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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": 3,
+   "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, 160, num_registros),\n",
+    "       'Preco_Unitario': np.round(np.random.uniform(10, 110, num_registros), 2)\n",
+    "   }\n",
+    "df = pd.DataFrame(data)\n",
+    "df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Carregando dados CSV\n",
+    "df = pd.read_csv('dados_vendas.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(365, 5)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Descobrir quantidade de linhas e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 1: Quantas linhas e colunas existem no DataFrame carregado?**\n",
+    "Existem 365 linhas e 5 colunas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Mostrar o tipo de dados\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 2: Quais são os tipos de dados (dtypes) das colunas?**\n",
+    "\n",
+    "Os dados das colunas são: data sendo um object, categoria sendo um object, quantidade vendida sendo int, preço unitario sendo float, valor total sendo float."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Data   Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0  2023-10-13  Brinquedos                 153           52.87      8089.11\n",
+      "1  2023-05-25  Brinquedos                  52          109.46      5691.92\n",
+      "2  2023-04-24      Livros                 123           42.90      5276.70\n",
+      "3  2023-06-22      Roupas                  51           32.85      1675.35\n",
+      "4  2023-07-02      Livros                  54           13.11       707.94\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exibe as 5 primeiras linhas\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estão presentes 5 categorias únicas de produto.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quantidade de categorias unicas\n",
+    "categorias_unicas = df['Categoria'].unique()\n",
+    "numero_categorias = len(categorias_unicas)\n",
+    "print(f\"Estão presentes {numero_categorias} categorias únicas de produto.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "6    2023-07-11  Eletrônicos                  23           38.95       895.85\n",
+      "9    2023-11-12  Eletrônicos                  33           33.90      1118.70\n",
+      "13   2023-03-27  Eletrônicos                 146           84.14     12284.44\n",
+      "23   2023-06-23  Eletrônicos                 155           34.69      5376.95\n",
+      "25   2023-01-15  Eletrônicos                  85           91.94      7814.90\n",
+      "..          ...          ...                 ...             ...          ...\n",
+      "348  2023-11-19  Eletrônicos                  39           91.62      3573.18\n",
+      "350  2023-01-18  Eletrônicos                  36           73.05      2629.80\n",
+      "354  2023-02-13  Eletrônicos                 125           96.21     12026.25\n",
+      "360  2023-04-02  Eletrônicos                 118           67.36      7948.48\n",
+      "363  2023-08-20  Eletrônicos                 113           42.17      4765.21\n",
+      "\n",
+      "[70 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+    "print(eletronicos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Média do valor total das vendas na categoria 'Eletrônicos'?\n",
+    "agrupado_media = df.groupby('Categoria')['Valor_Total'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Categoria\n",
+       "Alimentos      4658.316061\n",
+       "Brinquedos     5314.170909\n",
+       "Eletrônicos    4594.863857\n",
+       "Livros         4584.918571\n",
+       "Roupas         4212.797541\n",
+       "Name: Valor_Total, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_media"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 4: Qual é a média do valor total das vendas na categoria 'Eletrônicos'?**\n",
+    "A média d valor total das vendas da categoria eletrônicos é R$ 4751.63"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0    2023-10-13   Brinquedos                 153           52.87      8089.11\n",
+      "1    2023-05-25   Brinquedos                  52          109.46      5691.92\n",
+      "2    2023-04-24       Livros                 123           42.90      5276.70\n",
+      "3    2023-06-22       Roupas                  51           32.85      1675.35\n",
+      "5    2023-09-15   Brinquedos                  82           78.56      6441.92\n",
+      "..          ...          ...                 ...             ...          ...\n",
+      "360  2023-04-02  Eletrônicos                 118           67.36      7948.48\n",
+      "361  2023-01-11       Roupas                  46           50.70      2332.20\n",
+      "362  2023-06-15   Brinquedos                  59           72.05      4250.95\n",
+      "363  2023-08-20  Eletrônicos                 113           42.17      4765.21\n",
+      "364  2023-04-24       Livros                  85           55.89      4750.65\n",
+      "\n",
+      "[315 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filtrar as vendas acima de R$ 1000:\n",
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+    "print(vendas_acima_1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estão presentes 5 categorias únicas de produto.\n"
+     ]
+    }
+   ],
+   "source": [
+    "categorias_unicas = df['Categoria'].unique()\n",
+    "numero_categorias = len(categorias_unicas)\n",
+    "print(f\"Estão presentes {numero_categorias} categorias únicas de produto.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "315\n"
+     ]
+    }
+   ],
+   "source": [
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+    "quantidade_vendas_acima_1000 = len(vendas_acima_1000)\n",
+    "print(quantidade_vendas_acima_1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**Pergunta 5: Quantas vendas acima de R$ 1000 ocorreram?**\n",
+    "\n",
+    "Houve 355 vendas com valor cima de R$ 1000.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Categoria  Valor_Total\n",
+      "0    Alimentos    307448.86\n",
+      "1   Brinquedos    409191.16\n",
+      "2  Eletrônicos    321640.47\n",
+      "3       Livros    417227.59\n",
+      "4       Roupas    256980.65\n"
+     ]
+    }
+   ],
+   "source": [
+    "##**Agrupar os dados por categoria e calcular o total de vendas por categoria:##\n",
+    "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+    "print(total_por_categoria)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 6: Qual categoria teve o maior valor total de vendas?**\n",
+    "\n",
+    "A categoria que teve o maior valor de vendas foi de eletrônico\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##Criar um gráfico de barras mostrando o total de vendas por categoria:\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='green')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "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..265fa70
--- /dev/null
+++ b/exercicios/para-casa/dados_vendas.csv
@@ -0,0 +1,366 @@
+Data,Categoria,Quantidade_Vendida,Preco_Unitario,Valor_Total
+2023-10-13,Brinquedos,153,52.87,8089.11
+2023-05-25,Brinquedos,52,109.46,5691.92
+2023-04-24,Livros,123,42.9,5276.7
+2023-06-22,Roupas,51,32.85,1675.3500000000001
+2023-07-02,Livros,54,13.11,707.9399999999999
+2023-09-15,Brinquedos,82,78.56,6441.92
+2023-07-11,Eletrônicos,23,38.95,895.85
+2023-10-16,Livros,45,88.51,3982.9500000000003
+2023-10-25,Brinquedos,16,102.55,1640.8
+2023-11-12,Eletrônicos,33,33.9,1118.7
+2023-06-10,Alimentos,55,83.49,4591.95
+2023-06-26,Brinquedos,24,71.55,1717.1999999999998
+2023-01-02,Alimentos,120,85.52,10262.4
+2023-03-27,Eletrônicos,146,84.14,12284.44
+2023-01-22,Alimentos,83,43.78,3633.7400000000002
+2023-07-19,Livros,55,108.67,5976.85
+2023-04-22,Alimentos,9,51.65,464.84999999999997
+2023-03-18,Roupas,47,77.17,3626.9900000000002
+2023-09-09,Livros,42,90.94,3819.48
+2023-06-02,Brinquedos,124,83.65,10372.6
+2023-05-21,Livros,136,56.53,7688.08
+2023-08-07,Livros,33,84.57,2790.81
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+2023-02-20,Roupas,113,60.93,6885.09
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+2023-10-20,Brinquedos,122,23.78,2901.1600000000003
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+2023-08-30,Alimentos,126,71.09,8957.34
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