diff --git "a/exercicios/para-casa/Exerc\303\255cio_Karine_S12.ipynb" "b/exercicios/para-casa/Exerc\303\255cio_Karine_S12.ipynb"
new file mode 100644
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@@ -0,0 +1,840 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Exercício para Casa, semana 12:** "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Parte 1: Preparação do Ambiente**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Importando as bibliotecas pandas, numpy e matplotlib:\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configurando a seed para reprodutibilidade\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gerando dados falsos\n",
+    "categorias = ['Eletrônicos', 'Roupas', 'Alimentos', 'Livros', 'Brinquedos']\n",
+    "datas = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')\n",
+    "num_registros = len(datas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Criando dataframe:\n",
+    "data = {\n",
+    "       'Data': np.random.choice(datas, num_registros),\n",
+    "       'Categoria': np.random.choice(categorias, num_registros),\n",
+    "       'Quantidade_Vendida': np.random.randint(1, 100, num_registros),\n",
+    "       'Preco_Unitario': np.round(np.random.uniform(10, 500, num_registros), 2)\n",
+    "   }\n",
+    "df = pd.DataFrame(data)\n",
+    "df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Salvando o DataFrame em um arquivo CSV\n",
+    "df.to_csv('dados_vendas.csv', index=False)\n",
+    "\n",
+    "print(\"Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Parte 2: Consulta e Filtragem de Dados**"
+   ]
+  },
+  {
+   "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>Data</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>74</td>\n",
+       "      <td>60.43</td>\n",
+       "      <td>4471.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-15</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>83</td>\n",
+       "      <td>272.88</td>\n",
+       "      <td>22649.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-09-28</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>17</td>\n",
+       "      <td>195.62</td>\n",
+       "      <td>3325.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-04-17</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>85</td>\n",
+       "      <td>233.93</td>\n",
+       "      <td>19884.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>78</td>\n",
+       "      <td>305.94</td>\n",
+       "      <td>23863.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>2023-10-07</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>66</td>\n",
+       "      <td>12.49</td>\n",
+       "      <td>824.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>2023-04-24</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>10</td>\n",
+       "      <td>350.90</td>\n",
+       "      <td>3509.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>2023-10-15</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>5</td>\n",
+       "      <td>33.90</td>\n",
+       "      <td>169.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>2023-12-08</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>74</td>\n",
+       "      <td>270.98</td>\n",
+       "      <td>20052.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>2023-05-31</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>97</td>\n",
+       "      <td>481.44</td>\n",
+       "      <td>46699.68</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>365 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Data   Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+       "0    2023-04-13      Roupas                  74           60.43      4471.82\n",
+       "1    2023-12-15   Alimentos                  83          272.88     22649.04\n",
+       "2    2023-09-28      Roupas                  17          195.62      3325.54\n",
+       "3    2023-04-17      Roupas                  85          233.93     19884.05\n",
+       "4    2023-03-13      Roupas                  78          305.94     23863.32\n",
+       "..          ...         ...                 ...             ...          ...\n",
+       "360  2023-10-07  Brinquedos                  66           12.49       824.34\n",
+       "361  2023-04-24  Brinquedos                  10          350.90      3509.00\n",
+       "362  2023-10-15  Brinquedos                   5           33.90       169.50\n",
+       "363  2023-12-08  Brinquedos                  74          270.98     20052.52\n",
+       "364  2023-05-31      Roupas                  97          481.44     46699.68\n",
+       "\n",
+       "[365 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Carregar os dados gerados:\n",
+    "df = pd.read_csv('dados_vendas.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 1: Quantas linhas e colunas existem no DataFrame carregado?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(365, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Verificando quantas linhas e colunas existem no DataFrame:\n",
+    "\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 2: Quais são os tipos de dados (dtypes) das colunas?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Data                   object\n",
+       "Categoria              object\n",
+       "Quantidade_Vendida      int64\n",
+       "Preco_Unitario        float64\n",
+       "Valor_Total           float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Exibir os tipos de dados das colunas:\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 3: Exibir as primeiras 5 linhas do DataFrame:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>Data</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>74</td>\n",
+       "      <td>60.43</td>\n",
+       "      <td>4471.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-15</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>83</td>\n",
+       "      <td>272.88</td>\n",
+       "      <td>22649.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-09-28</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>17</td>\n",
+       "      <td>195.62</td>\n",
+       "      <td>3325.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-04-17</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>85</td>\n",
+       "      <td>233.93</td>\n",
+       "      <td>19884.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>78</td>\n",
+       "      <td>305.94</td>\n",
+       "      <td>23863.32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Data  Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+       "0  2023-04-13     Roupas                  74           60.43      4471.82\n",
+       "1  2023-12-15  Alimentos                  83          272.88     22649.04\n",
+       "2  2023-09-28     Roupas                  17          195.62      3325.54\n",
+       "3  2023-04-17     Roupas                  85          233.93     19884.05\n",
+       "4  2023-03-13     Roupas                  78          305.94     23863.32"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Exibir as 5 primeiras linhas \n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Quantas categorias únicas de produtos estão presentes no DataFrame?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Roupas', 'Alimentos', 'Eletrônicos', 'Brinquedos', 'Livros'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorias_unicas = df['Categoria'].unique()\n",
+    "categorias_unicas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Filtrar os dados para mostrar apenas as vendas da categoria 'Eletrônicos':**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "5    2023-07-08  Eletrônicos                  73          256.12     18696.76\n",
+      "6    2023-01-21  Eletrônicos                   1          274.53       274.53\n",
+      "7    2023-04-13  Eletrônicos                  51          248.32     12664.32\n",
+      "14   2023-06-01  Eletrônicos                  34          303.52     10319.68\n",
+      "18   2023-09-15  Eletrônicos                  39          121.40      4734.60\n",
+      "..          ...          ...                 ...             ...          ...\n",
+      "347  2023-09-10  Eletrônicos                  33          431.46     14238.18\n",
+      "349  2023-09-13  Eletrônicos                  27          201.67      5445.09\n",
+      "351  2023-05-08  Eletrônicos                  29          243.57      7063.53\n",
+      "353  2023-10-08  Eletrônicos                  57          427.57     24371.49\n",
+      "356  2023-02-27  Eletrônicos                  55          466.48     25656.40\n",
+      "\n",
+      "[73 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+    "print(eletronicos)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Qual é a média do valor total das vendas na categoria 'Eletrônicos'?\"**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Média do valor total das vendas na categoria 'Eletrônicos': 12291.256301369864\n"
+     ]
+    }
+   ],
+   "source": [
+    "eletronicos_df = df[df['Categoria'] == 'Eletrônicos']\n",
+    "media_valor_total_eletronicos = eletronicos_df['Valor_Total'].mean()\n",
+    "print(\"Média do valor total das vendas na categoria 'Eletrônicos':\", media_valor_total_eletronicos)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Filtrar as vendas acima de R$ 1000:**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data   Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0    2023-04-13      Roupas                  74           60.43      4471.82\n",
+      "1    2023-12-15   Alimentos                  83          272.88     22649.04\n",
+      "2    2023-09-28      Roupas                  17          195.62      3325.54\n",
+      "3    2023-04-17      Roupas                  85          233.93     19884.05\n",
+      "4    2023-03-13      Roupas                  78          305.94     23863.32\n",
+      "..          ...         ...                 ...             ...          ...\n",
+      "358  2023-12-26  Brinquedos                  68          147.88     10055.84\n",
+      "359  2023-06-23   Alimentos                  86          133.80     11506.80\n",
+      "361  2023-04-24  Brinquedos                  10          350.90      3509.00\n",
+      "363  2023-12-08  Brinquedos                  74          270.98     20052.52\n",
+      "364  2023-05-31      Roupas                  97          481.44     46699.68\n",
+      "\n",
+      "[330 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "vendas_acima_1000 = df[df['Valor_Total']> 1000]\n",
+    "print(vendas_acima_1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Quantas vendas acima de R$ 1000 ocorreram?**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data   Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0    2023-04-13      Roupas                  74           60.43      4471.82\n",
+      "1    2023-12-15   Alimentos                  83          272.88     22649.04\n",
+      "2    2023-09-28      Roupas                  17          195.62      3325.54\n",
+      "3    2023-04-17      Roupas                  85          233.93     19884.05\n",
+      "4    2023-03-13      Roupas                  78          305.94     23863.32\n",
+      "..          ...         ...                 ...             ...          ...\n",
+      "358  2023-12-26  Brinquedos                  68          147.88     10055.84\n",
+      "359  2023-06-23   Alimentos                  86          133.80     11506.80\n",
+      "361  2023-04-24  Brinquedos                  10          350.90      3509.00\n",
+      "363  2023-12-08  Brinquedos                  74          270.98     20052.52\n",
+      "364  2023-05-31      Roupas                  97          481.44     46699.68\n",
+      "\n",
+      "[330 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Filtrar os dados:\n",
+    "vendas_acima_1000 = df[df['Valor_Total']> 1000]\n",
+    "#Função len para verificar a quantidade de registros:\n",
+    "quantidade = len(vendas_acima_1000)\n",
+    "print(vendas_acima_1000)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Parte 3: Agrupamento e Agregação:**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. **Agrupar os dados por categoria e calcular o total de vendas por categoria:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Categoria  Valor_Total\n",
+      "0    Alimentos    742457.74\n",
+      "1   Brinquedos   1187964.04\n",
+      "2  Eletrônicos    897261.71\n",
+      "3       Livros    963685.46\n",
+      "4       Roupas    991215.29\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+    "print(total_por_categoria)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. **Qual categoria teve o maior valor total de vendas?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A categoria com o maior valor total de vendas é: Brinquedos e o valor máximo de venda foi R$ 1187964.04\n"
+     ]
+    }
+   ],
+   "source": [
+    "vendas_por_categoria = df.groupby('Categoria')['Valor_Total'].sum()\n",
+    "categoria_maior_venda = vendas_por_categoria.idxmax()\n",
+    "valor_maximo_venda = vendas_por_categoria.max()\n",
+    "print(f\"A categoria com o maior valor total de vendas é: {categoria_maior_venda} e o valor máximo de venda foi R$ {valor_maximo_venda}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. **Calcular a quantidade média vendida por categoria:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Categoria  Quantidade_Vendida\n",
+      "0    Alimentos           55.618182\n",
+      "1   Brinquedos           47.247191\n",
+      "2  Eletrônicos           46.780822\n",
+      "3       Livros           50.917808\n",
+      "4       Roupas           47.200000\n"
+     ]
+    }
+   ],
+   "source": [
+    "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n",
+    "print(media_quantidade_categoria)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. : **Qual categoria teve a maior quantidade média vendida?**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Categoria  Quantidade_Vendida\n",
+      "0    Alimentos           55.618182\n",
+      "1   Brinquedos           47.247191\n",
+      "2  Eletrônicos           46.780822\n",
+      "3       Livros           50.917808\n",
+      "4       Roupas           47.200000\n",
+      "Os alimentos tiveram a maior quantidade de vendas, totalizando 55.618182\n"
+     ]
+    }
+   ],
+   "source": [
+    "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n",
+    "print(media_quantidade_categoria)\n",
+    "print(\"Os alimentos tiveram a maior quantidade de vendas, totalizando 55.618182\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Parte 4: Visualização de Dados**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. **Criar um gráfico de barras mostrando o total de vendas por categoria:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='skyblue')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. **Pergunta 8: Com base no gráfico, qual categoria visualmente se destaca em termos de vendas totais?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A categoria com maior destaque é brinquedos, com um número maior de vendas totais. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. **Criar um gráfico de linha mostrando a variação diária das vendas de 'Eletrônicos':**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='purple')\n",
+    "plt.xlabel('Data')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Variação Diária das Vendas de Eletrônicos')\n",
+    "plt.xticks(ticks=vendas_diarias['Data'][::5], rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. **Pergunta 9: Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Com a análise do gráfico é possível observar que não há uma linearidade, ora as vendas estão altas, ora caem bruscamente. Isso indica que as vendas não são constantes. Por fim, não há um padrão ou tendência. "
+   ]
+  }
+ ],
+ "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/dados_vendas.csv b/exercicios/para-casa/dados_vendas.csv
new file mode 100644
index 0000000..c98fa34
--- /dev/null
+++ b/exercicios/para-casa/dados_vendas.csv
@@ -0,0 +1,366 @@
+Data,Categoria,Quantidade_Vendida,Preco_Unitario,Valor_Total
+2023-04-13,Roupas,74,60.43,4471.82
+2023-12-15,Alimentos,83,272.88,22649.04
+2023-09-28,Roupas,17,195.62,3325.54
+2023-04-17,Roupas,85,233.93,19884.05
+2023-03-13,Roupas,78,305.94,23863.32
+2023-07-08,Eletrônicos,73,256.12,18696.760000000002
+2023-01-21,Eletrônicos,1,274.53,274.53
+2023-04-13,Eletrônicos,51,248.32,12664.32
+2023-05-02,Alimentos,45,210.39,9467.55
+2023-08-03,Brinquedos,77,388.22,29892.940000000002
+2023-11-27,Roupas,4,15.98,63.92
+2023-03-29,Roupas,62,303.24,18800.88
+2023-04-10,Alimentos,65,287.1,18661.5
+2023-12-26,Roupas,32,360.93,11549.76
+2023-06-01,Eletrônicos,34,303.52,10319.68
+2023-05-11,Brinquedos,92,415.13,38191.96
+2023-05-30,Livros,95,479.95,45595.25
+2023-11-05,Roupas,72,177.84,12804.48
+2023-09-15,Eletrônicos,39,121.4,4734.6
+2023-12-10,Livros,26,217.56,5656.56
+2023-10-21,Brinquedos,34,151.09,5137.06
+2023-07-11,Livros,54,311.33,16811.82
+2023-10-04,Eletrônicos,3,456.81,1370.43
+2023-06-10,Livros,50,78.17,3908.5
+2023-11-10,Alimentos,12,59.39,712.6800000000001
+2023-01-22,Livros,65,135.45,8804.25
+2023-09-10,Roupas,54,365.79,19752.66
+2023-08-24,Roupas,5,300.55,1502.75
+2023-12-11,Alimentos,94,60.08,5647.5199999999995
+2023-02-18,Eletrônicos,94,460.19,43257.86
+2023-02-28,Roupas,57,397.14,22636.98
+2023-06-19,Brinquedos,17,21.28,361.76
+2023-07-07,Roupas,47,329.17,15470.990000000002
+2023-09-28,Roupas,23,387.91,8921.93
+2023-07-09,Eletrônicos,79,193.47,15284.13
+2023-06-24,Livros,85,43.77,3720.4500000000003
+2023-02-20,Roupas,14,47.89,670.46
+2023-12-30,Alimentos,66,61.08,4031.2799999999997
+2023-02-24,Livros,75,421.82,31636.5
+2023-09-01,Brinquedos,51,456.24,23268.24
+2023-11-16,Eletrônicos,38,70.18,2666.84
+2023-05-11,Brinquedos,64,125.59,8037.76
+2023-11-03,Livros,98,91.11,8928.78
+2023-05-15,Livros,38,101.3,3849.4
+2023-01-21,Livros,50,420.37,21018.5
+2023-11-25,Brinquedos,98,172.75,16929.5
+2023-06-16,Livros,82,162.61,13334.02
+2023-10-01,Brinquedos,30,121.42,3642.6
+2023-03-30,Livros,79,307.87,24321.73
+2023-11-12,Alimentos,91,195.86,17823.260000000002
+2023-01-14,Livros,51,374.68,19108.68
+2023-08-30,Brinquedos,63,110.73,6975.990000000001
+2023-09-22,Roupas,98,396.02,38809.96
+2023-12-12,Livros,52,305.82,15902.64
+2023-02-22,Roupas,38,65.99,2507.62
+2023-12-06,Alimentos,97,213.11,20671.670000000002
+2023-04-02,Eletrônicos,88,433.12,38114.56
+2023-09-21,Alimentos,79,462.25,36517.75
+2023-02-04,Livros,30,238.19,7145.7
+2023-07-25,Roupas,51,245.61,12526.11
+2023-03-22,Roupas,81,460.04,37263.240000000005
+2023-02-19,Brinquedos,5,297.66,1488.3000000000002
+2023-12-26,Roupas,29,26.09,756.61
+2023-01-02,Brinquedos,4,457.25,1829.0
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