diff --git a/exercicios/para-casa/Rafaella_Roden_Casa.ipynb b/exercicios/para-casa/Rafaella_Roden_Casa.ipynb
new file mode 100644
index 0000000..1bd8327
--- /dev/null
+++ b/exercicios/para-casa/Rafaella_Roden_Casa.ipynb
@@ -0,0 +1,1409 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "uxO_3dCD2Nb1",
+        "AnlU-49l2fdQ",
+        "FTQdCD5I3mP-",
+        "LJ_Y59rF6MeR"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "aJbyXE1ywyPd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Parte 1: Geração de Dados Fictícios**"
+      ],
+      "metadata": {
+        "id": "uxO_3dCD2Nb1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Geração de Dados Fictícios:**\n",
+        "\n",
+        "#Use o script abaixo para gerar um conjunto de dados falso que simula informações de vendas de produtos em uma loja:\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "   # Configurando a seed para reprodutibilidade\n",
+        "np.random.seed(42)\n",
+        "\n",
+        "   # 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)\n",
+        "\n",
+        "   # 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",
+        "\n",
+        "   # 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'.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VCgbTQGAw5cN",
+        "outputId": "0dff86aa-16db-4a30-e23d-4c8e54826a45"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Anotações da etapa:\n",
+        "Começamos com a importação das bibliotecas pandas e numpy para fazer os códigos e marplotilip para fazer os gráficos.\n",
+        "\n",
+        "\n",
+        "Depois disso, foi criado os dados falsos e incluidos eles dentro de um dataframe - DF. Se os dados já existisse, poderia conectar com o drive e puxar - *estou usando o Colab, por isso considerei isso.*\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "PM56rBPt_u9_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Parte 2: Consulta e Filtragem de Dados**\n"
+      ],
+      "metadata": {
+        "id": "AnlU-49l2fdQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Carregar os dados: neste parte vc deve caregar o csv gerado para podemos continuar**\n"
+      ],
+      "metadata": {
+        "id": "ZTz5atIz2kMF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv(\"dados_vendas.csv\")\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "9gfbgDVWxX7N",
+        "outputId": "32d87b27-7235-4b2d-8a00-a8854646aba5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a2cd8326-b362-4979-b72d-70e19c601436\" class=\"colab-df-container\">\n",
+              "    <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>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2cd8326-b362-4979-b72d-70e19c601436')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a2cd8326-b362-4979-b72d-70e19c601436 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a2cd8326-b362-4979-b72d-70e19c601436');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-4ec8e5aa-cde9-41da-8708-892099313972\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ec8e5aa-cde9-41da-8708-892099313972')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
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+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4ec8e5aa-cde9-41da-8708-892099313972 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 365,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 217,\n        \"samples\": [\n          \"2023-06-30\",\n          \"2023-04-24\",\n          \"2023-08-13\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Categoria\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Alimentos\",\n          \"Livros\",\n          \"Eletr\\u00f4nicos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantidade_Vendida\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          19,\n          52,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Preco_Unitario\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140.544313886131,\n        \"min\": 10.12,\n        \"max\": 499.68,\n        \"num_unique_values\": 364,\n        \"samples\": [\n          387.78,\n          387.91,\n          415.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valor_Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11374.75726385857,\n        \"min\": 31.64,\n        \"max\": 46699.68,\n        \"num_unique_values\": 365,\n        \"samples\": [\n          12796.74,\n          8921.93,\n          38191.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 1: Quantas linhas e colunas existem no DataFrame carregado? Use a função `shape` para descobrir**\n"
+      ],
+      "metadata": {
+        "id": "SWXDmUsu2s3t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RpCqBFbbyEwN",
+        "outputId": "640c6f29-9942-4b8c-81dc-0c8803ccf123"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(365, 5)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 2: Quais são os tipos de dados (dtypes) das colunas? Use a função `dtypes` para listar os tipos**"
+      ],
+      "metadata": {
+        "id": "iFMIeFhR215t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.dtypes"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "C1FI10bryUkm",
+        "outputId": "4b099d0c-6e49-4975-bb1b-f50408b0d29b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Data                   object\n",
+              "Categoria              object\n",
+              "Quantidade_Vendida      int64\n",
+              "Preco_Unitario        float64\n",
+              "Valor_Total           float64\n",
+              "dtype: object"
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+              "      <th>Categoria</th>\n",
+              "      <td>object</td>\n",
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+              "      <th>Quantidade_Vendida</th>\n",
+              "      <td>int64</td>\n",
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+              "      <th>Preco_Unitario</th>\n",
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+              "      <td>float64</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Exibir as primeiras 5 linhas do DataFrame**"
+      ],
+      "metadata": {
+        "id": "2a4iKiaB2-Yt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "cjnrePCpydt9",
+        "outputId": "d79be8f0-a52d-4c16-b3df-9cd482ec74e1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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"
+            ],
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+              "      <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",
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+              "      <th>4</th>\n",
+              "      <td>2023-03-13</td>\n",
+              "      <td>Roupas</td>\n",
+              "      <td>78</td>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 365,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 217,\n        \"samples\": [\n          \"2023-06-30\",\n          \"2023-04-24\",\n          \"2023-08-13\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Categoria\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Alimentos\",\n          \"Livros\",\n          \"Eletr\\u00f4nicos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantidade_Vendida\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          19,\n          52,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Preco_Unitario\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140.544313886131,\n        \"min\": 10.12,\n        \"max\": 499.68,\n        \"num_unique_values\": 364,\n        \"samples\": [\n          387.78,\n          387.91,\n          415.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valor_Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11374.75726385857,\n        \"min\": 31.64,\n        \"max\": 46699.68,\n        \"num_unique_values\": 365,\n        \"samples\": [\n          12796.74,\n          8921.93,\n          38191.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 3: Quantas categorias únicas de produtos estão presentes no DataFrame? Utilize a função `unique` na coluna 'Categoria' para responder.**"
+      ],
+      "metadata": {
+        "id": "uMxXvMb53Dbm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Categoria'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xKGSPHoFyo1k",
+        "outputId": "73764dce-38c1-4b0a-ccf5-935a733d3c50"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Roupas', 'Alimentos', 'Eletrônicos', 'Brinquedos', 'Livros'],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Filtrar os dados para mostrar apenas as vendas da categoria 'Eletrônicos'**"
+      ],
+      "metadata": {
+        "id": "pLo3HQq43Jv9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#    ```python\n",
+        "#    eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+        "#    print(eletronicos)\n",
+        "\n",
+        "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+        "print(eletronicos)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0Hgq5XUKzJGt",
+        "outputId": "02b0b0b9-29e5-4c20-d259-b956a8e04e16"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 4: Qual é a média do valor total das vendas na categoria 'Eletrônicos'?**"
+      ],
+      "metadata": {
+        "id": "BbvHTjRy3OLF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#    - Utilize a média na coluna 'Valor_Total' do DataFrame filtrado.\n",
+        "\n",
+        "media_eletronicos = eletronicos['Valor_Total'].mean()\n",
+        "print(media_eletronicos)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "I3czwttGzfHd",
+        "outputId": "3c32d2dc-b7ea-4533-9eda-3e86da1e6b7d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "12291.256301369864\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Filtrar as vendas acima de R$ 1000**"
+      ],
+      "metadata": {
+        "id": "q5KF8EST3Tkc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+        "print(vendas_acima_1000)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SkC1BCQz1Ife",
+        "outputId": "5cc20d20-7404-4719-a2a1-f1b2c0aeb1a7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 5: Quantas vendas acima de R$ 1000 ocorreram? Utilize a função `len` ou `shape` para descobrir a quantidade de registros.**"
+      ],
+      "metadata": {
+        "id": "bDvLO1fb3Xg8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "quantidade_acima_1000 = len(vendas_acima_1000)\n",
+        "print(quantidade_acima_1000)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zctieX3M1PvF",
+        "outputId": "2d806885-6ee5-44bb-9dee-8e65aa5b29cb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "330\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Anotações da etapa:\n",
+        "Começamos lendo o arquivo e pra testar se estava tudo certo usei o head, se a gente colocar entre os parenteses do head algum numero, ele vai puxar o numero de linhas que vai aparecer.\n",
+        "\n",
+        "Com o dtypes, é possível verificar qual o tipo de dado que consta, isso é importante pra saber se vai ser possível fazer os calculos também.\n",
+        "\n",
+        "Com o unique e selecionando uma coluna, foi possível verificar as opções que constavam nela dentro do arquivo.\n",
+        "\n",
+        "Depois disso, foi criado uma variavel com um código que puxa apenas a categoria eletronicos de dentro da planilha.\n",
+        "\n",
+        "Com essa informação, foi possível obter uma média das vendas da categoria eletronicos.\n",
+        "\n",
+        "Criamos também uma variavel pra puxar apenas os as vendas com valor total acima de 1000 e com essa informação foi possível saber quantas vendas existem com valor acima de 1000."
+      ],
+      "metadata": {
+        "id": "ksZoV06oAkCG"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Parte 3: Agrupamento e Agregação**\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "FTQdCD5I3mP-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Agrupar os dados por categoria e calcular o total de vendas por categoria:**\n",
+        "  "
+      ],
+      "metadata": {
+        "id": "edGQJpL53qV2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+        "print(total_por_categoria)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Fj6Df4Q533AG",
+        "outputId": "a939df60-9578-4325-95fe-78b666b12b02"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Qual categoria teve o maior valor total de vendas?**"
+      ],
+      "metadata": {
+        "id": "Ql5o1Ufw4Rx4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "categoria_max_vendas = total_por_categoria.loc[total_por_categoria['Valor_Total'].idxmax(), 'Categoria']\n",
+        "print(f\"A categoria com o maior valor total de vendas é: {categoria_max_vendas}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yIuR-STo4T3e",
+        "outputId": "8f314d41-a69b-403a-efda-c37dfcfd8812"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "A categoria com o maior valor total de vendas é: Brinquedos\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Calcular a quantidade média vendida por categoria:**\n"
+      ],
+      "metadata": {
+        "id": "8CZbDok-5GhF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n",
+        "print(media_quantidade_categoria)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Edc7zfbm5IqF",
+        "outputId": "41037c7e-ca00-4cdb-a728-9786403d8ef5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 7: Qual categoria teve a maior quantidade média vendida?**"
+      ],
+      "metadata": {
+        "id": "TafLAMQK5Smt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "categoria_mean_vendida = media_quantidade_categoria.loc[media_quantidade_categoria['Quantidade_Vendida'].idxmax(), 'Categoria']\n",
+        "print(f\"A categoria com maior quantidade vendida é: {categoria_mean_vendida}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X-tUbsLU5TH8",
+        "outputId": "5560aa38-3707-42c9-ca33-a0d4e617739e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "A categoria com maior quantidade vendida é: Alimentos\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Anotações da etapa:\n",
+        "Aqui já começamos agrupando algumas informações para visualizar e gerar o total de vendas por categoria e isso foi um passo pra conseguir fazer a média por categoria e encontrar a maior meédia.\n",
+        "O mesmo para Quantidade Vendida.\n",
+        "\n",
+        "O uso do .loc foi para ele fazer a seleção da categoria que foi filtrada.\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "QwGfER91B35m"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Parte 4: Visualização de Dados**\n"
+      ],
+      "metadata": {
+        "id": "LJ_Y59rF6MeR"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Criar um gráfico de barras mostrando o total de vendas por categoria:**\n"
+      ],
+      "metadata": {
+        "id": "cjop4b_F7cPv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='pink')\n",
+        "plt.xlabel('Categoria')\n",
+        "plt.ylabel('Valor Total de Vendas (R$)')\n",
+        "plt.title('Total de Vendas por Categoria')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "l02OjKXD7eve",
+        "outputId": "4a90d35a-8b2f-454d-d7a8-05cbb14cecfd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 8: Com base no gráfico, qual categoria visualmente se destaca em termos de vendas totais?**\n",
+        "\n",
+        "\n",
+        "> Resposta: Observando o gráfico, a categoria que se destaca é de Brinquedos, onde demonstra que obteve o maior faturamento em vendas por categoria. Na sequencia, identificamos roupas, livros, eletrônicos e por fim, alimentos.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "Art8ICni7ssu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Criar um gráfico de linha mostrando a variação diária das vendas de 'Eletrônicos'**"
+      ],
+      "metadata": {
+        "id": "f9wtBdkJ8aDu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n",
+        "\n",
+        "plt.figure(figsize=(25, 6))\n",
+        "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='purple')\n",
+        "plt.xlabel('Data')\n",
+        "plt.ylabel('Valor Total de Vendas (R$)')\n",
+        "plt.title('Variação Diária das Vendas de Eletrônicos')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 548
+        },
+        "id": "QI5eXmxl8bhO",
+        "outputId": "007c11e2-e582-493b-8c02-b1f5ecdafe46"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2500x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pergunta 9: Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?**\n",
+        "\n",
+        "\n",
+        "\n",
+        "> A grande maioria das vendas mais altas acontecem no inicio do mês, entre os dias 05 - 10 de cada mês. A empresa teve uma queda nas vendas considerando o gráfico de janeiro até dezembro.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "DF3lanWK8xBf"
+      }
+    }
+  ]
+}
\ No newline at end of file