Analise_Varejo.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "BytjxFMs2WNk"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BHxvTEOW-a_8",
        "outputId": "aad96522-acfc-40a7-e4fc-5e4c79f1b7e8"
      },
      "outputs": [
        {
          "data": {
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              "    }\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>idcompra</th>\n",
              "      <th>idcanalvenda</th>\n",
              "      <th>bandeira</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "      <th>Nome_Departamento</th>\n",
              "      <th>estado</th>\n",
              "      <th>cliente_Log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>393835</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>1582.560</td>\n",
              "      <td>791.280</td>\n",
              "      <td>Telefones e Celulares</td>\n",
              "      <td>SP</td>\n",
              "      <td>15042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>411885</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-17</td>\n",
              "      <td>679.200</td>\n",
              "      <td>679.200</td>\n",
              "      <td>Tablets</td>\n",
              "      <td>SP</td>\n",
              "      <td>17590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>437305</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>35.920</td>\n",
              "      <td>37.512</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>SP</td>\n",
              "      <td>15478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>440055</td>\n",
              "      <td>APP</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-01-13</td>\n",
              "      <td>2239.416</td>\n",
              "      <td>2239.416</td>\n",
              "      <td>TVs e Acessorios</td>\n",
              "      <td>SP</td>\n",
              "      <td>2933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.200</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte e Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   idcompra idcanalvenda bandeira       Data     Preço  Preço_com_frete  \\\n",
              "0    393835       Mobile        A 2023-03-05  1582.560          791.280   \n",
              "1    411885       Mobile        A 2023-03-17   679.200          679.200   \n",
              "2    437305       Mobile        A 2023-03-06    35.920           37.512   \n",
              "3    440055          APP        A 2023-01-13  2239.416         2239.416   \n",
              "4    455795       Mobile        B 2023-03-31  1743.200         1785.112   \n",
              "\n",
              "       Nome_Departamento estado  cliente_Log  \n",
              "0  Telefones e Celulares     SP        15042  \n",
              "1                Tablets     SP        17590  \n",
              "2                  Bebes     SP        15478  \n",
              "3       TVs e Acessorios     SP         2933  \n",
              "4        Esporte e Lazer     RS        19790  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas = pd.read_excel(\"varejo.xlsx\")\n",
        "vendas.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlxfFoPi-mYA",
        "outputId": "3df4da3d-cf87-4e4b-c2a5-5b8c71d90f34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 80228 entries, 0 to 80227\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   idcompra           80228 non-null  int64         \n",
            " 1   idcanalvenda       80228 non-null  object        \n",
            " 2   bandeira           80228 non-null  object        \n",
            " 3   Data               80228 non-null  datetime64[ns]\n",
            " 4   Preço              79926 non-null  float64       \n",
            " 5   Preço_com_frete    80228 non-null  float64       \n",
            " 6   Nome_Departamento  80228 non-null  object        \n",
            " 7   estado             79926 non-null  object        \n",
            " 8   cliente_Log        80228 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(2), int64(2), object(4)\n",
            "memory usage: 5.5+ MB\n"
          ]
        }
      ],
      "source": [
        "vendas.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqSBAq6S-6LB",
        "outputId": "2b1aa4cd-6070-4530-d569-6592ad50e4ef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "idcanalvenda\n",
              "APP             139\n",
              "Aplicativo    21425\n",
              "Internet      24515\n",
              "Mobile        24732\n",
              "Name: idcompra, dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Verificar colunas com o nunique\n",
        "vendas.groupby(\"idcanalvenda\").idcompra.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NUmISzve_UPL",
        "outputId": "b1762fce-550d-4c7c-cbd0-4abdca4e9de9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "idcanalvenda\n",
              "Aplicativo    21539\n",
              "Internet      24515\n",
              "Mobile        24732\n",
              "Name: idcompra, dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Mudar categoria APP para Aplicativo utilizando replace e depois consultando com o nunique\n",
        "vendas[\"idcanalvenda\"] = vendas[\"idcanalvenda\"].str.replace(\"APP\", \"Aplicativo\")\n",
        "vendas.groupby(\"idcanalvenda\").idcompra.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jjpxUv2_1Rn",
        "outputId": "af1ad015-6f5b-4f88-dd3e-8f5450b2290e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "bandeira\n",
              "A    27679\n",
              "B    38483\n",
              "Name: idcompra, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas.groupby(\"bandeira\").idcompra.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ybJ7vTr2_8ux",
        "outputId": "7b5ae8ba-6e26-4b6b-a61c-8c1bf3ff2390"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Nome_Departamento\n",
              "Acessorios e Inovacoes     3548\n",
              "Alimentos                     2\n",
              "Ar e Ventilacao             951\n",
              "Audio                      1794\n",
              "Automotivo                  895\n",
              "Bebes                      4531\n",
              "Beleza e Saude             4626\n",
              "Brinquedos                 1320\n",
              "Cama, Mesa e Banho          153\n",
              "Casa e Construcao           263\n",
              "Cine e Foto                  11\n",
              "Decoracao                   134\n",
              "Eletrodomesticos          10501\n",
              "Eletroportateis            9593\n",
              "Esporte e Lazer             531\n",
              "Ferramentas                1111\n",
              "Games                       764\n",
              "Informatica                5290\n",
              "Instrumentos Musicais         1\n",
              "Linha Industrial             86\n",
              "Malas e Mochilas            114\n",
              "Moda                          3\n",
              "Moveis                     4175\n",
              "Perfumaria                  624\n",
              "Pet Shop                     43\n",
              "Produtos de Limpeza         713\n",
              "Relogios                    134\n",
              "TV e Video                    5\n",
              "TVs e Acessorios           5326\n",
              "Tablets                    1770\n",
              "Telefones e Celulares     14495\n",
              "Utilidades Domesticas      3728\n",
              "Name: idcompra, dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas.groupby(\"Nome_Departamento\").idcompra.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phKrnV5RABRL",
        "outputId": "fec0b21c-cc4f-4bbd-8ae7-a5633fd4be7b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "estado\n",
              "AC       10\n",
              "AL      461\n",
              "AM       15\n",
              "AP        3\n",
              "BA     2028\n",
              "CE     1007\n",
              "DF     1902\n",
              "ES     1254\n",
              "GO     1603\n",
              "MA      129\n",
              "MG    10538\n",
              "MT      510\n",
              "PA      108\n",
              "PB      536\n",
              "PE     1632\n",
              "PI      247\n",
              "PR     2754\n",
              "RJ    16213\n",
              "RN      463\n",
              "RO       15\n",
              "RR        7\n",
              "RS     4099\n",
              "SC     1932\n",
              "SE      340\n",
              "SP    25962\n",
              "TO      101\n",
              "Name: idcompra, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas.groupby(\"estado\").idcompra.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwJS_5ldAMIC",
        "outputId": "cd2acb77-04b9-4494-8922-5128f8633a72"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Nome_Departamento\n",
              "Acessorios_e_Inovacoes     3548\n",
              "Alimentos                     2\n",
              "Ar_e_Ventilacao             951\n",
              "Audio                      1794\n",
              "Automotivo                  895\n",
              "Bebes                      4531\n",
              "Beleza_e_Saude             4626\n",
              "Brinquedos                 1320\n",
              "Cama,_Mesa_e_Banho          153\n",
              "Casa_e_Construcao           263\n",
              "Cine_e_Foto                  11\n",
              "Decoracao                   134\n",
              "Eletrodomesticos          10501\n",
              "Eletroportateis            9593\n",
              "Esporte_e_Lazer             531\n",
              "Ferramentas                1111\n",
              "Games                       764\n",
              "Informatica                5290\n",
              "Instrumentos_Musicais         1\n",
              "Linha_Industrial             86\n",
              "Malas_e_Mochilas            114\n",
              "Moda                          3\n",
              "Moveis                     4175\n",
              "Perfumaria                  624\n",
              "Pet_Shop                     43\n",
              "Produtos_de_Limpeza         713\n",
              "Relogios                    134\n",
              "TV_e_Video                    5\n",
              "TVs_e_Acessorios           5326\n",
              "Tablets                    1770\n",
              "Telefones_e_Celulares     14495\n",
              "Utilidades_Domesticas      3728\n",
              "Name: idcompra, dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Substituindo o Espaço em branco pelo _\n",
        "vendas[\"Nome_Departamento\"] = vendas[\"Nome_Departamento\"].str.replace(\" \",\"_\")\n",
        "vendas.groupby(\"Nome_Departamento\").idcompra.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3N5FHZwBKjQ",
        "outputId": "be211f20-c602-44d9-8139-a67b8859951f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 80228 entries, 0 to 80227\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   idcompra           80228 non-null  int64         \n",
            " 1   idcanalvenda       80228 non-null  object        \n",
            " 2   bandeira           80228 non-null  object        \n",
            " 3   Data               80228 non-null  datetime64[ns]\n",
            " 4   Preço              79926 non-null  float64       \n",
            " 5   Preço_com_frete    80228 non-null  float64       \n",
            " 6   Nome_Departamento  80228 non-null  object        \n",
            " 7   estado             79926 non-null  object        \n",
            " 8   cliente_Log        80228 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(2), int64(2), object(4)\n",
            "memory usage: 5.5+ MB\n"
          ]
        }
      ],
      "source": [
        "vendas.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UB7yXX1ID6_h",
        "outputId": "25ca8719-c555-48c7-8419-a6048b8fb648"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idcompra</th>\n",
              "      <th>idcanalvenda</th>\n",
              "      <th>bandeira</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "      <th>Nome_Departamento</th>\n",
              "      <th>estado</th>\n",
              "      <th>cliente_Log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>382380</td>\n",
              "      <td>Internet</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-04-26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>879.200</td>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>346055</td>\n",
              "      <td>Internet</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87.648</td>\n",
              "      <td>Ar_e_Ventilacao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>172555</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.840</td>\n",
              "      <td>Ar_e_Ventilacao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>446380</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1703.120</td>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>770</th>\n",
              "      <td>40425</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-04-21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1759.200</td>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78610</th>\n",
              "      <td>399680</td>\n",
              "      <td>Aplicativo</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-04-19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>339.912</td>\n",
              "      <td>Eletroportateis</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78718</th>\n",
              "      <td>320895</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000.800</td>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79465</th>\n",
              "      <td>389240</td>\n",
              "      <td>Aplicativo</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-01-14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>279.200</td>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79548</th>\n",
              "      <td>124585</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-04-21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2051.240</td>\n",
              "      <td>TVs_e_Acessorios</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80016</th>\n",
              "      <td>104235</td>\n",
              "      <td>Aplicativo</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-04-17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1199.200</td>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21315</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>302 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       idcompra idcanalvenda bandeira       Data  Preço  Preço_com_frete  \\\n",
              "96       382380     Internet        A 2023-04-26    NaN          879.200   \n",
              "284      346055     Internet        A 2023-03-23    NaN           87.648   \n",
              "402      172555       Mobile        A 2023-03-19    NaN          135.840   \n",
              "411      446380       Mobile        A 2023-03-09    NaN         1703.120   \n",
              "770       40425       Mobile        B 2023-04-21    NaN         1759.200   \n",
              "...         ...          ...      ...        ...    ...              ...   \n",
              "78610    399680   Aplicativo        A 2023-04-19    NaN          339.912   \n",
              "78718    320895       Mobile        B 2023-01-20    NaN         1000.800   \n",
              "79465    389240   Aplicativo        A 2023-01-14    NaN          279.200   \n",
              "79548    124585       Mobile        A 2023-04-21    NaN         2051.240   \n",
              "80016    104235   Aplicativo        A 2023-04-17    NaN         1199.200   \n",
              "\n",
              "           Nome_Departamento estado  cliente_Log  \n",
              "96     Telefones_e_Celulares    NaN         5804  \n",
              "284          Ar_e_Ventilacao    NaN        16359  \n",
              "402          Ar_e_Ventilacao    NaN        13691  \n",
              "411         Eletrodomesticos    NaN        13024  \n",
              "770    Telefones_e_Celulares    NaN         9121  \n",
              "...                      ...    ...          ...  \n",
              "78610        Eletroportateis    NaN        20873  \n",
              "78718  Telefones_e_Celulares    NaN        17772  \n",
              "79465       Eletrodomesticos    NaN        20399  \n",
              "79548       TVs_e_Acessorios    NaN        17784  \n",
              "80016  Telefones_e_Celulares    NaN        21315  \n",
              "\n",
              "[302 rows x 9 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Analisar quais dados então nulos dentro da tabela\n",
        "vendas[vendas[\"estado\"].isnull()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1fyBV5cEETn",
        "outputId": "94003498-9009-439b-a7ca-90accb26f07c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 79926 entries, 0 to 80227\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   idcompra           79926 non-null  int64         \n",
            " 1   idcanalvenda       79926 non-null  object        \n",
            " 2   bandeira           79926 non-null  object        \n",
            " 3   Data               79926 non-null  datetime64[ns]\n",
            " 4   Preço              79926 non-null  float64       \n",
            " 5   Preço_com_frete    79926 non-null  float64       \n",
            " 6   Nome_Departamento  79926 non-null  object        \n",
            " 7   estado             79926 non-null  object        \n",
            " 8   cliente_Log        79926 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(2), int64(2), object(4)\n",
            "memory usage: 6.1+ MB\n"
          ]
        }
      ],
      "source": [
        "#Retirando os nulos da tabela\n",
        "vendas_sem_nulos = vendas.dropna()\n",
        "vendas_sem_nulos.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "GSGirib2E9pE"
      },
      "outputs": [],
      "source": [
        "vendas[\"estado\"] = vendas[\"estado\"].fillna(\"MS\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Gw18luLhEg8R"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Nickson\\AppData\\Local\\Temp\\ipykernel_2308\\3316148249.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  vendas[\"estado\"].fillna(\"MS\", inplace=True)\n"
          ]
        }
      ],
      "source": [
        "#Manter os dados nulos com outras informações no BD\n",
        "vendas[\"estado\"].fillna(\"MS\", inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-IOyuWIFE7E",
        "outputId": "19282df7-126d-4720-9b74-65b38f600634"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 80228 entries, 0 to 80227\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   idcompra           80228 non-null  int64         \n",
            " 1   idcanalvenda       80228 non-null  object        \n",
            " 2   bandeira           80228 non-null  object        \n",
            " 3   Data               80228 non-null  datetime64[ns]\n",
            " 4   Preço              79926 non-null  float64       \n",
            " 5   Preço_com_frete    80228 non-null  float64       \n",
            " 6   Nome_Departamento  80228 non-null  object        \n",
            " 7   estado             80228 non-null  object        \n",
            " 8   cliente_Log        80228 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(2), int64(2), object(4)\n",
            "memory usage: 5.5+ MB\n"
          ]
        }
      ],
      "source": [
        "vendas.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "uTvBzEViFG4-"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Nickson\\AppData\\Local\\Temp\\ipykernel_2308\\162248293.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  vendas[\"Preço\"].fillna(media_preco, inplace= True)\n"
          ]
        }
      ],
      "source": [
        "#Substituir as informações nulas pela média\n",
        "media_preco = vendas[\"Preço\"].mean()\n",
        "\n",
        "vendas[\"Preço\"].fillna(media_preco, inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TsqPMgxyFe9u",
        "outputId": "7ed886e5-4f2e-4c5d-fe7f-3d075c8baafc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "906.5891285439033"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "media_preco"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zct_dxIFgQ9",
        "outputId": "9d1acccf-91cf-46b5-f64b-e5ed6a5d63c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 80228 entries, 0 to 80227\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   idcompra           80228 non-null  int64         \n",
            " 1   idcanalvenda       80228 non-null  object        \n",
            " 2   bandeira           80228 non-null  object        \n",
            " 3   Data               80228 non-null  datetime64[ns]\n",
            " 4   Preço              80228 non-null  float64       \n",
            " 5   Preço_com_frete    80228 non-null  float64       \n",
            " 6   Nome_Departamento  80228 non-null  object        \n",
            " 7   estado             80228 non-null  object        \n",
            " 8   cliente_Log        80228 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(2), int64(2), object(4)\n",
            "memory usage: 5.5+ MB\n"
          ]
        }
      ],
      "source": [
        "vendas.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzpKXnkXFiif",
        "outputId": "de6a10f0-899a-4262-80a8-9094e6957167"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "532.448"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mediana_preco = vendas[\"Preço\"].median()\n",
        "mediana_preco"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EjZTG2eDGB7a",
        "outputId": "faf57748-a45e-4f56-c925-dbf927ac1b64"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idcompra</th>\n",
              "      <th>idcanalvenda</th>\n",
              "      <th>bandeira</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
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              "      <th>cliente_Log</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>393835</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>1582.560</td>\n",
              "      <td>791.280</td>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>SP</td>\n",
              "      <td>15042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>411885</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-17</td>\n",
              "      <td>679.200</td>\n",
              "      <td>679.200</td>\n",
              "      <td>Tablets</td>\n",
              "      <td>SP</td>\n",
              "      <td>17590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>437305</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>35.920</td>\n",
              "      <td>37.512</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>SP</td>\n",
              "      <td>15478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>440055</td>\n",
              "      <td>Aplicativo</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-01-13</td>\n",
              "      <td>2239.416</td>\n",
              "      <td>2239.416</td>\n",
              "      <td>TVs_e_Acessorios</td>\n",
              "      <td>SP</td>\n",
              "      <td>2933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.200</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   idcompra idcanalvenda bandeira       Data     Preço  Preço_com_frete  \\\n",
              "0    393835       Mobile        A 2023-03-05  1582.560          791.280   \n",
              "1    411885       Mobile        A 2023-03-17   679.200          679.200   \n",
              "2    437305       Mobile        A 2023-03-06    35.920           37.512   \n",
              "3    440055   Aplicativo        A 2023-01-13  2239.416         2239.416   \n",
              "4    455795       Mobile        B 2023-03-31  1743.200         1785.112   \n",
              "\n",
              "       Nome_Departamento estado  cliente_Log  \n",
              "0  Telefones_e_Celulares     SP        15042  \n",
              "1                Tablets     SP        17590  \n",
              "2                  Bebes     SP        15478  \n",
              "3       TVs_e_Acessorios     SP         2933  \n",
              "4        Esporte_e_Lazer     RS        19790  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WFN8B2zgH-q6",
        "outputId": "0896686b-5bb3-4df2-998d-9f933de413a1"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>80228.000000</td>\n",
              "      <td>80228.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>906.589129</td>\n",
              "      <td>908.619823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1057.611490</td>\n",
              "      <td>1057.583101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.320000</td>\n",
              "      <td>6.320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>147.920000</td>\n",
              "      <td>150.706000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>532.448000</td>\n",
              "      <td>535.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1199.200000</td>\n",
              "      <td>1215.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14439.240000</td>\n",
              "      <td>14439.240000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "              Preço  Preço_com_frete\n",
              "count  80228.000000     80228.000000\n",
              "mean     906.589129       908.619823\n",
              "std     1057.611490      1057.583101\n",
              "min        6.320000         6.320000\n",
              "25%      147.920000       150.706000\n",
              "50%      532.448000       535.120000\n",
              "75%     1199.200000      1215.240000\n",
              "max    14439.240000     14439.240000"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas[[\"Preço\",\"Preço_com_frete\"]].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "TVFer00LIMqY"
      },
      "outputs": [],
      "source": [
        "preco_errado = vendas.query(\"Preço > Preço_com_frete\")\n",
        "\n",
        "vendas_correto = vendas.query(\"Preço < Preço_com_frete\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
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          "height": 300
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        "id": "KnlMehjHJPcT",
        "outputId": "f4b0a319-1c14-4e9c-a574-62fffa12b49f"
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              "      <th></th>\n",
              "      <th>idcompra</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>22047.000000</td>\n",
              "      <td>22047</td>\n",
              "      <td>22047.000000</td>\n",
              "      <td>22047.000000</td>\n",
              "      <td>22047.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>253799.955550</td>\n",
              "      <td>2023-03-02 05:19:31.261395968</td>\n",
              "      <td>549.518401</td>\n",
              "      <td>567.332688</td>\n",
              "      <td>13856.313013</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>2023-01-02 00:00:00</td>\n",
              "      <td>8.720000</td>\n",
              "      <td>9.512000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>127670.000000</td>\n",
              "      <td>2023-02-01 00:00:00</td>\n",
              "      <td>93.528000</td>\n",
              "      <td>100.800000</td>\n",
              "      <td>6858.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>257885.000000</td>\n",
              "      <td>2023-03-02 00:00:00</td>\n",
              "      <td>183.200000</td>\n",
              "      <td>193.912000</td>\n",
              "      <td>13845.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>379705.000000</td>\n",
              "      <td>2023-04-01 00:00:00</td>\n",
              "      <td>661.680000</td>\n",
              "      <td>676.736000</td>\n",
              "      <td>20801.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>499960.000000</td>\n",
              "      <td>2023-04-30 00:00:00</td>\n",
              "      <td>14391.200000</td>\n",
              "      <td>14407.120000</td>\n",
              "      <td>27982.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>144794.628563</td>\n",
              "      <td>NaN</td>\n",
              "      <td>846.295127</td>\n",
              "      <td>863.458763</td>\n",
              "      <td>8049.120135</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            idcompra                           Data         Preço  \\\n",
              "count   22047.000000                          22047  22047.000000   \n",
              "mean   253799.955550  2023-03-02 05:19:31.261395968    549.518401   \n",
              "min        25.000000            2023-01-02 00:00:00      8.720000   \n",
              "25%    127670.000000            2023-02-01 00:00:00     93.528000   \n",
              "50%    257885.000000            2023-03-02 00:00:00    183.200000   \n",
              "75%    379705.000000            2023-04-01 00:00:00    661.680000   \n",
              "max    499960.000000            2023-04-30 00:00:00  14391.200000   \n",
              "std    144794.628563                            NaN    846.295127   \n",
              "\n",
              "       Preço_com_frete   cliente_Log  \n",
              "count     22047.000000  22047.000000  \n",
              "mean        567.332688  13856.313013  \n",
              "min           9.512000      1.000000  \n",
              "25%         100.800000   6858.000000  \n",
              "50%         193.912000  13845.000000  \n",
              "75%         676.736000  20801.000000  \n",
              "max       14407.120000  27982.000000  \n",
              "std         863.458763   8049.120135  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_1CwFSX6JSeB",
        "outputId": "f16a9f7b-7007-4f71-da81-fdd9ce506582"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idcompra</th>\n",
              "      <th>idcanalvenda</th>\n",
              "      <th>bandeira</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "      <th>Nome_Departamento</th>\n",
              "      <th>estado</th>\n",
              "      <th>cliente_Log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>437305</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>35.92</td>\n",
              "      <td>37.512</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>SP</td>\n",
              "      <td>15478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.20</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>75525</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-02-19</td>\n",
              "      <td>679.20</td>\n",
              "      <td>695.120</td>\n",
              "      <td>Moveis</td>\n",
              "      <td>SP</td>\n",
              "      <td>3442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>258580</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>107.76</td>\n",
              "      <td>109.344</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>ES</td>\n",
              "      <td>7662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>306675</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-02-17</td>\n",
              "      <td>471.20</td>\n",
              "      <td>511.120</td>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>RJ</td>\n",
              "      <td>15060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    idcompra idcanalvenda bandeira       Data    Preço  Preço_com_frete  \\\n",
              "2     437305       Mobile        A 2023-03-06    35.92           37.512   \n",
              "4     455795       Mobile        B 2023-03-31  1743.20         1785.112   \n",
              "8      75525       Mobile        B 2023-02-19   679.20          695.120   \n",
              "19    258580       Mobile        A 2023-03-27   107.76          109.344   \n",
              "22    306675       Mobile        B 2023-02-17   471.20          511.120   \n",
              "\n",
              "   Nome_Departamento estado  cliente_Log  \n",
              "2              Bebes     SP        15478  \n",
              "4    Esporte_e_Lazer     RS        19790  \n",
              "8             Moveis     SP         3442  \n",
              "19             Bebes     ES         7662  \n",
              "22  Eletrodomesticos     RJ        15060  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "IL4C5WyQJchJ",
        "outputId": "801d4eab-3e1c-4e8f-d553-ec8101c17f88"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idcompra</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "      <th>cliente_Log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>168.000000</td>\n",
              "      <td>168</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>168.00000</td>\n",
              "      <td>168.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>265130.744048</td>\n",
              "      <td>2023-03-05 23:51:25.714285824</td>\n",
              "      <td>1070.083095</td>\n",
              "      <td>1098.71319</td>\n",
              "      <td>14368.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2145.000000</td>\n",
              "      <td>2023-01-02 00:00:00</td>\n",
              "      <td>10.896000</td>\n",
              "      <td>16.11200</td>\n",
              "      <td>210.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>157955.000000</td>\n",
              "      <td>2023-02-03 18:00:00</td>\n",
              "      <td>527.200000</td>\n",
              "      <td>545.32000</td>\n",
              "      <td>7717.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>253702.500000</td>\n",
              "      <td>2023-03-07 00:00:00</td>\n",
              "      <td>1108.008000</td>\n",
              "      <td>1136.24800</td>\n",
              "      <td>15281.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>380616.250000</td>\n",
              "      <td>2023-04-09 00:00:00</td>\n",
              "      <td>1599.200000</td>\n",
              "      <td>1618.88400</td>\n",
              "      <td>20538.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>496910.000000</td>\n",
              "      <td>2023-04-30 00:00:00</td>\n",
              "      <td>2399.200000</td>\n",
              "      <td>2432.74400</td>\n",
              "      <td>27837.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>140435.252208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>617.138494</td>\n",
              "      <td>620.66244</td>\n",
              "      <td>7890.233136</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            idcompra                           Data        Preço  \\\n",
              "count     168.000000                            168   168.000000   \n",
              "mean   265130.744048  2023-03-05 23:51:25.714285824  1070.083095   \n",
              "min      2145.000000            2023-01-02 00:00:00    10.896000   \n",
              "25%    157955.000000            2023-02-03 18:00:00   527.200000   \n",
              "50%    253702.500000            2023-03-07 00:00:00  1108.008000   \n",
              "75%    380616.250000            2023-04-09 00:00:00  1599.200000   \n",
              "max    496910.000000            2023-04-30 00:00:00  2399.200000   \n",
              "std    140435.252208                            NaN   617.138494   \n",
              "\n",
              "       Preço_com_frete   cliente_Log  \n",
              "count        168.00000    168.000000  \n",
              "mean        1098.71319  14368.571429  \n",
              "min           16.11200    210.000000  \n",
              "25%          545.32000   7717.500000  \n",
              "50%         1136.24800  15281.000000  \n",
              "75%         1618.88400  20538.000000  \n",
              "max         2432.74400  27837.000000  \n",
              "std          620.66244   7890.233136  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto.query(\"Nome_Departamento == 'Esporte_e_Lazer' and estado == 'SP'\").describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XSf_fNnHJ4Co",
        "outputId": "4aade110-3cf1-422a-f4c3-d7bbcf7d253d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "        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>Nome_Departamento</th>\n",
              "      <th>idcompra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bebes</td>\n",
              "      <td>4353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>4201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Eletroportateis</td>\n",
              "      <td>2223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Moveis</td>\n",
              "      <td>2036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Utilidades_Domesticas</td>\n",
              "      <td>1366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Acessorios_e_Inovacoes</td>\n",
              "      <td>1243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Beleza_e_Saude</td>\n",
              "      <td>1054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Brinquedos</td>\n",
              "      <td>686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Perfumaria</td>\n",
              "      <td>563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Produtos_de_Limpeza</td>\n",
              "      <td>522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Automotivo</td>\n",
              "      <td>457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Audio</td>\n",
              "      <td>374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Ar_e_Ventilacao</td>\n",
              "      <td>337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Ferramentas</td>\n",
              "      <td>287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Informatica</td>\n",
              "      <td>284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Games</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>TVs_e_Acessorios</td>\n",
              "      <td>158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Decoracao</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Malas_e_Mochilas</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Cama,_Mesa_e_Banho</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Tablets</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Casa_e_Construcao</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Relogios</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Pet_Shop</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Linha_Industrial</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Cine_e_Foto</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Moda</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Alimentos</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Instrumentos_Musicais</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Nome_Departamento  idcompra\n",
              "0                    Bebes      4353\n",
              "1         Eletrodomesticos      4201\n",
              "2          Eletroportateis      2223\n",
              "3                   Moveis      2036\n",
              "4    Utilidades_Domesticas      1366\n",
              "5   Acessorios_e_Inovacoes      1243\n",
              "6           Beleza_e_Saude      1054\n",
              "7               Brinquedos       686\n",
              "8               Perfumaria       563\n",
              "9      Produtos_de_Limpeza       522\n",
              "10   Telefones_e_Celulares       493\n",
              "11              Automotivo       457\n",
              "12         Esporte_e_Lazer       446\n",
              "13                   Audio       374\n",
              "14         Ar_e_Ventilacao       337\n",
              "15             Ferramentas       287\n",
              "16             Informatica       284\n",
              "17                   Games       200\n",
              "18        TVs_e_Acessorios       158\n",
              "19               Decoracao       127\n",
              "20        Malas_e_Mochilas        91\n",
              "21      Cama,_Mesa_e_Banho        71\n",
              "22                 Tablets        64\n",
              "23       Casa_e_Construcao        58\n",
              "24                Relogios        45\n",
              "25                Pet_Shop        40\n",
              "26        Linha_Industrial         9\n",
              "27             Cine_e_Foto         8\n",
              "28                    Moda         3\n",
              "29               Alimentos         2\n",
              "30   Instrumentos_Musicais         1"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto.groupby(\"Nome_Departamento\").idcompra.nunique().sort_values(ascending=False).reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "t450SPqkKv4R",
        "outputId": "bbfc09f0-a4a3-4e99-f612-01dd6cfb1f50"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nome_Departamento</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TVs_e_Acessorios</td>\n",
              "      <td>3469.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Informatica</td>\n",
              "      <td>2159.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Linha_Industrial</td>\n",
              "      <td>2027.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cine_e_Foto</td>\n",
              "      <td>1851.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>1442.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>1400.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>1123.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Tablets</td>\n",
              "      <td>1100.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Games</td>\n",
              "      <td>942.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Instrumentos_Musicais</td>\n",
              "      <td>834.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Automotivo</td>\n",
              "      <td>681.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Moveis</td>\n",
              "      <td>541.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Ar_e_Ventilacao</td>\n",
              "      <td>518.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Audio</td>\n",
              "      <td>480.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Casa_e_Construcao</td>\n",
              "      <td>433.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Pet_Shop</td>\n",
              "      <td>237.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Acessorios_e_Inovacoes</td>\n",
              "      <td>229.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Eletroportateis</td>\n",
              "      <td>223.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ferramentas</td>\n",
              "      <td>217.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Cama,_Mesa_e_Banho</td>\n",
              "      <td>210.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Malas_e_Mochilas</td>\n",
              "      <td>194.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Relogios</td>\n",
              "      <td>186.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Brinquedos</td>\n",
              "      <td>176.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Utilidades_Domesticas</td>\n",
              "      <td>155.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Decoracao</td>\n",
              "      <td>144.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Bebes</td>\n",
              "      <td>130.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Beleza_e_Saude</td>\n",
              "      <td>119.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Alimentos</td>\n",
              "      <td>104.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Perfumaria</td>\n",
              "      <td>80.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Produtos_de_Limpeza</td>\n",
              "      <td>68.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Moda</td>\n",
              "      <td>47.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Nome_Departamento  Preço_com_frete\n",
              "0         TVs_e_Acessorios          3469.38\n",
              "1              Informatica          2159.78\n",
              "2         Linha_Industrial          2027.11\n",
              "3              Cine_e_Foto          1851.20\n",
              "4    Telefones_e_Celulares          1442.19\n",
              "5         Eletrodomesticos          1400.66\n",
              "6          Esporte_e_Lazer          1123.14\n",
              "7                  Tablets          1100.23\n",
              "8                    Games           942.90\n",
              "9    Instrumentos_Musicais           834.61\n",
              "10              Automotivo           681.57\n",
              "11                  Moveis           541.03\n",
              "12         Ar_e_Ventilacao           518.79\n",
              "13                   Audio           480.35\n",
              "14       Casa_e_Construcao           433.53\n",
              "15                Pet_Shop           237.26\n",
              "16  Acessorios_e_Inovacoes           229.14\n",
              "17         Eletroportateis           223.77\n",
              "18             Ferramentas           217.59\n",
              "19      Cama,_Mesa_e_Banho           210.70\n",
              "20        Malas_e_Mochilas           194.26\n",
              "21                Relogios           186.95\n",
              "22              Brinquedos           176.45\n",
              "23   Utilidades_Domesticas           155.02\n",
              "24               Decoracao           144.50\n",
              "25                   Bebes           130.90\n",
              "26          Beleza_e_Saude           119.59\n",
              "27               Alimentos           104.24\n",
              "28              Perfumaria            80.91\n",
              "29     Produtos_de_Limpeza            68.79\n",
              "30                    Moda            47.01"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "round(vendas_correto.groupby(\"Nome_Departamento\")[\"Preço_com_frete\"].agg(\"mean\").sort_values(ascending=False).reset_index(),2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-q-nChdELQRi",
        "outputId": "c05d44f3-3bfa-40ed-a2dd-10ee81e0f403"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Data\n",
              "2023-02-13    226\n",
              "2023-01-20    218\n",
              "2023-03-07    215\n",
              "2023-03-30    214\n",
              "2023-02-20    214\n",
              "             ... \n",
              "2023-02-15    162\n",
              "2023-04-04    162\n",
              "2023-02-25    161\n",
              "2023-02-22    156\n",
              "2023-02-12    152\n",
              "Name: idcompra, Length: 119, dtype: int64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto.groupby(\"Data\").idcompra.nunique().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "NQ6PGScgMKFu",
        "outputId": "3a5786d4-4666-4ca9-a5af-48ab7c8a8058"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Nickson\\AppData\\Local\\Temp\\ipykernel_2308\\296731108.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  vendas_correto[\"mes\"] = vendas_correto[\"Data\"].dt.month\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idcompra</th>\n",
              "      <th>idcanalvenda</th>\n",
              "      <th>bandeira</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "      <th>Nome_Departamento</th>\n",
              "      <th>estado</th>\n",
              "      <th>cliente_Log</th>\n",
              "      <th>mes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>437305</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>35.92</td>\n",
              "      <td>37.512</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>SP</td>\n",
              "      <td>15478</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.20</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>75525</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-02-19</td>\n",
              "      <td>679.20</td>\n",
              "      <td>695.120</td>\n",
              "      <td>Moveis</td>\n",
              "      <td>SP</td>\n",
              "      <td>3442</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>258580</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>107.76</td>\n",
              "      <td>109.344</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>ES</td>\n",
              "      <td>7662</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>306675</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-02-17</td>\n",
              "      <td>471.20</td>\n",
              "      <td>511.120</td>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>RJ</td>\n",
              "      <td>15060</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    idcompra idcanalvenda bandeira       Data    Preço  Preço_com_frete  \\\n",
              "2     437305       Mobile        A 2023-03-06    35.92           37.512   \n",
              "4     455795       Mobile        B 2023-03-31  1743.20         1785.112   \n",
              "8      75525       Mobile        B 2023-02-19   679.20          695.120   \n",
              "19    258580       Mobile        A 2023-03-27   107.76          109.344   \n",
              "22    306675       Mobile        B 2023-02-17   471.20          511.120   \n",
              "\n",
              "   Nome_Departamento estado  cliente_Log  mes  \n",
              "2              Bebes     SP        15478    3  \n",
              "4    Esporte_e_Lazer     RS        19790    3  \n",
              "8             Moveis     SP         3442    2  \n",
              "19             Bebes     ES         7662    3  \n",
              "22  Eletrodomesticos     RJ        15060    2  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto[\"mes\"] = vendas_correto[\"Data\"].dt.month\n",
        "vendas_correto.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7Sgp7ZBMcYl",
        "outputId": "2856d571-df4f-47a5-ffc5-415bb818eac0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "mes\n",
              "3    5590\n",
              "4    5454\n",
              "1    5309\n",
              "2    5138\n",
              "Name: idcompra, dtype: int64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto.groupby(\"mes\").idcompra.nunique().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1CCDFizWMijQ",
        "outputId": "10a496b0-bf47-4a2a-e1bb-c89d980976ed"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cliente_Log</th>\n",
              "      <th>idade</th>\n",
              "      <th>renda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15042</td>\n",
              "      <td>89</td>\n",
              "      <td>11337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17590</td>\n",
              "      <td>54</td>\n",
              "      <td>2050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15478</td>\n",
              "      <td>47</td>\n",
              "      <td>8258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2933</td>\n",
              "      <td>39</td>\n",
              "      <td>4671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19790</td>\n",
              "      <td>26</td>\n",
              "      <td>9207</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cliente_Log  idade  renda\n",
              "0        15042     89  11337\n",
              "1        17590     54   2050\n",
              "2        15478     47   8258\n",
              "3         2933     39   4671\n",
              "4        19790     26   9207"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cliente = pd.read_excel(\"cliente_varejo.xlsx\")\n",
        "cliente.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bNdXy0LYr9vy",
        "outputId": "ad1a760f-244c-4345-9a26-a6d5cb465216"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idcompra</th>\n",
              "      <th>idcanalvenda</th>\n",
              "      <th>bandeira</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "      <th>Nome_Departamento</th>\n",
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              "      <th>cliente_Log</th>\n",
              "      <th>mes</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>437305</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>35.92</td>\n",
              "      <td>37.512</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>SP</td>\n",
              "      <td>15478</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.20</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>75525</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-02-19</td>\n",
              "      <td>679.20</td>\n",
              "      <td>695.120</td>\n",
              "      <td>Moveis</td>\n",
              "      <td>SP</td>\n",
              "      <td>3442</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>258580</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>107.76</td>\n",
              "      <td>109.344</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>ES</td>\n",
              "      <td>7662</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>306675</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-02-17</td>\n",
              "      <td>471.20</td>\n",
              "      <td>511.120</td>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>RJ</td>\n",
              "      <td>15060</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    idcompra idcanalvenda bandeira       Data    Preço  Preço_com_frete  \\\n",
              "2     437305       Mobile        A 2023-03-06    35.92           37.512   \n",
              "4     455795       Mobile        B 2023-03-31  1743.20         1785.112   \n",
              "8      75525       Mobile        B 2023-02-19   679.20          695.120   \n",
              "19    258580       Mobile        A 2023-03-27   107.76          109.344   \n",
              "22    306675       Mobile        B 2023-02-17   471.20          511.120   \n",
              "\n",
              "   Nome_Departamento estado  cliente_Log  mes  \n",
              "2              Bebes     SP        15478    3  \n",
              "4    Esporte_e_Lazer     RS        19790    3  \n",
              "8             Moveis     SP         3442    2  \n",
              "19             Bebes     ES         7662    3  \n",
              "22  Eletrodomesticos     RJ        15060    2  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_correto.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "uaNzDm83sD8D",
        "outputId": "d6bbedd3-8ff3-45f9-f903-e51d3e70cdfc"
      },
      "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>idade</th>\n",
              "      <th>renda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>26425.000000</td>\n",
              "      <td>26425.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.412564</td>\n",
              "      <td>8241.470350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20.698825</td>\n",
              "      <td>3899.871482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>4859.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>8234.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>71.000000</td>\n",
              "      <td>11629.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>89.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              idade         renda\n",
              "count  26425.000000  26425.000000\n",
              "mean      53.412564   8241.470350\n",
              "std       20.698825   3899.871482\n",
              "min       18.000000   1500.000000\n",
              "25%       35.000000   4859.000000\n",
              "50%       53.000000   8234.000000\n",
              "75%       71.000000  11629.000000\n",
              "max       89.000000  15000.000000"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cliente[[\"idade\", \"renda\"]].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GurDOaysO0i",
        "outputId": "78a5abd0-94a6-410e-beb5-c0f4f53597f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26425 entries, 0 to 26424\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype\n",
            "---  ------       --------------  -----\n",
            " 0   cliente_Log  26425 non-null  int64\n",
            " 1   idade        26425 non-null  int64\n",
            " 2   renda        26425 non-null  int64\n",
            "dtypes: int64(3)\n",
            "memory usage: 619.5 KB\n"
          ]
        }
      ],
      "source": [
        "cliente.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKfzTx6YsfOh",
        "outputId": "8c8eee07-7347-4344-a1cf-888fa6b38f07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26425 entries, 0 to 26424\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   cliente_Log  26425 non-null  int64  \n",
            " 1   idade        26425 non-null  int64  \n",
            " 2   renda        26425 non-null  float64\n",
            "dtypes: float64(1), int64(2)\n",
            "memory usage: 619.5 KB\n"
          ]
        }
      ],
      "source": [
        "cliente = cliente.astype({\"renda\":\"float\"})\n",
        "cliente.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vsVtlMcks3Ud",
        "outputId": "60cbb970-a5a8-4619-de99-c2b95bba5722"
      },
      "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>idcompra</th>\n",
              "      <th>idcanalvenda</th>\n",
              "      <th>bandeira</th>\n",
              "      <th>Data</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "      <th>Nome_Departamento</th>\n",
              "      <th>estado</th>\n",
              "      <th>cliente_Log</th>\n",
              "      <th>mes</th>\n",
              "      <th>idade</th>\n",
              "      <th>renda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>437305</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>A</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>35.92</td>\n",
              "      <td>37.512</td>\n",
              "      <td>Bebes</td>\n",
              "      <td>SP</td>\n",
              "      <td>15478</td>\n",
              "      <td>3</td>\n",
              "      <td>47</td>\n",
              "      <td>8258.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.20</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "      <td>3</td>\n",
              "      <td>26</td>\n",
              "      <td>9207.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.20</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "      <td>3</td>\n",
              "      <td>58</td>\n",
              "      <td>9204.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>455795</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>1743.20</td>\n",
              "      <td>1785.112</td>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>RS</td>\n",
              "      <td>19790</td>\n",
              "      <td>3</td>\n",
              "      <td>48</td>\n",
              "      <td>2796.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75525</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>B</td>\n",
              "      <td>2023-02-19</td>\n",
              "      <td>679.20</td>\n",
              "      <td>695.120</td>\n",
              "      <td>Moveis</td>\n",
              "      <td>SP</td>\n",
              "      <td>3442</td>\n",
              "      <td>2</td>\n",
              "      <td>78</td>\n",
              "      <td>6596.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   idcompra idcanalvenda bandeira       Data    Preço  Preço_com_frete  \\\n",
              "0    437305       Mobile        A 2023-03-06    35.92           37.512   \n",
              "1    455795       Mobile        B 2023-03-31  1743.20         1785.112   \n",
              "2    455795       Mobile        B 2023-03-31  1743.20         1785.112   \n",
              "3    455795       Mobile        B 2023-03-31  1743.20         1785.112   \n",
              "4     75525       Mobile        B 2023-02-19   679.20          695.120   \n",
              "\n",
              "  Nome_Departamento estado  cliente_Log  mes  idade   renda  \n",
              "0             Bebes     SP        15478    3     47  8258.0  \n",
              "1   Esporte_e_Lazer     RS        19790    3     26  9207.0  \n",
              "2   Esporte_e_Lazer     RS        19790    3     58  9204.0  \n",
              "3   Esporte_e_Lazer     RS        19790    3     48  2796.0  \n",
              "4            Moveis     SP         3442    2     78  6596.0  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vendas_cliente = vendas_correto.merge(cliente, how= \"left\", on=\"cliente_Log\")\n",
        "vendas_cliente.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "fYsZEql3uQ6b",
        "outputId": "86664048-a1e9-4ddb-acc9-63aff77bf915"
      },
      "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>idcanalvenda</th>\n",
              "      <th>renda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aplicativo</td>\n",
              "      <td>8244.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mobile</td>\n",
              "      <td>8222.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Internet</td>\n",
              "      <td>8211.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  idcanalvenda    renda\n",
              "0   Aplicativo  8244.85\n",
              "1       Mobile  8222.72\n",
              "2     Internet  8211.84"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "agg_idcanal_renda = round(vendas_cliente.groupby(\"idcanalvenda\")[\"renda\"].agg(\"mean\").sort_values(ascending=False).reset_index(),2)\n",
        "agg_idcanal_renda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "tYA07OJXu09R",
        "outputId": "4dd76a5c-83a6-412d-f219-84649e7e0d96"
      },
      "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>bandeira</th>\n",
              "      <th>idade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>53.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B</td>\n",
              "      <td>53.29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  bandeira  idade\n",
              "0        A  53.30\n",
              "1        B  53.29"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "agg_idade_bandeira = round(vendas_cliente.groupby(\"bandeira\")[\"idade\"].agg(\"mean\").sort_values(ascending=False).reset_index(),2)\n",
        "agg_idade_bandeira"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "7RGmuVw4vR2l"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "HZA4oI0Ayk4_",
        "outputId": "3aace709-c44c-4040-e89d-a6663b55a980"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(7,4))\n",
        "\n",
        "plt.bar(agg_idade_bandeira[\"bandeira\"], agg_idade_bandeira[\"idade\"], color=\"lightblue\")\n",
        "\n",
        "plt.ylabel(\"Média de idade\")\n",
        "\n",
        "plt.title(\"Idade média por Bandeira\", loc=\"center\", fontsize=18, fontweight=\"bold\", color= \"black\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "rJxsFttBzLji",
        "outputId": "77e67596-72ff-454c-9913-68c709f24803"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(7,4))\n",
        "\n",
        "plt.bar(agg_idcanal_renda[\"idcanalvenda\"], agg_idcanal_renda[\"renda\"], color=\"lightblue\")\n",
        "\n",
        "plt.ylabel(\"Renda média\")\n",
        "\n",
        "plt.title(\"Renda média por canal de venda\", loc=\"center\", fontsize=18, fontweight=\"bold\", color=\"black\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "MLpE4UUI0BLV",
        "outputId": "40d2527d-1744-4cd7-b172-c9318e298b02"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data</th>\n",
              "      <th>idcompra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-03</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-04</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-05</td>\n",
              "      <td>204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-06</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2023-04-26</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>2023-04-27</td>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>2023-04-28</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>2023-04-29</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>2023-04-30</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Data  idcompra\n",
              "0   2023-01-02       193\n",
              "1   2023-01-03       163\n",
              "2   2023-01-04       188\n",
              "3   2023-01-05       204\n",
              "4   2023-01-06       198\n",
              "..         ...       ...\n",
              "114 2023-04-26       200\n",
              "115 2023-04-27       212\n",
              "116 2023-04-28       192\n",
              "117 2023-04-29       187\n",
              "118 2023-04-30       192\n",
              "\n",
              "[119 rows x 2 columns]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "venda_por_data = vendas_correto.groupby(\"Data\").idcompra.nunique().reset_index()\n",
        "venda_por_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "YYPNzEFM0Uc5",
        "outputId": "0c5d5ac9-9eaf-4843-c11d-ee3fe64fb31c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(20,12))\n",
        "\n",
        "\n",
        "plt.xlabel(\"Data da venda\")\n",
        "plt.ylabel(\"Quantidade de vendas\")\n",
        "x = venda_por_data[\"Data\"]\n",
        "y = venda_por_data[\"idcompra\"]\n",
        "\n",
        "plt.plot(x,y, color = \"lightblue\", linewidth = \"2\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "gbA8yTO11Dw_"
      },
      "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>Nome_Departamento</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TVs_e_Acessorios</td>\n",
              "      <td>3469.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Informatica</td>\n",
              "      <td>2159.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Linha_Industrial</td>\n",
              "      <td>2027.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cine_e_Foto</td>\n",
              "      <td>1851.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>1442.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>1400.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>1123.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Tablets</td>\n",
              "      <td>1100.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Games</td>\n",
              "      <td>942.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Instrumentos_Musicais</td>\n",
              "      <td>834.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Automotivo</td>\n",
              "      <td>681.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Moveis</td>\n",
              "      <td>541.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Ar_e_Ventilacao</td>\n",
              "      <td>518.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Audio</td>\n",
              "      <td>480.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Casa_e_Construcao</td>\n",
              "      <td>433.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Pet_Shop</td>\n",
              "      <td>237.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Acessorios_e_Inovacoes</td>\n",
              "      <td>229.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Eletroportateis</td>\n",
              "      <td>223.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ferramentas</td>\n",
              "      <td>217.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Cama,_Mesa_e_Banho</td>\n",
              "      <td>210.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Malas_e_Mochilas</td>\n",
              "      <td>194.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Relogios</td>\n",
              "      <td>186.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Brinquedos</td>\n",
              "      <td>176.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Utilidades_Domesticas</td>\n",
              "      <td>155.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Decoracao</td>\n",
              "      <td>144.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Bebes</td>\n",
              "      <td>130.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Beleza_e_Saude</td>\n",
              "      <td>119.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Alimentos</td>\n",
              "      <td>104.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Perfumaria</td>\n",
              "      <td>80.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Produtos_de_Limpeza</td>\n",
              "      <td>68.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Moda</td>\n",
              "      <td>47.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Nome_Departamento  Preço_com_frete\n",
              "0         TVs_e_Acessorios          3469.38\n",
              "1              Informatica          2159.78\n",
              "2         Linha_Industrial          2027.11\n",
              "3              Cine_e_Foto          1851.20\n",
              "4    Telefones_e_Celulares          1442.19\n",
              "5         Eletrodomesticos          1400.66\n",
              "6          Esporte_e_Lazer          1123.14\n",
              "7                  Tablets          1100.23\n",
              "8                    Games           942.90\n",
              "9    Instrumentos_Musicais           834.61\n",
              "10              Automotivo           681.57\n",
              "11                  Moveis           541.03\n",
              "12         Ar_e_Ventilacao           518.79\n",
              "13                   Audio           480.35\n",
              "14       Casa_e_Construcao           433.53\n",
              "15                Pet_Shop           237.26\n",
              "16  Acessorios_e_Inovacoes           229.14\n",
              "17         Eletroportateis           223.77\n",
              "18             Ferramentas           217.59\n",
              "19      Cama,_Mesa_e_Banho           210.70\n",
              "20        Malas_e_Mochilas           194.26\n",
              "21                Relogios           186.95\n",
              "22              Brinquedos           176.45\n",
              "23   Utilidades_Domesticas           155.02\n",
              "24               Decoracao           144.50\n",
              "25                   Bebes           130.90\n",
              "26          Beleza_e_Saude           119.59\n",
              "27               Alimentos           104.24\n",
              "28              Perfumaria            80.91\n",
              "29     Produtos_de_Limpeza            68.79\n",
              "30                    Moda            47.01"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "agg_dept_preco = round(vendas_correto.groupby(\"Nome_Departamento\")[\"Preço_com_frete\"].agg(\"mean\").sort_values(ascending=False).reset_index(),2)\n",
        "agg_dept_preco"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792
        },
        "id": "Wuj6u6Dh1qkg",
        "outputId": "6d1e2389-6c92-4d55-ef4c-9baf8a9b714b"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 3000x2000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(30,20))\n",
        "\n",
        "plt.bar(agg_dept_preco[\"Nome_Departamento\"], agg_dept_preco[\"Preço_com_frete\"], color=\"lightblue\")\n",
        "\n",
        "plt.ylabel(\"Preço médio\")\n",
        "\n",
        "plt.title(\"Preço médio por departamento\", loc=\"center\", fontsize=18, fontweight=\"bold\", color=\"black\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "56_VRpk017sb"
      },
      "outputs": [],
      "source": [
        "%pip install plotly\n",
        "%pip install cufflinks\n",
        "%pip install chart-studio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "a00TQQMV2bD2"
      },
      "outputs": [],
      "source": [
        "import chart_studio.plotly as py\n",
        "import cufflinks as cf\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "loNx_dM43gBI",
        "outputId": "ce606d28-7b35-4433-c63f-1193bd6eb15e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "plotlyServerURL": "https://plot.ly"
              },
              "data": [
                {
                  "alignmentgroup": "True",
                  "hovertemplate": "bandeira=%{x}<br>idade=%{y}<extra></extra>",
                  "legendgroup": "",
                  "marker": {
                    "color": "#636efa",
                    "pattern": {
                      "shape": ""
                    }
                  },
                  "name": "",
                  "offsetgroup": "",
                  "orientation": "v",
                  "showlegend": false,
                  "textposition": "auto",
                  "type": "bar",
                  "x": [
                    "A",
                    "B"
                  ],
                  "xaxis": "x",
                  "y": [
                    53.3,
                    53.29
                  ],
                  "yaxis": "y"
                }
              ],
              "layout": {
                "barmode": "relative",
                "legend": {
                  "tracegroupgap": 0
                },
                "margin": {
                  "t": 60
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              "      <th>Nome_Departamento</th>\n",
              "      <th>Preço_com_frete</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TVs_e_Acessorios</td>\n",
              "      <td>3469.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Informatica</td>\n",
              "      <td>2159.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Linha_Industrial</td>\n",
              "      <td>2027.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cine_e_Foto</td>\n",
              "      <td>1851.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Telefones_e_Celulares</td>\n",
              "      <td>1442.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Eletrodomesticos</td>\n",
              "      <td>1400.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Esporte_e_Lazer</td>\n",
              "      <td>1123.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Tablets</td>\n",
              "      <td>1100.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Games</td>\n",
              "      <td>942.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Instrumentos_Musicais</td>\n",
              "      <td>834.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Automotivo</td>\n",
              "      <td>681.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Moveis</td>\n",
              "      <td>541.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Ar_e_Ventilacao</td>\n",
              "      <td>518.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Audio</td>\n",
              "      <td>480.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Casa_e_Construcao</td>\n",
              "      <td>433.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Pet_Shop</td>\n",
              "      <td>237.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Acessorios_e_Inovacoes</td>\n",
              "      <td>229.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Eletroportateis</td>\n",
              "      <td>223.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ferramentas</td>\n",
              "      <td>217.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Cama,_Mesa_e_Banho</td>\n",
              "      <td>210.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Malas_e_Mochilas</td>\n",
              "      <td>194.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Relogios</td>\n",
              "      <td>186.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Brinquedos</td>\n",
              "      <td>176.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Utilidades_Domesticas</td>\n",
              "      <td>155.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Decoracao</td>\n",
              "      <td>144.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Bebes</td>\n",
              "      <td>130.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Beleza_e_Saude</td>\n",
              "      <td>119.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Alimentos</td>\n",
              "      <td>104.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Perfumaria</td>\n",
              "      <td>80.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Produtos_de_Limpeza</td>\n",
              "      <td>68.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Moda</td>\n",
              "      <td>47.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
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              "         Nome_Departamento  Preço_com_frete\n",
              "0         TVs_e_Acessorios          3469.38\n",
              "1              Informatica          2159.78\n",
              "2         Linha_Industrial          2027.11\n",
              "3              Cine_e_Foto          1851.20\n",
              "4    Telefones_e_Celulares          1442.19\n",
              "5         Eletrodomesticos          1400.66\n",
              "6          Esporte_e_Lazer          1123.14\n",
              "7                  Tablets          1100.23\n",
              "8                    Games           942.90\n",
              "9    Instrumentos_Musicais           834.61\n",
              "10              Automotivo           681.57\n",
              "11                  Moveis           541.03\n",
              "12         Ar_e_Ventilacao           518.79\n",
              "13                   Audio           480.35\n",
              "14       Casa_e_Construcao           433.53\n",
              "15                Pet_Shop           237.26\n",
              "16  Acessorios_e_Inovacoes           229.14\n",
              "17         Eletroportateis           223.77\n",
              "18             Ferramentas           217.59\n",
              "19      Cama,_Mesa_e_Banho           210.70\n",
              "20        Malas_e_Mochilas           194.26\n",
              "21                Relogios           186.95\n",
              "22              Brinquedos           176.45\n",
              "23   Utilidades_Domesticas           155.02\n",
              "24               Decoracao           144.50\n",
              "25                   Bebes           130.90\n",
              "26          Beleza_e_Saude           119.59\n",
              "27               Alimentos           104.24\n",
              "28              Perfumaria            80.91\n",
              "29     Produtos_de_Limpeza            68.79\n",
              "30                    Moda            47.01"
            ]
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                    "colorway": [
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                      "#B6E880",
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                    ],
                    "font": {
                      "color": "#2a3f5f"
                    },
                    "geo": {
                      "bgcolor": "white",
                      "lakecolor": "white",
                      "landcolor": "#E5ECF6",
                      "showlakes": true,
                      "showland": true,
                      "subunitcolor": "white"
                    },
                    "hoverlabel": {
                      "align": "left"
                    },
                    "hovermode": "closest",
                    "mapbox": {
                      "style": "light"
                    },
                    "paper_bgcolor": "white",
                    "plot_bgcolor": "#E5ECF6",
                    "polar": {
                      "angularaxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      },
                      "bgcolor": "#E5ECF6",
                      "radialaxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      }
                    },
                    "scene": {
                      "xaxis": {
                        "backgroundcolor": "#E5ECF6",
                        "gridcolor": "white",
                        "gridwidth": 2,
                        "linecolor": "white",
                        "showbackground": true,
                        "ticks": "",
                        "zerolinecolor": "white"
                      },
                      "yaxis": {
                        "backgroundcolor": "#E5ECF6",
                        "gridcolor": "white",
                        "gridwidth": 2,
                        "linecolor": "white",
                        "showbackground": true,
                        "ticks": "",
                        "zerolinecolor": "white"
                      },
                      "zaxis": {
                        "backgroundcolor": "#E5ECF6",
                        "gridcolor": "white",
                        "gridwidth": 2,
                        "linecolor": "white",
                        "showbackground": true,
                        "ticks": "",
                        "zerolinecolor": "white"
                      }
                    },
                    "shapedefaults": {
                      "line": {
                        "color": "#2a3f5f"
                      }
                    },
                    "ternary": {
                      "aaxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      },
                      "baxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      },
                      "bgcolor": "#E5ECF6",
                      "caxis": {
                        "gridcolor": "white",
                        "linecolor": "white",
                        "ticks": ""
                      }
                    },
                    "title": {
                      "x": 0.05
                    },
                    "xaxis": {
                      "automargin": true,
                      "gridcolor": "white",
                      "linecolor": "white",
                      "ticks": "",
                      "title": {
                        "standoff": 15
                      },
                      "zerolinecolor": "white",
                      "zerolinewidth": 2
                    },
                    "yaxis": {
                      "automargin": true,
                      "gridcolor": "white",
                      "linecolor": "white",
                      "ticks": "",
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                    }
                  }
                },
                "xaxis": {
                  "anchor": "y",
                  "domain": [
                    0,
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                  "title": {
                    "text": "Nome_Departamento"
                  }
                },
                "yaxis": {
                  "anchor": "x",
                  "domain": [
                    0,
                    1
                  ],
                  "title": {
                    "text": "Preço_com_frete"
                  }
                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "px.bar(agg_dept_preco, x=\"Nome_Departamento\", y=\"Preço_com_frete\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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  },
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  "nbformat_minor": 0
}