Aula_8_analise_exploratoria.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aula_8_analise-exploratoria.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMEJSNCi4nKPzH2YSDVT4wj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luisriego/Analise-de-dados-com-Python-e-Pandas/blob/main/Aula_8_analise_exploratoria.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xc5PJO-DpBtD"
      },
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use(\"seaborn\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "1miZ8iwKprZi",
        "outputId": "70f46d8e-2cc3-458f-e469-446e89c318a6"
      },
      "source": [
        "# Upload de arquivos\n",
        "from google.colab import files\n",
        "arq = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f3a3baab-60c5-4589-90b7-f2c5fe06c63d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-f3a3baab-60c5-4589-90b7-f2c5fe06c63d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Saving AdventureWorks.xlsx to AdventureWorks.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mav6H8GNqVwU"
      },
      "source": [
        "# Criar o dataframe\n",
        "df = pd.read_excel(\"AdventureWorks.xlsx\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "5bOzJ0zqrFfn",
        "outputId": "38a8e23c-10ba-4fb8-f479-542806848d9b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "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 Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
              "      <th>Produto</th>\n",
              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-05-12</td>\n",
              "      <td>2008-05-17</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19051</td>\n",
              "      <td>200805123CS567</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-05-14</td>\n",
              "      <td>2008-05-20</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19052</td>\n",
              "      <td>200805143CS576</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-05-21</td>\n",
              "      <td>2008-05-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19052</td>\n",
              "      <td>200805213CS576</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-06-20</td>\n",
              "      <td>2008-06-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19053</td>\n",
              "      <td>200806203CS586</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ...            Marca   Classe  Cor\n",
              "0 2008-05-09 2008-05-29      199  ...  Adventure Works  Regular  Red\n",
              "1 2008-05-12 2008-05-17      306  ...  Adventure Works  Regular  Red\n",
              "2 2008-05-14 2008-05-20      306  ...  Adventure Works  Regular  Red\n",
              "3 2008-05-21 2008-05-27      306  ...  Adventure Works  Regular  Red\n",
              "4 2008-06-20 2008-06-27      306  ...  Adventure Works  Regular  Red\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaZ7TkMIrMtA",
        "outputId": "d69001a4-5ceb-44fb-89eb-a647b77de1f7"
      },
      "source": [
        "# Quantidade de linhas e colunas\n",
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(904, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHASsn9IrjiK",
        "outputId": "3edb9a77-609d-4aba-e15a-355e8736ad6d"
      },
      "source": [
        "# Verificação tipo de datos\n",
        "df.dtypes"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data Venda        datetime64[ns]\n",
              "Data Envio        datetime64[ns]\n",
              "ID Loja                    int64\n",
              "ID Produto                 int64\n",
              "ID Cliente                 int64\n",
              "No. Venda                 object\n",
              "Custo Unitário           float64\n",
              "Preço Unitário           float64\n",
              "Quantidade                 int64\n",
              "Valor Desconto           float64\n",
              "Valor Venda              float64\n",
              "Produto                   object\n",
              "Fabricante                object\n",
              "Marca                     object\n",
              "Classe                    object\n",
              "Cor                       object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqVALQSpr4Z7",
        "outputId": "2907c6ab-6b0b-4b8f-f765-9bc121a36471"
      },
      "source": [
        "# Qual a receita total\n",
        "df[\"Valor Venda\"].sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5984606.1426"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JD-Sx-uLsf93"
      },
      "source": [
        "# Criamos a coluna custo\n",
        "df[\"Custo\"] = df[\"Custo Unitário\"].mul(df[\"Quantidade\"])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "L2NIdCprtni5",
        "outputId": "2f67fefe-f3d8-419d-bb5a-674e206669ee"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th></th>\n",
              "      <th>Data Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
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              "      <th>Custo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
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              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-05-12</td>\n",
              "      <td>2008-05-17</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19051</td>\n",
              "      <td>200805123CS567</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-05-14</td>\n",
              "      <td>2008-05-20</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19052</td>\n",
              "      <td>200805143CS576</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-05-21</td>\n",
              "      <td>2008-05-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19052</td>\n",
              "      <td>200805213CS576</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-06-20</td>\n",
              "      <td>2008-06-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19053</td>\n",
              "      <td>200806203CS586</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ...   Classe  Cor    Custo\n",
              "0 2008-05-09 2008-05-29      199  ...  Regular  Red  2091.48\n",
              "1 2008-05-12 2008-05-17      306  ...  Regular  Red  2091.48\n",
              "2 2008-05-14 2008-05-20      306  ...  Regular  Red  2091.48\n",
              "3 2008-05-21 2008-05-27      306  ...  Regular  Red  2091.48\n",
              "4 2008-06-20 2008-06-27      306  ...  Regular  Red  2091.48\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSabKwp6tuYu",
        "outputId": "ee3c3632-f71f-4b50-e22b-fb71138e1e1b"
      },
      "source": [
        "# Custo total\n",
        "round(df[\"Custo\"].sum(),2)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2486783.05"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3c0PxmxUuFPZ"
      },
      "source": [
        "df[\"Lucro\"] = df[\"Valor Venda\"] - df[\"Custo\"]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "H4nwBb6Cu0L1",
        "outputId": "755eb786-b9ba-4601-af3c-9ec0d7625ef9"
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Data Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
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              "      <th>Cor</th>\n",
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              "      <th>Lucro</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
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              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ID Produto  ...   Classe  Cor    Custo    Lucro\n",
              "0 2008-05-09 2008-05-29      199         384  ...  Regular  Red  2091.48  2456.52\n",
              "\n",
              "[1 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUdlWpYgu21t",
        "outputId": "fad2211e-1831-45c1-cdb1-99b627f93f7c"
      },
      "source": [
        "# Total de lucro\n",
        "round(df[\"Lucro\"].sum(),2)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3497823.09"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1wzMU5dvKrP"
      },
      "source": [
        "# Coluna com total de dias para enviar um pedido\n",
        "df[\"Tempo de Envio\"] = df[\"Data Envio\"] - df[\"Data Venda\"]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "W3X-NifYwD85",
        "outputId": "00fab42d-941b-49d5-c68b-e25f7b76300c"
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
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              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
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              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
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              "      <th>Cor</th>\n",
              "      <th>Custo</th>\n",
              "      <th>Lucro</th>\n",
              "      <th>Tempo de Envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>20 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ...    Custo    Lucro Tempo de Envio\n",
              "0 2008-05-09 2008-05-29      199  ...  2091.48  2456.52        20 days\n",
              "\n",
              "[1 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-DAMjirwF8M"
      },
      "source": [
        "# Coluna com total de dias para enviar um pedido em dias\n",
        "df[\"Tempo de Envio\"] = (df[\"Data Envio\"] - df[\"Data Venda\"]).dt.days"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "kHJ06_C_wlB3",
        "outputId": "cc539448-d5ff-4d56-e0e5-6abd7e2c705e"
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>ID Cliente</th>\n",
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              "      <th>Custo Unitário</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ...    Custo    Lucro Tempo de Envio\n",
              "0 2008-05-09 2008-05-29      199  ...  2091.48  2456.52             20\n",
              "\n",
              "[1 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nj3MSkgGwpQR",
        "outputId": "a209b15e-144a-4a2e-fe42-31f1afd8ea7d"
      },
      "source": [
        "df[\"Tempo de Envio\"].dtype"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JVA8nHbxHBC",
        "outputId": "0bbaa389-9b4e-4c51-9946-1d35ff819938"
      },
      "source": [
        "# Media de tempo de envio por marca\n",
        "df.groupby(\"Marca\")[\"Tempo de Envio\"].mean()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Marca\n",
              "Adventure Works    8.663866\n",
              "Contoso            8.470930\n",
              "Fabrikam           8.510121\n",
              "Name: Tempo de Envio, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0bR1Eycxxhe",
        "outputId": "a2b1ff5c-bbf2-438e-ee03-96f089427c05"
      },
      "source": [
        "# Verificar se há valores ausentes\n",
        "df.isnull().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data Venda        0\n",
              "Data Envio        0\n",
              "ID Loja           0\n",
              "ID Produto        0\n",
              "ID Cliente        0\n",
              "No. Venda         0\n",
              "Custo Unitário    0\n",
              "Preço Unitário    0\n",
              "Quantidade        0\n",
              "Valor Desconto    0\n",
              "Valor Venda       0\n",
              "Produto           0\n",
              "Fabricante        0\n",
              "Marca             0\n",
              "Classe            0\n",
              "Cor               0\n",
              "Custo             0\n",
              "Lucro             0\n",
              "Tempo de Envio    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VF9tcq9OyMpg",
        "outputId": "c4fb915a-d6ad-4e40-af75-5179fde3d2d0"
      },
      "source": [
        "# Vamos a agrupar por ano e marca\n",
        "df.groupby([df[\"Data Venda\"].dt.year, \"Marca\"])[\"Lucro\"].sum()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data Venda  Marca          \n",
              "2008        Adventure Works             306,641.16\n",
              "            Contoso                      56,416.00\n",
              "            Fabrikam                  1,557,020.55\n",
              "2009        Adventure Works             405,395.08\n",
              "            Contoso                     138,258.95\n",
              "            Fabrikam                  1,034,091.35\n",
              "Name: Lucro, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYcyGee-zb48"
      },
      "source": [
        "# Mostrar os valores com notação decimal con 2 casilhas decimales\n",
        "pd.options.display.float_format = '{:20,.2f}'.format"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "qxiYDqP00P-1",
        "outputId": "820406d3-75c3-4ead-a957-638f98b9e4a1"
      },
      "source": [
        "# Apagar o index\n",
        "lucro_ano = df.groupby([df[\"Data Venda\"].dt.year, \"Marca\"])[\"Lucro\"].sum().reset_index()\n",
        "lucro_ano"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "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 Venda</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Lucro</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>306,641.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>56,416.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008</td>\n",
              "      <td>Fabrikam</td>\n",
              "      <td>1,557,020.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>405,395.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009</td>\n",
              "      <td>Contoso</td>\n",
              "      <td>138,258.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009</td>\n",
              "      <td>Fabrikam</td>\n",
              "      <td>1,034,091.35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Data Venda            Marca                Lucro\n",
              "0        2008  Adventure Works           306,641.16\n",
              "1        2008          Contoso            56,416.00\n",
              "2        2008         Fabrikam         1,557,020.55\n",
              "3        2009  Adventure Works           405,395.08\n",
              "4        2009          Contoso           138,258.95\n",
              "5        2009         Fabrikam         1,034,091.35"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORR8MVYc1Gpx",
        "outputId": "75eed3dc-a87a-45f8-ea38-852b841c9f57"
      },
      "source": [
        "# Qual o total de produtos vendidos\n",
        "df.groupby(\"Produto\")[\"Quantidade\"].sum().sort_values(ascending=False)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Produto\n",
              "Headphone Adapter for Contoso Phone E130 Silver    25232\n",
              "Headphone Adapter for Contoso Phone E130 White     25008\n",
              "Adventure Works Laptop15.4W M1548 Black             1089\n",
              "Fabrikam Trendsetter 2/3'' 17mm X100 Grey           1087\n",
              "Adventure Works Laptop15.4W M1548 Red               1047\n",
              "Fabrikam Trendsetter 2/3'' 17mm X100 Black           926\n",
              "Fabrikam Trendsetter 1/3'' 8.5mm X200 Black          884\n",
              "Fabrikam Trendsetter 1/3'' 8.5mm X200 Grey           845\n",
              "Fabrikam Trendsetter 1/3'' 8.5mm X200 White          789\n",
              "Name: Quantidade, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "kITf6Oin1yv-",
        "outputId": "0d8ce7ad-86e3-414f-9945-878c998726b5"
      },
      "source": [
        "# Gráfico do total de produtos vendidos\n",
        "df.groupby(\"Produto\")[\"Quantidade\"].sum().sort_values(ascending=True).plot.barh(title=\"Total Produtos vendidos\")\n",
        "plt.xlabel(\"Total\")\n",
        "plt.ylabel(\"Produto\");"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "fztprxHN20EE",
        "outputId": "46dacfc1-eb57-4753-d635-7225686c400b"
      },
      "source": [
        "# Gráfico lucro por ano\n",
        "df.groupby(df[\"Data Venda\"].dt.year)[\"Lucro\"].sum().plot.bar(title=\"Lucro x Ano\")\n",
        "plt.xlabel(\"Ano\")\n",
        "plt.ylabel(\"Receita\");"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGC25pzV3wHb",
        "outputId": "a0cc68ab-591c-4444-f029-2c7e233d914d"
      },
      "source": [
        "df.groupby(df[\"Data Venda\"].dt.year)[\"Lucro\"].sum()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data Venda\n",
              "2008           1,920,077.71\n",
              "2009           1,577,745.38\n",
              "Name: Lucro, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9c0UjlOd4CED"
      },
      "source": [
        "# Seleção apenas das vendas de 2009\n",
        "df_2009 = df[df[\"Data Venda\"].dt.year == 2009]"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "GtQXDE054gNo",
        "outputId": "e6545202-0fb2-44b8-b4b0-e269878ec530"
      },
      "source": [
        "df_2009.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "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 Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
              "      <th>Produto</th>\n",
              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "      <th>Custo</th>\n",
              "      <th>Lucro</th>\n",
              "      <th>Tempo de Envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2009-05-02</td>\n",
              "      <td>2009-05-14</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18938</td>\n",
              "      <td>200905023CS847</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4,548.00</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2,091.48</td>\n",
              "      <td>2,456.52</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2009-05-16</td>\n",
              "      <td>2009-05-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19067</td>\n",
              "      <td>200905163CS746</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4,548.00</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2,091.48</td>\n",
              "      <td>2,456.52</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2009-05-31</td>\n",
              "      <td>2009-06-12</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19068</td>\n",
              "      <td>200905313CS748</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4,548.00</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2,091.48</td>\n",
              "      <td>2,456.52</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2009-06-28</td>\n",
              "      <td>2009-07-11</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18959</td>\n",
              "      <td>200906283CS870</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4,548.00</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2,091.48</td>\n",
              "      <td>2,456.52</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2009-07-08</td>\n",
              "      <td>2009-07-12</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19070</td>\n",
              "      <td>200907083CS757</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4,548.00</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2,091.48</td>\n",
              "      <td>2,456.52</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Data Venda Data Envio  ...                Lucro  Tempo de Envio\n",
              "11 2009-05-02 2009-05-14  ...             2,456.52              12\n",
              "12 2009-05-16 2009-05-27  ...             2,456.52              11\n",
              "13 2009-05-31 2009-06-12  ...             2,456.52              12\n",
              "14 2009-06-28 2009-07-11  ...             2,456.52              13\n",
              "15 2009-07-08 2009-07-12  ...             2,456.52               4\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "rNlWNfL_4lW1",
        "outputId": "9d29a9cb-561f-41cf-cb60-ff5c24cc1280"
      },
      "source": [
        "# Gráfico lucro por mês em 2009\n",
        "df_2009.groupby(df_2009[\"Data Venda\"].dt.month)[\"Lucro\"].sum().plot(title=\"Lucro x Mês\")\n",
        "plt.xlabel(\"Mês\")\n",
        "plt.ylabel(\"Lucro\");"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "-rqTVDni5JXS",
        "outputId": "bee4fa19-bf33-4a31-f4f2-f22e022d98bd"
      },
      "source": [
        "# Gráfico lucro por mês\n",
        "df_2009.groupby(\"Marca\")[\"Lucro\"].sum().plot.bar(title=\"Lucro x Marca\")\n",
        "plt.xlabel(\"Marca\")\n",
        "plt.ylabel(\"Lucro\")\n",
        "plt.xticks(rotation=\"horizontal\");"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "pOKsSwK76G9_",
        "outputId": "f1db026b-9335-4d3a-d737-8b9b5e8ef211"
      },
      "source": [
        "# Gráfico lucro por classe em 2009\n",
        "df_2009.groupby(\"Classe\")[\"Lucro\"].sum().plot.bar(title=\"Lucro x Classe\")\n",
        "plt.xlabel(\"Classe\")\n",
        "plt.ylabel(\"Lucro\")\n",
        "plt.xticks(rotation=\"horizontal\");"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNZlZPa66354",
        "outputId": "a5004a15-6837-402f-a2c0-73e5e58e51c4"
      },
      "source": [
        "df[\"Tempo de Envio\"].describe()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                 904.00\n",
              "mean                    8.54\n",
              "std                     3.06\n",
              "min                     4.00\n",
              "25%                     6.00\n",
              "50%                     9.00\n",
              "75%                    11.00\n",
              "max                    20.00\n",
              "Name: Tempo de Envio, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "I-e1QEyb7Mhp",
        "outputId": "9105d76d-67ec-49c6-9ff8-aaecb4d27f22"
      },
      "source": [
        "# Gráfico de Boxplot\n",
        "plt.boxplot(df[\"Tempo de Envio\"]);"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "PJ2LAPqF7zWZ",
        "outputId": "d6d7ebd5-529a-4434-b0df-c7e9f752ab6e"
      },
      "source": [
        "# Histograma\n",
        "plt.hist(df[\"Tempo de Envio\"]);"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "on4wjVgx8llm",
        "outputId": "fc8b58c7-3dbb-47f5-a19f-36a938ec2861"
      },
      "source": [
        "# Tempo máximo de envio\n",
        "df[\"Tempo de Envio\"].max()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "id": "TVqVjneN8y4X",
        "outputId": "15622937-0dc4-4240-ef24-e5e7e1d77167"
      },
      "source": [
        "# Histograma\n",
        "df[df[\"Tempo de Envio\"] == 20]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "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 Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
              "      <th>Produto</th>\n",
              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "      <th>Custo</th>\n",
              "      <th>Lucro</th>\n",
              "      <th>Tempo de Envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4,548.00</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2,091.48</td>\n",
              "      <td>2,456.52</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ...                Lucro  Tempo de Envio\n",
              "0 2008-05-09 2008-05-29  ...             2,456.52              20\n",
              "\n",
              "[1 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jciuZ-HE9FFB"
      },
      "source": [
        "df.to_csv(\"df_vendas_novo.csv\", index=False)"
      ],
      "execution_count": 47,
      "outputs": []
    }
  ]
}