From d717c2dad4c4ff655025213e6f0f76572c89e3f7 Mon Sep 17 00:00:00 2001
From: Barbara Reimao <barbarareimao625@gmail.com>
Date: Thu, 29 Aug 2024 20:57:20 -0300
Subject: [PATCH] exercicio semana 12

---
 exercicios/para-casa/exercicio_casa.ipynb | 976 ++++++++++++++++++++++
 1 file changed, 976 insertions(+)
 create mode 100644 exercicios/para-casa/exercicio_casa.ipynb

diff --git a/exercicios/para-casa/exercicio_casa.ipynb b/exercicios/para-casa/exercicio_casa.ipynb
new file mode 100644
index 0000000..aeb3f85
--- /dev/null
+++ b/exercicios/para-casa/exercicio_casa.ipynb
@@ -0,0 +1,976 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "df = pd.read_csv(\"dados_vendas.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(365, 5)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Informando numeros de linhas e colunas que formam o DataFrame\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "O DataFrame gerado possui 365 linhas e 5 colunas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Data                   object\n",
+       "Categoria              object\n",
+       "Quantidade_Vendida      int64\n",
+       "Preco_Unitario        float64\n",
+       "Valor_Total           float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Informando os tipos de dados das colunas que compõem o DataFrame.\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "O DataFrame possui as colunas Data e Categoria com dados do tipo object. A coluna Quantidade_Vendida é composta por dados do tipo inteiro e as colunas Preco_Unitario e Valor_Total são formadas por dados do tipo ponto flutuante."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>74</td>\n",
+       "      <td>60.43</td>\n",
+       "      <td>4471.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-15</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>83</td>\n",
+       "      <td>272.88</td>\n",
+       "      <td>22649.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-09-28</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>17</td>\n",
+       "      <td>195.62</td>\n",
+       "      <td>3325.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-04-17</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>85</td>\n",
+       "      <td>233.93</td>\n",
+       "      <td>19884.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>78</td>\n",
+       "      <td>305.94</td>\n",
+       "      <td>23863.32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Data  Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+       "0  2023-04-13     Roupas                  74           60.43      4471.82\n",
+       "1  2023-12-15  Alimentos                  83          272.88     22649.04\n",
+       "2  2023-09-28     Roupas                  17          195.62      3325.54\n",
+       "3  2023-04-17     Roupas                  85          233.93     19884.05\n",
+       "4  2023-03-13     Roupas                  78          305.94     23863.32"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Exibindo as 5 primeiras linhas do DataFrame\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eletronicos = df[df['Categoria'] == 'Eletrônicos']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2023-07-08</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>73</td>\n",
+       "      <td>256.12</td>\n",
+       "      <td>18696.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2023-01-21</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>1</td>\n",
+       "      <td>274.53</td>\n",
+       "      <td>274.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>51</td>\n",
+       "      <td>248.32</td>\n",
+       "      <td>12664.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2023-06-01</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>34</td>\n",
+       "      <td>303.52</td>\n",
+       "      <td>10319.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>2023-09-15</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>39</td>\n",
+       "      <td>121.40</td>\n",
+       "      <td>4734.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>2023-09-10</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>33</td>\n",
+       "      <td>431.46</td>\n",
+       "      <td>14238.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2023-09-13</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>27</td>\n",
+       "      <td>201.67</td>\n",
+       "      <td>5445.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>2023-05-08</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>29</td>\n",
+       "      <td>243.57</td>\n",
+       "      <td>7063.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>2023-10-08</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>57</td>\n",
+       "      <td>427.57</td>\n",
+       "      <td>24371.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>2023-02-27</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>55</td>\n",
+       "      <td>466.48</td>\n",
+       "      <td>25656.40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>73 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+       "5    2023-07-08  Eletrônicos                  73          256.12     18696.76\n",
+       "6    2023-01-21  Eletrônicos                   1          274.53       274.53\n",
+       "7    2023-04-13  Eletrônicos                  51          248.32     12664.32\n",
+       "14   2023-06-01  Eletrônicos                  34          303.52     10319.68\n",
+       "18   2023-09-15  Eletrônicos                  39          121.40      4734.60\n",
+       "..          ...          ...                 ...             ...          ...\n",
+       "347  2023-09-10  Eletrônicos                  33          431.46     14238.18\n",
+       "349  2023-09-13  Eletrônicos                  27          201.67      5445.09\n",
+       "351  2023-05-08  Eletrônicos                  29          243.57      7063.53\n",
+       "353  2023-10-08  Eletrônicos                  57          427.57     24371.49\n",
+       "356  2023-02-27  Eletrônicos                  55          466.48     25656.40\n",
+       "\n",
+       "[73 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eletronicos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "media = eletronicos['Valor_Total'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(12291.256301369864)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "media"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A média do valor total das vendas na categoria 'Eletrônicos' é de 12291.26"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>74</td>\n",
+       "      <td>60.43</td>\n",
+       "      <td>4471.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-15</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>83</td>\n",
+       "      <td>272.88</td>\n",
+       "      <td>22649.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-09-28</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>17</td>\n",
+       "      <td>195.62</td>\n",
+       "      <td>3325.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-04-17</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>85</td>\n",
+       "      <td>233.93</td>\n",
+       "      <td>19884.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>78</td>\n",
+       "      <td>305.94</td>\n",
+       "      <td>23863.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>2023-12-26</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>68</td>\n",
+       "      <td>147.88</td>\n",
+       "      <td>10055.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>2023-06-23</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>86</td>\n",
+       "      <td>133.80</td>\n",
+       "      <td>11506.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>2023-04-24</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>10</td>\n",
+       "      <td>350.90</td>\n",
+       "      <td>3509.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>2023-12-08</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>74</td>\n",
+       "      <td>270.98</td>\n",
+       "      <td>20052.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>2023-05-31</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>97</td>\n",
+       "      <td>481.44</td>\n",
+       "      <td>46699.68</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>330 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Data   Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+       "0    2023-04-13      Roupas                  74           60.43      4471.82\n",
+       "1    2023-12-15   Alimentos                  83          272.88     22649.04\n",
+       "2    2023-09-28      Roupas                  17          195.62      3325.54\n",
+       "3    2023-04-17      Roupas                  85          233.93     19884.05\n",
+       "4    2023-03-13      Roupas                  78          305.94     23863.32\n",
+       "..          ...         ...                 ...             ...          ...\n",
+       "358  2023-12-26  Brinquedos                  68          147.88     10055.84\n",
+       "359  2023-06-23   Alimentos                  86          133.80     11506.80\n",
+       "361  2023-04-24  Brinquedos                  10          350.90      3509.00\n",
+       "363  2023-12-08  Brinquedos                  74          270.98     20052.52\n",
+       "364  2023-05-31      Roupas                  97          481.44     46699.68\n",
+       "\n",
+       "[330 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vendas_acima_1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(330, 5)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vendas_acima_1000.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Foram registradas 330 vendas com valor total maior que 1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>Categoria</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>742457.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>1187964.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>897261.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Livros</td>\n",
+       "      <td>963685.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>991215.29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Categoria  Valor_Total\n",
+       "0    Alimentos    742457.74\n",
+       "1   Brinquedos   1187964.04\n",
+       "2  Eletrônicos    897261.71\n",
+       "3       Livros    963685.46\n",
+       "4       Roupas    991215.29"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_por_categoria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A categoria Brinquedos apresentou o maior valor total de vendas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>55.618182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>47.247191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>46.780822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Livros</td>\n",
+       "      <td>50.917808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>47.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Categoria  Quantidade_Vendida\n",
+       "0    Alimentos           55.618182\n",
+       "1   Brinquedos           47.247191\n",
+       "2  Eletrônicos           46.780822\n",
+       "3       Livros           50.917808\n",
+       "4       Roupas           47.200000"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "media_quantidade_categoria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A categoria Alimentos teve a maior quantidade média vendida."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='skyblue')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Com base na visualização do gráfico, a Categoria Brinquedos apresentou o maior valor total de vendas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>15198.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-01-21</td>\n",
+       "      <td>274.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-01-27</td>\n",
+       "      <td>12175.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-02-05</td>\n",
+       "      <td>21956.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-02-06</td>\n",
+       "      <td>9625.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>2023-11-23</td>\n",
+       "      <td>12677.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>2023-12-04</td>\n",
+       "      <td>6724.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>2023-12-12</td>\n",
+       "      <td>6993.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>2023-12-25</td>\n",
+       "      <td>14750.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>2023-12-26</td>\n",
+       "      <td>12856.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>64 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Data  Valor_Total\n",
+       "0   2023-01-09     15198.60\n",
+       "1   2023-01-21       274.53\n",
+       "2   2023-01-27     12175.60\n",
+       "3   2023-02-05     21956.48\n",
+       "4   2023-02-06      9625.70\n",
+       "..         ...          ...\n",
+       "59  2023-11-23     12677.00\n",
+       "60  2023-12-04      6724.81\n",
+       "61  2023-12-12      6993.20\n",
+       "62  2023-12-25     14750.80\n",
+       "63  2023-12-26     12856.48\n",
+       "\n",
+       "[64 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vendas_diarias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='purple')\n",
+    "plt.xlabel('Data')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Variação Diária das Vendas de Eletrônicos')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualizando o gráfico é possível inferir que existe uma enorme variação na venda total diária de eletronicos: dias de pico extremamente elevado de vendas, enquanto em outros dias uma acentuada baixa no volume de vendas. É recomendável que seja feita uma diluição na visualização dos dados, ou seja, gráficos apresentados semanalmente, com o intuito de diagnosticar, com mais precisão e confiabilidade, os motivos que contribuem para esse historico de vendas tão irregular."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}