diff --git a/exercicios/para-casa/atividade_casa_s12.ipynb b/exercicios/para-casa/atividade_casa_s12.ipynb
new file mode 100644
index 0000000..bd133a8
--- /dev/null
+++ b/exercicios/para-casa/atividade_casa_s12.ipynb
@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_titanic = pd.read_csv('/Users/laismeirelesalves/Estudos/semana_12/on26-python-s12-pandas-numpy-II/material/titanic.csv')\n",
+    "df_titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Quantidade de passageiros')"
+      ]
+     },
+     "execution_count": 41,
+     "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": [
+    "plt.hist(x = df_titanic['Age'], color='orange', edgecolor='black')\n",
+    "\n",
+    "plt.xlabel('Idade', loc='right')\n",
+    "plt.ylabel('Quantidade de passageiros')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'function' object has no attribute 'plot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[48], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m taxa_tarifa_por_genero \u001b[39m=\u001b[39m df_titanic\u001b[39m.\u001b[39mgroupby(\u001b[39m'\u001b[39m\u001b[39mSex\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m'\u001b[39m\u001b[39mFare\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mmean\n\u001b[1;32m      2\u001b[0m taxa_tarifa_por_genero\n\u001b[0;32m----> 3\u001b[0m taxa_tarifa_por_genero\u001b[39m.\u001b[39;49mplot\u001b[39m.\u001b[39mbar()\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'plot'"
+     ]
+    }
+   ],
+   "source": [
+    "taxa_tarifa_por_genero = df_titanic.groupby('Sex')['Fare'].mean\n",
+    "taxa_tarifa_por_genero\n",
+    "taxa_tarifa_por_genero.plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_titanic.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "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>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>223.5000</td>\n",
+       "      <td>446.0000</td>\n",
+       "      <td>668.5</td>\n",
+       "      <td>891.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Survived</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>714.0</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>20.1250</td>\n",
+       "      <td>28.0000</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>80.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare</th>\n",
+       "      <td>891.0</td>\n",
+       "      <td>32.204208</td>\n",
+       "      <td>49.693429</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.9104</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>512.3292</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count        mean         std   min       25%       50%    75%  \\\n",
+       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
+       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
+       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
+       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
+       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
+       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
+       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
+       "\n",
+       "                  max  \n",
+       "PassengerId  891.0000  \n",
+       "Survived       1.0000  \n",
+       "Pclass         3.0000  \n",
+       "Age           80.0000  \n",
+       "SibSp          8.0000  \n",
+       "Parch          6.0000  \n",
+       "Fare         512.3292  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_titanic.describe().T\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coluna_idade = df_titanic['Age']\n",
+    "coluna_idade\n",
+    "type(coluna_idade)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/exercicio_s12.ipynb b/exercicios/para-sala/exercicio_s12.ipynb
new file mode 100644
index 0000000..47c7754
--- /dev/null
+++ b/exercicios/para-sala/exercicio_s12.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('/Users/laismeirelesalves/Estudos/semana_12/on26-python-s12-pandas-numpy-II/material/titanic.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of      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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(305, 12)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filtrado = df[df['Age'] > 30]\n",
+    "df_filtrado.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(103, 12)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filtrado = df[(df['Age'] > 30) & (df['Sex'] == 'female')]\n",
+    "df_filtrado.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(52, 12)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filtrado = df[df['Name'].str.contains('John')]\n",
+    "df_filtrado.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(675, 12)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_consulta = df.query('Pclass in (2,3)')\n",
+    "df_consulta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Slicing --> obter somente uma parte do dataset\n",
+    "\n",
+    "df[:30]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>1</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>0</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Survived   Age\n",
+       "0           0  22.0\n",
+       "1           1  38.0\n",
+       "2           1  26.0\n",
+       "3           1  35.0\n",
+       "4           0  35.0\n",
+       "..        ...   ...\n",
+       "886         0  27.0\n",
+       "887         1  19.0\n",
+       "888         0   NaN\n",
+       "889         1  26.0\n",
+       "890         0  32.0\n",
+       "\n",
+       "[891 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[:, ['Survived', 'Age']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sex\n",
+      "female    314\n",
+      "male      577\n",
+      "Name: PassengerId, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_agrupado = df.groupby('Sex')\n",
+    "df_agrupado\n",
+    "\n",
+    "contagem_por_genero = df_agrupado['PassengerId'].count()\n",
+    "print(contagem_por_genero)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sex\n",
+      "female    27.915709\n",
+      "male      30.726645\n",
+      "Name: Age, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "media_idade_por_genero = df_agrupado['Age'].mean()\n",
+    "print(media_idade_por_genero)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sex\n",
+      "female    512.3292\n",
+      "male      512.3292\n",
+      "Name: Fare, dtype: float64\n",
+      "Sex\n",
+      "female    6.75\n",
+      "male      0.00\n",
+      "Name: Fare, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "valor_max_tarifa_por_genero = df_agrupado['Fare'].max()\n",
+    "valor_min_tarifa_por_genero = df_agrupado['Fare'].min()\n",
+    "\n",
+    "print(valor_max_tarifa_por_genero)\n",
+    "print(valor_min_tarifa_por_genero)"
+   ]
+  }
+ ],
+ "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"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/part_2_ex_s12.ipynb b/exercicios/para-sala/part_2_ex_s12.ipynb
new file mode 100644
index 0000000..3b614a2
--- /dev/null
+++ b/exercicios/para-sala/part_2_ex_s12.ipynb
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('/Users/laismeirelesalves/Estudos/semana_12/on26-python-s12-pandas-numpy-II/material/titanic.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contagem_passageiros = df['Sex'].value_counts()\n",
+    "contagem_passageiros.plot(kind='bar');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Pclass'>"
+      ]
+     },
+     "execution_count": 4,
+     "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": [
+    "taxa_sobrev_classe = df.groupby('Pclass')['Survived'].mean()\n",
+    "taxa_sobrev_classe.plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
+       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  }
+ ],
+ "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"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/material/aula_s12.ipynb b/material/aula_s12.ipynb
index 6a3a013..969ac67 100644
--- a/material/aula_s12.ipynb
+++ b/material/aula_s12.ipynb
@@ -3268,6 +3268,18 @@
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot([1, 2, 3, 4], [2, 4, 6, 8]);"
+<<<<<<< HEAD
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install matplotlib"
+=======
+>>>>>>> d0272b0e971aa8c3e9bbbff7edc83af7e132fef5
    ]
   },
   {