Analise_exploratoria.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7415cfbb-f646-4df4-9cf4-8e2d6fc4246e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37ffc85-0e1b-43c9-b968-259470786e14",
   "metadata": {},
   "source": [
    "## Importando os arquivos do dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9ac104d-ac1d-4c54-a74e-283a711200c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"dataset/AdventureWorks.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccc528c-5e0b-4444-807e-7f6ebc8cb035",
   "metadata": {},
   "source": [
    "## Análise inicial da estrutura de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ffa0bced-40c7-4445-9b6f-c5c96dc0a4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Venda</th>\n",
       "      <th>Data Envio</th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>No. Venda</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "      <th>Produto</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Classe</th>\n",
       "      <th>Cor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-05-09</td>\n",
       "      <td>2008-05-29</td>\n",
       "      <td>199</td>\n",
       "      <td>384</td>\n",
       "      <td>18839</td>\n",
       "      <td>200805093CS607</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-05-12</td>\n",
       "      <td>2008-05-17</td>\n",
       "      <td>306</td>\n",
       "      <td>384</td>\n",
       "      <td>19051</td>\n",
       "      <td>200805123CS567</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-05-14</td>\n",
       "      <td>2008-05-20</td>\n",
       "      <td>306</td>\n",
       "      <td>384</td>\n",
       "      <td>19052</td>\n",
       "      <td>200805143CS576</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-05-21</td>\n",
       "      <td>2008-05-27</td>\n",
       "      <td>306</td>\n",
       "      <td>384</td>\n",
       "      <td>19052</td>\n",
       "      <td>200805213CS576</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-06-20</td>\n",
       "      <td>2008-06-27</td>\n",
       "      <td>306</td>\n",
       "      <td>384</td>\n",
       "      <td>19053</td>\n",
       "      <td>200806203CS586</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Venda Data Envio  ID Loja  ID Produto  ID Cliente       No. Venda  \\\n",
       "0 2008-05-09 2008-05-29      199         384       18839  200805093CS607   \n",
       "1 2008-05-12 2008-05-17      306         384       19051  200805123CS567   \n",
       "2 2008-05-14 2008-05-20      306         384       19052  200805143CS576   \n",
       "3 2008-05-21 2008-05-27      306         384       19052  200805213CS576   \n",
       "4 2008-06-20 2008-06-27      306         384       19053  200806203CS586   \n",
       "\n",
       "   Custo Unitário  Preço Unitário  Quantidade  Valor Desconto  Valor Venda  \\\n",
       "0          348.58           758.0           6             0.0       4548.0   \n",
       "1          348.58           758.0           6             0.0       4548.0   \n",
       "2          348.58           758.0           6             0.0       4548.0   \n",
       "3          348.58           758.0           6             0.0       4548.0   \n",
       "4          348.58           758.0           6             0.0       4548.0   \n",
       "\n",
       "                                 Produto       Fabricante            Marca  \\\n",
       "0  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "1  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "2  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "3  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "4  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "\n",
       "    Classe  Cor  \n",
       "0  Regular  Red  \n",
       "1  Regular  Red  \n",
       "2  Regular  Red  \n",
       "3  Regular  Red  \n",
       "4  Regular  Red  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ca743a1-3d5e-4f34-85be-d6cb7bf2108d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 904 entries, 0 to 903\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Data Venda      904 non-null    datetime64[ns]\n",
      " 1   Data Envio      904 non-null    datetime64[ns]\n",
      " 2   ID Loja         904 non-null    int64         \n",
      " 3   ID Produto      904 non-null    int64         \n",
      " 4   ID Cliente      904 non-null    int64         \n",
      " 5   No. Venda       904 non-null    object        \n",
      " 6   Custo Unitário  904 non-null    float64       \n",
      " 7   Preço Unitário  904 non-null    float64       \n",
      " 8   Quantidade      904 non-null    int64         \n",
      " 9   Valor Desconto  904 non-null    float64       \n",
      " 10  Valor Venda     904 non-null    float64       \n",
      " 11  Produto         904 non-null    object        \n",
      " 12  Fabricante      904 non-null    object        \n",
      " 13  Marca           904 non-null    object        \n",
      " 14  Classe          904 non-null    object        \n",
      " 15  Cor             904 non-null    object        \n",
      "dtypes: datetime64[ns](2), float64(4), int64(4), object(6)\n",
      "memory usage: 113.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c35b55d-275d-4f26-8a71-39b3201e68b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data Venda        False\n",
       "Data Envio        False\n",
       "ID Loja           False\n",
       "ID Produto        False\n",
       "ID Cliente        False\n",
       "No. Venda         False\n",
       "Custo Unitário    False\n",
       "Preço Unitário    False\n",
       "Quantidade        False\n",
       "Valor Desconto    False\n",
       "Valor Venda       False\n",
       "Produto           False\n",
       "Fabricante        False\n",
       "Marca             False\n",
       "Classe            False\n",
       "Cor               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d154b292-4fe7-43f2-915c-6ace1041c37e",
   "metadata": {},
   "source": [
    "## Análises exploratórias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d33cdec-c917-40e6-b362-d4543ad2c29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>904.000000</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>904.000000</td>\n",
       "      <td>904.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>270.015487</td>\n",
       "      <td>1225.238938</td>\n",
       "      <td>19029.092920</td>\n",
       "      <td>272.006150</td>\n",
       "      <td>742.527965</td>\n",
       "      <td>62.950221</td>\n",
       "      <td>689.123294</td>\n",
       "      <td>6620.139538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50.955280</td>\n",
       "      <td>706.698742</td>\n",
       "      <td>96.427655</td>\n",
       "      <td>129.765051</td>\n",
       "      <td>368.589889</td>\n",
       "      <td>148.945964</td>\n",
       "      <td>822.363117</td>\n",
       "      <td>4319.407257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>18815.000000</td>\n",
       "      <td>5.090000</td>\n",
       "      <td>9.990000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>159.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>18964.750000</td>\n",
       "      <td>324.690000</td>\n",
       "      <td>758.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>306.000000</td>\n",
       "      <td>1202.000000</td>\n",
       "      <td>19060.000000</td>\n",
       "      <td>330.660000</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>461.166000</td>\n",
       "      <td>5798.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>307.000000</td>\n",
       "      <td>1226.000000</td>\n",
       "      <td>19107.000000</td>\n",
       "      <td>348.580000</td>\n",
       "      <td>998.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1005.075000</td>\n",
       "      <td>7986.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>307.000000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>19145.000000</td>\n",
       "      <td>348.580000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>6272.000000</td>\n",
       "      <td>29400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID Loja   ID Produto    ID Cliente  Custo Unitário  Preço Unitário  \\\n",
       "count  904.000000   904.000000    904.000000      904.000000      904.000000   \n",
       "mean   270.015487  1225.238938  19029.092920      272.006150      742.527965   \n",
       "std     50.955280   706.698742     96.427655      129.765051      368.589889   \n",
       "min    199.000000   366.000000  18815.000000        5.090000        9.990000   \n",
       "25%    199.000000   384.000000  18964.750000      324.690000      758.000000   \n",
       "50%    306.000000  1202.000000  19060.000000      330.660000      980.000000   \n",
       "75%    307.000000  1226.000000  19107.000000      348.580000      998.000000   \n",
       "max    307.000000  2497.000000  19145.000000      348.580000      999.000000   \n",
       "\n",
       "        Quantidade  Valor Desconto   Valor Venda  \n",
       "count   904.000000      904.000000    904.000000  \n",
       "mean     62.950221      689.123294   6620.139538  \n",
       "std     148.945964      822.363117   4319.407257  \n",
       "min       2.000000        0.000000    159.840000  \n",
       "25%       6.000000        0.000000   3920.000000  \n",
       "50%       9.000000      461.166000   5798.700000  \n",
       "75%      17.000000     1005.075000   7986.000000  \n",
       "max    1560.000000     6272.000000  29400.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d3a31dd-7839-4a81-a51e-17f00c2701cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Venda</th>\n",
       "      <th>Data Envio</th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>No. Venda</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "      <th>Produto</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Classe</th>\n",
       "      <th>Cor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-05-09</td>\n",
       "      <td>2008-05-29</td>\n",
       "      <td>199</td>\n",
       "      <td>384</td>\n",
       "      <td>18839</td>\n",
       "      <td>200805093CS607</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Venda Data Envio  ID Loja  ID Produto  ID Cliente       No. Venda  \\\n",
       "0 2008-05-09 2008-05-29      199         384       18839  200805093CS607   \n",
       "\n",
       "   Custo Unitário  Preço Unitário  Quantidade  Valor Desconto  Valor Venda  \\\n",
       "0          348.58           758.0           6             0.0       4548.0   \n",
       "\n",
       "                                 Produto       Fabricante            Marca  \\\n",
       "0  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "\n",
       "    Classe  Cor  \n",
       "0  Regular  Red  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc987b3-6b63-49c1-baae-eeae5550894e",
   "metadata": {},
   "source": [
    "### Qual a receita total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6f3c58ea-d182-49d3-b3b7-e79fa57e5800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5984606.1426"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Valor Venda'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ee42c2-4d6d-4494-9e0f-2e03187fc5d6",
   "metadata": {},
   "source": [
    "### Qual o custo total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a7e38d1b-640f-4adc-ab04-17aec9db9ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2486783.05"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sem modificar o dataset\n",
    "(df['Custo Unitário'] * df['Quantidade']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "41db56bd-9075-42d8-8618-d91564035664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>Data Venda</th>\n",
       "      <th>Data Envio</th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>No. Venda</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "      <th>Produto</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Classe</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Custo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-05-09</td>\n",
       "      <td>2008-05-29</td>\n",
       "      <td>199</td>\n",
       "      <td>384</td>\n",
       "      <td>18839</td>\n",
       "      <td>200805093CS607</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "      <td>2091.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Venda Data Envio  ID Loja  ID Produto  ID Cliente       No. Venda  \\\n",
       "0 2008-05-09 2008-05-29      199         384       18839  200805093CS607   \n",
       "\n",
       "   Custo Unitário  Preço Unitário  Quantidade  Valor Desconto  Valor Venda  \\\n",
       "0          348.58           758.0           6             0.0       4548.0   \n",
       "\n",
       "                                 Produto       Fabricante            Marca  \\\n",
       "0  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "\n",
       "    Classe  Cor    Custo  \n",
       "0  Regular  Red  2091.48  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicionando coluna custo\n",
    "\n",
    "df['Custo'] = df['Custo Unitário'] * df['Quantidade']\n",
    "\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2ced9ef9-b50f-464c-981b-9506f188afaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2486783.05"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Custo'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a678f2-421b-4917-b26e-7ae15ec3b985",
   "metadata": {},
   "source": [
    "### Lucro das operações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61f2c152-4e76-4a77-acf3-96ff0eea83c4",
   "metadata": {},
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Venda</th>\n",
       "      <th>Data Envio</th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>No. Venda</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "      <th>Produto</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Classe</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Custo</th>\n",
       "      <th>Lucro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-05-09</td>\n",
       "      <td>2008-05-29</td>\n",
       "      <td>199</td>\n",
       "      <td>384</td>\n",
       "      <td>18839</td>\n",
       "      <td>200805093CS607</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "      <td>2091.48</td>\n",
       "      <td>2456.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Venda Data Envio  ID Loja  ID Produto  ID Cliente       No. Venda  \\\n",
       "0 2008-05-09 2008-05-29      199         384       18839  200805093CS607   \n",
       "\n",
       "   Custo Unitário  Preço Unitário  Quantidade  Valor Desconto  Valor Venda  \\\n",
       "0          348.58           758.0           6             0.0       4548.0   \n",
       "\n",
       "                                 Produto       Fabricante            Marca  \\\n",
       "0  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "\n",
       "    Classe  Cor    Custo    Lucro  \n",
       "0  Regular  Red  2091.48  2456.52  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Lucro'] = df['Valor Venda'] - df['Custo']\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f78203b4-2139-485d-90f3-45799a8db488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3497823.09"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df['Lucro'].sum(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4c4328-6b80-4e95-8086-09d9910209f8",
   "metadata": {},
   "source": [
    "### Tempo de envio dos produtos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4a7a0501-648f-443a-85cd-134370032ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Venda</th>\n",
       "      <th>Data Envio</th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>No. Venda</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "      <th>Produto</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Classe</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Custo</th>\n",
       "      <th>Lucro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-05-09</td>\n",
       "      <td>2008-05-29</td>\n",
       "      <td>199</td>\n",
       "      <td>384</td>\n",
       "      <td>18839</td>\n",
       "      <td>200805093CS607</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "      <td>2091.48</td>\n",
       "      <td>2456.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Venda Data Envio  ID Loja  ID Produto  ID Cliente       No. Venda  \\\n",
       "0 2008-05-09 2008-05-29      199         384       18839  200805093CS607   \n",
       "\n",
       "   Custo Unitário  Preço Unitário  Quantidade  Valor Desconto  Valor Venda  \\\n",
       "0          348.58           758.0           6             0.0       4548.0   \n",
       "\n",
       "                                 Produto       Fabricante            Marca  \\\n",
       "0  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "\n",
       "    Classe  Cor    Custo    Lucro  \n",
       "0  Regular  Red  2091.48  2456.52  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c48db45d-7930-4350-a026-e45de76aa87c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Tempo_envio'] = df['Data Envio'] - df['Data Venda']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6dbe005f-f772-4ef5-b44a-e52a3cf133f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Venda</th>\n",
       "      <th>Data Envio</th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>No. Venda</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "      <th>Produto</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Classe</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Custo</th>\n",
       "      <th>Lucro</th>\n",
       "      <th>Tempo_envio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-05-09</td>\n",
       "      <td>2008-05-29</td>\n",
       "      <td>199</td>\n",
       "      <td>384</td>\n",
       "      <td>18839</td>\n",
       "      <td>200805093CS607</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4548.0</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "      <td>2091.48</td>\n",
       "      <td>2456.52</td>\n",
       "      <td>20 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Venda Data Envio  ID Loja  ID Produto  ID Cliente       No. Venda  \\\n",
       "0 2008-05-09 2008-05-29      199         384       18839  200805093CS607   \n",
       "\n",
       "   Custo Unitário  Preço Unitário  Quantidade  Valor Desconto  Valor Venda  \\\n",
       "0          348.58           758.0           6             0.0       4548.0   \n",
       "\n",
       "                                 Produto       Fabricante            Marca  \\\n",
       "0  Adventure Works Laptop15.4W M1548 Red  Adventure Works  Adventure Works   \n",
       "\n",
       "    Classe  Cor    Custo    Lucro Tempo_envio  \n",
       "0  Regular  Red  2091.48  2456.52     20 days  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "46a50ec7-b818-4835-aaaa-e6dd0cfde1e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Tempo_envio'] = (df['Data Envio'] - df['Data Venda']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8a6cf8f1-d6a7-4d0f-9b69-005f1759bc47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data Venda        datetime64[ns]\n",
       "Data Envio        datetime64[ns]\n",
       "ID Loja                    int64\n",
       "ID Produto                 int64\n",
       "ID Cliente                 int64\n",
       "No. Venda                 object\n",
       "Custo Unitário           float64\n",
       "Preço Unitário           float64\n",
       "Quantidade                 int64\n",
       "Valor Desconto           float64\n",
       "Valor Venda              float64\n",
       "Produto                   object\n",
       "Fabricante                object\n",
       "Marca                     object\n",
       "Classe                    object\n",
       "Cor                       object\n",
       "Custo                    float64\n",
       "Lucro                    float64\n",
       "Tempo_envio                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ab8818c4-8054-4d95-8748-ba9b8a23cd43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fabricante\n",
       "Adventure Works    8.663866\n",
       "Contoso, Ltd       8.470930\n",
       "Fabrikam, Inc.     8.510121\n",
       "Name: Tempo_envio, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analisando o tempo médio de envio por fabricante\n",
    "df.groupby('Fabricante')['Tempo_envio'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4467aae-6f01-4245-8b77-88901bb6ad71",
   "metadata": {},
   "source": [
    "### Lucro das marcas por ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "71489aad-33ab-4dbd-b83f-e0a7c011606a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data Venda  Marca          \n",
       "2008        Adventure Works    3.066412e+05\n",
       "            Contoso            5.641600e+04\n",
       "            Fabrikam           1.557021e+06\n",
       "2009        Adventure Works    4.053951e+05\n",
       "            Contoso            1.382590e+05\n",
       "            Fabrikam           1.034091e+06\n",
       "Name: Lucro, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([df['Data Venda'].dt.year, \"Marca\"])['Lucro'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b51ed37e-6d60-45d6-aab9-454ea07dd5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configurando formatação\n",
    "pd.options.display.float_format = '{:20,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "24c1a8a0-bb88-4f88-af48-564ab7944d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data Venda  Marca          \n",
       "2008        Adventure Works             306,641.16\n",
       "            Contoso                      56,416.00\n",
       "            Fabrikam                  1,557,020.55\n",
       "2009        Adventure Works             405,395.08\n",
       "            Contoso                     138,258.95\n",
       "            Fabrikam                  1,034,091.35\n",
       "Name: Lucro, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([df['Data Venda'].dt.year, \"Marca\"])['Lucro'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b9c81dc0-f70c-4b40-99bf-62c27a8c7b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Venda</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Lucro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>306,641.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>56,416.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>Fabrikam</td>\n",
       "      <td>1,557,020.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>405,395.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>138,258.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009</td>\n",
       "      <td>Fabrikam</td>\n",
       "      <td>1,034,091.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data Venda            Marca                Lucro\n",
       "0        2008  Adventure Works           306,641.16\n",
       "1        2008          Contoso            56,416.00\n",
       "2        2008         Fabrikam         1,557,020.55\n",
       "3        2009  Adventure Works           405,395.08\n",
       "4        2009          Contoso           138,258.95\n",
       "5        2009         Fabrikam         1,034,091.35"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([df['Data Venda'].dt.year, \"Marca\"])['Lucro'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "656aeb50-c91e-4b43-8223-86c3b21a37a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lucro_ano = df.groupby([df['Data Venda'].dt.year, \"Marca\"])['Lucro'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f67fbf8-c640-438f-af62-0002c0ac3e8f",
   "metadata": {},
   "source": [
    "### Total de produtos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "361eaef3-295f-4ca8-82ba-a1ded7d6f76f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Produto\n",
       "Headphone Adapter for Contoso Phone E130 Silver    25232\n",
       "Headphone Adapter for Contoso Phone E130 White     25008\n",
       "Adventure Works Laptop15.4W M1548 Black             1089\n",
       "Fabrikam Trendsetter 2/3'' 17mm X100 Grey           1087\n",
       "Adventure Works Laptop15.4W M1548 Red               1047\n",
       "Fabrikam Trendsetter 2/3'' 17mm X100 Black           926\n",
       "Fabrikam Trendsetter 1/3'' 8.5mm X200 Black          884\n",
       "Fabrikam Trendsetter 1/3'' 8.5mm X200 Grey           845\n",
       "Fabrikam Trendsetter 1/3'' 8.5mm X200 White          789\n",
       "Name: Quantidade, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Produto\")['Quantidade'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff45995-7579-4e55-b8fa-290b3e36997e",
   "metadata": {},
   "source": [
    "## Análise Gráfica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75205952-b6e1-40ec-9e7c-ac81f171f8e5",
   "metadata": {},
   "source": [
    "### Número de produtos vendidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "20d8abc0-935c-4c73-a44e-14f9c51e79c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"Produto\")['Quantidade'].sum().sort_values(ascending=False).plot.bar();\n",
    "plt.xlabel('Produto');\n",
    "plt.ylabel('Unidades Vendidas');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3baa50ec-c476-4043-a14a-7e54beff7f02",
   "metadata": {},
   "source": [
    "### Lucro por ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3277427c-61d8-442b-beb4-fd09abd9d222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(df[\"Data Venda\"].dt.year)['Lucro'].sum().sort_values(ascending=False).plot.bar(title = 'Lucro por ano');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e049ff-a9ee-4b8d-8098-464b9b293781",
   "metadata": {},
   "source": [
    "### Vendas de 2009"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f8fca26b-368f-4e96-ad85-6773140163f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Venda</th>\n",
       "      <th>Data Envio</th>\n",
       "      <th>ID Loja</th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>ID Cliente</th>\n",
       "      <th>No. Venda</th>\n",
       "      <th>Custo Unitário</th>\n",
       "      <th>Preço Unitário</th>\n",
       "      <th>Quantidade</th>\n",
       "      <th>Valor Desconto</th>\n",
       "      <th>Valor Venda</th>\n",
       "      <th>Produto</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Marca</th>\n",
       "      <th>Classe</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Custo</th>\n",
       "      <th>Lucro</th>\n",
       "      <th>Tempo_envio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2009-05-02</td>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>199</td>\n",
       "      <td>384</td>\n",
       "      <td>18938</td>\n",
       "      <td>200905023CS847</td>\n",
       "      <td>348.58</td>\n",
       "      <td>758.00</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,548.00</td>\n",
       "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Adventure Works</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Red</td>\n",
       "      <td>2,091.48</td>\n",
       "      <td>2,456.52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data Venda Data Envio  ID Loja  ID Produto  ID Cliente       No. Venda  \\\n",
       "11 2009-05-02 2009-05-14      199         384       18938  200905023CS847   \n",
       "\n",
       "         Custo Unitário       Preço Unitário  Quantidade       Valor Desconto  \\\n",
       "11               348.58               758.00           6                 0.00   \n",
       "\n",
       "            Valor Venda                                Produto  \\\n",
       "11             4,548.00  Adventure Works Laptop15.4W M1548 Red   \n",
       "\n",
       "         Fabricante            Marca   Classe  Cor                Custo  \\\n",
       "11  Adventure Works  Adventure Works  Regular  Red             2,091.48   \n",
       "\n",
       "                  Lucro  Tempo_envio  \n",
       "11             2,456.52           12  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2009 = df[df['Data Venda'].dt.year == 2009]\n",
    "df_2009.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "42a6b947-e026-4028-a0be-a3aa9e4d8372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Lucro')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2009.groupby(df_2009['Data Venda'].dt.month)['Lucro'].sum().plot(title = 'Lucro por mês');\n",
    "plt.xlabel(\"Mês em 2009\")\n",
    "plt.ylabel(\"Lucro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8ba7eaf5-913f-4928-834a-27654bc4ff65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2009.groupby('Marca')['Lucro'].sum().plot.bar(title = \"Lucro por marca em 2009\")\n",
    "plt.xticks(rotation='horizontal');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cab877ef-123c-4140-987d-dd237cf2b39a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lambkQlJTU7VgwQKlpqY6ph3HjBmjxMRELViwQFOmTNHBgwd15MgRLVu2TO+++64KCgo0evRo3X333frqq68MHwGupvnz5ysxMbFI+8iRIzVw4ECNHTtWd911l5566inHraDBwcFOf/ibNWum8ePH64UXXlDnzp31l7/8RXa7XZs2bVK9evUUHx9/wRrGjRunDz74QFFRUXrqqadUo0YNLVq0SIcOHdJHH31U5KLY4oSEhCgyMtLp1l7pt1uOz3vxxRe1Zs0aderUSU888YQ8PDz05ptvKi8vT6+88kpp37IiBg8erHfffVcxMTHauHGjOnfurNzcXH355Zd64okndOeddxY77vbbb9d7770nX19ftWrVSsnJyfryyy9Vs2ZNp36tWrXSLbfconbt2qlGjRravHmzli9frhEjRkj6bQbo1ltv1YABA9SqVSt5eHjok08+UUZGhuPZGD4+PpozZ44eeOAB3XDDDRo4cKBq166t1NRUJSQk6Oabby42TJ33zDPP6LPPPtPtt9+uBx98UO3atVNubq527Nih5cuX6/Dhw0632F5p9913nzZu3KiHHnpIu3fvdnq2SNWqVdW3b1/H65deekk33XSTunbtqkceeUQ//PCDpk2bpp49e6pXr16OfuHh4erfv79iY2N19OhRNWvWTIsWLdLhw4f1zjvvOO1/wIABqlevnlq1aqXs7GzNnz9fBw8eVEJCQpFTRcAVZfRenssgyfrkk08crz///HNLklWlShWnxcPDwxowYIBlWZY1bNgwS5K1d+9ex7gtW7ZYkqw9e/aU9SGgDJy/fbWkJS0tzbIsy1q9erUVEhJieXp6Wi1atLAWL15c5Nbe8+bPn2+FhYVZdrvd8vPzs7p27WqtWbPGsb5Ro0Yl3qKakpJi3X333Vb16tUtLy8vq0OHDtbnn39eqmORZA0fPtxavHix1bx5c8tut1thYWHW2rVri/TdunWrFRkZaVWtWtXy9va2unXrZm3YsKHY98aVp9OeOnXKGj9+vNW4cWOrUqVKVkBAgHX33Xc73UasP9za++uvv1rR0dFWrVq1rKpVq1qRkZHWnj17rEaNGllDhgxx9HvxxRetDh06WNWrV7cqV65sXXfdddZLL71k5efnW5ZlWcePH7eGDx9uXXfddVaVKlUsX19fKzw83Prwww+L1Ll27VorMjLS8vX1tby8vKymTZtaDz74oNNt0SU5efKkFRsbazVr1szy9PS0atWqZd10003Wa6+95qjl/K29f7zNeO3atZYka9myZU7tpX2vz98WXtxS3O3n//rXv6ybbrrJ8vLysmrXrm0NHz7cys7OLtLv9OnT1pgxY6yAgADLbrdbN954Y5Fbzy3LsqZOnWpdd911lpeXl+Xn52fdcccd1rZt2y7yjgGXz2ZZpbyi6k/GZrPpk08+cXxTWLp0qe677z7t2rWryMVjVatWVUBAgCZOnFhk+vb06dPy9vbW6tWrHQ9WAv6MbDabhg8ffsFv9gBQHlWY0zRhYWEqKCjQ0aNH1blz52L73HzzzTp37pxSUlIcF7rt27dPUukuHgQAAFdeuQojOTk5OnDggOP1oUOHtH37dtWoUUPBwcG67777NHjwYE2bNk1hYWE6duyYkpKS1KZNG/Xu3VsRERG64YYb9NBDD2nGjBkqLCzU8OHD1aNHjyK37AEAgLJRru6m2bx5s8LCwhQWFiZJiomJUVhYmOLi4iRJCxYs0ODBg/X000+rRYsW6tu3rzZt2uR4MJGbm5v++c9/qlatWurSpYt69+6tli1basmSJcaOCQCAa125vWYEAABUDOVqZgQAAFQ8hBEAAGBUubiAtbCwUD/99JOqVat2wV8HBQAAfx6WZenkyZOqV6/eBR/sWC7CyE8//XTJv6cBAADMSktLU4MGDUpcXy7CyPnHEKelpV3y72oAAICylZ2drcDAwIv+nEC5CCPnT834+PgQRgAAKGcudokFF7ACAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADDKw3QBAHCtChqXYLoElKHDL/c2XcKfFjMjAADAKMIIAAAwijACAACMIowAAACjXA4j69atU58+fVSvXj3ZbDatWLGi1GPXr18vDw8PtW3b1tXdAgCACsrlMJKbm6vQ0FDNmjXLpXGZmZkaPHiwbr31Vld3CQAAKjCXb+2NiopSVFSUyzt67LHHdO+998rd3d2l2RQAAFCxlck1IwsWLNDBgwc1ceLEUvXPy8tTdna20wIAACqmqx5G9u/fr3Hjxmnx4sXy8CjdREx8fLx8fX0dS2Bg4FWuEgAAmHJVw0hBQYHuvfdeTZ48WcHBwaUeFxsbq6ysLMeSlpZ2FasEAAAmXdXHwZ88eVKbN2/Wtm3bNGLECElSYWGhLMuSh4eHVq9ere7duxcZZ7fbZbfbr2ZpAADgT+KqhhEfHx/t2LHDqW327Nn66quvtHz5cjVu3Phq7h4AAJQDLoeRnJwcHThwwPH60KFD2r59u2rUqKGGDRsqNjZWP/74o9599125ubkpJCTEaXydOnXk5eVVpB0AAFybXA4jmzdvVrdu3RyvY2JiJElDhgzRwoUL9fPPPys1NfXKVQgAACo0m2VZlukiLiY7O1u+vr7KysqSj4+P6XIA4IoIGpdgugSUocMv9zZdQpkr7d9vfpsGAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGuRxG1q1bpz59+qhevXqy2WxasWLFBft//PHH6tGjh2rXri0fHx917NhRq1atutR6AQBABeNyGMnNzVVoaKhmzZpVqv7r1q1Tjx49tHLlSm3ZskXdunVTnz59tG3bNpeLBQAAFY+HqwOioqIUFRVV6v4zZsxwej1lyhR9+umn+uc//6mwsDBXdw8AACoYl8PI5SosLNTJkydVo0aNEvvk5eUpLy/P8To7O7ssSgMAAAaU+QWsr732mnJycjRgwIAS+8THx8vX19exBAYGlmGFAACgLJVpGHn//fc1efJkffjhh6pTp06J/WJjY5WVleVY0tLSyrBKAABQlsrsNM2SJUs0dOhQLVu2TBERERfsa7fbZbfby6gyAABgUpnMjHzwwQeKjo7WBx98oN69e5fFLgEAQDnh8sxITk6ODhw44Hh96NAhbd++XTVq1FDDhg0VGxurH3/8Ue+++66k307NDBkyRH/7298UHh6u9PR0SVLlypXl6+t7hQ4DAACUVy7PjGzevFlhYWGO23JjYmIUFhamuLg4SdLPP/+s1NRUR/+33npL586d0/Dhw1W3bl3HMnLkyCt0CAAAoDxzeWbklltukWVZJa5fuHCh0+uvv/7a1V0AAIBrCL9NAwAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAo1wOI+vWrVOfPn1Ur1492Ww2rVix4qJjvv76a91www2y2+1q1qyZFi5ceAmlAgCAisjlMJKbm6vQ0FDNmjWrVP0PHTqk3r17q1u3btq+fbtGjRqloUOHatWqVS4XCwAAKh4PVwdERUUpKiqq1P3nzp2rxo0ba9q0aZKkli1b6ttvv9Xrr7+uyMhIV3cPAAAqmKt+zUhycrIiIiKc2iIjI5WcnFzimLy8PGVnZzstAACgYrrqYSQ9PV3+/v5Obf7+/srOztbp06eLHRMfHy9fX1/HEhgYeLXLBAAAhvwp76aJjY1VVlaWY0lLSzNdEgAAuEpcvmbEVQEBAcrIyHBqy8jIkI+PjypXrlzsGLvdLrvdfrVLAwAAfwJXfWakY8eOSkpKcmpbs2aNOnbseLV3DQAAygGXw0hOTo62b9+u7du3S/rt1t3t27crNTVV0m+nWAYPHuzo/9hjj+ngwYN69tlntWfPHs2ePVsffvihRo8efWWOAAAAlGsuh5HNmzcrLCxMYWFhkqSYmBiFhYUpLi5OkvTzzz87gokkNW7cWAkJCVqzZo1CQ0M1bdo0vf3229zWCwAAJEk2y7Is00VcTHZ2tnx9fZWVlSUfHx/T5QDAFRE0LsF0CShDh1/ubbqEMlfav99/yrtpAADAtYMwAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjLqkMDJr1iwFBQXJy8tL4eHh2rhx4wX7z5gxQy1atFDlypUVGBio0aNH68yZM5dUMAAAqFhcDiNLly5VTEyMJk6cqK1btyo0NFSRkZE6evRosf3ff/99jRs3ThMnTtTu3bv1zjvvaOnSpfrrX/962cUDAIDyz+UwMn36dA0bNkzR0dFq1aqV5s6dK29vb82fP7/Y/hs2bNDNN9+se++9V0FBQerZs6cGDRp00dkUAABwbXApjOTn52vLli2KiIj43wbc3BQREaHk5ORix9x0003asmWLI3wcPHhQK1eu1G233VbifvLy8pSdne20AACAisnDlc7Hjx9XQUGB/P39ndr9/f21Z8+eYsfce++9On78uDp16iTLsnTu3Dk99thjFzxNEx8fr8mTJ7tSGgAAKKeu+t00X3/9taZMmaLZs2dr69at+vjjj5WQkKAXXnihxDGxsbHKyspyLGlpaVe7TAAAYIhLMyO1atWSu7u7MjIynNozMjIUEBBQ7JgJEybogQce0NChQyVJrVu3Vm5urh555BGNHz9ebm5F85DdbpfdbnelNAAAUE65NDPi6empdu3aKSkpydFWWFiopKQkdezYsdgxp06dKhI43N3dJUmWZblaLwAAqGBcmhmRpJiYGA0ZMkTt27dXhw4dNGPGDOXm5io6OlqSNHjwYNWvX1/x8fGSpD59+mj69OkKCwtTeHi4Dhw4oAkTJqhPnz6OUAIAAK5dLoeRe+65R8eOHVNcXJzS09PVtm1bJSYmOi5qTU1NdZoJee6552Sz2fTcc8/pxx9/VO3atdWnTx+99NJLV+4oAABAuWWzysG5kuzsbPn6+iorK0s+Pj6mywGAKyJoXILpElCGDr/c23QJZa60f7/5bRoAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABh1SWFk1qxZCgoKkpeXl8LDw7Vx48YL9s/MzNTw4cNVt25d2e12BQcHa+XKlZdUMAAAqFg8XB2wdOlSxcTEaO7cuQoPD9eMGTMUGRmpvXv3qk6dOkX65+fnq0ePHqpTp46WL1+u+vXr68iRI6pevfqVqB8AAJRzLoeR6dOna9iwYYqOjpYkzZ07VwkJCZo/f77GjRtXpP/8+fN14sQJbdiwQZUqVZIkBQUFXV7VAACgwnDpNE1+fr62bNmiiIiI/23AzU0RERFKTk4udsxnn32mjh07avjw4fL391dISIimTJmigoKCEveTl5en7OxspwUAAFRMLoWR48ePq6CgQP7+/k7t/v7+Sk9PL3bMwYMHtXz5chUUFGjlypWaMGGCpk2bphdffLHE/cTHx8vX19exBAYGulImAAAoR6763TSFhYWqU6eO3nrrLbVr10733HOPxo8fr7lz55Y4JjY2VllZWY4lLS3tapcJAAAMcemakVq1asnd3V0ZGRlO7RkZGQoICCh2TN26dVWpUiW5u7s72lq2bKn09HTl5+fL09OzyBi73S673e5KaQAAoJxyaWbE09NT7dq1U1JSkqOtsLBQSUlJ6tixY7Fjbr75Zh04cECFhYWOtn379qlu3brFBhEAAHBtcfk0TUxMjObNm6dFixZp9+7devzxx5Wbm+u4u2bw4MGKjY119H/88cd14sQJjRw5Uvv27VNCQoKmTJmi4cOHX7mjAAAA5ZbLt/bec889OnbsmOLi4pSenq62bdsqMTHRcVFramqq3Nz+l3ECAwO1atUqjR49Wm3atFH9+vU1cuRIjR079sodBQAAKLdslmVZpou4mOzsbPn6+iorK0s+Pj6mywGAKyJoXILpElCGDr/c23QJZa60f7/5bRoAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABh1SWFk1qxZCgoKkpeXl8LDw7Vx48ZSjVuyZIlsNpv69u17KbsFAAAVkMthZOnSpYqJidHEiRO1detWhYaGKjIyUkePHr3guMOHD2vMmDHq3LnzJRcLAAAqHpfDyPTp0zVs2DBFR0erVatWmjt3rry9vTV//vwSxxQUFOi+++7T5MmT1aRJk8sqGAAAVCwuhZH8/Hxt2bJFERER/9uAm5siIiKUnJxc4rjnn39ederU0cMPP1yq/eTl5Sk7O9tpAQAAFZNLYeT48eMqKCiQv7+/U7u/v7/S09OLHfPtt9/qnXfe0bx580q9n/j4ePn6+jqWwMBAV8oEAADlyFW9m+bkyZN64IEHNG/ePNWqVavU42JjY5WVleVY0tLSrmKVAADAJA9XOteqVUvu7u7KyMhwas/IyFBAQECR/ikpKTp8+LD69OnjaCssLPxtxx4e2rt3r5o2bVpknN1ul91ud6U0AABQTrk0M+Lp6al27dopKSnJ0VZYWKikpCR17NixSP/rrrtOO3bs0Pbt2x3LHXfcoW7dumn79u2cfgEAAK7NjEhSTEyMhgwZovbt26tDhw6aMWOGcnNzFR0dLUkaPHiw6tevr/j4eHl5eSkkJMRpfPXq1SWpSDsAALg2uRxG7rnnHh07dkxxcXFKT09X27ZtlZiY6LioNTU1VW5uPNgVAACUjs2yLMt0EReTnZ0tX19fZWVlycfHx3Q5AHBFBI1LMF0CytDhl3ubLqHMlfbvN1MYAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAw6pLCyKxZsxQUFCQvLy+Fh4dr48aNJfadN2+eOnfuLD8/P/n5+SkiIuKC/QEAwLXF5TCydOlSxcTEaOLEidq6datCQ0MVGRmpo0ePFtv/66+/1qBBg7R27VolJycrMDBQPXv21I8//njZxQMAgPLPZlmW5cqA8PBw3XjjjZo5c6YkqbCwUIGBgXryySc1bty4i44vKCiQn5+fZs6cqcGDB5dqn9nZ2fL19VVWVpZ8fHxcKRcA/rSCxiWYLgFl6PDLvU2XUOZK+/fbpZmR/Px8bdmyRREREf/bgJubIiIilJycXKptnDp1SmfPnlWNGjVK7JOXl6fs7GynBQAAVEwuhZHjx4+roKBA/v7+Tu3+/v5KT08v1TbGjh2revXqOQWaP4qPj5evr69jCQwMdKVMAABQjpTp3TQvv/yylixZok8++UReXl4l9ouNjVVWVpZjSUtLK8MqAQBAWfJwpXOtWrXk7u6ujIwMp/aMjAwFBARccOxrr72ml19+WV9++aXatGlzwb52u112u92V0gAAQDnl0syIp6en2rVrp6SkJEdbYWGhkpKS1LFjxxLHvfLKK3rhhReUmJio9u3bX3q1AACgwnFpZkSSYmJiNGTIELVv314dOnTQjBkzlJubq+joaEnS4MGDVb9+fcXHx0uSpk6dqri4OL3//vsKCgpyXFtStWpVVa1a9QoeCgAAKI9cDiP33HOPjh07pri4OKWnp6tt27ZKTEx0XNSampoqN7f/TbjMmTNH+fn5uvvuu522M3HiRE2aNOnyqgcAAOWey88ZMYHnjACoiHjOyLWF54xcoeeMAAAAXGmEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGufwEVpQtHop0bbkWH4oEAMyMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMuqQwMmvWLAUFBcnLy0vh4eHauHHjBfsvW7ZM1113nby8vNS6dWutXLnykooFAAAVj8thZOnSpYqJidHEiRO1detWhYaGKjIyUkePHi22/4YNGzRo0CA9/PDD2rZtm/r27au+fftq586dl108AAAo/1wOI9OnT9ewYcMUHR2tVq1aae7cufL29tb8+fOL7f+3v/1NvXr10jPPPKOWLVvqhRde0A033KCZM2dedvEAAKD8cymM5Ofna8uWLYqIiPjfBtzcFBERoeTk5GLHJCcnO/WXpMjIyBL7AwCAa4uHK52PHz+ugoIC+fv7O7X7+/trz549xY5JT08vtn96enqJ+8nLy1NeXp7jdVZWliQpOzvblXIrhMK8U6ZLQBm6Fv8/fi3j831tuRY/3+eP2bKsC/ZzKYyUlfj4eE2ePLlIe2BgoIFqgLLjO8N0BQCulmv5833y5En5+vqWuN6lMFKrVi25u7srIyPDqT0jI0MBAQHFjgkICHCpvyTFxsYqJibG8bqwsFAnTpxQzZo1ZbPZXCkZ5VB2drYCAwOVlpYmHx8f0+UAuIL4fF9bLMvSyZMnVa9evQv2cymMeHp6ql27dkpKSlLfvn0l/RYUkpKSNGLEiGLHdOzYUUlJSRo1apSjbc2aNerYsWOJ+7Hb7bLb7U5t1atXd6VUVAA+Pj78YwVUUHy+rx0XmhE5z+XTNDExMRoyZIjat2+vDh06aMaMGcrNzVV0dLQkafDgwapfv77i4+MlSSNHjlTXrl01bdo09e7dW0uWLNHmzZv11ltvubprAABQAbkcRu655x4dO3ZMcXFxSk9PV9u2bZWYmOi4SDU1NVVubv+7Seemm27S+++/r+eee05//etf1bx5c61YsUIhISFX7igAAEC5ZbMudokrUMby8vIUHx+v2NjYIqfrAJRvfL5RHMIIAAAwih/KAwAARhFGAACAUYQRAABgFGEEAPCn8uCDDzqeZYVrA2EEJXrwwQdls9mKLL169TJdGoCr4Pef+UqVKqlx48Z69tlndebMGdOloYL7U/42Df48evXqpQULFji1cTseUHGd/8yfPXtWW7Zs0ZAhQ2Sz2TR16lTTpbnk7NmzqlSpkukyUErMjOCC7Ha7AgICnBY/Pz9JUmZmph599FH5+/vLy8tLISEh+vzzzx1jP/roI11//fWy2+0KCgrStGnTnLYdFBSkKVOm6KGHHlK1atXUsGHDIk/m3bFjh7p3767KlSurZs2aeuSRR5STk+NYf346d8qUKfL391f16tX1/PPP69y5c3rmmWdUo0YNNWjQwClQde/evcjPFxw7dkyenp5KSkq6Yu8dUB6d/8wHBgaqb9++ioiI0Jo1ayT99vMf8fHxaty4sSpXrqzQ0FAtX77cafxnn32m5s2by8vLS926ddOiRYtks9mUmZkpSZo0aZLatm3rNGbGjBkKCgoqsabExER16tRJ1atXV82aNXX77bcrJSXFsf7w4cOy2WxaunSpunbtKi8vL/3jH/+4Iu8HygZhBJeksLBQUVFRWr9+vRYvXqzvv/9eL7/8stzd3SVJW7Zs0YABAzRw4EDt2LFDkyZN0oQJE7Rw4UKn7UybNk3t27fXtm3b9MQTT+jxxx/X3r17JUm5ubmKjIyUn5+fNm3apGXLlunLL78sEiS++uor/fTTT1q3bp2mT5+uiRMn6vbbb5efn5++++47PfbYY3r00Uf1ww8/SJKGDh2q999/X3l5eY5tLF68WPXr11f37t2v4rsGlC87d+7Uhg0b5OnpKem3X1R/9913NXfuXO3atUujR4/W/fffr2+++UaSdOjQId19993q27ev/vOf/+jRRx/V+PHjL7uO3NxcxcTEaPPmzUpKSpKbm5vuuusuFRYWOvUbN26cRo4cqd27dysyMvKy94syZAElGDJkiOXu7m5VqVLFaXnppZesVatWWW5ubtbevXuLHXvvvfdaPXr0cGp75plnrFatWjleN2rUyLr//vsdrwsLC606depYc+bMsSzLst566y3Lz8/PysnJcfRJSEiw3NzcrPT0dEeNjRo1sgoKChx9WrRoYXXu3Nnx+ty5c1aVKlWsDz74wLIsyzp9+rTl5+dnLV261NGnTZs21qRJk1x+j4CK5Pefebvdbkmy3NzcrOXLl1tnzpyxvL29rQ0bNjiNefjhh61BgwZZlmVZY8eOtUJCQpzWjx8/3pJk/frrr5ZlWdbEiROt0NBQpz6vv/661ahRI6c67rzzzhLrPHbsmCXJ2rFjh2VZlnXo0CFLkjVjxoxLO3AYxzUjuKBu3bppzpw5Tm01atTQ22+/rQYNGig4OLjYcbt379add97p1HbzzTdrxowZKigocMygtGnTxrHeZrMpICBAR48edWwjNDRUVapUcdpGYWGh9u7d6/g9pOuvv97p95D8/f2dfvvI3d1dNWvWdGzXy8tLDzzwgObPn68BAwZo69at2rlzpz777DOX3x+gojn/mc/NzdXrr78uDw8P9evXT7t27dKpU6fUo0cPp/75+fkKCwuTJO3du1c33nij0/oOHTpcdk379+9XXFycvvvuOx0/ftwxI5Kamur0WW/fvv1l7wtmEEZwQVWqVFGzZs2KtFeuXPmKbP+PF5jZbLYiU6+Xso2LbXfo0KFq27atfvjhBy1YsEDdu3dXo0aNXKweqHh+/5mfP3++QkND9c477zj+6CckJKh+/fpOY1y5qN3NzU3WH36F5OzZsxcc06dPHzVq1Ejz5s1TvXr1VFhYqJCQEOXn5xepHeUTYQSXpE2bNvrhhx+0b9++YmdHWrZsqfXr1zu1rV+/XsHBwY5ZkYtp2bKlFi5cqNzcXMc/MuvXr5ebm5tatGhxWfW3bt1a7du317x58/T+++9r5syZl7U9oCJyc3PTX//6V8XExGjfvn2y2+1KTU1V165di+3fokULrVy50qlt06ZNTq9r166t9PR0WZYlm80mSdq+fXuJNfzyyy/au3ev5s2bp86dO0uSvv3228s4KvwZcQErLigvL0/p6elOy/Hjx9W1a1d16dJF/fr105o1a3To0CF98cUXSkxMlCQ9/fTTSkpK0gsvvKB9+/Zp0aJFmjlzpsaMGVPqfd93333y8vLSkCFDtHPnTq1du1ZPPvmkHnjgAccpmssxdOhQvfzyy7IsS3fddddlbw+oiPr37y93d3e9+eabGjNmjEaPHq1FixYpJSVFW7du1RtvvKFFixZJkh599FHt2bNHY8eO1b59+/Thhx86Llo/HzxuueUWHTt2TK+88opSUlI0a9YsffHFFyXu38/PTzVr1tRbb72lAwcO6KuvvlJMTMxVP26ULcIILigxMVF169Z1Wjp16iTpt1t3b7zxRg0aNEitWrXSs88+q4KCAknSDTfcoA8//FBLlixRSEiI4uLi9Pzzz+vBBx8s9b69vb21atUqnThxQjfeeKPuvvtu3XrrrVdsFmPQoEHy8PDQoEGD5OXldUW2CVQ0Hh4eGjFihF555RXFxsZqwoQJio+PV8uWLdWrVy8lJCSocePGkqTGjRtr+fLl+vjjj9WmTRvNmTPHcTfN+VM5LVu21OzZszVr1iyFhoZq48aNF/yS4ubmpiVLlmjLli0KCQnR6NGj9eqrr179A0eZsll/PHkHXCMOHz6spk2batOmTbrhhhtMlwNUSC+99JLmzp2rtLQ006XgT4xrRnDNOXv2rH755Rc999xz+r//+z+CCHAFzZ49WzfeeKNq1qyp9evX69VXXy3ybCDgjwgjuOasX79e3bp1U3BwcJGnRwK4PPv379eLL76oEydOqGHDhnr66acVGxtruiz8yXGaBgAAGMUFrAAAwCjCCAAAMIowAgAAjCKMAAAAowgjAK44m82mFStWmC4DQDlBGAHgsvT0dD355JNq0qSJ7Ha7AgMD1adPHyUlJZkuDUA5xHNGALjk8OHDuvnmm1W9enW9+uqrat26tc6ePatVq1Zp+PDh2rNnj+kSAZQzzIwAcMkTTzwhm82mjRs3ql+/fgoODtb111+vmJgY/fvf/y52zNixYxUcHCxvb281adJEEyZMcPrZ+P/85z/q1q2bqlWrJh8fH7Vr106bN2+WJB05ckR9+vSRn5+fqlSpouuvv97pl2F37typqKgoVa1aVf7+/nrggQd0/Pjxq/smALiiCCMASu3EiRNKTEzU8OHDVaVKlSLrq1evXuy4atWqaeHChfr+++/1t7/9TfPmzdPrr7/uWH/fffepQYMG2rRpk7Zs2aJx48apUqVKkqThw4crLy9P69at044dOzR16lRVrVpVkpSZmanu3bsrLCxMmzdvVmJiojIyMjRgwIArf/AArhpO0wAotQMHDsiyLF133XUujXvuuecc/zsoKEhjxozRkiVL9Oyzz0qSUlNT9cwzzzi227x5c0f/1NRU9evXT61bt5YkNWnSxLFu5syZCgsL05QpUxxt8+fPV2BgoPbt26fg4GDXDxJAmSOMACi1S/31iKVLl+rvf/+7UlJSlJOTo3PnzsnHx8exPiYmRkOHDtV7772niIgI9e/fX02bNpUkPfXUU3r88ce1evVqRUREqF+/fmrTpo2k307vrF271jFT8nspKSmEEaCc4DQNgFJr3ry5bDabSxepJicn67777tNtt92mzz//XNu2bdP48eOVn5/v6DNp0iTt2rVLvXv31ldffaVWrVrpk08+kSQNHTpUBw8e1AMPPKAdO3aoffv2euONNyRJOTk56tOnj7Zv3+607N+/X126dLmyBw/gquGH8gC4JCoqSjt27NDevXuLXDeSmZmp6tWry2az6ZNPPlHfvn01bdo0zZ49WykpKY5+Q4cO1fLly5WZmVnsPgYNGqTc3Fx99tlnRdbFxsYqISFB//3vfzV+/Hh99NFH2rlzpzw8mOgFyitmRgC4ZNasWSooKFCHDh300Ucfaf/+/dq9e7f+/ve/q2PHjkX6N2/eXKmpqVqyZIlSUlL097//3THrIUmnT5/WiBEj9PXXX+vIkSNav369Nm3apJYtW0qSRo0apVWrVunQoUPaunWr1q5d61g3fPhwnThxQoMGDdKmTZuUkpKiVatWKTo6WgUFBWXzhgC4bIQRAC5p0qSJtm7dqm7duunpp59WSEiIevTooaSkJM2ZM6dI/zvuuEOjR4/WiBEj1LZtW23YsEETJkxwrHd3d9cvv/yiwYMHKzg4WAMGDFBUVJQmT54sSSooKNDw4cPVsmVL9erVS8HBwZo9e7YkqV69elq/fr0KCgrUs2dPtW7dWqNGjVL16tXl5sY/b0B5wWkaAABgFF8dAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARv0/KRNNTUoDB2EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2009.groupby('Classe')['Lucro'].sum().plot.bar(title = \"Lucro por classe em 2009\")\n",
    "plt.xticks(rotation='horizontal');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a5fcf8-fa7b-46ac-ad5b-6145cc9a2389",
   "metadata": {},
   "source": [
    "## Analises estatísticas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "18ea4d86-98ca-4f6f-aab8-7d4960d0ffac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                 904.00\n",
       "mean                    8.54\n",
       "std                     3.06\n",
       "min                     4.00\n",
       "25%                     6.00\n",
       "50%                     9.00\n",
       "75%                    11.00\n",
       "max                    20.00\n",
       "Name: Tempo_envio, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tempo_envio'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f93bc9ef-a5c3-4151-9969-50607e1d1d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot - Visualização/Detecção de outliers\n",
    "sb.boxplot(df['Tempo_envio']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "62742db7-f878-4b06-811f-3e90aeb0c03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma: Distribuição dos temos de envio\n",
    "sb.histplot(df['Tempo_envio']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e40283c9-804a-4102-959c-240fbb330ff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo de envio máximo: 20\n",
      "Tempo de envio mínimo: 4\n"
     ]
    }
   ],
   "source": [
    "print(f\"Tempo de envio máximo: {df['Tempo_envio'].max()}\")\n",
    "print(f\"Tempo de envio mínimo: {df['Tempo_envio'].min()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ac9aa87-c590-4412-8d3b-5494c3c89680",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}