diff --git a/exercicios/para-casa/README.md b/exercicios/para-casa/README.md
index ac13de2..18333dd 100644
--- a/exercicios/para-casa/README.md
+++ b/exercicios/para-casa/README.md
@@ -11,7 +11,7 @@ Formato do notebook:
 
     ●	Processamento
         ○	Função de processamento
-            ○	Leitura e tratamento do DF
+        ○	Leitura e tratamento do DF
 
     ●	Visualização dos Dados
         ○	gerar gráficos
diff --git a/exercicios/para-casa/ex_casaj.ipynb b/exercicios/para-casa/ex_casaj.ipynb
new file mode 100644
index 0000000..01d3364
--- /dev/null
+++ b/exercicios/para-casa/ex_casaj.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Um breve cruzamento de dados, analisando a lista dos passageiros do titanic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"titanic.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Taxa de Sobrevivência por Porto')"
+      ]
+     },
+     "execution_count": 53,
+     "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": [
+    "\n",
+    "#sobreviventes x porto \n",
+    "sobreviventes_por_porto = df.groupby('Embarked')['Survived'].value_counts()\n",
+    "sobreviventes_por_porto.plot.barh()\n",
+    "\n",
+    "\n",
+    "plt.xlabel('Embarked')\n",
+    "plt.ylabel('Survived')\n",
+    "plt.title('Taxa de Sobrevivência por Porto')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Número de pessoas por porto')"
+      ]
+     },
+     "execution_count": 50,
+     "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": [
+    "criancas_por_porto = df['Embarked'].value_counts()\n",
+    "criancas_por_porto.plot.bar()\n",
+    "\n",
+    "plt.xlabel('Embarked')\n",
+    "plt.ylabel('Quantidade')\n",
+    "plt.title('Número de pessoas por porto')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#quantidade de sobreviventes menores de 15 anos por clasee\n",
+    "sobreviventes_por_porto = df[df['Age'] < 15].groupby(['Age', 'Pclass']).size().unstack().plot.bar(stacked=True)\n",
+    "\n",
+    "# Set axis labels and title\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Count of Passengers')\n",
+    "plt.title('Sobreviventes menores de 15 anos por classe')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "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": "3.11.6"
+  }
+ },
+ "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
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
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