20Soares/estudos/on26-python-s12-pandas-numpy-II/exercicios/para-casa/desafiosemana12.ipynb#W2sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m contagem_passageiros \u001b[39m=\u001b[39m agrupando_genero[\u001b[39m'\u001b[39m\u001b[39mIdPassageiro\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mcount()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Valcineia%20Soares/estudos/on26-python-s12-pandas-numpy-II/exercicios/para-casa/desafiosemana12.ipynb#W2sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mContagem de passageiros\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Valcineia Soares\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:8872\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[0;32m   8869\u001b[0m \u001b[39mif\u001b[39;00m level \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m by \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   8870\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mYou have to supply one of \u001b[39m\u001b[39m'\u001b[39m\u001b[39mby\u001b[39m\u001b[39m'\u001b[39m\u001b[39m and \u001b[39m\u001b[39m'\u001b[39m\u001b[39mlevel\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m-> 8872\u001b[0m \u001b[39mreturn\u001b[39;00m DataFrameGroupBy(\n\u001b[0;32m   8873\u001b[0m     obj\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m,\n\u001b[0;32m   8874\u001b[0m     keys\u001b[39m=\u001b[39;49mby,\n\u001b[0;32m   8875\u001b[0m     axis\u001b[39m=\u001b[39;49maxis,\n\u001b[0;32m   8876\u001b[0m     level\u001b[39m=\u001b[39;49mlevel,\n\u001b[0;32m   8877\u001b[0m     as_index\u001b[39m=\u001b[39;49mas_index,\n\u001b[0;32m   8878\u001b[0m     sort\u001b[39m=\u001b[39;49msort,\n\u001b[0;32m   8879\u001b[0m     group_keys\u001b[39m=\u001b[39;49mgroup_keys,\n\u001b[0;32m   8880\u001b[0m     observed\u001b[39m=\u001b[39;49mobserved,\n\u001b[0;32m   8881\u001b[0m     dropna\u001b[39m=\u001b[39;49mdropna,\n\u001b[0;32m   8882\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\Valcineia Soares\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1274\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[0;32m   1271\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdropna \u001b[39m=\u001b[39m dropna\n\u001b[0;32m   1273\u001b[0m \u001b[39mif\u001b[39;00m grouper \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m-> 1274\u001b[0m     grouper, exclusions, obj \u001b[39m=\u001b[39m get_grouper(\n\u001b[0;32m   1275\u001b[0m         obj,\n\u001b[0;32m   1276\u001b[0m         keys,\n\u001b[0;32m   1277\u001b[0m         axis\u001b[39m=\u001b[39;49maxis,\n\u001b[0;32m   1278\u001b[0m         level\u001b[39m=\u001b[39;49mlevel,\n\u001b[0;32m   1279\u001b[0m         sort\u001b[39m=\u001b[39;49msort,\n\u001b[0;32m   1280\u001b[0m         observed\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m \u001b[39mif\u001b[39;49;00m observed \u001b[39mis\u001b[39;49;00m lib\u001b[39m.\u001b[39;49mno_default \u001b[39melse\u001b[39;49;00m observed,\n\u001b[0;32m   1281\u001b[0m         dropna\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdropna,\n\u001b[0;32m   1282\u001b[0m     )\n\u001b[0;32m   1284\u001b[0m \u001b[39mif\u001b[39;00m observed \u001b[39mis\u001b[39;00m lib\u001b[39m.\u001b[39mno_default:\n\u001b[0;32m   1285\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(ping\u001b[39m.\u001b[39m_passed_categorical \u001b[39mfor\u001b[39;00m ping \u001b[39min\u001b[39;00m grouper\u001b[39m.\u001b[39mgroupings):\n",
+      "File \u001b[1;32mc:\\Users\\Valcineia Soares\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\groupby\\grouper.py:1009\u001b[0m, in \u001b[0;36mget_grouper\u001b[1;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[0;32m   1007\u001b[0m         in_axis, level, gpr \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m, gpr, \u001b[39mNone\u001b[39;00m\n\u001b[0;32m   1008\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1009\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(gpr)\n\u001b[0;32m   1010\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(gpr, Grouper) \u001b[39mand\u001b[39;00m gpr\u001b[39m.\u001b[39mkey \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   1011\u001b[0m     \u001b[39m# Add key to exclusions\u001b[39;00m\n\u001b[0;32m   1012\u001b[0m     exclusions\u001b[39m.\u001b[39madd(gpr\u001b[39m.\u001b[39mkey)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Gênero'"
+     ]
+    }
+   ],
+   "source": [
+    "agrupando_genero = df.groupby('Gênero')\n",
+    "contagem_passageiros = agrupando_genero['IdPassageiro'].count()\n",
+    "\n",
+    "print(\"Contagem de passageiros\")\n",
+    "print(contagem_passageiros)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'agrupando_genero' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Valcineia Soares\\estudos\\on26-python-s12-pandas-numpy-II\\exercicios\\para-casa\\desafiosemana12.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Valcineia%20Soares/estudos/on26-python-s12-pandas-numpy-II/exercicios/para-casa/desafiosemana12.ipynb#W3sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m media_idade \u001b[39m=\u001b[39m agrupando_genero[\u001b[39m'\u001b[39m\u001b[39mIdade\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mmean()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Valcineia%20Soares/estudos/on26-python-s12-pandas-numpy-II/exercicios/para-casa/desafiosemana12.ipynb#W3sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mMédia de Idade de passageiros por gênero\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Valcineia%20Soares/estudos/on26-python-s12-pandas-numpy-II/exercicios/para-casa/desafiosemana12.ipynb#W3sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mprint\u001b[39m(media_idade)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'agrupando_genero' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "media_idade = agrupando_genero['Idade'].mean()\n",
+    "\n",
+    "print(\"Média de Idade de passageiros por gênero\")\n",
+    "print(media_idade)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valor_max = agrupando_genero['PrecoTicket'].max()\n",
+    "valor_min = agrupando_genero['PrecoTicket'].min()\n",
+    "\n",
+    "\n",
+    "print(f'Valor mínimo do preço da passagem por gênero {valor_min}, {valor_max}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Idade'].plot.hist(bins=20, edgecolor='black')\n",
+    "\n",
+    "plt.xlabel('Idades')\n",
+    "plt.ylabel('Quantidade')\n",
+    "plt.title('Distribuição de Idades')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CRIANDO ARRAYS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "nv_array=np.random.randint(5,50,500)\n",
+    "serie_array=pd.Series(nv_array)\n",
+    "serie_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lista_python = [[10,15,20],[2,4,6]]\n",
+    "array_cis = np.array(lista_python)\n",
+    "\n",
+    "df = pd.DataFrame(array_cis, columns=['x', 'y', 'z'])\n",
+    "\n",
+    "df"
+   ]
+  }
+ ],
+ "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
+}